system

Abstract

We provide the system. [Solution] A means of receiving user requests, An analysis means for analyzing a request received using the aforementioned receiving means, Based on the information analyzed by the aforementioned analysis means, an acquisition means acquires information through an external data connection means, The means of providing the acquired information to the user, and simultaneously selecting and displaying highly relevant advertisements, A means of communication for receiving feedback to the user interface, An optimization means for optimizing the content and presentation method of the advertisement based on the aforementioned feedback, An information provision system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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] [[ID=!]] Conventional artificial intelligence agent services have the problem that a subscription contract is required for use, which imposes an economic burden on users. Also, in order to obtain advertising revenue, it is necessary to effectively present advertisements suitable for the interests and behaviors of users, but there is also the problem that it is difficult to efficiently select and optimize advertisements. Furthermore, a method for effectively utilizing feedback from users by artificial intelligence agents to improve the quality of services remains an unsolved problem.

Means for Solving the Problems

[0005] This invention includes means for providing a widely accessible service that reduces the financial burden on users by offering a free artificial intelligence agent system with advertisements. Specifically, it includes a configuration that displays advertisements after the information is provided, in addition to an artificial intelligence agent that analyzes user requests, acquires and provides appropriate information. Furthermore, it provides means for improving the effectiveness of advertisements by conducting surveys periodically and optimizing the advertisements based on the results. In addition, it aims to improve the quality of the service by updating the artificial intelligence model based on user feedback. As a result, advertisers can conduct effective marketing, and users benefit from being able to use a high-quality service for free.

[0006] "User" refers to an individual or organization that uses the artificial intelligence agent function of this system free of charge.

[0007] "Input means" refers to an interface or device for receiving user requests.

[0008] "Analysis means" refers to a function that interprets user requests received via input means and performs appropriate information processing.

[0009] "External data connection means" refers to a connection function for obtaining necessary information from an external data source based on the analyzed request.

[0010] "Output means" refers to a function for presenting acquired information and analysis results to the user.

[0011] "Means of displaying advertisements" refers to a function that displays and prompts users to view advertisements after providing them with information.

[0012] "Means of presenting questionnaires" refers to a function for conducting questionnaires with users and collecting their responses.

[0013] "Optimization measures" refer to functions that optimize the content and presentation methods of advertisements based on the collected survey results.

[0014] "Update mechanisms" refer to functions that improve the artificial intelligence model based on user feedback and enhance the quality of the service. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

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

[0017] [[ID=IS15]]First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention is a system that balances service convenience and advertising revenue generation by providing users with a free artificial intelligence agent with advertisements. This system is mainly based on interactions between a server, a terminal, and a user.

[0037] The user installs the AI ​​agent application on their device and registers and logs in to an account within the app. Afterward, the user inputs questions into the AI ​​agent to obtain various information they need on a daily basis (e.g., weather, news, schedule). The device sends the entered questions to the server for immediate analysis.

[0038] The server analyzes questions submitted by users and retrieves necessary data using external data connection methods. This allows it to generate accurate answers and send them to the user's device. When presenting information, the system maximizes advertising effectiveness by displaying highly relevant advertisements selected through optimization methods.

[0039] Furthermore, the system periodically presents users with surveys and uses the results to optimize the content and presentation methods of advertisements. It also updates its artificial intelligence model based on user feedback to improve service quality. This allows users to enjoy high-quality services for free, while the server side earns advertising revenue.

[0040] For example, if a user makes a request such as "Tell me the weather for tomorrow," the device sends the request to the server, which collects and analyzes weather data and returns the results to the device. At the same time, weather-related advertisements (e.g., rain gear promotions) are displayed to the user. In this way, the system effectively provides information and advertisements to the user.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user installs the AI ​​agent application on their device, registers an account, and logs in. The user enters their information, the device sends that information to the server, and after authentication by the server, login is permitted.

[0044] Step 2:

[0045] The user inputs questions or tasks to the AI ​​agent from the device (e.g., "Tell me today's news"). The device then formats the user's input into a format that is easy for the server to process and sends it to the server.

[0046] Step 3:

[0047] The server parses the received request and retrieves the necessary information from an appropriate external data source (e.g., a news API). In this process, the parsing mechanism is used to understand the user's request and select the relevant data.

[0048] Step 4:

[0049] The server generates results based on the information it analyzes and sends them to the terminal. The terminal receives this information and displays it to the user.

[0050] Step 5:

[0051] After the information is displayed, the server sends advertising data to the device. The device then displays relevant advertisements to the user for 30 seconds. The advertisements are optimized based on the user's interests.

[0052] Step 6:

[0053] The server periodically generates a survey and sends it to the terminal. The terminal presents the survey to the user and prompts them to answer. The user answers the survey, and the terminal sends the answers to the server.

[0054] Step 7:

[0055] The server analyzes the survey data it collects and uses it to optimize advertisements. It also updates its AI model based on user feedback to improve the accuracy of the service. The server aims for continuous service improvement by repeatedly making these improvements.

[0056] (Example 1)

[0057] 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."

[0058] Currently, many information provision systems provide information to users, but the display of advertisements does not always align with user interests, resulting in limited advertising effectiveness. Furthermore, the lack of mechanisms to efficiently utilize user feedback to improve the system makes it difficult to improve the quality of services.

[0059] 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.

[0060] In this invention, the server includes an input means for receiving user requests, an analysis means for analyzing the received requests and using natural language processing technology, and an external connection means for acquiring information. This enables the provision of information related to the user's interests and the optimal display of advertisements. Furthermore, the quality of the service can be continuously improved through advertisement optimization and model updates based on feedback.

[0061] An "input means" is a device or mechanism for receiving requests from users.

[0062] "Analysis means" refers to a mechanism for analyzing received requests and understanding the information through a generative AI model using natural language processing technology.

[0063] "External connection means" refers to a connection device or process for obtaining appropriate information from an external information source.

[0064] "Output means" refers to a device or method for selecting highly relevant advertisements based on acquired information and displaying a combination of that information and the advertisements to the user.

[0065] A "secure communication protocol" is a communication method for safely transmitting information and advertisements to a device.

[0066] An "optimization method" is a system that optimizes the content and display method of advertisements based on the results of a survey.

[0067] The "update mechanism" is a system that updates the artificial intelligence model based on user feedback to improve the quality of information.

[0068] This invention aims to build a system that utilizes a free artificial intelligence agent with advertisements to provide users with useful information while generating advertising revenue. This system relies on collaboration among three parties: the server, the terminal, and the user.

[0069] Users can access this system by first installing an AI agent application on their device. Users must register an account and log in within the application. Afterward, users use their device to obtain various information needed in their daily lives, inputting questions to the AI ​​agent. This input is in the form of a prompt, such as "What's the weather like tomorrow?" or "What's the schedule for the next meeting?"

[0070] The terminal sends the user's input to the server. A secure communication protocol is used to ensure data safety. The server parses the received prompt. This parsing utilizes natural language processing techniques and generative AI models to understand the user's intent and prepare to collect appropriate information.

[0071] Next, the server uses external data connection means to retrieve data from necessary information sources. Typically, weather APIs and news APIs are used for this purpose. The retrieved information is processed within the server and formatted as a response for the user. Simultaneously, optimization means are used to select advertisements relevant to the user and incorporate them into the response data.

[0072] Ultimately, the server sends this information and advertisements to the device. The device then displays the data received from the server to the user. The information presentation is designed with maximum consideration for usability, using an optimal user interface. For example, when a user inquires about weather information, promotional advertisements for related rain gear may be displayed simultaneously.

[0073] In this way, users can obtain useful information for free, and the server can generate revenue through advertising. This system allows for continuous improvement of the service based on user feedback.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user requests information. The user launches the AI ​​agent application on their device and enters specific questions about the information they need. These questions take the form of prompts. An example of a request entered is, "Tell me the weather tomorrow." The output of the device is the prompt entered by the user.

[0077] Step 2:

[0078] The terminal sends a prompt message to the server. The terminal sends the input prompt message to the server using a secure communication protocol (e.g., HTTPS). In this case, the prompt message is the input, and sending the request to the server is the output.

[0079] Step 3:

[0080] The server parses the prompt message. The server receives the prompt message and performs analysis using natural language processing techniques. This analysis uses a generative AI model to perform data calculations to understand the user's intent. The input is the prompt message, and the output is the analysis result.

[0081] Step 4:

[0082] The server retrieves data from an external information source. Based on the analysis results, the server uses external data connection means to retrieve the necessary data from an appropriate information source (e.g., weather API). The input is the analysis results, and the output is the retrieved data.

[0083] Step 5:

[0084] The server generates answers and advertisements. Based on the acquired data, the server forms answers to the user's questions and simultaneously selects highly relevant advertisements. Advertisements are selected using optimization methods. The input is the acquired data, and the output is answer data and advertisement data.

[0085] Step 6:

[0086] The server sends the responses and advertisements to the device. The generated response data and advertisement data are then sent back to the device using a secure communication protocol. The input is the response data and advertisement data, and the output is the transmission to the device.

[0087] Step 7:

[0088] The device displays information and advertisements to the user. The device receives data sent from the server and displays information and advertisements to the user. The user reviews this data and obtains the necessary information. The input is data from the server, and the output is the display on the device.

[0089] (Application Example 1)

[0090] 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."

[0091] In modern society, users want to easily access information while simultaneously avoiding the burden of excessive advertising. Furthermore, the advertising industry demands more effective ad delivery, but accurate ad suggestions based on user interests are insufficient. Additionally, there is a lack of mechanisms to improve the performance of AI agents by utilizing user feedback, and improvements in this area are needed.

[0092] 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.

[0093] In this invention, the server includes receiving means for receiving user requests, analysis means for analyzing the received requests, and acquisition means for acquiring information through external data connection means based on the analysis results. This enables information acquisition in response to user requests, optimal display of advertisements related to that information, and automatic updating of the AI ​​agent using feedback.

[0094] A "receiving means" is a means of transmitting requests from a user from a terminal to a server.

[0095] "Analysis tools" are means used to understand user requests and identify information related to those requests.

[0096] "Acquisition means" refers to the means of collecting necessary information from external data sources based on the analyzed information.

[0097] "Means for selecting and displaying highly relevant advertisements" refers to means for automatically selecting advertisements related to acquired information and providing those advertisements to users.

[0098] "Communication method" refers to a method for receiving feedback from users and sending it to the server.

[0099] "Optimization methods" refer to techniques for improving the content and presentation of advertisements based on user feedback and research results.

[0100] This invention realizes a system that uses a smartphone to provide information in response to various user requests and effectively displays relevant advertisements. The terminal has an application installed for receiving requests from the user. Through this application, the user can input questions and requests for information.

[0101] The server utilizes the AWS® cloud platform to build a platform for advanced analytical processing. It uses the Django framework to analyze request data sent by users. Based on the analyzed data, it retrieves necessary information from external data sources, such as the Ticketmaster API. The retrieved information is then automatically selected and provided to users with relevant advertisements using Google® AdSense.

[0102] For example, if a user enters "Tell me about events next weekend" into the application, the server parses the request and retrieves event information for the next weekend from the Ticketmaster API. Then, it uses Google AdSense to select relevant advertisements and displays them on the device along with the information.

[0103] User feedback is sent from the device to the server via communication methods. Based on this feedback, the server optimizes the content and presentation methods of advertisements and updates the AI ​​agent model to improve service quality.

[0104] One example of a prompt is the instruction, "Analyze the provided question, retrieve event information for the next weekend, and retrieve relevant ads from Google AdSense." This allows users to efficiently obtain the information they need while naturally enjoying interesting advertisements.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The terminal receives requests entered by the user into the application. These inputs include questions and information requests entered by the user via the smartphone interface. The entered data is sent directly to the server, serving as foundational data for subsequent processing.

[0108] Step 2:

[0109] The server parses the received request using the Django framework. The input here is the request data sent from the terminal in step 1. The parsing process identifies the content of the user's request and determines the type of external data needed in the next step. As a result of the parsing, specific information retrieval instructions are generated in the form of prompt statements.

[0110] Step 3:

[0111] Based on the analysis results, the server uses external data connection means to retrieve information from an external API. The input for this step is the prompt statement generated in step 2. For example, it might call the Ticketmaster API to collect specific event information. The collected data is prepared for use in providing information to the user.

[0112] Step 4:

[0113] The server uses Google AdSense to select ads relevant to the acquired event information. The input here is the external data obtained in step 3. It selects ads relevant to the obtained information and creates a data package for display to the user. The selected ads are presented along with the information provided.

[0114] Step 5:

[0115] The device displays information and selected advertisements sent from the server to the user. The input consists of the information and advertisement data provided by the server in step 4. The information and advertisements are displayed on the smartphone screen, and the user can review them.

[0116] Step 6:

[0117] The user inputs feedback on the displayed information and advertisements into their device via the application and sends it back to the server. This input constitutes the user's feedback data. The submitted feedback is used in the next step to optimize advertisements and update the AI ​​model.

[0118] Step 7:

[0119] Based on the feedback received, the server optimizes the ad content and presentation method, and updates the AI ​​agent's model. The input for this step is the user feedback obtained in step 6. By analyzing the feedback and improving the generated AI model, future information provision and ad displays will be more tailored to the user. This process improves the overall system performance.

[0120] 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.

[0121] This invention is a free, ad-supported artificial intelligence agent system incorporating an emotion engine, which highly optimizes user interaction and provides a personalized experience. This system operates based on communication between a server, a terminal, and the user.

[0122] Users install the AI ​​agent application on their device, register, and log in. When users input specific tasks or questions into the AI ​​agent, the input is sent from the device to the server. Before analyzing the request, the server uses an emotion engine to analyze the user input and recognize the user's emotions at that time. This emotion data influences the information provided to the user, enabling customized responses.

[0123] Once an information request is parsed, the server collects the necessary information from external data sources and generates a response for the user. When the generated information is returned to the device, appropriate ad content is selected and provided to the user based on the emotions analyzed by the emotion engine. By presenting the ad as content that matches the user's situation and mood, the effectiveness of the ad is maximized.

[0124] Furthermore, user sentiment information is reflected in regularly conducted surveys and used to optimize ads and update AI models. For example, if a user requests "Please tell me the best music to help me relax," the device's sentiment engine recognizes from the unintended nuances and tone that the user wants to relax, and displays calming ad content that matches that. This allows users to have a pleasant experience while advertisers can achieve effective marketing.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user launches the AI ​​agent application installed on their device and performs the login operation. The device sends the user's authentication information to the server, which verifies it and grants permission to log in.

[0128] Step 2:

[0129] The user inputs specific questions or tasks to the AI ​​agent. During this process, the device collects the user's voice and input, and an emotion engine analyzes the user's emotions.

[0130] Step 3:

[0131] The terminal sends input data, including the results of the emotion engine's analysis, to the server. The server analyzes this data and identifies the type of information requested.

[0132] Step 4:

[0133] The server uses external data connection means to obtain information based on the analyzed request from an external data source. At this time, it compares the obtained information with sentiment data to generate the optimal response.

[0134] Step 5:

[0135] Along with the generated response, the server selects advertisements that match the user's emotions and sends them to the device.

[0136] Step 6:

[0137] The device displays information and advertisements received from the server to the user. The advertisements are customized based on the user's current emotional state.

[0138] Step 7:

[0139] Based on the user's usage history and sentiment data, the server periodically presents the user with surveys. The device sends the survey responses back to the server, which uses this data to further optimize advertisements and update the AI ​​model.

[0140] (Example 2)

[0141] 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".

[0142] In modern society, users desire to obtain diverse information quickly and accurately. However, traditional information acquisition methods struggle to provide information that appropriately addresses users' emotional states and individual needs, sometimes resulting in a poor user experience. Furthermore, traditional advertising often presents one-sided content without considering users' emotions, failing to maximize advertising effectiveness. To solve these problems, it is necessary to analyze users' emotional states and provide information and advertisements based on that analysis.

[0143] 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.

[0144] In this invention, the server includes means for analyzing user requests, means for recognizing emotions using an emotion analysis algorithm, and means for generating responses using a generative AI model. This enables the provision of appropriate information and optimization of advertisements according to the user's emotions.

[0145] "Terminal equipment" refers to electronic devices used by users to input and display information, and includes devices such as computers and smartphones.

[0146] A "server" is a centralized computing device that analyzes data received from terminal devices via a network, connects to external data sources, and processes information.

[0147] "Processing means" refers to functions that include algorithms and programs for analyzing and understanding the user's request received.

[0148] An "emotion analysis algorithm" is a mathematical method or program that analyzes user input information and uses that information to determine the user's emotional state.

[0149] "Means of collecting external information" refers to technologies that include APIs and database connection functions for obtaining relevant information from the internet or different data sources.

[0150] A "generative AI model" is an algorithm and framework that uses artificial intelligence technology to learn from data and generate optimal responses and suggestions for users.

[0151] "Means for selecting and displaying advertisements" refers to a program that takes into account the user's emotions and usage situation to select the most effective advertisement and displays it on the user's device.

[0152] This system is an emotion-based information delivery system realized through the collaboration of users, terminals, and servers. Users install an AI agent application on their terminal and start the service. The terminal is an electronic device such as a smartphone or computer, and functions as a medium for users to input information and receive responses.

[0153] When a user enters a prompt, such as "Tell me some music that's good for relaxing," the device sends that information to the server. The server then uses a sentiment analysis algorithm that employs natural language processing to analyze the received request. This analysis identifies the user's emotional state and deepens the understanding of the request.

[0154] Next, the server collects the necessary information from external data sources. APIs from music streaming services, for example, are often used for this purpose. Then, a generative AI model is used to generate the most appropriate response for the user. The generative AI model uses the server's computing resources to assemble information tailored to the user's preferences and emotions.

[0155] The server also selects appropriate advertisements based on the analyzed sentiment data. These advertisements are designed to harmonize with the user's emotions and context, and are strategically designed to enhance marketing effectiveness. The selected advertisements and generated responses are displayed to the user via their device.

[0156] Through this process, users can receive personalized information that resonates with their emotions, allowing them to enjoy a comfortable experience. This system offers new value at the boundary between information provision and advertising, benefiting both users and businesses.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The user launches the AI ​​agent application using their device and enters a prompt. For example, they might enter something like, "Recommend some relaxing music for a tiring day." This becomes the input to the system. The device receives this input and prepares to send it to the server.

[0160] Step 2:

[0161] The terminal receives input data from the user and sends it to the server via a secure communication protocol. Once this output reaches the server, the server's receiving module holds the data in a state ready for analysis.

[0162] Step 3:

[0163] The server analyzes the received user request. First, it uses natural language processing (NLP) techniques to understand the meaning of the input text. Then, it uses an emotion analysis algorithm to extract the user's emotional state (in this example, the emotion of "wanting to relax"). The input text data is analyzed and output in the form of the user's emotional state.

[0164] Step 4:

[0165] The server queries external data sources based on the analyzed emotional state. It accesses external music streaming APIs, for example, to collect lists of music suitable for relaxation. This is the collected output data. This data is processed within the server and converted into a specific format.

[0166] Step 5:

[0167] The server utilizes a generative AI model to construct appropriate responses for the user based on collected music data. This AI model generates more personalized suggestions based on the user's past preferences and similar requests. The responses generated here are shown in the output below.

[0168] Step 6:

[0169] The server uses the data obtained from sentiment analysis to select the most suitable advertisement from the advertising database. The selected advertisement is tailored to the user's emotional state. This advertising data also becomes part of the final output.

[0170] Step 7:

[0171] The server sends the generated response and selected advertisements to the device. The device receives this information and displays it to the user. The user can review advertisements designed to pique their interest while viewing the specific music suggestions provided in the response. The output data is visualized on the user's device, completing the user experience.

[0172] (Application Example 2)

[0173] 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".

[0174] In today's world, interaction between users and artificial intelligence agents is becoming increasingly important, but a challenge remains: the lack of systems that understand users' emotions and provide information and advertisements accordingly. Traditional advertising systems deliver ads based on general user information, making it difficult to provide personalized ads that match users' emotions and moods at that time. Therefore, there is a need to develop systems that can create a valuable advertising experience for users.

[0175] 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.

[0176] In this invention, the server includes emotion analysis means for analyzing the user's emotions, data acquisition means for acquiring external data based on emotion information, and information provision means for providing information and advertisements to the user. This makes it possible to provide optimal information and advertisements to the user in a timely manner based on their emotions.

[0177] "Information receiving means" refers to components for receiving requests and inputs from users.

[0178] An "emotional analysis tool" is a device or program that analyzes a user's emotions based on the information received and extracts that emotional information.

[0179] "External data connection means" refers to an interface or function for connecting to external information sources or databases to obtain data.

[0180] "Data acquisition means" refers to means for acquiring appropriate information and advertisements based on emotional information obtained through emotion analysis means.

[0181] "Information provision means" refers to the devices or functions necessary to present acquired information and advertisements to users.

[0182] "Advertising display means" refers to means for displaying advertisements selected based on the user's emotional information.

[0183] "Ad optimization methods" refer to means of adjusting and optimizing the content and delivery methods of advertisements based on user evaluation criteria.

[0184] A "model update method" is a means of updating the structure of artificial intelligence based on user feedback to improve its accuracy and performance.

[0185] The system realizing this invention is operated by the user using an application installed on their terminal. When the user inputs a request, the terminal sends that request to the server. The server first extracts emotional data from the user's request using an emotional analysis means. Natural language processing technologies such as the Google Cloud Natural Language API are used for this emotional analysis.

[0186] Once sentiment data is acquired, the server retrieves information and advertisements tailored to the user's sentiment via external data connection means. Here, the advertisements are specifically selected to match the user's sentiment, and a personalized algorithm is applied to the selection process.

[0187] The acquired information and advertisements are transmitted to the device and presented to the user by the information provision means. In this process, the advertisement display means plays a role in displaying advertisement content that corresponds to the user's emotions. For example, if the user requests "relaxing music," the device uses emotion analysis means to interpret the request as a "relaxed" state and selects content advertisements suitable for relaxation.

[0188] Furthermore, user feedback information is collected through advertising optimization methods, and this information is used on the server to update the AI ​​model. Machine learning frameworks such as TENSORFLOW® and PyTorch are used for these model updates, and model adjustments are made to further enhance the effectiveness of the advertisements.

[0189] For example, when a user enters "I want to relieve stress," the device that receives the input displays advertisements for healing music and aromatherapy-related products. Through this entire process, users receive individually filtered information while gaining a valuable advertising experience.

[0190] Examples of prompts for a generative AI model include: "If the system determines that the user is seeking relaxation, please suggest what kind of advertisements should be shown."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The user opens an application installed on their device and enters a request into a text input field. This input includes the user's current needs or questions. Once the input is complete, the device sends the input data as a digital signal to the server.

[0194] Step 2:

[0195] The server uses sentiment analysis tools to analyze the received input data. Natural Language Processing (NLP) techniques are used to analyze the text and extract the underlying emotions expressed in the input. In this process, sentiment analysis algorithms evaluate keywords and context within the text and output sentiment labels (e.g., "relaxed," "stressed," "excited").

[0196] Step 3:

[0197] Based on the sentiment data obtained by the sentiment analysis means, the server uses external data connection means to retrieve appropriate information and advertising data. In this step, database queries are executed to search for and filter information that matches the sentiment label. The output is a list of specific information and advertising content corresponding to that sentiment.

[0198] Step 4:

[0199] The server sends the acquired information and advertising data back to the device. The device uses information delivery methods to present the information and advertisements to the user. Advertisements customized to the user's emotions are displayed on the screen, and the information is presented visually.

[0200] Step 5:

[0201] Users are required to provide feedback on the advertisements they see. This feedback data is sent from the device to the server and used to verify the effectiveness of the advertisements and user responses. The results are processed by ad optimization tools on the server and used to update the generated AI model.

[0202] Step 6:

[0203] The server uses the collected feedback information to train an AI model. Here, machine learning frameworks such as TensorFlow and PyTorch are used to adjust the algorithm's parameters. An example of a prompt might be, "If the user is determined to be seeking relaxation, suggest what kind of advertisement should be shown." As a result, this continuously improves the performance of the advertisements.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] This invention is a system that balances service convenience and advertising revenue generation by providing users with a free artificial intelligence agent with advertisements. This system is mainly based on interactions between a server, a terminal, and a user.

[0221] The user installs the AI ​​agent application on their device and registers and logs in to an account within the app. Afterward, the user inputs questions into the AI ​​agent to obtain various information they need on a daily basis (e.g., weather, news, schedule). The device sends the entered questions to the server for immediate analysis.

[0222] The server analyzes questions submitted by users and retrieves necessary data using external data connection methods. This allows it to generate accurate answers and send them to the user's device. When presenting information, the system maximizes advertising effectiveness by displaying highly relevant advertisements selected through optimization methods.

[0223] Furthermore, the system periodically presents users with surveys and uses the results to optimize the content and presentation methods of advertisements. It also updates its artificial intelligence model based on user feedback to improve service quality. This allows users to enjoy high-quality services for free, while the server side earns advertising revenue.

[0224] For example, if a user makes a request such as "Tell me the weather for tomorrow," the device sends the request to the server, which collects and analyzes weather data and returns the results to the device. At the same time, weather-related advertisements (e.g., rain gear promotions) are displayed to the user. In this way, the system effectively provides information and advertisements to the user.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] The user installs the AI ​​agent application on their device, registers an account, and logs in. The user enters their information, the device sends that information to the server, and after authentication by the server, login is permitted.

[0228] Step 2:

[0229] The user inputs questions or tasks to the AI ​​agent from the device (e.g., "Tell me today's news"). The device then formats the user's input into a format that is easy for the server to process and sends it to the server.

[0230] Step 3:

[0231] The server parses the received request and retrieves the necessary information from an appropriate external data source (e.g., a news API). In this process, the parsing mechanism is used to understand the user's request and select the relevant data.

[0232] Step 4:

[0233] The server generates results based on the information it analyzes and sends them to the terminal. The terminal receives this information and displays it to the user.

[0234] Step 5:

[0235] After the information is displayed, the server sends advertising data to the device. The device then displays relevant advertisements to the user for 30 seconds. The advertisements are optimized based on the user's interests.

[0236] Step 6:

[0237] The server periodically generates a survey and sends it to the terminal. The terminal presents the survey to the user and prompts them to answer. The user answers the survey, and the terminal sends the answers to the server.

[0238] Step 7:

[0239] The server analyzes the survey data it collects and uses it to optimize advertisements. It also updates its AI model based on user feedback to improve the accuracy of the service. The server aims for continuous service improvement by repeatedly making these improvements.

[0240] (Example 1)

[0241] 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."

[0242] Currently, many information provision systems provide information to users, but the display of advertisements does not always align with user interests, resulting in limited advertising effectiveness. Furthermore, the lack of mechanisms to efficiently utilize user feedback to improve the system makes it difficult to improve the quality of services.

[0243] 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.

[0244] In this invention, the server includes an input means for receiving user requests, an analysis means for analyzing the received requests and using natural language processing technology, and an external connection means for acquiring information. This enables the provision of information related to the user's interests and the optimal display of advertisements. Furthermore, the quality of the service can be continuously improved through advertisement optimization and model updates based on feedback.

[0245] An "input means" is a device or mechanism for receiving requests from users.

[0246] "Analysis means" refers to a mechanism for analyzing received requests and understanding the information through a generative AI model using natural language processing technology.

[0247] "External connection means" refers to a connection device or process for obtaining appropriate information from an external information source.

[0248] "Output means" refers to a device or method for selecting highly relevant advertisements based on acquired information and displaying a combination of that information and the advertisements to the user.

[0249] A "secure communication protocol" is a communication method for safely transmitting information and advertisements to a device.

[0250] An "optimization method" is a system that optimizes the content and display method of advertisements based on the results of a survey.

[0251] The "update mechanism" is a system that updates the artificial intelligence model based on user feedback to improve the quality of information.

[0252] This invention aims to build a system that utilizes a free artificial intelligence agent with advertisements to provide users with useful information while generating advertising revenue. This system relies on collaboration among three parties: the server, the terminal, and the user.

[0253] Users can access this system by first installing an AI agent application on their device. Users must register an account and log in within the application. Afterward, users use their device to obtain various information needed in their daily lives, inputting questions to the AI ​​agent. This input is in the form of a prompt, such as "What's the weather like tomorrow?" or "What's the schedule for the next meeting?"

[0254] The terminal sends the user's input to the server. A secure communication protocol is used to ensure data safety. The server parses the received prompt. This parsing utilizes natural language processing techniques and generative AI models to understand the user's intent and prepare to collect appropriate information.

[0255] Next, the server uses external data connection means to retrieve data from necessary information sources. Typically, weather APIs and news APIs are used for this purpose. The retrieved information is processed within the server and formatted as a response for the user. Simultaneously, optimization means are used to select advertisements relevant to the user and incorporate them into the response data.

[0256] Ultimately, the server sends this information and advertisements to the device. The device then displays the data received from the server to the user. The information presentation is designed with maximum consideration for usability, using an optimal user interface. For example, when a user inquires about weather information, promotional advertisements for related rain gear may be displayed simultaneously.

[0257] In this way, users can obtain useful information for free, and the server can generate revenue through advertising. This system allows for continuous improvement of the service based on user feedback.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The user requests information. The user launches the AI ​​agent application on their device and enters specific questions about the information they need. These questions take the form of prompts. An example of a request entered is, "Tell me the weather tomorrow." The output of the device is the prompt entered by the user.

[0261] Step 2:

[0262] The terminal sends a prompt message to the server. The terminal sends the input prompt message to the server using a secure communication protocol (e.g., HTTPS). In this case, the prompt message is the input, and sending the request to the server is the output.

[0263] Step 3:

[0264] The server parses the prompt message. The server receives the prompt message and performs analysis using natural language processing techniques. This analysis uses a generative AI model to perform data calculations to understand the user's intent. The input is the prompt message, and the output is the analysis result.

[0265] Step 4:

[0266] The server retrieves data from an external information source. Based on the analysis results, the server uses external data connection means to retrieve the necessary data from an appropriate information source (e.g., weather API). The input is the analysis results, and the output is the retrieved data.

[0267] Step 5:

[0268] The server generates answers and advertisements. Based on the acquired data, the server forms answers to the user's questions and simultaneously selects highly relevant advertisements. Advertisements are selected using optimization methods. The input is the acquired data, and the output is answer data and advertisement data.

[0269] Step 6:

[0270] The server sends the responses and advertisements to the device. The generated response data and advertisement data are then sent back to the device using a secure communication protocol. The input is the response data and advertisement data, and the output is the transmission to the device.

[0271] Step 7:

[0272] The device displays information and advertisements to the user. The device receives data sent from the server and displays information and advertisements to the user. The user reviews this data and obtains the necessary information. The input is data from the server, and the output is the display on the device.

[0273] (Application Example 1)

[0274] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0275] In modern society, users want to easily access information while simultaneously avoiding the burden of excessive advertising. Furthermore, the advertising industry demands more effective ad delivery, but accurate ad suggestions based on user interests are insufficient. Additionally, there is a lack of mechanisms to improve the performance of AI agents by utilizing user feedback, and improvements in this area are needed.

[0276] 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.

[0277] In this invention, the server includes receiving means for receiving user requests, analysis means for analyzing the received requests, and acquisition means for acquiring information through external data connection means based on the analysis results. This enables information acquisition in response to user requests, optimal display of advertisements related to that information, and automatic updating of the AI ​​agent using feedback.

[0278] A "receiving means" is a means of transmitting requests from a user from a terminal to a server.

[0279] "Analysis tools" are means used to understand user requests and identify information related to those requests.

[0280] "Acquisition means" refers to the means of collecting necessary information from external data sources based on the analyzed information.

[0281] "Means for selecting and displaying highly relevant advertisements" refers to means for automatically selecting advertisements related to acquired information and providing those advertisements to users.

[0282] "Communication method" refers to a method for receiving feedback from users and sending it to the server.

[0283] The "optimization means" is a means to improve the content and presentation method of advertisements based on user feedback and survey results.

[0284] This invention realizes a system that uses a smartphone to provide information according to various requirements of users and effectively display relevant advertisements. An application for receiving requests from users is installed on the terminal. Through this application, users can input questions and requests for information.

[0285] The server utilizes the AWS cloud platform to build a platform for performing advanced analysis processing. Using the Django framework, it analyzes the request data sent from users. Based on the analyzed data, it obtains the necessary information from external data sources, such as external APIs like the Ticketmaster API. The obtained information is used to automatically select advertisements related to that information using Google AdSense and provide them to users.

[0286] As a specific example, when a user inputs "Tell me about the events next weekend" into the application, the server analyzes the request and obtains the event information for the next weekend from the Ticketmaster API. Then, it selects advertisements related to that information using Google AdSense and displays them on the terminal together with the information.

[0287] User feedback is sent from the terminal to the server using communication means. Based on this feedback, the server optimizes the content and presentation method of advertisements and further updates the model of the AI agent to improve the service quality.

[0288] As an example of a prompt sentence, there is an instruction like "Analyze the provided question, obtain the event information for the next weekend, and obtain relevant advertisements from Google AdSense". Thereby, users can efficiently obtain the target information and at the same time naturally enjoy interesting advertisements.

[0289] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0290] Step 1:

[0291] The terminal receives requests entered by the user into the application. These inputs include questions and information requests entered by the user via the smartphone interface. The entered data is sent directly to the server, serving as foundational data for subsequent processing.

[0292] Step 2:

[0293] The server parses the received request using the Django framework. The input here is the request data sent from the terminal in step 1. The parsing process identifies the content of the user's request and determines the type of external data needed in the next step. As a result of the parsing, specific information retrieval instructions are generated in the form of prompt statements.

[0294] Step 3:

[0295] Based on the analysis results, the server uses external data connection means to retrieve information from an external API. The input for this step is the prompt statement generated in step 2. For example, it might call the Ticketmaster API to collect specific event information. The collected data is prepared for use in providing information to the user.

[0296] Step 4:

[0297] The server uses Google AdSense to select ads relevant to the acquired event information. The input here is the external data obtained in step 3. It selects ads relevant to the obtained information and creates a data package for display to the user. The selected ads are presented along with the information provided.

[0298] Step 5:

[0299] The device displays information and selected advertisements sent from the server to the user. The input consists of the information and advertisement data provided by the server in step 4. The information and advertisements are displayed on the smartphone screen, and the user can review them.

[0300] Step 6:

[0301] The user inputs feedback on the displayed information and advertisements into their device via the application and sends it back to the server. This input constitutes the user's feedback data. The submitted feedback is used in the next step to optimize advertisements and update the AI ​​model.

[0302] Step 7:

[0303] Based on the feedback received, the server optimizes the ad content and presentation method, and updates the AI ​​agent's model. The input for this step is the user feedback obtained in step 6. By analyzing the feedback and improving the generated AI model, future information provision and ad displays will be more tailored to the user. This process improves the overall system performance.

[0304] 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.

[0305] This invention is a free, ad-supported artificial intelligence agent system incorporating an emotion engine, which highly optimizes user interaction and provides a personalized experience. This system operates based on communication between a server, a terminal, and the user.

[0306] The user installs the AI agent application on the terminal and performs registration and login. When the user enters a specific task or question into the AI agent, the input is sent from the terminal to the server. Before analyzing the request, the server uses the emotion engine to analyze the user input and recognize the emotion at that time. This emotion data affects the information provided to the user and enables customized responses.

[0307] When the information request is analyzed, the server collects the necessary information from external data sources and generates an answer to the user based on it. When the generated information is returned to the terminal, appropriate advertisement content is selected and provided to the user based on the emotion analyzed by the emotion engine. By presenting the advertisement as content suitable for the user's situation and mood, the effectiveness of the advertisement is maximized.

[0308] Also, the user's emotion information is reflected in the questionnaire surveys conducted regularly and is used for advertisement optimization and AI model update. For example, when the user requests "Please teach me the most suitable music for relaxation", the terminal recognizes through the emotion engine that the user feels like relaxing from the unintended nuances and tone, and displays gentle advertisement content that matches it. As a result, the user can obtain a comfortable experience, and at the same time, the advertiser can achieve effective marketing.

[0309] The processing flow will be described below.

[0310] Step 1:

[0311] [[ID=2,0]]The user launches the AI agent application installed on the terminal and performs a login operation. The terminal sends the user's authentication information to the server, and the server confirms this and permits the login.

[0312] Step 2:

[0313] The user inputs specific questions or tasks to the AI ​​agent. During this process, the device collects the user's voice and input, and an emotion engine analyzes the user's emotions.

[0314] Step 3:

[0315] The terminal sends input data, including the results of the emotion engine's analysis, to the server. The server analyzes this data and identifies the type of information requested.

[0316] Step 4:

[0317] The server uses external data connection means to obtain information based on the analyzed request from an external data source. At this time, it compares the obtained information with sentiment data to generate the optimal response.

[0318] Step 5:

[0319] Along with the generated response, the server selects advertisements that match the user's emotions and sends them to the device.

[0320] Step 6:

[0321] The device displays information and advertisements received from the server to the user. The advertisements are customized based on the user's current emotional state.

[0322] Step 7:

[0323] Based on the user's usage history and sentiment data, the server periodically presents the user with surveys. The device sends the survey responses back to the server, which uses this data to further optimize advertisements and update the AI ​​model.

[0324] (Example 2)

[0325] 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".

[0326] In modern society, users desire to obtain diverse information quickly and accurately. However, traditional information acquisition methods struggle to provide information that appropriately addresses users' emotional states and individual needs, sometimes resulting in a poor user experience. Furthermore, traditional advertising often presents one-sided content without considering users' emotions, failing to maximize advertising effectiveness. To solve these problems, it is necessary to analyze users' emotional states and provide information and advertisements based on that analysis.

[0327] 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.

[0328] In this invention, the server includes means for analyzing user requests, means for recognizing emotions using an emotion analysis algorithm, and means for generating responses using a generative AI model. This enables the provision of appropriate information and optimization of advertisements according to the user's emotions.

[0329] "Terminal equipment" refers to electronic devices used by users to input and display information, and includes devices such as computers and smartphones.

[0330] A "server" is a centralized computing device that analyzes data received from terminal devices via a network, connects to external data sources, and processes information.

[0331] "Processing means" refers to functions that include algorithms and programs for analyzing and understanding the user's request received.

[0332] An "emotion analysis algorithm" is a mathematical method or program that analyzes user input information and uses that information to determine the user's emotional state.

[0333] "Means of collecting external information" refers to technologies that include APIs and database connection functions for obtaining relevant information from the internet or different data sources.

[0334] A "generative AI model" is an algorithm and framework that uses artificial intelligence technology to learn from data and generate optimal responses and suggestions for users.

[0335] "Means for selecting and displaying advertisements" refers to a program that takes into account the user's emotions and usage situation to select the most effective advertisement and displays it on the user's device.

[0336] This system is an emotion-based information delivery system realized through the collaboration of users, terminals, and servers. Users install an AI agent application on their terminal and start the service. The terminal is an electronic device such as a smartphone or computer, and functions as a medium for users to input information and receive responses.

[0337] When a user enters a prompt, such as "Tell me some music that's good for relaxing," the device sends that information to the server. The server then uses a sentiment analysis algorithm that employs natural language processing to analyze the received request. This analysis identifies the user's emotional state and deepens the understanding of the request.

[0338] Next, the server collects the necessary information from external data sources. APIs from music streaming services, for example, are often used for this purpose. Then, a generative AI model is used to generate the most appropriate response for the user. The generative AI model uses the server's computing resources to assemble information tailored to the user's preferences and emotions.

[0339] The server also selects appropriate advertisements based on the analyzed sentiment data. These advertisements are designed to harmonize with the user's emotions and context, and are strategically designed to enhance marketing effectiveness. The selected advertisements and generated responses are displayed to the user via their device.

[0340] Through this process, users can receive personalized information that resonates with their emotions, allowing them to enjoy a comfortable experience. This system offers new value at the boundary between information provision and advertising, benefiting both users and businesses.

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] The user launches the AI ​​agent application using their device and enters a prompt. For example, they might enter something like, "Recommend some relaxing music for a tiring day." This becomes the input to the system. The device receives this input and prepares to send it to the server.

[0344] Step 2:

[0345] The terminal receives input data from the user and sends it to the server via a secure communication protocol. Once this output reaches the server, the server's receiving module holds the data in a state ready for analysis.

[0346] Step 3:

[0347] The server analyzes the received user request. First, it uses natural language processing (NLP) techniques to understand the meaning of the input text. Then, it uses an emotion analysis algorithm to extract the user's emotional state (in this example, the emotion of "wanting to relax"). The input text data is analyzed and output in the form of the user's emotional state.

[0348] Step 4:

[0349] The server queries external data sources based on the analyzed emotional state. It accesses external music streaming APIs, for example, to collect lists of music suitable for relaxation. This is the collected output data. This data is processed within the server and converted into a specific format.

[0350] Step 5:

[0351] The server utilizes a generative AI model to construct appropriate responses for the user based on collected music data. This AI model generates more personalized suggestions based on the user's past preferences and similar requests. The responses generated here are shown in the output below.

[0352] Step 6:

[0353] The server uses the data obtained from sentiment analysis to select the most suitable advertisement from the advertising database. The selected advertisement is tailored to the user's emotional state. This advertising data also becomes part of the final output.

[0354] Step 7:

[0355] The server sends the generated response and selected advertisements to the device. The device receives this information and displays it to the user. The user can review advertisements designed to pique their interest while viewing the specific music suggestions provided in the response. The output data is visualized on the user's device, completing the user experience.

[0356] (Application Example 2)

[0357] 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."

[0358] In today's world, interaction between users and artificial intelligence agents is becoming increasingly important, but a challenge remains: the lack of systems that understand users' emotions and provide information and advertisements accordingly. Traditional advertising systems deliver ads based on general user information, making it difficult to provide personalized ads that match users' emotions and moods at that time. Therefore, there is a need to develop systems that can create a valuable advertising experience for users.

[0359] 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.

[0360] In this invention, the server includes emotion analysis means for analyzing the user's emotions, data acquisition means for acquiring external data based on emotion information, and information provision means for providing information and advertisements to the user. This makes it possible to provide optimal information and advertisements to the user in a timely manner based on their emotions.

[0361] "Information receiving means" refers to components for receiving requests and inputs from users.

[0362] An "emotional analysis tool" is a device or program that analyzes a user's emotions based on the information received and extracts that emotional information.

[0363] "External data connection means" refers to an interface or function for connecting to external information sources or databases to obtain data.

[0364] "Data acquisition means" refers to means for acquiring appropriate information and advertisements based on emotional information obtained through emotion analysis means.

[0365] "Information provision means" refers to the devices or functions necessary to present acquired information and advertisements to users.

[0366] "Advertising display means" refers to means for displaying advertisements selected based on the user's emotional information.

[0367] "Ad optimization methods" refer to means of adjusting and optimizing the content and delivery methods of advertisements based on user evaluation criteria.

[0368] A "model update method" is a means of updating the structure of artificial intelligence based on user feedback to improve its accuracy and performance.

[0369] The system realizing this invention is operated by the user using an application installed on their terminal. When the user inputs a request, the terminal sends that request to the server. The server first extracts emotional data from the user's request using an emotional analysis means. Natural language processing technologies such as the Google Cloud Natural Language API are used for this emotional analysis.

[0370] Once sentiment data is acquired, the server retrieves information and advertisements tailored to the user's sentiment via external data connection means. Here, the advertisements are specifically selected to match the user's sentiment, and a personalized algorithm is applied to the selection process.

[0371] The acquired information and advertisements are transmitted to the device and presented to the user by the information provision means. In this process, the advertisement display means plays a role in displaying advertisement content that corresponds to the user's emotions. For example, if the user requests "relaxing music," the device uses emotion analysis means to interpret the request as a "relaxed" state and selects content advertisements suitable for relaxation.

[0372] Furthermore, user feedback information is collected through advertising optimization methods, and this information is used on the server to update the AI ​​model. Machine learning frameworks such as TensorFlow and PyTorch are used for these model updates, and the model is adjusted to further enhance the effectiveness of the advertisements.

[0373] For example, when a user enters "I want to relieve stress," the device that receives the input displays advertisements for healing music and aromatherapy-related products. Through this entire process, users receive individually filtered information while gaining a valuable advertising experience.

[0374] Examples of prompts for a generative AI model include: "If the system determines that the user is seeking relaxation, please suggest what kind of advertisements should be shown."

[0375] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0376] Step 1:

[0377] The user opens an application installed on their device and enters a request into a text input field. This input includes the user's current needs or questions. Once the input is complete, the device sends the input data as a digital signal to the server.

[0378] Step 2:

[0379] The server uses sentiment analysis tools to analyze the received input data. Natural Language Processing (NLP) techniques are used to analyze the text and extract the underlying emotions expressed in the input. In this process, sentiment analysis algorithms evaluate keywords and context within the text and output sentiment labels (e.g., "relaxed," "stressed," "excited").

[0380] Step 3:

[0381] Based on the sentiment data obtained by the sentiment analysis means, the server uses external data connection means to retrieve appropriate information and advertising data. In this step, database queries are executed to search for and filter information that matches the sentiment label. The output is a list of specific information and advertising content corresponding to that sentiment.

[0382] Step 4:

[0383] The server sends the acquired information and advertising data back to the device. The device uses information delivery methods to present the information and advertisements to the user. Advertisements customized to the user's emotions are displayed on the screen, and the information is presented visually.

[0384] Step 5:

[0385] Users are required to provide feedback on the advertisements they see. This feedback data is sent from the device to the server and used to verify the effectiveness of the advertisements and user responses. The results are processed by ad optimization tools on the server and used to update the generated AI model.

[0386] Step 6:

[0387] The server uses the collected feedback information to train an AI model. Here, machine learning frameworks such as TensorFlow and PyTorch are used to adjust the algorithm's parameters. An example of a prompt might be, "If the user is determined to be seeking relaxation, suggest what kind of advertisement should be shown." As a result, this continuously improves the performance of the advertisements.

[0388] 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.

[0389] 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.

[0390] 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.

[0391] [Third Embodiment]

[0392] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0393] 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.

[0394] 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).

[0395] 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.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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".

[0404] This invention is a system that balances service convenience and advertising revenue generation by providing users with a free artificial intelligence agent with advertisements. This system is mainly based on interactions between a server, a terminal, and a user.

[0405] The user installs the AI ​​agent application on their device and registers and logs in to an account within the app. Afterward, the user inputs questions into the AI ​​agent to obtain various information they need on a daily basis (e.g., weather, news, schedule). The device sends the entered questions to the server for immediate analysis.

[0406] The server analyzes questions submitted by users and retrieves necessary data using external data connection methods. This allows it to generate accurate answers and send them to the user's device. When presenting information, the system maximizes advertising effectiveness by displaying highly relevant advertisements selected through optimization methods.

[0407] Furthermore, the system periodically presents users with surveys and uses the results to optimize the content and presentation methods of advertisements. It also updates its artificial intelligence model based on user feedback to improve service quality. This allows users to enjoy high-quality services for free, while the server side earns advertising revenue.

[0408] For example, if a user makes a request such as "Tell me the weather for tomorrow," the device sends the request to the server, which collects and analyzes weather data and returns the results to the device. At the same time, weather-related advertisements (e.g., rain gear promotions) are displayed to the user. In this way, the system effectively provides information and advertisements to the user.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The user installs the AI ​​agent application on their device, registers an account, and logs in. The user enters their information, the device sends that information to the server, and after authentication by the server, login is permitted.

[0412] Step 2:

[0413] The user inputs questions or tasks to the AI ​​agent from the device (e.g., "Tell me today's news"). The device then formats the user's input into a format that is easy for the server to process and sends it to the server.

[0414] Step 3:

[0415] The server parses the received request and retrieves the necessary information from an appropriate external data source (e.g., a news API). In this process, the parsing mechanism is used to understand the user's request and select the relevant data.

[0416] Step 4:

[0417] The server generates results based on the information it analyzes and sends them to the terminal. The terminal receives this information and displays it to the user.

[0418] Step 5:

[0419] After the information is displayed, the server sends advertising data to the device. The device then displays relevant advertisements to the user for 30 seconds. The advertisements are optimized based on the user's interests.

[0420] Step 6:

[0421] The server periodically generates a survey and sends it to the terminal. The terminal presents the survey to the user and prompts them to answer. The user answers the survey, and the terminal sends the answers to the server.

[0422] Step 7:

[0423] The server analyzes the survey data it collects and uses it to optimize advertisements. It also updates its AI model based on user feedback to improve the accuracy of the service. The server aims for continuous service improvement by repeatedly making these improvements.

[0424] (Example 1)

[0425] 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."

[0426] Currently, many information provision systems provide information to users, but the display of advertisements does not always align with user interests, resulting in limited advertising effectiveness. Furthermore, the lack of mechanisms to efficiently utilize user feedback to improve the system makes it difficult to improve the quality of services.

[0427] 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.

[0428] In this invention, the server includes an input means for receiving user requests, an analysis means for analyzing the received requests and using natural language processing technology, and an external connection means for acquiring information. This enables the provision of information related to the user's interests and the optimal display of advertisements. Furthermore, the quality of the service can be continuously improved through advertisement optimization and model updates based on feedback.

[0429] An "input means" is a device or mechanism for receiving requests from users.

[0430] "Analysis means" refers to a mechanism for analyzing received requests and understanding the information through a generative AI model using natural language processing technology.

[0431] "External connection means" refers to a connection device or process for obtaining appropriate information from an external information source.

[0432] "Output means" refers to a device or method for selecting highly relevant advertisements based on acquired information and displaying a combination of that information and the advertisements to the user.

[0433] A "secure communication protocol" is a communication method for safely transmitting information and advertisements to a device.

[0434] An "optimization method" is a system that optimizes the content and display method of advertisements based on the results of a survey.

[0435] The "update mechanism" is a system that updates the artificial intelligence model based on user feedback to improve the quality of information.

[0436] This invention aims to build a system that utilizes a free artificial intelligence agent with advertisements to provide users with useful information while generating advertising revenue. This system relies on collaboration among three parties: the server, the terminal, and the user.

[0437] Users can access this system by first installing an AI agent application on their device. Users must register an account and log in within the application. Afterward, users use their device to obtain various information needed in their daily lives, inputting questions to the AI ​​agent. This input is in the form of a prompt, such as "What's the weather like tomorrow?" or "What's the schedule for the next meeting?"

[0438] The terminal sends the user's input to the server. A secure communication protocol is used to ensure data safety. The server parses the received prompt. This parsing utilizes natural language processing techniques and generative AI models to understand the user's intent and prepare to collect appropriate information.

[0439] Next, the server uses external data connection means to retrieve data from necessary information sources. Typically, weather APIs and news APIs are used for this purpose. The retrieved information is processed within the server and formatted as a response for the user. Simultaneously, optimization means are used to select advertisements relevant to the user and incorporate them into the response data.

[0440] Ultimately, the server sends this information and advertisements to the device. The device then displays the data received from the server to the user. The information presentation is designed with maximum consideration for usability, using an optimal user interface. For example, when a user inquires about weather information, promotional advertisements for related rain gear may be displayed simultaneously.

[0441] In this way, users can obtain useful information for free, and the server can generate revenue through advertising. This system allows for continuous improvement of the service based on user feedback.

[0442] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0443] Step 1:

[0444] The user requests information. The user launches the AI ​​agent application on their device and enters specific questions about the information they need. These questions take the form of prompts. An example of a request entered is, "Tell me the weather tomorrow." The output of the device is the prompt entered by the user.

[0445] Step 2:

[0446] The terminal sends a prompt message to the server. The terminal sends the input prompt message to the server using a secure communication protocol (e.g., HTTPS). In this case, the prompt message is the input, and sending the request to the server is the output.

[0447] Step 3:

[0448] The server parses the prompt message. The server receives the prompt message and performs analysis using natural language processing techniques. This analysis uses a generative AI model to perform data calculations to understand the user's intent. The input is the prompt message, and the output is the analysis result.

[0449] Step 4:

[0450] The server retrieves data from an external information source. Based on the analysis results, the server uses external data connection means to retrieve the necessary data from an appropriate information source (e.g., weather API). The input is the analysis results, and the output is the retrieved data.

[0451] Step 5:

[0452] The server generates answers and advertisements. Based on the acquired data, the server forms answers to the user's questions and simultaneously selects highly relevant advertisements. Advertisements are selected using optimization methods. The input is the acquired data, and the output is answer data and advertisement data.

[0453] Step 6:

[0454] The server sends the responses and advertisements to the device. The generated response data and advertisement data are then sent back to the device using a secure communication protocol. The input is the response data and advertisement data, and the output is the transmission to the device.

[0455] Step 7:

[0456] The device displays information and advertisements to the user. The device receives data sent from the server and displays information and advertisements to the user. The user reviews this data and obtains the necessary information. The input is data from the server, and the output is the display on the device.

[0457] (Application Example 1)

[0458] 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."

[0459] In modern society, users want to easily access information while simultaneously avoiding the burden of excessive advertising. Furthermore, the advertising industry demands more effective ad delivery, but accurate ad suggestions based on user interests are insufficient. Additionally, there is a lack of mechanisms to improve the performance of AI agents by utilizing user feedback, and improvements in this area are needed.

[0460] 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.

[0461] In this invention, the server includes receiving means for receiving user requests, analysis means for analyzing the received requests, and acquisition means for acquiring information through external data connection means based on the analysis results. This enables information acquisition in response to user requests, optimal display of advertisements related to that information, and automatic updating of the AI ​​agent using feedback.

[0462] A "receiving means" is a means of transmitting requests from a user from a terminal to a server.

[0463] "Analysis tools" are means used to understand user requests and identify information related to those requests.

[0464] "Acquisition means" refers to the means of collecting necessary information from external data sources based on the analyzed information.

[0465] "Means for selecting and displaying highly relevant advertisements" refers to means for automatically selecting advertisements related to acquired information and providing those advertisements to users.

[0466] "Communication method" refers to a method for receiving feedback from users and sending it to the server.

[0467] "Optimization methods" refer to techniques for improving the content and presentation of advertisements based on user feedback and research results.

[0468] This invention realizes a system that uses a smartphone to provide information in response to various user requests and effectively displays relevant advertisements. The terminal has an application installed for receiving requests from the user. Through this application, the user can input questions and requests for information.

[0469] The server leverages the AWS cloud platform to build a platform for advanced analytical processing. It uses the Django framework to analyze request data sent by users. Based on the analyzed data, it retrieves necessary information from external data sources, such as the Ticketmaster API. The retrieved information is then automatically selected and presented to users with relevant advertisements using Google AdSense.

[0470] For example, if a user enters "Tell me about events next weekend" into the application, the server parses the request and retrieves event information for the next weekend from the Ticketmaster API. Then, it uses Google AdSense to select relevant advertisements and displays them on the device along with the information.

[0471] User feedback is sent from the device to the server via communication methods. Based on this feedback, the server optimizes the content and presentation methods of advertisements and updates the AI ​​agent model to improve service quality.

[0472] One example of a prompt is the instruction, "Analyze the provided question, retrieve event information for the next weekend, and retrieve relevant ads from Google AdSense." This allows users to efficiently obtain the information they need while naturally enjoying interesting advertisements.

[0473] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0474] Step 1:

[0475] The terminal receives requests entered by the user into the application. These inputs include questions and information requests entered by the user via the smartphone interface. The entered data is sent directly to the server, serving as foundational data for subsequent processing.

[0476] Step 2:

[0477] The server parses the received request using the Django framework. The input here is the request data sent from the terminal in step 1. The parsing process identifies the content of the user's request and determines the type of external data needed in the next step. As a result of the parsing, specific information retrieval instructions are generated in the form of prompt statements.

[0478] Step 3:

[0479] Based on the analysis results, the server uses external data connection means to retrieve information from an external API. The input for this step is the prompt statement generated in step 2. For example, it might call the Ticketmaster API to collect specific event information. The collected data is prepared for use in providing information to the user.

[0480] Step 4:

[0481] The server uses Google AdSense to select ads relevant to the acquired event information. The input here is the external data obtained in step 3. It selects ads relevant to the obtained information and creates a data package for display to the user. The selected ads are presented along with the information provided.

[0482] Step 5:

[0483] The device displays information and selected advertisements sent from the server to the user. The input consists of the information and advertisement data provided by the server in step 4. The information and advertisements are displayed on the smartphone screen, and the user can review them.

[0484] Step 6:

[0485] The user inputs feedback on the displayed information and advertisements into their device via the application and sends it back to the server. This input constitutes the user's feedback data. The submitted feedback is used in the next step to optimize advertisements and update the AI ​​model.

[0486] Step 7:

[0487] Based on the feedback received, the server optimizes the ad content and presentation method, and updates the AI ​​agent's model. The input for this step is the user feedback obtained in step 6. By analyzing the feedback and improving the generated AI model, future information provision and ad displays will be more tailored to the user. This process improves the overall system performance.

[0488] 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.

[0489] This invention is a free, ad-supported artificial intelligence agent system incorporating an emotion engine, which highly optimizes user interaction and provides a personalized experience. This system operates based on communication between a server, a terminal, and the user.

[0490] Users install the AI ​​agent application on their device, register, and log in. When users input specific tasks or questions into the AI ​​agent, the input is sent from the device to the server. Before analyzing the request, the server uses an emotion engine to analyze the user input and recognize the user's emotions at that time. This emotion data influences the information provided to the user, enabling customized responses.

[0491] Once an information request is parsed, the server collects the necessary information from external data sources and generates a response for the user. When the generated information is returned to the device, appropriate ad content is selected and provided to the user based on the emotions analyzed by the emotion engine. By presenting the ad as content that matches the user's situation and mood, the effectiveness of the ad is maximized.

[0492] Furthermore, user sentiment information is reflected in regularly conducted surveys and used to optimize ads and update AI models. For example, if a user requests "Please tell me the best music to help me relax," the device's sentiment engine recognizes from the unintended nuances and tone that the user wants to relax, and displays calming ad content that matches that. This allows users to have a pleasant experience while advertisers can achieve effective marketing.

[0493] The following describes the processing flow.

[0494] Step 1:

[0495] The user launches the AI ​​agent application installed on their device and performs the login operation. The device sends the user's authentication information to the server, which verifies it and grants permission to log in.

[0496] Step 2:

[0497] The user inputs specific questions or tasks to the AI ​​agent. During this process, the device collects the user's voice and input, and an emotion engine analyzes the user's emotions.

[0498] Step 3:

[0499] The terminal sends input data, including the results of the emotion engine's analysis, to the server. The server analyzes this data and identifies the type of information requested.

[0500] Step 4:

[0501] The server uses external data connection means to obtain information based on the analyzed request from an external data source. At this time, it compares the obtained information with sentiment data to generate the optimal response.

[0502] Step 5:

[0503] Along with the generated response, the server selects advertisements that match the user's emotions and sends them to the device.

[0504] Step 6:

[0505] The device displays information and advertisements received from the server to the user. The advertisements are customized based on the user's current emotional state.

[0506] Step 7:

[0507] Based on the user's usage history and sentiment data, the server periodically presents the user with surveys. The device sends the survey responses back to the server, which uses this data to further optimize advertisements and update the AI ​​model.

[0508] (Example 2)

[0509] 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."

[0510] In modern society, users desire to obtain diverse information quickly and accurately. However, traditional information acquisition methods struggle to provide information that appropriately addresses users' emotional states and individual needs, sometimes resulting in a poor user experience. Furthermore, traditional advertising often presents one-sided content without considering users' emotions, failing to maximize advertising effectiveness. To solve these problems, it is necessary to analyze users' emotional states and provide information and advertisements based on that analysis.

[0511] 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.

[0512] In this invention, the server includes means for analyzing user requests, means for recognizing emotions using an emotion analysis algorithm, and means for generating responses using a generative AI model. This enables the provision of appropriate information and optimization of advertisements according to the user's emotions.

[0513] "Terminal equipment" refers to electronic devices used by users to input and display information, and includes devices such as computers and smartphones.

[0514] A "server" is a centralized computing device that analyzes data received from terminal devices via a network, connects to external data sources, and processes information.

[0515] "Processing means" refers to functions that include algorithms and programs for analyzing and understanding the user's request received.

[0516] An "emotion analysis algorithm" is a mathematical method or program that analyzes user input information and uses that information to determine the user's emotional state.

[0517] "Means of collecting external information" refers to technologies that include APIs and database connection functions for obtaining relevant information from the internet or different data sources.

[0518] A "generative AI model" is an algorithm and framework that uses artificial intelligence technology to learn from data and generate optimal responses and suggestions for users.

[0519] "Means for selecting and displaying advertisements" refers to a program that takes into account the user's emotions and usage situation to select the most effective advertisement and displays it on the user's device.

[0520] This system is an emotion-based information delivery system realized through the collaboration of users, terminals, and servers. Users install an AI agent application on their terminal and start the service. The terminal is an electronic device such as a smartphone or computer, and functions as a medium for users to input information and receive responses.

[0521] When a user enters a prompt, such as "Tell me some music that's good for relaxing," the device sends that information to the server. The server then uses a sentiment analysis algorithm that employs natural language processing to analyze the received request. This analysis identifies the user's emotional state and deepens the understanding of the request.

[0522] Next, the server collects the necessary information from external data sources. APIs from music streaming services, for example, are often used for this purpose. Then, a generative AI model is used to generate the most appropriate response for the user. The generative AI model uses the server's computing resources to assemble information tailored to the user's preferences and emotions.

[0523] The server also selects appropriate advertisements based on the analyzed sentiment data. These advertisements are designed to harmonize with the user's emotions and context, and are strategically designed to enhance marketing effectiveness. The selected advertisements and generated responses are displayed to the user via their device.

[0524] Through this process, users can receive personalized information that resonates with their emotions, allowing them to enjoy a comfortable experience. This system offers new value at the boundary between information provision and advertising, benefiting both users and businesses.

[0525] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0526] Step 1:

[0527] The user launches the AI ​​agent application using their device and enters a prompt. For example, they might enter something like, "Recommend some relaxing music for a tiring day." This becomes the input to the system. The device receives this input and prepares to send it to the server.

[0528] Step 2:

[0529] The terminal receives input data from the user and sends it to the server via a secure communication protocol. Once this output reaches the server, the server's receiving module holds the data in a state ready for analysis.

[0530] Step 3:

[0531] The server analyzes the received user request. First, it uses natural language processing (NLP) techniques to understand the meaning of the input text. Then, it uses an emotion analysis algorithm to extract the user's emotional state (in this example, the emotion of "wanting to relax"). The input text data is analyzed and output in the form of the user's emotional state.

[0532] Step 4:

[0533] The server queries external data sources based on the analyzed emotional state. It accesses external music streaming APIs, for example, to collect lists of music suitable for relaxation. This is the collected output data. This data is processed within the server and converted into a specific format.

[0534] Step 5:

[0535] The server utilizes a generative AI model to construct appropriate responses for the user based on collected music data. This AI model generates more personalized suggestions based on the user's past preferences and similar requests. The responses generated here are shown in the output below.

[0536] Step 6:

[0537] The server uses the data obtained from sentiment analysis to select the most suitable advertisement from the advertising database. The selected advertisement is tailored to the user's emotional state. This advertising data also becomes part of the final output.

[0538] Step 7:

[0539] The server sends the generated response and selected advertisements to the device. The device receives this information and displays it to the user. The user can review advertisements designed to pique their interest while viewing the specific music suggestions provided in the response. The output data is visualized on the user's device, completing the user experience.

[0540] (Application Example 2)

[0541] 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."

[0542] In today's world, interaction between users and artificial intelligence agents is becoming increasingly important, but a challenge remains: the lack of systems that understand users' emotions and provide information and advertisements accordingly. Traditional advertising systems deliver ads based on general user information, making it difficult to provide personalized ads that match users' emotions and moods at that time. Therefore, there is a need to develop systems that can create a valuable advertising experience for users.

[0543] 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.

[0544] In this invention, the server includes emotion analysis means for analyzing the user's emotions, data acquisition means for acquiring external data based on emotion information, and information provision means for providing information and advertisements to the user. This makes it possible to provide optimal information and advertisements to the user in a timely manner based on their emotions.

[0545] "Information receiving means" refers to components for receiving requests and inputs from users.

[0546] An "emotional analysis tool" is a device or program that analyzes a user's emotions based on the information received and extracts that emotional information.

[0547] "External data connection means" refers to an interface or function for connecting to external information sources or databases to obtain data.

[0548] "Data acquisition means" refers to means for acquiring appropriate information and advertisements based on emotional information obtained through emotion analysis means.

[0549] "Information provision means" refers to the devices or functions necessary to present acquired information and advertisements to users.

[0550] "Advertising display means" refers to means for displaying advertisements selected based on the user's emotional information.

[0551] "Ad optimization methods" refer to means of adjusting and optimizing the content and delivery methods of advertisements based on user evaluation criteria.

[0552] A "model update method" is a means of updating the structure of artificial intelligence based on user feedback to improve its accuracy and performance.

[0553] The system realizing this invention is operated by the user using an application installed on their terminal. When the user inputs a request, the terminal sends that request to the server. The server first extracts emotional data from the user's request using an emotional analysis means. Natural language processing technologies such as the Google Cloud Natural Language API are used for this emotional analysis.

[0554] Once sentiment data is acquired, the server retrieves information and advertisements tailored to the user's sentiment via external data connection means. Here, the advertisements are specifically selected to match the user's sentiment, and a personalized algorithm is applied to the selection process.

[0555] The acquired information and advertisements are transmitted to the device and presented to the user by the information provision means. In this process, the advertisement display means plays a role in displaying advertisement content that corresponds to the user's emotions. For example, if the user requests "relaxing music," the device uses emotion analysis means to interpret the request as a "relaxed" state and selects content advertisements suitable for relaxation.

[0556] Furthermore, user feedback information is collected through advertising optimization methods, and this information is used on the server to update the AI ​​model. Machine learning frameworks such as TensorFlow and PyTorch are used for these model updates, and the model is adjusted to further enhance the effectiveness of the advertisements.

[0557] For example, when a user enters "I want to relieve stress," the device that receives the input displays advertisements for healing music and aromatherapy-related products. Through this entire process, users receive individually filtered information while gaining a valuable advertising experience.

[0558] Examples of prompts for a generative AI model include: "If the system determines that the user is seeking relaxation, please suggest what kind of advertisements should be shown."

[0559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0560] Step 1:

[0561] The user opens an application installed on their device and enters a request into a text input field. This input includes the user's current needs or questions. Once the input is complete, the device sends the input data as a digital signal to the server.

[0562] Step 2:

[0563] The server uses sentiment analysis tools to analyze the received input data. Natural Language Processing (NLP) techniques are used to analyze the text and extract the underlying emotions expressed in the input. In this process, sentiment analysis algorithms evaluate keywords and context within the text and output sentiment labels (e.g., "relaxed," "stressed," "excited").

[0564] Step 3:

[0565] Based on the sentiment data obtained by the sentiment analysis means, the server uses external data connection means to retrieve appropriate information and advertising data. In this step, database queries are executed to search for and filter information that matches the sentiment label. The output is a list of specific information and advertising content corresponding to that sentiment.

[0566] Step 4:

[0567] The server sends the acquired information and advertising data back to the device. The device uses information delivery methods to present the information and advertisements to the user. Advertisements customized to the user's emotions are displayed on the screen, and the information is presented visually.

[0568] Step 5:

[0569] Users are required to provide feedback on the advertisements they see. This feedback data is sent from the device to the server and used to verify the effectiveness of the advertisements and user responses. The results are processed by ad optimization tools on the server and used to update the generated AI model.

[0570] Step 6:

[0571] The server uses the collected feedback information to train an AI model. Here, machine learning frameworks such as TensorFlow and PyTorch are used to adjust the algorithm's parameters. An example of a prompt might be, "If the user is determined to be seeking relaxation, suggest what kind of advertisement should be shown." As a result, this continuously improves the performance of the advertisements.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] 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.

[0578] 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).

[0579] 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.

[0580] 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.

[0581] 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).

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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.

[0588] 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".

[0589] This invention is a system that balances service convenience and advertising revenue generation by providing users with a free artificial intelligence agent with advertisements. This system is mainly based on interactions between a server, a terminal, and a user.

[0590] The user installs the AI ​​agent application on their device and registers and logs in to an account within the app. Afterward, the user inputs questions into the AI ​​agent to obtain various information they need on a daily basis (e.g., weather, news, schedule). The device sends the entered questions to the server for immediate analysis.

[0591] The server analyzes questions submitted by users and retrieves necessary data using external data connection methods. This allows it to generate accurate answers and send them to the user's device. When presenting information, the system maximizes advertising effectiveness by displaying highly relevant advertisements selected through optimization methods.

[0592] Furthermore, the system periodically presents users with surveys and uses the results to optimize the content and presentation methods of advertisements. It also updates its artificial intelligence model based on user feedback to improve service quality. This allows users to enjoy high-quality services for free, while the server side earns advertising revenue.

[0593] For example, if a user makes a request such as "Tell me the weather for tomorrow," the device sends the request to the server, which collects and analyzes weather data and returns the results to the device. At the same time, weather-related advertisements (e.g., rain gear promotions) are displayed to the user. In this way, the system effectively provides information and advertisements to the user.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] The user installs the AI ​​agent application on their device, registers an account, and logs in. The user enters their information, the device sends that information to the server, and after authentication by the server, login is permitted.

[0597] Step 2:

[0598] The user inputs questions or tasks to the AI ​​agent from the device (e.g., "Tell me today's news"). The device then formats the user's input into a format that is easy for the server to process and sends it to the server.

[0599] Step 3:

[0600] The server parses the received request and retrieves the necessary information from an appropriate external data source (e.g., a news API). In this process, the parsing mechanism is used to understand the user's request and select the relevant data.

[0601] Step 4:

[0602] The server generates results based on the information it analyzes and sends them to the terminal. The terminal receives this information and displays it to the user.

[0603] Step 5:

[0604] After the information is displayed, the server sends advertising data to the device. The device then displays relevant advertisements to the user for 30 seconds. The advertisements are optimized based on the user's interests.

[0605] Step 6:

[0606] The server periodically generates a survey and sends it to the terminal. The terminal presents the survey to the user and prompts them to answer. The user answers the survey, and the terminal sends the answers to the server.

[0607] Step 7:

[0608] The server analyzes the survey data it collects and uses it to optimize advertisements. It also updates its AI model based on user feedback to improve the accuracy of the service. The server aims for continuous service improvement by repeatedly making these improvements.

[0609] (Example 1)

[0610] 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".

[0611] Currently, many information provision systems provide information to users, but the display of advertisements does not always align with user interests, resulting in limited advertising effectiveness. Furthermore, the lack of mechanisms to efficiently utilize user feedback to improve the system makes it difficult to improve the quality of services.

[0612] 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.

[0613] In this invention, the server includes an input means for receiving user requests, an analysis means for analyzing the received requests and using natural language processing technology, and an external connection means for acquiring information. This enables the provision of information related to the user's interests and the optimal display of advertisements. Furthermore, the quality of the service can be continuously improved through advertisement optimization and model updates based on feedback.

[0614] An "input means" is a device or mechanism for receiving requests from users.

[0615] "Analysis means" refers to a mechanism for analyzing received requests and understanding the information through a generative AI model using natural language processing technology.

[0616] "External connection means" refers to a connection device or process for obtaining appropriate information from an external information source.

[0617] "Output means" refers to a device or method for selecting highly relevant advertisements based on acquired information and displaying a combination of that information and the advertisements to the user.

[0618] A "secure communication protocol" is a communication method for safely transmitting information and advertisements to a device.

[0619] An "optimization method" is a system that optimizes the content and display method of advertisements based on the results of a survey.

[0620] The "update mechanism" is a system that updates the artificial intelligence model based on user feedback to improve the quality of information.

[0621] This invention aims to build a system that utilizes a free artificial intelligence agent with advertisements to provide users with useful information while generating advertising revenue. This system relies on collaboration among three parties: the server, the terminal, and the user.

[0622] Users can access this system by first installing an AI agent application on their device. Users must register an account and log in within the application. Afterward, users use their device to obtain various information needed in their daily lives, inputting questions to the AI ​​agent. This input is in the form of a prompt, such as "What's the weather like tomorrow?" or "What's the schedule for the next meeting?"

[0623] The terminal sends the user's input to the server. A secure communication protocol is used to ensure data safety. The server parses the received prompt. This parsing utilizes natural language processing techniques and generative AI models to understand the user's intent and prepare to collect appropriate information.

[0624] Next, the server uses external data connection means to retrieve data from necessary information sources. Typically, weather APIs and news APIs are used for this purpose. The retrieved information is processed within the server and formatted as a response for the user. Simultaneously, optimization means are used to select advertisements relevant to the user and incorporate them into the response data.

[0625] Ultimately, the server sends this information and advertisements to the device. The device then displays the data received from the server to the user. The information presentation is designed with maximum consideration for usability, using an optimal user interface. For example, when a user inquires about weather information, promotional advertisements for related rain gear may be displayed simultaneously.

[0626] In this way, users can obtain useful information for free, and the server can generate revenue through advertising. This system allows for continuous improvement of the service based on user feedback.

[0627] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0628] Step 1:

[0629] The user requests information. The user launches the AI ​​agent application on their device and enters specific questions about the information they need. These questions take the form of prompts. An example of a request entered is, "Tell me the weather tomorrow." The output of the device is the prompt entered by the user.

[0630] Step 2:

[0631] The terminal sends a prompt message to the server. The terminal sends the input prompt message to the server using a secure communication protocol (e.g., HTTPS). In this case, the prompt message is the input, and sending the request to the server is the output.

[0632] Step 3:

[0633] The server parses the prompt message. The server receives the prompt message and performs analysis using natural language processing techniques. This analysis uses a generative AI model to perform data calculations to understand the user's intent. The input is the prompt message, and the output is the analysis result.

[0634] Step 4:

[0635] The server retrieves data from an external information source. Based on the analysis results, the server uses external data connection means to retrieve the necessary data from an appropriate information source (e.g., weather API). The input is the analysis results, and the output is the retrieved data.

[0636] Step 5:

[0637] The server generates answers and advertisements. Based on the acquired data, the server forms answers to the user's questions and simultaneously selects highly relevant advertisements. Advertisements are selected using optimization methods. The input is the acquired data, and the output is answer data and advertisement data.

[0638] Step 6:

[0639] The server sends the responses and advertisements to the device. The generated response data and advertisement data are then sent back to the device using a secure communication protocol. The input is the response data and advertisement data, and the output is the transmission to the device.

[0640] Step 7:

[0641] The device displays information and advertisements to the user. The device receives data sent from the server and displays information and advertisements to the user. The user reviews this data and obtains the necessary information. The input is data from the server, and the output is the display on the device.

[0642] (Application Example 1)

[0643] 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".

[0644] In modern society, users want to easily access information while simultaneously avoiding the burden of excessive advertising. Furthermore, the advertising industry demands more effective ad delivery, but accurate ad suggestions based on user interests are insufficient. Additionally, there is a lack of mechanisms to improve the performance of AI agents by utilizing user feedback, and improvements in this area are needed.

[0645] 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.

[0646] In this invention, the server includes receiving means for receiving user requests, analysis means for analyzing the received requests, and acquisition means for acquiring information through external data connection means based on the analysis results. This enables information acquisition in response to user requests, optimal display of advertisements related to that information, and automatic updating of the AI ​​agent using feedback.

[0647] A "receiving means" is a means of transmitting requests from a user from a terminal to a server.

[0648] "Analysis tools" are means used to understand user requests and identify information related to those requests.

[0649] "Acquisition means" refers to the means of collecting necessary information from external data sources based on the analyzed information.

[0650] "Means for selecting and displaying highly relevant advertisements" refers to means for automatically selecting advertisements related to acquired information and providing those advertisements to users.

[0651] "Communication method" refers to a method for receiving feedback from users and sending it to the server.

[0652] "Optimization methods" refer to techniques for improving the content and presentation of advertisements based on user feedback and research results.

[0653] This invention realizes a system that uses a smartphone to provide information in response to various user requests and effectively displays relevant advertisements. The terminal has an application installed for receiving requests from the user. Through this application, the user can input questions and requests for information.

[0654] The server leverages the AWS cloud platform to build a platform for advanced analytical processing. It uses the Django framework to analyze request data sent by users. Based on the analyzed data, it retrieves necessary information from external data sources, such as the Ticketmaster API. The retrieved information is then automatically selected and presented to users with relevant advertisements using Google AdSense.

[0655] For example, if a user enters "Tell me about events next weekend" into the application, the server parses the request and retrieves event information for the next weekend from the Ticketmaster API. Then, it uses Google AdSense to select relevant advertisements and displays them on the device along with the information.

[0656] User feedback is sent from the device to the server via communication methods. Based on this feedback, the server optimizes the content and presentation methods of advertisements and updates the AI ​​agent model to improve service quality.

[0657] One example of a prompt is the instruction, "Analyze the provided question, retrieve event information for the next weekend, and retrieve relevant ads from Google AdSense." This allows users to efficiently obtain the information they need while naturally enjoying interesting advertisements.

[0658] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0659] Step 1:

[0660] The terminal receives requests entered by the user into the application. These inputs include questions and information requests entered by the user via the smartphone interface. The entered data is sent directly to the server, serving as foundational data for subsequent processing.

[0661] Step 2:

[0662] The server parses the received request using the Django framework. The input here is the request data sent from the terminal in step 1. The parsing process identifies the content of the user's request and determines the type of external data needed in the next step. As a result of the parsing, specific information retrieval instructions are generated in the form of prompt statements.

[0663] Step 3:

[0664] Based on the analysis results, the server uses external data connection means to retrieve information from an external API. The input for this step is the prompt statement generated in step 2. For example, it might call the Ticketmaster API to collect specific event information. The collected data is prepared for use in providing information to the user.

[0665] Step 4:

[0666] The server uses Google AdSense to select ads relevant to the acquired event information. The input here is the external data obtained in step 3. It selects ads relevant to the obtained information and creates a data package for display to the user. The selected ads are presented along with the information provided.

[0667] Step 5:

[0668] The device displays information and selected advertisements sent from the server to the user. The input consists of the information and advertisement data provided by the server in step 4. The information and advertisements are displayed on the smartphone screen, and the user can review them.

[0669] Step 6:

[0670] The user inputs feedback on the displayed information and advertisements into their device via the application and sends it back to the server. This input constitutes the user's feedback data. The submitted feedback is used in the next step to optimize advertisements and update the AI ​​model.

[0671] Step 7:

[0672] Based on the feedback received, the server optimizes the ad content and presentation method, and updates the AI ​​agent's model. The input for this step is the user feedback obtained in step 6. By analyzing the feedback and improving the generated AI model, future information provision and ad displays will be more tailored to the user. This process improves the overall system performance.

[0673] 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.

[0674] This invention is a free, ad-supported artificial intelligence agent system incorporating an emotion engine, which highly optimizes user interaction and provides a personalized experience. This system operates based on communication between a server, a terminal, and the user.

[0675] Users install the AI ​​agent application on their device, register, and log in. When users input specific tasks or questions into the AI ​​agent, the input is sent from the device to the server. Before analyzing the request, the server uses an emotion engine to analyze the user input and recognize the user's emotions at that time. This emotion data influences the information provided to the user, enabling customized responses.

[0676] Once an information request is parsed, the server collects the necessary information from external data sources and generates a response for the user. When the generated information is returned to the device, appropriate ad content is selected and provided to the user based on the emotions analyzed by the emotion engine. By presenting the ad as content that matches the user's situation and mood, the effectiveness of the ad is maximized.

[0677] Furthermore, user sentiment information is reflected in regularly conducted surveys and used to optimize ads and update AI models. For example, if a user requests "Please tell me the best music to help me relax," the device's sentiment engine recognizes from the unintended nuances and tone that the user wants to relax, and displays calming ad content that matches that. This allows users to have a pleasant experience while advertisers can achieve effective marketing.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The user launches the AI ​​agent application installed on their device and performs the login operation. The device sends the user's authentication information to the server, which verifies it and grants permission to log in.

[0681] Step 2:

[0682] The user inputs specific questions or tasks to the AI ​​agent. During this process, the device collects the user's voice and input, and an emotion engine analyzes the user's emotions.

[0683] Step 3:

[0684] The terminal sends input data, including the results of the emotion engine's analysis, to the server. The server analyzes this data and identifies the type of information requested.

[0685] Step 4:

[0686] The server uses external data connection means to obtain information based on the analyzed request from an external data source. At this time, it compares the obtained information with sentiment data to generate the optimal response.

[0687] Step 5:

[0688] Along with the generated response, the server selects advertisements that match the user's emotions and sends them to the device.

[0689] Step 6:

[0690] The device displays information and advertisements received from the server to the user. The advertisements are customized based on the user's current emotional state.

[0691] Step 7:

[0692] Based on the user's usage history and sentiment data, the server periodically presents the user with surveys. The device sends the survey responses back to the server, which uses this data to further optimize advertisements and update the AI ​​model.

[0693] (Example 2)

[0694] 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".

[0695] In modern society, users desire to obtain diverse information quickly and accurately. However, traditional information acquisition methods struggle to provide information that appropriately addresses users' emotional states and individual needs, sometimes resulting in a poor user experience. Furthermore, traditional advertising often presents one-sided content without considering users' emotions, failing to maximize advertising effectiveness. To solve these problems, it is necessary to analyze users' emotional states and provide information and advertisements based on that analysis.

[0696] 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.

[0697] In this invention, the server includes means for analyzing user requests, means for recognizing emotions using an emotion analysis algorithm, and means for generating responses using a generative AI model. This enables the provision of appropriate information and optimization of advertisements according to the user's emotions.

[0698] "Terminal equipment" refers to electronic devices used by users to input and display information, and includes devices such as computers and smartphones.

[0699] A "server" is a centralized computing device that analyzes data received from terminal devices via a network, connects to external data sources, and processes information.

[0700] "Processing means" refers to functions that include algorithms and programs for analyzing and understanding the user's request received.

[0701] An "emotion analysis algorithm" is a mathematical method or program that analyzes user input information and uses that information to determine the user's emotional state.

[0702] "Means of collecting external information" refers to technologies that include APIs and database connection functions for obtaining relevant information from the internet or different data sources.

[0703] A "generative AI model" is an algorithm and framework that uses artificial intelligence technology to learn from data and generate optimal responses and suggestions for users.

[0704] "Means for selecting and displaying advertisements" refers to a program that takes into account the user's emotions and usage situation to select the most effective advertisement and displays it on the user's device.

[0705] This system is an emotion-based information delivery system realized through the collaboration of users, terminals, and servers. Users install an AI agent application on their terminal and start the service. The terminal is an electronic device such as a smartphone or computer, and functions as a medium for users to input information and receive responses.

[0706] When a user enters a prompt, such as "Tell me some music that's good for relaxing," the device sends that information to the server. The server then uses a sentiment analysis algorithm that employs natural language processing to analyze the received request. This analysis identifies the user's emotional state and deepens the understanding of the request.

[0707] Next, the server collects the necessary information from external data sources. APIs from music streaming services, for example, are often used for this purpose. Then, a generative AI model is used to generate the most appropriate response for the user. The generative AI model uses the server's computing resources to assemble information tailored to the user's preferences and emotions.

[0708] The server also selects appropriate advertisements based on the analyzed sentiment data. These advertisements are designed to harmonize with the user's emotions and context, and are strategically designed to enhance marketing effectiveness. The selected advertisements and generated responses are displayed to the user via their device.

[0709] Through this process, users can receive personalized information that resonates with their emotions, allowing them to enjoy a comfortable experience. This system offers new value at the boundary between information provision and advertising, benefiting both users and businesses.

[0710] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0711] Step 1:

[0712] The user launches the AI ​​agent application using their device and enters a prompt. For example, they might enter something like, "Recommend some relaxing music for a tiring day." This becomes the input to the system. The device receives this input and prepares to send it to the server.

[0713] Step 2:

[0714] The terminal receives input data from the user and sends it to the server via a secure communication protocol. Once this output reaches the server, the server's receiving module holds the data in a state ready for analysis.

[0715] Step 3:

[0716] The server analyzes the received user request. First, it uses natural language processing (NLP) techniques to understand the meaning of the input text. Then, it uses an emotion analysis algorithm to extract the user's emotional state (in this example, the emotion of "wanting to relax"). The input text data is analyzed and output in the form of the user's emotional state.

[0717] Step 4:

[0718] The server queries external data sources based on the analyzed emotional state. It accesses external music streaming APIs, for example, to collect lists of music suitable for relaxation. This is the collected output data. This data is processed within the server and converted into a specific format.

[0719] Step 5:

[0720] The server utilizes a generative AI model to construct appropriate responses for the user based on collected music data. This AI model generates more personalized suggestions based on the user's past preferences and similar requests. The responses generated here are shown in the output below.

[0721] Step 6:

[0722] The server uses the data obtained from sentiment analysis to select the most suitable advertisement from the advertising database. The selected advertisement is tailored to the user's emotional state. This advertising data also becomes part of the final output.

[0723] Step 7:

[0724] The server sends the generated response and selected advertisements to the device. The device receives this information and displays it to the user. The user can review advertisements designed to pique their interest while viewing the specific music suggestions provided in the response. The output data is visualized on the user's device, completing the user experience.

[0725] (Application Example 2)

[0726] 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".

[0727] In today's world, interaction between users and artificial intelligence agents is becoming increasingly important, but a challenge remains: the lack of systems that understand users' emotions and provide information and advertisements accordingly. Traditional advertising systems deliver ads based on general user information, making it difficult to provide personalized ads that match users' emotions and moods at that time. Therefore, there is a need to develop systems that can create a valuable advertising experience for users.

[0728] 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.

[0729] In this invention, the server includes emotion analysis means for analyzing the user's emotions, data acquisition means for acquiring external data based on emotion information, and information provision means for providing information and advertisements to the user. This makes it possible to provide optimal information and advertisements to the user in a timely manner based on their emotions.

[0730] "Information receiving means" refers to components for receiving requests and inputs from users.

[0731] An "emotional analysis tool" is a device or program that analyzes a user's emotions based on the information received and extracts that emotional information.

[0732] "External data connection means" refers to an interface or function for connecting to external information sources or databases to obtain data.

[0733] "Data acquisition means" refers to means for acquiring appropriate information and advertisements based on emotional information obtained through emotion analysis means.

[0734] "Information provision means" refers to the devices or functions necessary to present acquired information and advertisements to users.

[0735] "Advertising display means" refers to means for displaying advertisements selected based on the user's emotional information.

[0736] "Ad optimization methods" refer to means of adjusting and optimizing the content and delivery methods of advertisements based on user evaluation criteria.

[0737] A "model update method" is a means of updating the structure of artificial intelligence based on user feedback to improve its accuracy and performance.

[0738] The system realizing this invention is operated by the user using an application installed on their terminal. When the user inputs a request, the terminal sends that request to the server. The server first extracts emotional data from the user's request using an emotional analysis means. Natural language processing technologies such as the Google Cloud Natural Language API are used for this emotional analysis.

[0739] Once sentiment data is acquired, the server retrieves information and advertisements tailored to the user's sentiment via external data connection means. Here, the advertisements are specifically selected to match the user's sentiment, and a personalized algorithm is applied to the selection process.

[0740] The acquired information and advertisements are transmitted to the device and presented to the user by the information provision means. In this process, the advertisement display means plays a role in displaying advertisement content that corresponds to the user's emotions. For example, if the user requests "relaxing music," the device uses emotion analysis means to interpret the request as a "relaxed" state and selects content advertisements suitable for relaxation.

[0741] Furthermore, user feedback information is collected through advertising optimization methods, and this information is used on the server to update the AI ​​model. Machine learning frameworks such as TensorFlow and PyTorch are used for these model updates, and the model is adjusted to further enhance the effectiveness of the advertisements.

[0742] For example, when a user enters "I want to relieve stress," the device that receives the input displays advertisements for healing music and aromatherapy-related products. Through this entire process, users receive individually filtered information while gaining a valuable advertising experience.

[0743] Examples of prompts for a generative AI model include: "If the system determines that the user is seeking relaxation, please suggest what kind of advertisements should be shown."

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] The user opens an application installed on their device and enters a request into a text input field. This input includes the user's current needs or questions. Once the input is complete, the device sends the input data as a digital signal to the server.

[0747] Step 2:

[0748] The server uses sentiment analysis tools to analyze the received input data. Natural Language Processing (NLP) techniques are used to analyze the text and extract the underlying emotions expressed in the input. In this process, sentiment analysis algorithms evaluate keywords and context within the text and output sentiment labels (e.g., "relaxed," "stressed," "excited").

[0749] Step 3:

[0750] Based on the sentiment data obtained by the sentiment analysis means, the server uses external data connection means to retrieve appropriate information and advertising data. In this step, database queries are executed to search for and filter information that matches the sentiment label. The output is a list of specific information and advertising content corresponding to that sentiment.

[0751] Step 4:

[0752] The server sends the acquired information and advertising data back to the device. The device uses information delivery methods to present the information and advertisements to the user. Advertisements customized to the user's emotions are displayed on the screen, and the information is presented visually.

[0753] Step 5:

[0754] Users are required to provide feedback on the advertisements they see. This feedback data is sent from the device to the server and used to verify the effectiveness of the advertisements and user responses. The results are processed by ad optimization tools on the server and used to update the generated AI model.

[0755] Step 6:

[0756] The server uses the collected feedback information to train an AI model. Here, machine learning frameworks such as TensorFlow and PyTorch are used to adjust the algorithm's parameters. An example of a prompt might be, "If the user is determined to be seeking relaxation, suggest what kind of advertisement should be shown." As a result, this continuously improves the performance of the advertisements.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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."

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0778] The following is further disclosed regarding the embodiments described above.

[0779] (Claim 1)

[0780] An input method for receiving user requests,

[0781] An analysis means for analyzing the request received using the input means,

[0782] A means for acquiring information through an external data connection means based on the information analyzed by the aforementioned analysis means,

[0783] An output means for providing the acquired information to the user,

[0784] A means of displaying an advertisement following the provision of the aforementioned information,

[0785] A system that includes this.

[0786] (Claim 2)

[0787] The system according to claim 1, further comprising an optimization means for presenting a questionnaire to a user and optimizing the advertisement based on the results of the questionnaire.

[0788] (Claim 3)

[0789] The system according to claim 1, further comprising an update means for updating an artificial intelligence model based on user feedback using the advertising optimization means.

[0790] "Example 1"

[0791] (Claim 1)

[0792] An input method for receiving user requests,

[0793] An analysis means that analyzes the request received by the input means and understands the information through a generative AI model using natural language processing technology,

[0794] An external connection means that acquires appropriate information from an external information source based on the information analyzed by the aforementioned analysis means,

[0795] Based on the acquired information, a means of outputting information that selects highly relevant advertisements and displays a combination of information and advertisements to the user,

[0796] A means for transmitting the aforementioned information and advertisements to a terminal using a secure communication protocol,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, further comprising an optimization means for presenting a questionnaire to a user and optimizing the content and display method of an advertisement based on the results of the questionnaire.

[0800] (Claim 3)

[0801] The system according to claim 1, further comprising an update means for updating the artificial intelligence model based on user feedback using the optimization means to improve the quality of information.

[0802] "Application Example 1"

[0803] (Claim 1)

[0804] A means of receiving user requests,

[0805] An analysis means for analyzing a request received using the aforementioned receiving means,

[0806] Based on the information analyzed by the aforementioned analysis means, an acquisition means acquires information through an external data connection means,

[0807] The means of providing the acquired information to the user, and simultaneously selecting and displaying highly relevant advertisements,

[0808] A means of communication for receiving feedback to the user interface,

[0809] An optimization means for optimizing the content and presentation method of the advertisement based on the aforementioned feedback,

[0810] An information provision system that includes this.

[0811] (Claim 2)

[0812] The information provision system according to claim 1, further comprising an optimization means for presenting a survey to a user and optimizing an advertisement based on the results of the survey.

[0813] (Claim 3)

[0814] The information provision system according to claim 1, further comprising an update means for updating the model of an artificial intelligence agent based on feedback from a user using the optimization means.

[0815] "Example 2 of combining an emotion engine"

[0816] (Claim 1)

[0817] Terminal equipment that receives user requests,

[0818] A server processing means for analyzing requests received using the aforementioned terminal device,

[0819] A means for recognizing emotions using an emotion analysis algorithm based on the information analyzed by the processing means,

[0820] Based on the results of recognizing the aforementioned emotions, means for collecting external information,

[0821] Means for providing the collected information to users,

[0822] A means for selecting and displaying advertisements that are suitable for the provision of the aforementioned information,

[0823] A means for generating a response using a generative AI model,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, further comprising means for presenting a questionnaire to a user based on sentiment analysis and optimizing the advertisement based on the results of the questionnaire.

[0827] (Claim 3)

[0828] The system according to claim 1, further comprising means for updating an artificial intelligence model based on emotion-based feedback from users using the advertising optimization means.

[0829] "Application example 2 when combining with an emotional engine"

[0830] (Claim 1)

[0831] A means of receiving information that accepts user requests,

[0832] An emotion analysis means analyzes the requests received using the aforementioned information receiving means and analyzes the user's emotions,

[0833] A data acquisition means that acquires appropriate information and advertisements through an external data connection means based on the emotional information analyzed by the aforementioned emotional analysis means,

[0834] Information provision means for providing the acquired information and advertisements to users,

[0835] An advertising display means that selects and displays appropriate advertisements based on the user's emotions,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, further comprising advertising optimization means for presenting evaluation items to the user and optimizing the advertisement based on the results of the evaluation items.

[0839] (Claim 3)

[0840] The system according to claim 1, further comprising a model update means for updating the structure of artificial intelligence based on user responses using the advertising optimization means. [Explanation of symbols]

[0841] 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 receiving user requests, An analysis means for analyzing a request received using the aforementioned receiving means, Based on the information analyzed by the aforementioned analysis means, an acquisition means acquires information through an external data connection means, The means of providing the acquired information to the user and simultaneously selecting and displaying highly relevant advertisements, A means of communication for receiving feedback to the user interface, An optimization means for optimizing the content and presentation method of the advertisement based on the aforementioned feedback, An information provision system that includes this.

2. The information provision system according to claim 1, further comprising an optimization means for presenting a survey to a user and optimizing an advertisement based on the results of the survey.

3. The information provision system according to claim 1, further comprising an update means for updating the model of an artificial intelligence agent based on feedback from a user using the optimization means.

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