system

Abstract

We provide the system. [Solution] A means for analyzing user preferences based on initial settings information and browsing history obtained from the user, A means of receiving user inquiries, searching for relevant information using generation AI, and obtaining the latest data, A means of customizing information based on the user's individual information and preferences and presenting it in a chat format, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] [In modern times, users can obtain various knowledge from many information sources in their daily lives. However, due to the huge amount of information, it is difficult to distinguish useful information that suits their living environment. Also, the provided knowledge is not always up-to-date, and it is difficult to make an optimal judgment in a specific situation. In order to solve this problem and improve the efficiency of users' lives, a system that can automatically acquire and provide beneficial information that meets the individual needs of users is required.]

Means for Solving the Problems

[0005] [This invention provides means for analyzing user preferences based on initial setup information and browsing history obtained from the user, and for searching for relevant information using a generation AI in response to specific user inquiries, thereby always obtaining the latest data. Furthermore, the obtained information is customized based on the user's individual information and analyzed preferences, and presented in a chat format to make it easy for the user to intuitively understand. In addition, the service is improved by collecting user feedback and retraining the machine learning model. This series of means makes it possible to efficiently provide useful and appropriate information to the user.]

[0006] "User" refers to [an individual or group that uses this system, and is an entity with a specific living environment or information needs].

[0007] "Initial setup information" refers to [basic personal information provided by the user when registering with this system, specifically including family structure, residence, annual income, occupation, etc.].

[0008] "Browsing history" refers to [a record of web pages and search queries that a user has accessed on the internet in the past, and is data that indicates the user's interests and preferences].

[0009] "Preferences" refer to the tendencies that indicate a user's tastes and interests, and are the information preferences of individual users derived through analysis.

[0010] "Generative AI" refers to artificial intelligence technology that generates and provides relevant information based on user input, and possesses the ability to create new information.

[0011] "Customization" refers to the process of tailoring information to a user's specific needs and circumstances and providing it in an individualized format.

[0012] "Chat format" refers to a method of receiving information interactively via a computer or mobile device, characterized by user-friendliness and immediacy in its interface.

[0013] "Feedback" refers to the opinions and reactions provided by users, and is information used to improve and adjust the system.

[0014] A "machine learning model" is [an algorithm and its implementation that analyzes data, learns from it to identify patterns, and uses those patterns to make predictions and optimize information]. [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 Embodiment 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when combined with an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

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

[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, 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 implemented as a chat-based lifestyle concierge system for providing information optimized for individual users. Based on the user's initial settings and browsing history, this system analyzes the user's preferences, uses a generative AI to obtain relevant and up-to-date information, and provides it in a customized format. The specific operation of each element is described below.

[0037] First, the user enters initial setup information through their device and registers it with the system. Then, browsing history, naturally accumulated during normal internet use, is collected as data indicating the user's interests. The server analyzes this data and runs an algorithm to identify the user's preferences. This determines what kind of information the user needs.

[0038] Next, when a user requests specific information, the request is sent to the server via the terminal. The server uses generative AI technology to search for relevant information to answer the user's question. At this time, real-time information gathering technology is used to ensure the reliability and freshness of the information in order to obtain the latest data.

[0039] The resulting information is customized on the server based on the user's initial settings and analyzed preferences. This customized information is then delivered to the user in a chat format. The device displays this information on its user interface, allowing the user to understand it intuitively.

[0040] For example, if a user requests "Please suggest some affordable leisure activities that my family can enjoy this weekend," the server will search for and present local event information and affordable activities that meet that request. This information is optimized and presented based, for example, on the user's family size and past leisure preferences.

[0041] This system allows users to easily obtain information that is directly useful in their daily lives and to improve the efficiency of their daily routines.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user enters initial setup information using a terminal and registers it with the system. This information includes family structure, residential area, annual income, occupation, etc. The terminal sends this information to the server.

[0045] Step 2:

[0046] The server stores the received initial configuration information in a database. Furthermore, it collects the user's browsing history, including websites accessed and search queries, to prepare for analysis.

[0047] Step 3:

[0048] The server uses machine learning algorithms to analyze collected browsing history and identify user preferences. This analysis generates tags that indicate user interests based on past behavioral data.

[0049] Step 4:

[0050] Users can inquire about specific information via chat through their device. For example, they might enter a request such as, "Please tell me about local activities that can help me save money."

[0051] Step 5:

[0052] The terminal sends the user's inquiry to the server. The server uses generative AI to search for information related to the user's inquiry and collects the latest data in real time.

[0053] Step 6:

[0054] The server customizes search results based on the user's initial settings and preferences, generating optimized information. This information is summarized in a chat format.

[0055] Step 7:

[0056] The server sends optimized information to the terminal. The terminal displays the received information in a user interface, making it easily accessible to the user.

[0057] Step 8:

[0058] Users review the provided information and, if necessary, send feedback to the server via their device. This feedback is used to improve the service.

[0059] (Example 1)

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

[0061] There is a growing demand for internet information services that can provide highly relevant information to individual users quickly and on a personalized basis. However, current systems have problems in that they cannot respond to the diverse preferences of users and their real-time changing information needs, resulting in limited accuracy and suitability of the information provided.

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

[0063] In this invention, the server includes means for analyzing the user's preferences based on basic setting information and viewing history obtained from the user; means for searching for relevant information and obtaining fresh data using generative AI technology in response to the user's inquiry; and means for adjusting information based on the user's individual information and preferences and presenting it in a conversational format. This makes it possible to quickly provide the user with the latest information optimized for them.

[0064] "Users" refer to individual people or organizations that use the system and are the entities that acquire information according to their needs and preferences.

[0065] "Basic settings information" refers to the information that users provide when they first register with the system, and it is a collection of parameters that indicate their preferences and interests.

[0066] "Viewing history" refers to the websites a user has visited on the internet and the keywords they have searched for, representing a past record of the user's interests and behavior.

[0067] "Means of analyzing preferences" refers to methods that include algorithms and computational processes for analyzing data collected from users to identify their interests and concerns.

[0068] "Generative AI technology" refers to technologies that use artificial intelligence to generate information or generate relevant information in response to user inquiries.

[0069] "Means for searching for relevant information" refers to technical methods for searching and collecting necessary information from a network according to the user's needs.

[0070] "Fresh data" refers to the latest and most up-to-date information, including information acquired in real time to meet user demands.

[0071] "Means of information adjustment" refers to methods for appropriately organizing and editing collected information based on the individual circumstances and preferences of the users.

[0072] "Presenting information in a conversational format" refers to a method of presenting information to users in an intuitive and easy-to-understand manner through chat or messaging.

[0073] This invention is an advanced system for providing information optimized for users, specifically operating through a chat-based information provision interface. The system collects the user's basic settings information and viewing history, and provides personalized information by analyzing the user's preferences based on this data.

[0074] The server uses a generative AI model to search for relevant information in response to user inquiries and retrieve up-to-date data. This AI model has the capability to generate optimal responses to user questions, for example, by utilizing natural language processing techniques. The software used includes generative AI technology and machine learning algorithms. For example, prompts such as "Search for the nearest affordable family events" are used.

[0075] Users input initial information into the system via a terminal and request information based on their individual preferences. The terminal presents the user with the latest information and assists in visualizing the information through an intuitive user interface. This interface displays information in a conversational format, making it easy for users to understand.

[0076] For example, if a user enters "Please suggest some affordable leisure activities that my family can enjoy this weekend" into their terminal, the server receives the request, uses generative AI technology to search for local event information, and provides the most suitable information. By utilizing such prompts, the system can be customized to meet the user's needs, and by using real-time updated information, it can always provide up-to-date information.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users register by entering basic configuration information into the system via their terminal. This input includes categories and areas of interest. This information is sent to the server and stored as a dataset representing the user's personal preferences.

[0080] Step 2:

[0081] The device automatically collects the user's internet browsing history and sends it to a server for analysis. This history includes visited websites and searched keywords. The server retrieves this history and creates a new dataset to analyze the user's interests. This identifies the user's preferences and interests.

[0082] Step 3:

[0083] The server initiates a process to analyze user preferences based on their basic settings and viewing history. Specifically, it uses machine learning algorithms to cluster the data and classify the types of information the user prefers. This process extracts patterns that indicate specific user preferences, which will serve as a guide for future information provision.

[0084] Step 4:

[0085] To retrieve specific information, the user enters their request into the terminal. This request is structured as a question or based on keywords and is sent to the server. Upon receiving the request, the server generates a prompt corresponding to the question and uses a generative AI model to search for relevant information.

[0086] Step 5:

[0087] The server starts with a prompt sentence obtained using a generative AI model and searches for relevant information on the network in real time. This search procedure collects the latest and most reliable data. This information is organized according to the user's preferences and basic settings. For example, a prompt sentence such as "I want to find some affordable leisure activities that my family can enjoy this weekend" is used in this process.

[0088] Step 6:

[0089] The acquired information is processed on the server based on the user's individual information and preferences. The optimized information is formatted in a chat format and sent to the terminal. The terminal displays this information to the user using a conversational interface, allowing the user to intuitively understand the provided information.

[0090] (Application Example 1)

[0091] 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."

[0092] There is a challenge in improving the online shopping experience by providing optimal and timely product and promotional information tailored to individual user preferences. Furthermore, there is a need to build systems that can flexibly respond to diverse user needs.

[0093] 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.

[0094] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user and selecting relevant product information; means for receiving user inquiries, searching for product information and promotional information in real time using a generative AI model, and obtaining the latest data; and means for customizing the information to be provided based on the user's individual information and preferences and presenting it through an interactive interface. This enables the provision of a shopping experience optimized for each individual user and improves satisfaction.

[0095] "Analyzing user preferences" refers to the process of determining a user's preferences and interests based on initial settings information and browsing history data obtained from the user.

[0096] A "generative AI model" is an algorithm or artificial intelligence system that generates language or information by learning from large amounts of data.

[0097] "Real-time product information search" refers to the process of instantly extracting information from the latest product database in response to user inquiries.

[0098] A "conversational interface" is a user interface designed to allow users to exchange information using natural language.

[0099] "Customizing and presenting" means optimizing information for each individual based on their specific information and preferences, and then providing that information to them.

[0100] "Based on purchasing power" means adjusting the provision of products and services according to the user's economic background and the amount they can spend.

[0101] The system for implementing this invention includes a series of processes for providing product information optimized based on user preferences in real time. The server collects initial setup information and browsing history data, and analyzes user preferences based on this data. This analysis utilizes data analysis platforms such as Python and R, specifically using libraries such as Pandas and NumPy.

[0102] The server uses a generative AI model to search for the latest product information related to the user's inquiry. This search is performed by a web application using Django as the backend, and utilizes OpenAI®'s GPT and Anthropic's Claude as generative AIs. This allows the server to obtain product and campaign information that the user is interested in in real time.

[0103] The terminal presents this information to the user through an interactive user interface. The information is customized to be easily understood by the user, allowing them to proceed with the purchase process with intuitive operation.

[0104] For example, if a user enters "I want a cool jacket for summer," the server retrieves the latest jacket information and displays related promotions and recommended products based on the user's purchase history. This allows the user to easily select a product that suits their preferences and needs.

[0105] An example of a prompt message would be, "Based on the user's preferences and history, please suggest the latest promotions related to jackets." This allows the generative AI model to generate the most relevant information according to the user's preferences.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user enters initial setup information via a terminal and registers it with the system. In this step, the terminal converts the input information into a format and sends it to the server. The input includes the user's basic profile information, purchase history, and browsing history. The output is registration of the user profile in the server-side user profile database.

[0109] Step 2:

[0110] The server analyzes the received initial setup information and browsing history data. This analysis uses data analysis libraries such as Pandas and NumPy to extract patterns indicating the user's interests. The input is the user's profile data, and the output is the extracted preference patterns.

[0111] Step 3:

[0112] When a user inquires about a specific product or service through their device, the server uses a generative AI model to search for relevant information. It receives a prompt as input and uses the generative AI model to generate the most appropriate response. The output is the latest and most relevant product information corresponding to the user's question.

[0113] Step 4:

[0114] The server customizes the generated information based on the user's individual preferences. This customization process uses preference patterns derived from past purchase and browsing history to filter and rank the generated information. The output is a personalized recommendation list.

[0115] Step 5:

[0116] The device presents customized information to the user through an interactive user interface. The information is displayed in a chat format, allowing the user to easily compare options and make a purchase decision. The input is recommendation data received from a server, and the output is the presentation of information to the user.

[0117] Step 6:

[0118] Users evaluate the presented options and send feedback to the server. This feedback is used to improve the service and retrain machine learning algorithms. The input is user feedback information, and the output is data to support the continuous improvement of the service.

[0119] 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.

[0120] This invention realizes a lifestyle concierge system that recognizes the user's emotional state in real time and provides information optimized to the user's individual needs. This system analyzes a combination of the user's initial settings, browsing history, and emotional state, and uses a generating AI to acquire and customize relevant information.

[0121] First, the user enters initial setup information from the device and registers it with the system. This information is sent to the server and stored in the database. The device continuously collects data on the user's website visit history and search queries, and provides this data to the server for detailed preference analysis. Furthermore, the emotion engine installed in the device recognizes the user's emotions in real time through input, voice, and facial expressions, and sends the results to the server.

[0122] Based on this information, the server analyzes the user's preferences and uses data, including emotional states, to prepare to provide the information the user is looking for. When the user queries for information, the server uses the results of the emotion engine to provide the information in the most appropriate tone and content for the user. For example, if the user is feeling stressed, the server will choose to present information in a way that provides reassurance.

[0123] For example, if a user asks, "I want to know how to reduce work stress," the server uses a generative AI to retrieve the latest information on stress reduction. If the emotion engine detects that the user's stress level is high, the server will present the user with information on relaxing music and easy-to-implement relaxation methods in friendly and gentle language.

[0124] Furthermore, when users provide feedback on the information they receive, the emotion engine analyzes their reactions and collects more accurate data. The server uses this feedback data to further improve the service and the generative AI model. This system allows users to efficiently obtain useful information that takes their emotions into consideration.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user enters initial setup information via a terminal and registers it with the system. This information includes family structure, residence, and annual income. The terminal sends this information to the server and stores it in the database.

[0128] Step 2:

[0129] The device continuously records the user's web browsing history and search queries and sends them to the server. This allows the server to understand the user's interests.

[0130] Step 3:

[0131] The emotion engine built into the device analyzes the user's input, voice tone, and facial expressions to recognize the user's emotional state in real time. This emotional data is then sent to a server.

[0132] Step 4:

[0133] The server performs a detailed analysis of the user's preferences based on the acquired initial settings, browsing history, and sentiment data. This analysis determines the type and presentation of information the user needs.

[0134] Step 5:

[0135] Users inquire about specific information through their devices. These inquiries are often specific requests, such as "I want to know how to relax."

[0136] Step 6:

[0137] The server receives user inquiries and uses generative AI to search for relevant information. While retrieving the latest and most reliable information in real time, it adjusts the content and tone of the information, taking sentiment data into consideration.

[0138] Step 7:

[0139] The server generates emotionally sensitive, customized information for the user and sends it to the terminal in a chat format. The terminal displays this information in its user interface, presenting it to the user in a visually pleasing way.

[0140] Step 8:

[0141] Users review the provided information and provide feedback, including their thoughts and opinions. This feedback is sent from the device to the server and used to improve future services.

[0142] Step 9:

[0143] Based on feedback, the server updates the training datasets for the emotion engine and generative AI models to improve the accuracy of the service. This further enhances the user experience.

[0144] (Example 2)

[0145] 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".

[0146] In recent years, many people have become dependent on online information, creating a demand for systems that provide information optimized for individual users. Providing information tailored to users' emotions and interests is particularly important, but current technology makes this difficult to achieve. Therefore, there is a need to develop systems that can provide information in real time, tailored to each user's emotional state and interests.

[0147] 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.

[0148] In this invention, the server includes means for analyzing the user's interests based on initial setup data, visit history, and emotional state obtained from the user; means for receiving user inquiries, searching for relevant information using a generative model, and obtaining the latest information; and means for customizing information in a tone appropriate to the user's interests based on emotional state data and providing it to an information display terminal. This makes it possible to provide customized information according to the user's emotional state and interests.

[0149] "Initial setup data" refers to the basic information that users enter when registering with the system, and it is fundamental data used to understand the user's individual information and interests.

[0150] "Visit history" refers to a record of the websites a user has accessed and the queries they have searched for, and is data used to identify the user's interests and preferences.

[0151] "Emotional state" refers to a real-time evaluation of the user's psychological state, and is data obtained by analyzing the user's facial expressions, voice, and input information.

[0152] A "generative model" refers to a computer model that uses AI technology to search for, retrieve, and generate information in response to user inquiries.

[0153] An "information display terminal" refers to a device or interface used to present information optimized for the user, and is a means of enabling interaction with the user.

[0154] "Customization" is the process of adjusting information and how it is delivered based on the user's individual information and emotional state, and providing information in the most optimal form to meet the needs of each individual user.

[0155] This system is a lifestyle concierge system designed to provide real-time information based on the user's emotional state and interests. First, the user enters initial setup data (name, age, areas of interest, etc.) using a terminal, and this data is sent to the server. This initial setup data is stored in a database and serves as the basis for analysis based on the user's preferences and wishes.

[0156] The device continuously collects the user's website visit history and search queries. This data is provided to the server as material for detailed analysis of the user's interests. The device also has an emotion engine that recognizes the user's emotional state in real time through input, voice, and facial expressions, and sends the results to the server.

[0157] The server integrates and analyzes this initial setup data, visit history, and emotional state to identify user preferences. It uses a generative AI model to customize relevant information in response to user inquiries. When providing information, it considers the data from the emotion engine and presents the information in a tone appropriate to the user's emotions.

[0158] For example, if a user enters the prompt "I want to know how to reduce work stress" into their device, the server will use a generative AI model to search for relevant information. If the server detects that the user is experiencing high levels of stress, it will present information on relaxing music and easy-to-follow relaxation techniques in user-friendly language.

[0159] This system, upon receiving user feedback, uses an emotion engine to further analyze user reactions and utilizes that data to improve services and update its generative AI models, thereby enabling the provision of higher-quality information.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user enters initial setup data using a terminal. Specifically, they enter information such as their name, age, and areas of interest. This data is sent from the terminal to the server, which stores it in a database and uses it as individual user information.

[0163] Step 2:

[0164] The device continuously collects and records the user's website visit history and search queries. This data is necessary to analyze the user's interests and is sent to the server periodically. The server receives this data and registers it in the user's visit history database.

[0165] Step 3:

[0166] The emotion engine built into the device recognizes the user's emotional state in real time from input, voice, and facial expressions. This emotional data is sent from the device to a server, which uses it to update the user's emotional state database.

[0167] Step 4:

[0168] The server analyzes the received initial setup data, visit history, and emotional state. Specifically, it uses machine learning algorithms to identify user interests and preferences, and processes this data in an integrated manner. The analysis results are stored as the basis for personalized information delivery.

[0169] Step 5:

[0170] The user enters a query prompt into the terminal. This prompt is sent to the server. The server uses a generative AI model to search for relevant information corresponding to the prompt and retrieves the latest information.

[0171] Step 6:

[0172] The server customizes the acquired information based on the user's emotional state. Specifically, it selects the optimal tone based on emotional data and adjusts the information accordingly. It then sends the customized information to the device.

[0173] Step 7:

[0174] Users provide feedback on the information provided. This feedback data is sent to the server via the terminal. The server uses an emotion engine to analyze the feedback data and uses it to improve the service and the generative AI model.

[0175] (Application Example 2)

[0176] 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".

[0177] Improving the customer experience in physical stores requires appropriate customer service that aligns with the customer's emotions. However, traditional methods make it difficult for service staff to accurately and in real time understand the emotions of individual customers, and the quality of service often depends on the staff's experience and intuition. This presents a challenge in providing optimal customer service that is tailored to the emotional state of each customer.

[0178] 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.

[0179] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user; means for obtaining the latest information using generating AI and presenting it through audio and visual devices; and means for understanding the user's emotional state in real time and providing appropriate tones and product suggestions. This enables proactive customer service that responds to the emotions of customers visiting the store.

[0180] "Initial setup information" refers to basic data that users enter when they start using the system, and is used to understand the individual needs and preferences of the user.

[0181] "Browsing history" refers to a record of websites a user has visited in the past and search queries they have made. This data is used as basic information to analyze user preferences.

[0182] "Preference analysis" is the process of analyzing what kind of information and services each user desires, using their initial settings and browsing history.

[0183] "Generative AI" is a type of artificial intelligence that uses machine learning and natural language processing technologies to generate and provide information tailored to user needs.

[0184] "Audio and visual devices" are interfaces for presenting information to users, and are devices and technologies for transmitting information through sound and images.

[0185] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a given time. Recognizing and understanding this in real time provides fundamental information for providing services tailored to individual needs.

[0186] "Product recommendation" is the act of recommending specific products or services based on the user's emotions and preferences, and is a method for realizing a more personalized customer experience.

[0187] The system for realizing this invention analyzes the user's initial settings, browsing history, and emotional state, and provides appropriate information using a generative AI model. The system operates with the following configuration.

[0188] The terminals used are compact devices such as smart glasses or smartphones. These devices are equipped with a facial recognition system using OpenCV, which analyzes the emotional state of customers in real time. Voice input is converted into text data using Google® Cloud Speech-to-Text. This data is sent to a server, where the user's emotional state and preference information is analyzed.

[0189] Based on the received data, the server uses a generative AI model to create information tailored to the user's individual needs. This generated information is then presented to the customer through audio and visual devices. Specifically, information processing is performed on a cloud server using Python, and the information is transmitted via audio or text.

[0190] For example, if a customer says in an interview that they are looking for a gift, the server will use a generative AI model to prepare a response such as, "Let me help you find the perfect gift. First, what kind of material do you prefer?" and will choose particularly friendly language if the customer is not in a relaxed emotional state.

[0191] As an example of a prompt, input such as, "Please tell me the best way to suggest products when a user is confused and looking for a birthday present," allows the AI ​​to generate an appropriate response.

[0192] This system enables the provision of an efficient and intimate customer experience that takes into account the emotions of customers visiting physical stores.

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The device captures the customer's face through smart glasses. The input is image data of the customer's face, which is then analyzed using OpenCV to determine their emotional state, and output as numerical data.

[0196] Step 2:

[0197] The terminal collects customer speech as audio data via its microphone. The audio data is converted into text data using Google Cloud Speech-to-Text and sent to the server in text format.

[0198] Step 3:

[0199] The server analyzes the user's emotional state and interests based on facial recognition data and text data converted from speech. This analysis uses a computational algorithm implemented in Python. The output is structured data indicating the user's emotional state and interests.

[0200] Step 4:

[0201] The server uses an AI model to generate information best suited to the user based on structured data. Prompts are used to input information into the AI ​​model, which then generates and outputs information as text data tailored to the customer's emotions and interests.

[0202] Step 5:

[0203] The terminal receives the generated text data and presents it to the customer as audio or visual information through a speech synthesis system. The output is the audio or screen information provided to the customer.

[0204] Step 6:

[0205] The user provides feedback on the information provided, and the device sends that feedback data to the server. The server stores this data for use in retraining machine learning models.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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".

[0222] This invention is implemented as a chat-based lifestyle concierge system for providing information optimized for individual users. Based on the user's initial settings and browsing history, this system analyzes the user's preferences, uses a generative AI to obtain relevant and up-to-date information, and provides it in a customized format. The specific operation of each element is described below.

[0223] First, the user enters initial setup information through their device and registers it with the system. Then, browsing history, naturally accumulated during normal internet use, is collected as data indicating the user's interests. The server analyzes this data and runs an algorithm to identify the user's preferences. This determines what kind of information the user needs.

[0224] Next, when a user requests specific information, the request is sent to the server via the terminal. The server uses generative AI technology to search for relevant information to answer the user's question. At this time, real-time information gathering technology is used to ensure the reliability and freshness of the information in order to obtain the latest data.

[0225] The resulting information is customized on the server based on the user's initial settings and analyzed preferences. This customized information is then delivered to the user in a chat format. The device displays this information on its user interface, allowing the user to understand it intuitively.

[0226] For example, if a user requests "Please suggest some affordable leisure activities that my family can enjoy this weekend," the server will search for and present local event information and affordable activities that meet that request. This information is optimized and presented based, for example, on the user's family size and past leisure preferences.

[0227] This system allows users to easily obtain information that is directly useful in their daily lives and to improve the efficiency of their daily routines.

[0228] The following describes the processing flow.

[0229] Step 1:

[0230] The user enters initial setup information using a terminal and registers it with the system. This information includes family structure, residential area, annual income, occupation, etc. The terminal sends this information to the server.

[0231] Step 2:

[0232] The server stores the received initial configuration information in a database. Furthermore, it collects the user's browsing history, including websites accessed and search queries, to prepare for analysis.

[0233] Step 3:

[0234] The server uses machine learning algorithms to analyze collected browsing history and identify user preferences. This analysis generates tags that indicate user interests based on past behavioral data.

[0235] Step 4:

[0236] Users can inquire about specific information via chat through their device. For example, they might enter a request such as, "Please tell me about local activities that can help me save money."

[0237] Step 5:

[0238] The terminal sends the user's inquiry to the server. The server uses generative AI to search for information related to the user's inquiry and collects the latest data in real time.

[0239] Step 6:

[0240] The server customizes search results based on the user's initial settings and preferences, generating optimized information. This information is summarized in a chat format.

[0241] Step 7:

[0242] The server sends optimized information to the terminal. The terminal displays the received information in a user interface, making it easily accessible to the user.

[0243] Step 8:

[0244] Users review the provided information and, if necessary, send feedback to the server via their device. This feedback is used to improve the service.

[0245] (Example 1)

[0246] 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."

[0247] There is a growing demand for internet information services that can provide highly relevant information to individual users quickly and on a personalized basis. However, current systems have problems in that they cannot respond to the diverse preferences of users and their real-time changing information needs, resulting in limited accuracy and suitability of the information provided.

[0248] 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.

[0249] In this invention, the server includes means for analyzing the user's preferences based on basic setting information and viewing history obtained from the user; means for searching for relevant information and obtaining fresh data using generative AI technology in response to the user's inquiry; and means for adjusting information based on the user's individual information and preferences and presenting it in a conversational format. This makes it possible to quickly provide the user with the latest information optimized for them.

[0250] "Users" refer to individual people or organizations that use the system and are the entities that acquire information according to their needs and preferences.

[0251] "Basic settings information" refers to the information that users provide when they first register with the system, and it is a collection of parameters that indicate their preferences and interests.

[0252] "Viewing history" refers to the websites a user has visited on the internet and the keywords they have searched for, representing a past record of the user's interests and behavior.

[0253] "Means of analyzing preferences" refers to methods that include algorithms and computational processes for analyzing data collected from users to identify their interests and concerns.

[0254] "Generative AI technology" refers to technologies that use artificial intelligence to generate information or generate relevant information in response to user inquiries.

[0255] "Means for searching for relevant information" refers to technical methods for searching and collecting necessary information from a network according to the user's needs.

[0256] "Fresh data" refers to the latest and most up-to-date information, including information acquired in real time to meet user demands.

[0257] "Means of information adjustment" refers to methods for appropriately organizing and editing collected information based on the individual circumstances and preferences of the users.

[0258] "Presenting information in a conversational format" refers to a method of presenting information to users in an intuitive and easy-to-understand manner through chat or messaging.

[0259] This invention is an advanced system for providing information optimized for users, specifically operating through a chat-based information provision interface. The system collects the user's basic settings information and viewing history, and provides personalized information by analyzing the user's preferences based on this data.

[0260] The server uses a generative AI model to search for relevant information in response to user inquiries and retrieve up-to-date data. This AI model has the capability to generate optimal responses to user questions, for example, by utilizing natural language processing techniques. The software used includes generative AI technology and machine learning algorithms. For example, prompts such as "Search for the nearest affordable family events" are used.

[0261] Users input initial information into the system via a terminal and request information based on their individual preferences. The terminal presents the user with the latest information and assists in visualizing the information through an intuitive user interface. This interface displays information in a conversational format, making it easy for users to understand.

[0262] For example, if a user enters "Please suggest some affordable leisure activities that my family can enjoy this weekend" into their terminal, the server receives the request, uses generative AI technology to search for local event information, and provides the most suitable information. By utilizing such prompts, the system can be customized to meet the user's needs, and by using real-time updated information, it can always provide up-to-date information.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] Users register by entering basic configuration information into the system via their terminal. This input includes categories and areas of interest. This information is sent to the server and stored as a dataset representing the user's personal preferences.

[0266] Step 2:

[0267] The device automatically collects the user's internet browsing history and sends it to a server for analysis. This history includes visited websites and searched keywords. The server retrieves this history and creates a new dataset to analyze the user's interests. This identifies the user's preferences and interests.

[0268] Step 3:

[0269] The server initiates a process to analyze user preferences based on their basic settings and viewing history. Specifically, it uses machine learning algorithms to cluster the data and classify the types of information the user prefers. This process extracts patterns that indicate specific user preferences, which will serve as a guide for future information provision.

[0270] Step 4:

[0271] To retrieve specific information, the user enters their request into the terminal. This request is structured as a question or based on keywords and is sent to the server. Upon receiving the request, the server generates a prompt corresponding to the question and uses a generative AI model to search for relevant information.

[0272] Step 5:

[0273] The server starts with a prompt sentence obtained using a generative AI model and searches for relevant information on the network in real time. This search procedure collects the latest and most reliable data. This information is organized according to the user's preferences and basic settings. For example, a prompt sentence such as "I want to find some affordable leisure activities that my family can enjoy this weekend" is used in this process.

[0274] Step 6:

[0275] The acquired information is processed on the server based on the user's individual information and preferences. The optimized information is formatted in a chat format and sent to the terminal. The terminal displays this information to the user using a conversational interface, allowing the user to intuitively understand the provided information.

[0276] (Application Example 1)

[0277] 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."

[0278] There is a challenge in improving the online shopping experience by providing optimal and timely product and promotional information tailored to individual user preferences. Furthermore, there is a need to build systems that can flexibly respond to diverse user needs.

[0279] 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.

[0280] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user and selecting relevant product information; means for receiving user inquiries, searching for product information and promotional information in real time using a generative AI model, and obtaining the latest data; and means for customizing the information to be provided based on the user's individual information and preferences and presenting it through an interactive interface. This enables the provision of a shopping experience optimized for each individual user and improves satisfaction.

[0281] "Analyzing user preferences" refers to the process of determining a user's preferences and interests based on initial settings information and browsing history data obtained from the user.

[0282] A "generative AI model" is an algorithm or artificial intelligence system that generates language or information by learning from large amounts of data.

[0283] "Searching for product information in real time" refers to the process of immediately extracting information from the latest product database in response to a user's inquiry.

[0284] "Dialogue-based interface" refers to a user interface designed to enable users to exchange information in natural language.

[0285] "Customize and present" means optimizing information based on a user's individual information and preferences and providing it to the individual.

[0286] "Based on purchasing power" means adjusting the provision of products and services according to a user's economic background and disposable amount.

[0287] The system for implementing this invention includes a series of processes for providing product information optimized based on user preferences in real time. The server collects initial setting information and browsing history data and analyzes the user's preferences based on this. For this analysis, data analysis platforms such as Python and R are used, specifically libraries such as Pandas and numpy.

[0288] The server uses a generative AI model to search for the latest product information related to a user's inquiry. This search is performed in a web application using Django as the backend, and generative AIs such as OpenAI's GPT and Anthropic's Claude are utilized. As a result, product and campaign information that the user is interested in can be obtained in real time.

[0289] The terminal presents this information to the user via a dialogue-based user interface. The information is customized so that the user can easily understand it, and the purchase procedure can be advanced with intuitive operations.

[0290] For example, if a user enters "I want a cool jacket for summer," the server retrieves the latest jacket information and displays related promotions and recommended products based on the user's purchase history. This allows the user to easily select a product that suits their preferences and needs.

[0291] An example of a prompt message would be, "Based on the user's preferences and history, please suggest the latest promotions related to jackets." This allows the generative AI model to generate the most relevant information according to the user's preferences.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The user enters initial setup information via a terminal and registers it with the system. In this step, the terminal converts the input information into a format and sends it to the server. The input includes the user's basic profile information, purchase history, and browsing history. The output is registration of the user profile in the server-side user profile database.

[0295] Step 2:

[0296] The server analyzes the received initial setup information and browsing history data. This analysis uses data analysis libraries such as Pandas and NumPy to extract patterns indicating the user's interests. The input is the user's profile data, and the output is the extracted preference patterns.

[0297] Step 3:

[0298] When a user inquires about a specific product or service through their device, the server uses a generative AI model to search for relevant information. It receives a prompt as input and uses the generative AI model to generate the most appropriate response. The output is the latest and most relevant product information corresponding to the user's question.

[0299] Step 4:

[0300] The server customizes the generated information based on the user's individual preferences. This customization process uses preference patterns derived from past purchase and browsing history to filter and rank the generated information. The output is a personalized recommendation list.

[0301] Step 5:

[0302] The device presents customized information to the user through an interactive user interface. The information is displayed in a chat format, allowing the user to easily compare options and make a purchase decision. The input is recommendation data received from a server, and the output is the presentation of information to the user.

[0303] Step 6:

[0304] Users evaluate the presented options and send feedback to the server. This feedback is used to improve the service and retrain machine learning algorithms. The input is user feedback information, and the output is data to support the continuous improvement of the service.

[0305] 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.

[0306] This invention realizes a lifestyle concierge system that recognizes the user's emotional state in real time and provides information optimized to the user's individual needs. This system analyzes a combination of the user's initial settings, browsing history, and emotional state, and uses a generating AI to acquire and customize relevant information.

[0307] First, the user inputs initial setting information from the terminal and registers it with the system. This information is sent to the server and stored in the database. The terminal continuously collects data on the user's website visit history and search queries and provides it to the server for detailed preference analysis. Furthermore, the emotion engine installed on the terminal recognizes the user's emotions in real-time through input, voice, and expressions and sends the results to the server.

[0308] Based on this information, the server analyzes the user's preferences and uses the data including the emotional state to prepare for providing the information the user desires. When the user inquiries for information, referring to the results of the emotion engine, the server provides the information in the most appropriate tone and content for the user. For example, when the user is feeling stressed, the server selects an expression of information that gives a sense of security.

[0309] As a specific example, when the user inquires, "I want to know how to reduce work stress," the server uses a generative AI to obtain the latest information on stress reduction. At this time, if the emotion engine recognizes that the user's stress level is increasing, the server presents information on music with a relaxing tendency and relaxation methods that can be easily practiced for the user in a friendly and gentle language.

[0310] Also, when providing feedback on the information provided to the user, the emotion engine analyzes the user's reaction and collects more accurate data. The server uses this feedback data to further improve the service and the generative AI model. With this system, the user can efficiently obtain useful information that takes into account their emotions.

[0311] The following explains the process flow. A

[0312] Step 1:

[0313] The user enters initial setup information via a terminal and registers it with the system. This information includes family structure, residence, and annual income. The terminal sends this information to the server and stores it in the database.

[0314] Step 2:

[0315] The device continuously records the user's web browsing history and search queries and sends them to the server. This allows the server to understand the user's interests.

[0316] Step 3:

[0317] The emotion engine built into the device analyzes the user's input, voice tone, and facial expressions to recognize the user's emotional state in real time. This emotional data is then sent to a server.

[0318] Step 4:

[0319] The server performs a detailed analysis of the user's preferences based on the acquired initial settings, browsing history, and sentiment data. This analysis determines the type and presentation of information the user needs.

[0320] Step 5:

[0321] Users inquire about specific information through their devices. These inquiries are often specific requests, such as "I want to know how to relax."

[0322] Step 6:

[0323] The server receives user inquiries and uses generative AI to search for relevant information. While retrieving the latest and most reliable information in real time, it adjusts the content and tone of the information, taking sentiment data into consideration.

[0324] Step 7:

[0325] The server generates emotionally sensitive, customized information for the user and sends it to the terminal in a chat format. The terminal displays this information in its user interface, presenting it to the user in a visually pleasing way.

[0326] Step 8:

[0327] Users review the provided information and provide feedback, including their thoughts and opinions. This feedback is sent from the device to the server and used to improve future services.

[0328] Step 9:

[0329] Based on feedback, the server updates the training datasets for the emotion engine and generative AI models to improve the accuracy of the service. This further enhances the user experience.

[0330] (Example 2)

[0331] 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".

[0332] In recent years, many people have become dependent on online information, creating a demand for systems that provide information optimized for individual users. Providing information tailored to users' emotions and interests is particularly important, but current technology makes this difficult to achieve. Therefore, there is a need to develop systems that can provide information in real time, tailored to each user's emotional state and interests.

[0333] 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.

[0334] In this invention, the server includes means for analyzing the user's interests based on initial setup data, visit history, and emotional state obtained from the user; means for receiving user inquiries, searching for relevant information using a generative model, and obtaining the latest information; and means for customizing information in a tone appropriate to the user's interests based on emotional state data and providing it to an information display terminal. This makes it possible to provide customized information according to the user's emotional state and interests.

[0335] "Initial setup data" refers to the basic information that users enter when registering with the system, and it is fundamental data used to understand the user's individual information and interests.

[0336] "Visit history" refers to a record of the websites a user has accessed and the queries they have searched for, and is data used to identify the user's interests and preferences.

[0337] "Emotional state" refers to a real-time evaluation of the user's psychological state, and is data obtained by analyzing the user's facial expressions, voice, and input information.

[0338] A "generative model" refers to a computer model that uses AI technology to search for, retrieve, and generate information in response to user inquiries.

[0339] An "information display terminal" refers to a device or interface used to present information optimized for the user, and is a means of enabling interaction with the user.

[0340] "Customization" is the process of adjusting information and how it is delivered based on the user's individual information and emotional state, and providing information in the most optimal form to meet the needs of each individual user.

[0341] This system is a lifestyle concierge system designed to provide real-time information based on the user's emotional state and interests. First, the user enters initial setup data (name, age, areas of interest, etc.) using a terminal, and this data is sent to the server. This initial setup data is stored in a database and serves as the basis for analysis based on the user's preferences and wishes.

[0342] The device continuously collects the user's website visit history and search queries. This data is provided to the server as material for detailed analysis of the user's interests. The device also has an emotion engine that recognizes the user's emotional state in real time through input, voice, and facial expressions, and sends the results to the server.

[0343] The server integrates and analyzes this initial setup data, visit history, and emotional state to identify user preferences. It uses a generative AI model to customize relevant information in response to user inquiries. When providing information, it considers the data from the emotion engine and presents the information in a tone appropriate to the user's emotions.

[0344] For example, if a user enters the prompt "I want to know how to reduce work stress" into their device, the server will use a generative AI model to search for relevant information. If the server detects that the user is experiencing high levels of stress, it will present information on relaxing music and easy-to-follow relaxation techniques in user-friendly language.

[0345] This system, upon receiving user feedback, uses an emotion engine to further analyze user reactions and utilizes that data to improve services and update its generative AI models, thereby enabling the provision of higher-quality information.

[0346] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0347] Step 1:

[0348] The user enters initial setup data using a terminal. Specifically, they enter information such as their name, age, and areas of interest. This data is sent from the terminal to the server, which stores it in a database and uses it as individual user information.

[0349] Step 2:

[0350] The device continuously collects and records the user's website visit history and search queries. This data is necessary to analyze the user's interests and is sent to the server periodically. The server receives this data and registers it in the user's visit history database.

[0351] Step 3:

[0352] The emotion engine built into the device recognizes the user's emotional state in real time from input, voice, and facial expressions. This emotional data is sent from the device to a server, which uses it to update the user's emotional state database.

[0353] Step 4:

[0354] The server analyzes the received initial setup data, visit history, and emotional state. Specifically, it uses machine learning algorithms to identify user interests and preferences, and processes this data in an integrated manner. The analysis results are stored as the basis for personalized information delivery.

[0355] Step 5:

[0356] The user enters a query prompt into the terminal. This prompt is sent to the server. The server uses a generative AI model to search for relevant information corresponding to the prompt and retrieves the latest information.

[0357] Step 6:

[0358] The server customizes the acquired information based on the user's emotional state. Specifically, it selects the optimal tone based on emotional data and adjusts the information accordingly. It then sends the customized information to the device.

[0359] Step 7:

[0360] Users provide feedback on the information provided. This feedback data is sent to the server via the terminal. The server uses an emotion engine to analyze the feedback data and uses it to improve the service and the generative AI model.

[0361] (Application Example 2)

[0362] 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."

[0363] Improving the customer experience in physical stores requires appropriate customer service that aligns with the customer's emotions. However, traditional methods make it difficult for service staff to accurately and in real time understand the emotions of individual customers, and the quality of service often depends on the staff's experience and intuition. This presents a challenge in providing optimal customer service that is tailored to the emotional state of each customer.

[0364] 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.

[0365] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user; means for obtaining the latest information using generating AI and presenting it through audio and visual devices; and means for understanding the user's emotional state in real time and providing appropriate tones and product suggestions. This enables proactive customer service that responds to the emotions of customers visiting the store.

[0366] "Initial setup information" refers to basic data that users enter when they start using the system, and is used to understand the individual needs and preferences of the user.

[0367] "Browsing history" refers to a record of websites a user has visited in the past and search queries they have made. This data is used as basic information to analyze user preferences.

[0368] "Preference analysis" is the process of analyzing what kind of information and services each user desires, using their initial settings and browsing history.

[0369] "Generative AI" is a type of artificial intelligence that uses machine learning and natural language processing technologies to generate and provide information tailored to user needs.

[0370] "Audio and visual devices" are interfaces for presenting information to users, and are devices and technologies for transmitting information through sound and images.

[0371] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a given time. Recognizing and understanding this in real time provides fundamental information for providing services tailored to individual needs.

[0372] "Product recommendation" is the act of recommending specific products or services based on the user's emotions and preferences, and is a method for realizing a more personalized customer experience.

[0373] The system for realizing this invention analyzes the user's initial settings, browsing history, and emotional state, and provides appropriate information using a generative AI model. The system operates with the following configuration.

[0374] The terminals used are compact devices such as smart glasses or smartphones. These devices are equipped with a facial recognition system using OpenCV, which analyzes the emotional state of customers in real time. Voice input is converted into text data using Google Cloud Speech-to-Text. This data is sent to a server, where the user's emotional state and preferences are analyzed.

[0375] Based on the received data, the server uses a generative AI model to create information tailored to the user's individual needs. This generated information is then presented to the customer through audio and visual devices. Specifically, information processing is performed on a cloud server using Python, and the information is transmitted via audio or text.

[0376] For example, if a customer says in an interview that they are looking for a gift, the server will use a generative AI model to prepare a response such as, "Let me help you find the perfect gift. First, what kind of material do you prefer?" and will choose particularly friendly language if the customer is not in a relaxed emotional state.

[0377] As an example of a prompt, input such as, "Please tell me the best way to suggest products when a user is confused and looking for a birthday present," allows the AI ​​to generate an appropriate response.

[0378] This system enables the provision of an efficient and intimate customer experience that takes into account the emotions of customers visiting physical stores.

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The device captures the customer's face through smart glasses. The input is image data of the customer's face, which is then analyzed using OpenCV to determine their emotional state, and output as numerical data.

[0382] Step 2:

[0383] The terminal collects customer speech as audio data via its microphone. The audio data is converted into text data using Google Cloud Speech-to-Text and sent to the server in text format.

[0384] Step 3:

[0385] The server analyzes the user's emotional state and interests based on facial recognition data and text data converted from speech. This analysis uses a computational algorithm implemented in Python. The output is structured data indicating the user's emotional state and interests.

[0386] Step 4:

[0387] The server uses an AI model to generate information best suited to the user based on structured data. Prompts are used to input information into the AI ​​model, which then generates and outputs information as text data tailored to the customer's emotions and interests.

[0388] Step 5:

[0389] The terminal receives the generated text data and presents it to the customer as audio or visual information through a speech synthesis system. The output is the audio or screen information provided to the customer.

[0390] Step 6:

[0391] The user provides feedback on the information provided, and the device sends that feedback data to the server. The server stores this data for use in retraining machine learning models.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] [Third Embodiment]

[0396] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0397] 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.

[0398] 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).

[0399] 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.

[0400] 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.

[0401] 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).

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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.

[0406] 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.

[0407] 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".

[0408] This invention is implemented as a chat-based lifestyle concierge system for providing information optimized for individual users. Based on the user's initial settings and browsing history, this system analyzes the user's preferences, uses a generative AI to obtain relevant and up-to-date information, and provides it in a customized format. The specific operation of each element is described below.

[0409] First, the user enters initial setup information through their device and registers it with the system. Then, browsing history, naturally accumulated during normal internet use, is collected as data indicating the user's interests. The server analyzes this data and runs an algorithm to identify the user's preferences. This determines what kind of information the user needs.

[0410] Next, when a user requests specific information, the request is sent to the server via the terminal. The server uses generative AI technology to search for relevant information to answer the user's question. At this time, real-time information gathering technology is used to ensure the reliability and freshness of the information in order to obtain the latest data.

[0411] The resulting information is customized on the server based on the user's initial settings and analyzed preferences. This customized information is then delivered to the user in a chat format. The device displays this information on its user interface, allowing the user to understand it intuitively.

[0412] For example, if a user requests "Please suggest some affordable leisure activities that my family can enjoy this weekend," the server will search for and present local event information and affordable activities that meet that request. This information is optimized and presented based, for example, on the user's family size and past leisure preferences.

[0413] This system allows users to easily obtain information that is directly useful in their daily lives and to improve the efficiency of their daily routines.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The user enters initial setup information using a terminal and registers it with the system. This information includes family structure, residential area, annual income, occupation, etc. The terminal sends this information to the server.

[0417] Step 2:

[0418] The server stores the received initial configuration information in a database. Furthermore, it collects the user's browsing history, including websites accessed and search queries, to prepare for analysis.

[0419] Step 3:

[0420] The server uses machine learning algorithms to analyze collected browsing history and identify user preferences. This analysis generates tags that indicate user interests based on past behavioral data.

[0421] Step 4:

[0422] Users can inquire about specific information via chat through their device. For example, they might enter a request such as, "Please tell me about local activities that can help me save money."

[0423] Step 5:

[0424] The terminal sends the user's inquiry to the server. The server uses generative AI to search for information related to the user's inquiry and collects the latest data in real time.

[0425] Step 6:

[0426] The server customizes search results based on the user's initial settings and preferences, generating optimized information. This information is summarized in a chat format.

[0427] Step 7:

[0428] The server sends optimized information to the terminal. The terminal displays the received information in a user interface, making it easily accessible to the user.

[0429] Step 8:

[0430] Users review the provided information and, if necessary, send feedback to the server via their device. This feedback is used to improve the service.

[0431] (Example 1)

[0432] 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."

[0433] There is a growing demand for internet information services that can provide highly relevant information to individual users quickly and on a personalized basis. However, current systems have problems in that they cannot respond to the diverse preferences of users and their real-time changing information needs, resulting in limited accuracy and suitability of the information provided.

[0434] 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.

[0435] In this invention, the server includes means for analyzing the user's preferences based on basic setting information and viewing history obtained from the user; means for searching for relevant information and obtaining fresh data using generative AI technology in response to the user's inquiry; and means for adjusting information based on the user's individual information and preferences and presenting it in a conversational format. This makes it possible to quickly provide the user with the latest information optimized for them.

[0436] "Users" refer to individual people or organizations that use the system and are the entities that acquire information according to their needs and preferences.

[0437] "Basic settings information" refers to the information that users provide when they first register with the system, and it is a collection of parameters that indicate their preferences and interests.

[0438] "Viewing history" refers to the websites a user has visited on the internet and the keywords they have searched for, representing a past record of the user's interests and behavior.

[0439] "Means of analyzing preferences" refers to methods that include algorithms and computational processes for analyzing data collected from users to identify their interests and concerns.

[0440] "Generative AI technology" refers to technologies that use artificial intelligence to generate information or generate relevant information in response to user inquiries.

[0441] "Means for searching for relevant information" refers to technical methods for searching and collecting necessary information from a network according to the user's needs.

[0442] "Fresh data" refers to the latest and most up-to-date information, including information acquired in real time to meet user demands.

[0443] "Means of information adjustment" refers to methods for appropriately organizing and editing collected information based on the individual circumstances and preferences of the users.

[0444] "Presenting information in a conversational format" refers to a method of presenting information to users in an intuitive and easy-to-understand manner through chat or messaging.

[0445] This invention is an advanced system for providing information optimized for users, specifically operating through a chat-based information provision interface. The system collects the user's basic settings information and viewing history, and provides personalized information by analyzing the user's preferences based on this data.

[0446] The server uses a generative AI model to search for relevant information in response to user inquiries and retrieve up-to-date data. This AI model has the capability to generate optimal responses to user questions, for example, by utilizing natural language processing techniques. The software used includes generative AI technology and machine learning algorithms. For example, prompts such as "Search for the nearest affordable family events" are used.

[0447] Users input initial information into the system via a terminal and request information based on their individual preferences. The terminal presents the user with the latest information and assists in visualizing the information through an intuitive user interface. This interface displays information in a conversational format, making it easy for users to understand.

[0448] For example, if a user enters "Please suggest some affordable leisure activities that my family can enjoy this weekend" into their terminal, the server receives the request, uses generative AI technology to search for local event information, and provides the most suitable information. By utilizing such prompts, the system can be customized to meet the user's needs, and by using real-time updated information, it can always provide up-to-date information.

[0449] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0450] Step 1:

[0451] Users register by entering basic configuration information into the system via their terminal. This input includes categories and areas of interest. This information is sent to the server and stored as a dataset representing the user's personal preferences.

[0452] Step 2:

[0453] The device automatically collects the user's internet browsing history and sends it to a server for analysis. This history includes visited websites and searched keywords. The server retrieves this history and creates a new dataset to analyze the user's interests. This identifies the user's preferences and interests.

[0454] Step 3:

[0455] The server initiates a process to analyze user preferences based on their basic settings and viewing history. Specifically, it uses machine learning algorithms to cluster the data and classify the types of information the user prefers. This process extracts patterns that indicate specific user preferences, which will serve as a guide for future information provision.

[0456] Step 4:

[0457] To retrieve specific information, the user enters their request into the terminal. This request is structured as a question or based on keywords and is sent to the server. Upon receiving the request, the server generates a prompt corresponding to the question and uses a generative AI model to search for relevant information.

[0458] Step 5:

[0459] The server starts with a prompt sentence obtained using a generative AI model and searches for relevant information on the network in real time. This search procedure collects the latest and most reliable data. This information is organized according to the user's preferences and basic settings. For example, a prompt sentence such as "I want to find some affordable leisure activities that my family can enjoy this weekend" is used in this process.

[0460] Step 6:

[0461] The acquired information is processed on the server based on the user's individual information and preferences. The optimized information is formatted in a chat format and sent to the terminal. The terminal displays this information to the user using a conversational interface, allowing the user to intuitively understand the provided information.

[0462] (Application Example 1)

[0463] 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."

[0464] There is a challenge in improving the online shopping experience by providing optimal and timely product and promotional information tailored to individual user preferences. Furthermore, there is a need to build systems that can flexibly respond to diverse user needs.

[0465] 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.

[0466] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user and selecting relevant product information; means for receiving user inquiries, searching for product information and promotional information in real time using a generative AI model, and obtaining the latest data; and means for customizing the information to be provided based on the user's individual information and preferences and presenting it through an interactive interface. This enables the provision of a shopping experience optimized for each individual user and improves satisfaction.

[0467] "Analyzing user preferences" refers to the process of determining a user's preferences and interests based on initial settings information and browsing history data obtained from the user.

[0468] A "generative AI model" is an algorithm or artificial intelligence system that generates language or information by learning from large amounts of data.

[0469] "Real-time product information search" refers to the process of instantly extracting information from the latest product database in response to user inquiries.

[0470] A "conversational interface" is a user interface designed to allow users to exchange information using natural language.

[0471] "Customizing and presenting" means optimizing information for each individual based on their specific information and preferences, and then providing that information to them.

[0472] "Based on purchasing power" means adjusting the provision of products and services according to the user's economic background and the amount they can spend.

[0473] The system for implementing this invention includes a series of processes for providing product information optimized based on user preferences in real time. The server collects initial setup information and browsing history data, and analyzes user preferences based on this data. This analysis utilizes data analysis platforms such as Python and R, specifically using libraries such as Pandas and NumPy.

[0474] The server uses a generative AI model to search for the latest product information related to the user's inquiry. This search is performed by a web application using Django as the backend, and utilizes OpenAI's GPT and Anthropic's Claude as generative AIs. This allows the server to obtain product and campaign information that the user is interested in in real time.

[0475] The terminal presents this information to the user through an interactive user interface. The information is customized to be easily understood by the user, allowing them to proceed with the purchase process with intuitive operation.

[0476] For example, if a user enters "I want a cool jacket for summer," the server retrieves the latest jacket information and displays related promotions and recommended products based on the user's purchase history. This allows the user to easily select a product that suits their preferences and needs.

[0477] An example of a prompt message would be, "Based on the user's preferences and history, please suggest the latest promotions related to jackets." This allows the generative AI model to generate the most relevant information according to the user's preferences.

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The user enters initial setup information via a terminal and registers it with the system. In this step, the terminal converts the input information into a format and sends it to the server. The input includes the user's basic profile information, purchase history, and browsing history. The output is registration of the user profile in the server-side user profile database.

[0481] Step 2:

[0482] The server analyzes the received initial setup information and browsing history data. This analysis uses data analysis libraries such as Pandas and NumPy to extract patterns indicating the user's interests. The input is the user's profile data, and the output is the extracted preference patterns.

[0483] Step 3:

[0484] When a user inquires about a specific product or service through their device, the server uses a generative AI model to search for relevant information. It receives a prompt as input and uses the generative AI model to generate the most appropriate response. The output is the latest and most relevant product information corresponding to the user's question.

[0485] Step 4:

[0486] The server customizes the generated information based on the user's individual preferences. This customization process uses preference patterns derived from past purchase and browsing history to filter and rank the generated information. The output is a personalized recommendation list.

[0487] Step 5:

[0488] The device presents customized information to the user through an interactive user interface. The information is displayed in a chat format, allowing the user to easily compare options and make a purchase decision. The input is recommendation data received from a server, and the output is the presentation of information to the user.

[0489] Step 6:

[0490] Users evaluate the presented options and send feedback to the server. This feedback is used to improve the service and retrain machine learning algorithms. The input is user feedback information, and the output is data to support the continuous improvement of the service.

[0491] 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.

[0492] This invention realizes a lifestyle concierge system that recognizes the user's emotional state in real time and provides information optimized to the user's individual needs. This system analyzes a combination of the user's initial settings, browsing history, and emotional state, and uses a generating AI to acquire and customize relevant information.

[0493] First, the user enters initial setup information from the device and registers it with the system. This information is sent to the server and stored in the database. The device continuously collects data on the user's website visit history and search queries, and provides this data to the server for detailed preference analysis. Furthermore, the emotion engine installed in the device recognizes the user's emotions in real time through input, voice, and facial expressions, and sends the results to the server.

[0494] Based on this information, the server analyzes the user's preferences and uses data, including emotional states, to prepare to provide the information the user is looking for. When the user queries for information, the server uses the results of the emotion engine to provide the information in the most appropriate tone and content for the user. For example, if the user is feeling stressed, the server will choose to present information in a way that provides reassurance.

[0495] For example, if a user asks, "I want to know how to reduce work stress," the server uses a generative AI to retrieve the latest information on stress reduction. If the emotion engine detects that the user's stress level is high, the server will present the user with information on relaxing music and easy-to-implement relaxation methods in friendly and gentle language.

[0496] Furthermore, when users provide feedback on the information they receive, the emotion engine analyzes their reactions and collects more accurate data. The server uses this feedback data to further improve the service and the generative AI model. This system allows users to efficiently obtain useful information that takes their emotions into consideration.

[0497] The following describes the processing flow.

[0498] Step 1:

[0499] The user enters initial setup information via a terminal and registers it with the system. This information includes family structure, residence, and annual income. The terminal sends this information to the server and stores it in the database.

[0500] Step 2:

[0501] The device continuously records the user's web browsing history and search queries and sends them to the server. This allows the server to understand the user's interests.

[0502] Step 3:

[0503] The emotion engine built into the device analyzes the user's input, voice tone, and facial expressions to recognize the user's emotional state in real time. This emotional data is then sent to a server.

[0504] Step 4:

[0505] The server performs a detailed analysis of the user's preferences based on the acquired initial settings, browsing history, and sentiment data. This analysis determines the type and presentation of information the user needs.

[0506] Step 5:

[0507] Users inquire about specific information through their devices. These inquiries are often specific requests, such as "I want to know how to relax."

[0508] Step 6:

[0509] The server receives user inquiries and uses generative AI to search for relevant information. While retrieving the latest and most reliable information in real time, it adjusts the content and tone of the information, taking sentiment data into consideration.

[0510] Step 7:

[0511] The server generates emotionally sensitive, customized information for the user and sends it to the terminal in a chat format. The terminal displays this information in its user interface, presenting it to the user in a visually pleasing way.

[0512] Step 8:

[0513] Users review the provided information and provide feedback, including their thoughts and opinions. This feedback is sent from the device to the server and used to improve future services.

[0514] Step 9:

[0515] Based on feedback, the server updates the training datasets for the emotion engine and generative AI models to improve the accuracy of the service. This further enhances the user experience.

[0516] (Example 2)

[0517] 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."

[0518] In recent years, many people have become dependent on online information, creating a demand for systems that provide information optimized for individual users. Providing information tailored to users' emotions and interests is particularly important, but current technology makes this difficult to achieve. Therefore, there is a need to develop systems that can provide information in real time, tailored to each user's emotional state and interests.

[0519] 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.

[0520] In this invention, the server includes means for analyzing the user's interests based on initial setup data, visit history, and emotional state obtained from the user; means for receiving user inquiries, searching for relevant information using a generative model, and obtaining the latest information; and means for customizing information in a tone appropriate to the user's interests based on emotional state data and providing it to an information display terminal. This makes it possible to provide customized information according to the user's emotional state and interests.

[0521] "Initial setup data" refers to the basic information that users enter when registering with the system, and it is fundamental data used to understand the user's individual information and interests.

[0522] "Visit history" refers to a record of the websites a user has accessed and the queries they have searched for, and is data used to identify the user's interests and preferences.

[0523] "Emotional state" refers to a real-time evaluation of the user's psychological state, and is data obtained by analyzing the user's facial expressions, voice, and input information.

[0524] A "generative model" refers to a computer model that uses AI technology to search for, retrieve, and generate information in response to user inquiries.

[0525] An "information display terminal" refers to a device or interface used to present information optimized for the user, and is a means of enabling interaction with the user.

[0526] "Customization" is the process of adjusting information and how it is delivered based on the user's individual information and emotional state, and providing information in the most optimal form to meet the needs of each individual user.

[0527] This system is a lifestyle concierge system designed to provide real-time information based on the user's emotional state and interests. First, the user enters initial setup data (name, age, areas of interest, etc.) using a terminal, and this data is sent to the server. This initial setup data is stored in a database and serves as the basis for analysis based on the user's preferences and wishes.

[0528] The device continuously collects the user's website visit history and search queries. This data is provided to the server as material for detailed analysis of the user's interests. The device also has an emotion engine that recognizes the user's emotional state in real time through input, voice, and facial expressions, and sends the results to the server.

[0529] The server integrates and analyzes this initial setup data, visit history, and emotional state to identify user preferences. It uses a generative AI model to customize relevant information in response to user inquiries. When providing information, it considers the data from the emotion engine and presents the information in a tone appropriate to the user's emotions.

[0530] For example, if a user enters the prompt "I want to know how to reduce work stress" into their device, the server will use a generative AI model to search for relevant information. If the server detects that the user is experiencing high levels of stress, it will present information on relaxing music and easy-to-follow relaxation techniques in user-friendly language.

[0531] This system, upon receiving user feedback, uses an emotion engine to further analyze user reactions and utilizes that data to improve services and update its generative AI models, thereby enabling the provision of higher-quality information.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The user enters initial setup data using a terminal. Specifically, they enter information such as their name, age, and areas of interest. This data is sent from the terminal to the server, which stores it in a database and uses it as individual user information.

[0535] Step 2:

[0536] The device continuously collects and records the user's website visit history and search queries. This data is necessary to analyze the user's interests and is sent to the server periodically. The server receives this data and registers it in the user's visit history database.

[0537] Step 3:

[0538] The emotion engine built into the device recognizes the user's emotional state in real time from input, voice, and facial expressions. This emotional data is sent from the device to a server, which uses it to update the user's emotional state database.

[0539] Step 4:

[0540] The server analyzes the received initial setup data, visit history, and emotional state. Specifically, it uses machine learning algorithms to identify user interests and preferences, and processes this data in an integrated manner. The analysis results are stored as the basis for personalized information delivery.

[0541] Step 5:

[0542] The user enters a query prompt into the terminal. This prompt is sent to the server. The server uses a generative AI model to search for relevant information corresponding to the prompt and retrieves the latest information.

[0543] Step 6:

[0544] The server customizes the acquired information based on the user's emotional state. Specifically, it selects the optimal tone based on emotional data and adjusts the information accordingly. It then sends the customized information to the device.

[0545] Step 7:

[0546] Users provide feedback on the information provided. This feedback data is sent to the server via the terminal. The server uses an emotion engine to analyze the feedback data and uses it to improve the service and the generative AI model.

[0547] (Application Example 2)

[0548] 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."

[0549] Improving the customer experience in physical stores requires appropriate customer service that aligns with the customer's emotions. However, traditional methods make it difficult for service staff to accurately and in real time understand the emotions of individual customers, and the quality of service often depends on the staff's experience and intuition. This presents a challenge in providing optimal customer service that is tailored to the emotional state of each customer.

[0550] 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.

[0551] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user; means for obtaining the latest information using generating AI and presenting it through audio and visual devices; and means for understanding the user's emotional state in real time and providing appropriate tones and product suggestions. This enables proactive customer service that responds to the emotions of customers visiting the store.

[0552] "Initial setup information" refers to basic data that users enter when they start using the system, and is used to understand the individual needs and preferences of the user.

[0553] "Browsing history" refers to a record of websites a user has visited in the past and search queries they have made. This data is used as basic information to analyze user preferences.

[0554] "Preference analysis" is the process of analyzing what kind of information and services each user desires, using their initial settings and browsing history.

[0555] "Generative AI" is a type of artificial intelligence that uses machine learning and natural language processing technologies to generate and provide information tailored to user needs.

[0556] "Audio and visual devices" are interfaces for presenting information to users, and are devices and technologies for transmitting information through sound and images.

[0557] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a given time. Recognizing and understanding this in real time provides fundamental information for providing services tailored to individual needs.

[0558] "Product recommendation" is the act of recommending specific products or services based on the user's emotions and preferences, and is a method for realizing a more personalized customer experience.

[0559] The system for realizing this invention analyzes the user's initial settings, browsing history, and emotional state, and provides appropriate information using a generative AI model. The system operates with the following configuration.

[0560] The terminals used are compact devices such as smart glasses or smartphones. These devices are equipped with a facial recognition system using OpenCV, which analyzes the emotional state of customers in real time. Voice input is converted into text data using Google Cloud Speech-to-Text. This data is sent to a server, where the user's emotional state and preferences are analyzed.

[0561] Based on the received data, the server uses a generative AI model to create information tailored to the user's individual needs. This generated information is then presented to the customer through audio and visual devices. Specifically, information processing is performed on a cloud server using Python, and the information is transmitted via audio or text.

[0562] For example, if a customer says in an interview that they are looking for a gift, the server will use a generative AI model to prepare a response such as, "Let me help you find the perfect gift. First, what kind of material do you prefer?" and will choose particularly friendly language if the customer is not in a relaxed emotional state.

[0563] As an example of a prompt, input such as, "Please tell me the best way to suggest products when a user is confused and looking for a birthday present," allows the AI ​​to generate an appropriate response.

[0564] This system enables the provision of an efficient and intimate customer experience that takes into account the emotions of customers visiting physical stores.

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The device captures the customer's face through smart glasses. The input is image data of the customer's face, which is then analyzed using OpenCV to determine their emotional state, and output as numerical data.

[0568] Step 2:

[0569] The terminal collects customer speech as audio data via its microphone. The audio data is converted into text data using Google Cloud Speech-to-Text and sent to the server in text format.

[0570] Step 3:

[0571] The server analyzes the user's emotional state and interests based on facial recognition data and text data converted from speech. This analysis uses a computational algorithm implemented in Python. The output is structured data indicating the user's emotional state and interests.

[0572] Step 4:

[0573] The server uses an AI model to generate information best suited to the user based on structured data. Prompts are used to input information into the AI ​​model, which then generates and outputs information as text data tailored to the customer's emotions and interests.

[0574] Step 5:

[0575] The terminal receives the generated text data and presents it to the customer as audio or visual information through a speech synthesis system. The output is the audio or screen information provided to the customer.

[0576] Step 6:

[0577] The user provides feedback on the information provided, and the device sends that feedback data to the server. The server stores this data for use in retraining machine learning models.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] [Fourth Embodiment]

[0582] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0583] 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.

[0584] 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).

[0585] 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.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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".

[0595] This invention is implemented as a chat-based lifestyle concierge system for providing information optimized for individual users. Based on the user's initial settings and browsing history, this system analyzes the user's preferences, uses a generative AI to obtain relevant and up-to-date information, and provides it in a customized format. The specific operation of each element is described below.

[0596] First, the user enters initial setup information through their device and registers it with the system. Then, browsing history, naturally accumulated during normal internet use, is collected as data indicating the user's interests. The server analyzes this data and runs an algorithm to identify the user's preferences. This determines what kind of information the user needs.

[0597] Next, when a user requests specific information, the request is sent to the server via the terminal. The server uses generative AI technology to search for relevant information to answer the user's question. At this time, real-time information gathering technology is used to ensure the reliability and freshness of the information in order to obtain the latest data.

[0598] The resulting information is customized on the server based on the user's initial settings and analyzed preferences. This customized information is then delivered to the user in a chat format. The device displays this information on its user interface, allowing the user to understand it intuitively.

[0599] For example, if a user requests "Please suggest some affordable leisure activities that my family can enjoy this weekend," the server will search for and present local event information and affordable activities that meet that request. This information is optimized and presented based, for example, on the user's family size and past leisure preferences.

[0600] This system allows users to easily obtain information that is directly useful in their daily lives and to improve the efficiency of their daily routines.

[0601] The following describes the processing flow.

[0602] Step 1:

[0603] The user enters initial setup information using a terminal and registers it with the system. This information includes family structure, residential area, annual income, occupation, etc. The terminal sends this information to the server.

[0604] Step 2:

[0605] The server stores the received initial configuration information in a database. Furthermore, it collects the user's browsing history, including websites accessed and search queries, to prepare for analysis.

[0606] Step 3:

[0607] The server uses machine learning algorithms to analyze collected browsing history and identify user preferences. This analysis generates tags that indicate user interests based on past behavioral data.

[0608] Step 4:

[0609] Users can inquire about specific information via chat through their device. For example, they might enter a request such as, "Please tell me about local activities that can help me save money."

[0610] Step 5:

[0611] The terminal sends the user's inquiry to the server. The server uses generative AI to search for information related to the user's inquiry and collects the latest data in real time.

[0612] Step 6:

[0613] The server customizes search results based on the user's initial settings and preferences, generating optimized information. This information is summarized in a chat format.

[0614] Step 7:

[0615] The server sends optimized information to the terminal. The terminal displays the received information in a user interface, making it easily accessible to the user.

[0616] Step 8:

[0617] Users review the provided information and, if necessary, send feedback to the server via their device. This feedback is used to improve the service.

[0618] (Example 1)

[0619] 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".

[0620] There is a growing demand for internet information services that can provide highly relevant information to individual users quickly and on a personalized basis. However, current systems have problems in that they cannot respond to the diverse preferences of users and their real-time changing information needs, resulting in limited accuracy and suitability of the information provided.

[0621] 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.

[0622] In this invention, the server includes means for analyzing the user's preferences based on basic setting information and viewing history obtained from the user; means for searching for relevant information and obtaining fresh data using generative AI technology in response to the user's inquiry; and means for adjusting information based on the user's individual information and preferences and presenting it in a conversational format. This makes it possible to quickly provide the user with the latest information optimized for them.

[0623] "Users" refer to individual people or organizations that use the system and are the entities that acquire information according to their needs and preferences.

[0624] "Basic settings information" refers to the information that users provide when they first register with the system, and it is a collection of parameters that indicate their preferences and interests.

[0625] "Viewing history" refers to the websites a user has visited on the internet and the keywords they have searched for, representing a past record of the user's interests and behavior.

[0626] "Means of analyzing preferences" refers to methods that include algorithms and computational processes for analyzing data collected from users to identify their interests and concerns.

[0627] "Generative AI technology" refers to technologies that use artificial intelligence to generate information or generate relevant information in response to user inquiries.

[0628] "Means for searching for relevant information" refers to technical methods for searching and collecting necessary information from a network according to the user's needs.

[0629] "Fresh data" refers to the latest and most up-to-date information, including information acquired in real time to meet user demands.

[0630] "Means of information adjustment" refers to methods for appropriately organizing and editing collected information based on the individual circumstances and preferences of the users.

[0631] "Presenting information in a conversational format" refers to a method of presenting information to users in an intuitive and easy-to-understand manner through chat or messaging.

[0632] This invention is an advanced system for providing information optimized for users, specifically operating through a chat-based information provision interface. The system collects the user's basic settings information and viewing history, and provides personalized information by analyzing the user's preferences based on this data.

[0633] The server uses a generative AI model to search for relevant information in response to user inquiries and retrieve up-to-date data. This AI model has the capability to generate optimal responses to user questions, for example, by utilizing natural language processing techniques. The software used includes generative AI technology and machine learning algorithms. For example, prompts such as "Search for the nearest affordable family events" are used.

[0634] Users input initial information into the system via a terminal and request information based on their individual preferences. The terminal presents the user with the latest information and assists in visualizing the information through an intuitive user interface. This interface displays information in a conversational format, making it easy for users to understand.

[0635] For example, if a user enters "Please suggest some affordable leisure activities that my family can enjoy this weekend" into their terminal, the server receives the request, uses generative AI technology to search for local event information, and provides the most suitable information. By utilizing such prompts, the system can be customized to meet the user's needs, and by using real-time updated information, it can always provide up-to-date information.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] Users register by entering basic configuration information into the system via their terminal. This input includes categories and areas of interest. This information is sent to the server and stored as a dataset representing the user's personal preferences.

[0639] Step 2:

[0640] The device automatically collects the user's internet browsing history and sends it to a server for analysis. This history includes visited websites and searched keywords. The server retrieves this history and creates a new dataset to analyze the user's interests. This identifies the user's preferences and interests.

[0641] Step 3:

[0642] The server initiates a process to analyze user preferences based on their basic settings and viewing history. Specifically, it uses machine learning algorithms to cluster the data and classify the types of information the user prefers. This process extracts patterns that indicate specific user preferences, which will serve as a guide for future information provision.

[0643] Step 4:

[0644] To retrieve specific information, the user enters their request into the terminal. This request is structured as a question or based on keywords and is sent to the server. Upon receiving the request, the server generates a prompt corresponding to the question and uses a generative AI model to search for relevant information.

[0645] Step 5:

[0646] The server starts with a prompt sentence obtained using a generative AI model and searches for relevant information on the network in real time. This search procedure collects the latest and most reliable data. This information is organized according to the user's preferences and basic settings. For example, a prompt sentence such as "I want to find some affordable leisure activities that my family can enjoy this weekend" is used in this process.

[0647] Step 6:

[0648] The acquired information is processed on the server based on the user's individual information and preferences. The optimized information is formatted in a chat format and sent to the terminal. The terminal displays this information to the user using a conversational interface, allowing the user to intuitively understand the provided information.

[0649] (Application Example 1)

[0650] 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".

[0651] There is a challenge in improving the online shopping experience by providing optimal and timely product and promotional information tailored to individual user preferences. Furthermore, there is a need to build systems that can flexibly respond to diverse user needs.

[0652] 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.

[0653] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user and selecting relevant product information; means for receiving user inquiries, searching for product information and promotional information in real time using a generative AI model, and obtaining the latest data; and means for customizing the information to be provided based on the user's individual information and preferences and presenting it through an interactive interface. This enables the provision of a shopping experience optimized for each individual user and improves satisfaction.

[0654] "Analyzing user preferences" refers to the process of determining a user's preferences and interests based on initial settings information and browsing history data obtained from the user.

[0655] A "generative AI model" is an algorithm or artificial intelligence system that generates language or information by learning from large amounts of data.

[0656] "Real-time product information search" refers to the process of instantly extracting information from the latest product database in response to user inquiries.

[0657] A "conversational interface" is a user interface designed to allow users to exchange information using natural language.

[0658] "Customizing and presenting" means optimizing information for each individual based on their specific information and preferences, and then providing that information to them.

[0659] "Based on purchasing power" means adjusting the provision of products and services according to the user's economic background and the amount they can spend.

[0660] The system for implementing this invention includes a series of processes for providing product information optimized based on user preferences in real time. The server collects initial setup information and browsing history data, and analyzes user preferences based on this data. This analysis utilizes data analysis platforms such as Python and R, specifically using libraries such as Pandas and NumPy.

[0661] The server uses a generative AI model to search for the latest product information related to the user's inquiry. This search is performed by a web application using Django as the backend, and utilizes OpenAI's GPT and Anthropic's Claude as generative AIs. This allows the server to obtain product and campaign information that the user is interested in in real time.

[0662] The terminal presents this information to the user through an interactive user interface. The information is customized to be easily understood by the user, allowing them to proceed with the purchase process with intuitive operation.

[0663] For example, if a user enters "I want a cool jacket for summer," the server retrieves the latest jacket information and displays related promotions and recommended products based on the user's purchase history. This allows the user to easily select a product that suits their preferences and needs.

[0664] An example of a prompt message would be, "Based on the user's preferences and history, please suggest the latest promotions related to jackets." This allows the generative AI model to generate the most relevant information according to the user's preferences.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The user enters initial setup information via a terminal and registers it with the system. In this step, the terminal converts the input information into a format and sends it to the server. The input includes the user's basic profile information, purchase history, and browsing history. The output is registration of the user profile in the server-side user profile database.

[0668] Step 2:

[0669] The server analyzes the received initial setup information and browsing history data. This analysis uses data analysis libraries such as Pandas and NumPy to extract patterns indicating the user's interests. The input is the user's profile data, and the output is the extracted preference patterns.

[0670] Step 3:

[0671] When a user inquires about a specific product or service through their device, the server uses a generative AI model to search for relevant information. It receives a prompt as input and uses the generative AI model to generate the most appropriate response. The output is the latest and most relevant product information corresponding to the user's question.

[0672] Step 4:

[0673] The server customizes the generated information based on the user's individual preferences. This customization process uses preference patterns derived from past purchase and browsing history to filter and rank the generated information. The output is a personalized recommendation list.

[0674] Step 5:

[0675] The device presents customized information to the user through an interactive user interface. The information is displayed in a chat format, allowing the user to easily compare options and make a purchase decision. The input is recommendation data received from a server, and the output is the presentation of information to the user.

[0676] Step 6:

[0677] Users evaluate the presented options and send feedback to the server. This feedback is used to improve the service and retrain machine learning algorithms. The input is user feedback information, and the output is data to support the continuous improvement of the service.

[0678] 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.

[0679] This invention realizes a lifestyle concierge system that recognizes the user's emotional state in real time and provides information optimized to the user's individual needs. This system analyzes a combination of the user's initial settings, browsing history, and emotional state, and uses a generating AI to acquire and customize relevant information.

[0680] First, the user enters initial setup information from the device and registers it with the system. This information is sent to the server and stored in the database. The device continuously collects data on the user's website visit history and search queries, and provides this data to the server for detailed preference analysis. Furthermore, the emotion engine installed in the device recognizes the user's emotions in real time through input, voice, and facial expressions, and sends the results to the server.

[0681] Based on this information, the server analyzes the user's preferences and uses data, including emotional states, to prepare to provide the information the user is looking for. When the user queries for information, the server uses the results of the emotion engine to provide the information in the most appropriate tone and content for the user. For example, if the user is feeling stressed, the server will choose to present information in a way that provides reassurance.

[0682] For example, if a user asks, "I want to know how to reduce work stress," the server uses a generative AI to retrieve the latest information on stress reduction. If the emotion engine detects that the user's stress level is high, the server will present the user with information on relaxing music and easy-to-implement relaxation methods in friendly and gentle language.

[0683] Furthermore, when users provide feedback on the information they receive, the emotion engine analyzes their reactions and collects more accurate data. The server uses this feedback data to further improve the service and the generative AI model. This system allows users to efficiently obtain useful information that takes their emotions into consideration.

[0684] The following describes the processing flow.

[0685] Step 1:

[0686] The user enters initial setup information via a terminal and registers it with the system. This information includes family structure, residence, and annual income. The terminal sends this information to the server and stores it in the database.

[0687] Step 2:

[0688] The device continuously records the user's web browsing history and search queries and sends them to the server. This allows the server to understand the user's interests.

[0689] Step 3:

[0690] The emotion engine built into the device analyzes the user's input, voice tone, and facial expressions to recognize the user's emotional state in real time. This emotional data is then sent to a server.

[0691] Step 4:

[0692] The server performs a detailed analysis of the user's preferences based on the acquired initial settings, browsing history, and sentiment data. This analysis determines the type and presentation of information the user needs.

[0693] Step 5:

[0694] Users inquire about specific information through their devices. These inquiries are often specific requests, such as "I want to know how to relax."

[0695] Step 6:

[0696] The server receives user inquiries and uses generative AI to search for relevant information. While retrieving the latest and most reliable information in real time, it adjusts the content and tone of the information, taking sentiment data into consideration.

[0697] Step 7:

[0698] The server generates emotionally sensitive, customized information for the user and sends it to the terminal in a chat format. The terminal displays this information in its user interface, presenting it to the user in a visually pleasing way.

[0699] Step 8:

[0700] Users review the provided information and provide feedback, including their thoughts and opinions. This feedback is sent from the device to the server and used to improve future services.

[0701] Step 9:

[0702] Based on feedback, the server updates the training datasets for the emotion engine and generative AI models to improve the accuracy of the service. This further enhances the user experience.

[0703] (Example 2)

[0704] 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".

[0705] In recent years, many people have become dependent on online information, creating a demand for systems that provide information optimized for individual users. Providing information tailored to users' emotions and interests is particularly important, but current technology makes this difficult to achieve. Therefore, there is a need to develop systems that can provide information in real time, tailored to each user's emotional state and interests.

[0706] 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.

[0707] In this invention, the server includes means for analyzing the user's interests based on initial setup data, visit history, and emotional state obtained from the user; means for receiving user inquiries, searching for relevant information using a generative model, and obtaining the latest information; and means for customizing information in a tone appropriate to the user's interests based on emotional state data and providing it to an information display terminal. This makes it possible to provide customized information according to the user's emotional state and interests.

[0708] "Initial setup data" refers to the basic information that users enter when registering with the system, and it is fundamental data used to understand the user's individual information and interests.

[0709] "Visit history" refers to a record of the websites a user has accessed and the queries they have searched for, and is data used to identify the user's interests and preferences.

[0710] "Emotional state" refers to a real-time evaluation of the user's psychological state, and is data obtained by analyzing the user's facial expressions, voice, and input information.

[0711] A "generative model" refers to a computer model that uses AI technology to search for, retrieve, and generate information in response to user inquiries.

[0712] An "information display terminal" refers to a device or interface used to present information optimized for the user, and is a means of enabling interaction with the user.

[0713] "Customization" is the process of adjusting information and how it is delivered based on the user's individual information and emotional state, and providing information in the most optimal form to meet the needs of each individual user.

[0714] This system is a lifestyle concierge system designed to provide real-time information based on the user's emotional state and interests. First, the user enters initial setup data (name, age, areas of interest, etc.) using a terminal, and this data is sent to the server. This initial setup data is stored in a database and serves as the basis for analysis based on the user's preferences and wishes.

[0715] The device continuously collects the user's website visit history and search queries. This data is provided to the server as material for detailed analysis of the user's interests. The device also has an emotion engine that recognizes the user's emotional state in real time through input, voice, and facial expressions, and sends the results to the server.

[0716] The server integrates and analyzes this initial setup data, visit history, and emotional state to identify user preferences. It uses a generative AI model to customize relevant information in response to user inquiries. When providing information, it considers the data from the emotion engine and presents the information in a tone appropriate to the user's emotions.

[0717] For example, if a user enters the prompt "I want to know how to reduce work stress" into their device, the server will use a generative AI model to search for relevant information. If the server detects that the user is experiencing high levels of stress, it will present information on relaxing music and easy-to-follow relaxation techniques in user-friendly language.

[0718] This system, upon receiving user feedback, uses an emotion engine to further analyze user reactions and utilizes that data to improve services and update its generative AI models, thereby enabling the provision of higher-quality information.

[0719] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0720] Step 1:

[0721] The user enters initial setup data using a terminal. Specifically, they enter information such as their name, age, and areas of interest. This data is sent from the terminal to the server, which stores it in a database and uses it as individual user information.

[0722] Step 2:

[0723] The device continuously collects and records the user's website visit history and search queries. This data is necessary to analyze the user's interests and is sent to the server periodically. The server receives this data and registers it in the user's visit history database.

[0724] Step 3:

[0725] The emotion engine built into the device recognizes the user's emotional state in real time from input, voice, and facial expressions. This emotional data is sent from the device to a server, which uses it to update the user's emotional state database.

[0726] Step 4:

[0727] The server analyzes the received initial setup data, visit history, and emotional state. Specifically, it uses machine learning algorithms to identify user interests and preferences, and processes this data in an integrated manner. The analysis results are stored as the basis for personalized information delivery.

[0728] Step 5:

[0729] The user enters a query prompt into the terminal. This prompt is sent to the server. The server uses a generative AI model to search for relevant information corresponding to the prompt and retrieves the latest information.

[0730] Step 6:

[0731] The server customizes the acquired information based on the user's emotional state. Specifically, it selects the optimal tone based on emotional data and adjusts the information accordingly. It then sends the customized information to the device.

[0732] Step 7:

[0733] Users provide feedback on the information provided. This feedback data is sent to the server via the terminal. The server uses an emotion engine to analyze the feedback data and uses it to improve the service and the generative AI model.

[0734] (Application Example 2)

[0735] 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".

[0736] Improving the customer experience in physical stores requires appropriate customer service that aligns with the customer's emotions. However, traditional methods make it difficult for service staff to accurately and in real time understand the emotions of individual customers, and the quality of service often depends on the staff's experience and intuition. This presents a challenge in providing optimal customer service that is tailored to the emotional state of each customer.

[0737] 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.

[0738] In this invention, the server includes means for analyzing user preferences based on initial setup information and browsing history obtained from the user; means for obtaining the latest information using generating AI and presenting it through audio and visual devices; and means for understanding the user's emotional state in real time and providing appropriate tones and product suggestions. This enables proactive customer service that responds to the emotions of customers visiting the store.

[0739] "Initial setup information" refers to basic data that users enter when they start using the system, and is used to understand the individual needs and preferences of the user.

[0740] "Browsing history" refers to a record of websites a user has visited in the past and search queries they have made. This data is used as basic information to analyze user preferences.

[0741] "Preference analysis" is the process of analyzing what kind of information and services each user desires, using their initial settings and browsing history.

[0742] "Generative AI" is a type of artificial intelligence that uses machine learning and natural language processing technologies to generate and provide information tailored to user needs.

[0743] "Audio and visual devices" are interfaces for presenting information to users, and are devices and technologies for transmitting information through sound and images.

[0744] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a given time. Recognizing and understanding this in real time provides fundamental information for providing services tailored to individual needs.

[0745] "Product recommendation" is the act of recommending specific products or services based on the user's emotions and preferences, and is a method for realizing a more personalized customer experience.

[0746] The system for realizing this invention analyzes the user's initial settings, browsing history, and emotional state, and provides appropriate information using a generative AI model. The system operates with the following configuration.

[0747] The terminals used are compact devices such as smart glasses or smartphones. These devices are equipped with a facial recognition system using OpenCV, which analyzes the emotional state of customers in real time. Voice input is converted into text data using Google Cloud Speech-to-Text. This data is sent to a server, where the user's emotional state and preferences are analyzed.

[0748] Based on the received data, the server uses a generative AI model to create information tailored to the user's individual needs. This generated information is then presented to the customer through audio and visual devices. Specifically, information processing is performed on a cloud server using Python, and the information is transmitted via audio or text.

[0749] For example, if a customer says in an interview that they are looking for a gift, the server will use a generative AI model to prepare a response such as, "Let me help you find the perfect gift. First, what kind of material do you prefer?" and will choose particularly friendly language if the customer is not in a relaxed emotional state.

[0750] As an example of a prompt, input such as, "Please tell me the best way to suggest products when a user is confused and looking for a birthday present," allows the AI ​​to generate an appropriate response.

[0751] This system enables the provision of an efficient and intimate customer experience that takes into account the emotions of customers visiting physical stores.

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The device captures the customer's face through smart glasses. The input is image data of the customer's face, which is then analyzed using OpenCV to determine their emotional state, and output as numerical data.

[0755] Step 2:

[0756] The terminal collects customer speech as audio data via its microphone. The audio data is converted into text data using Google Cloud Speech-to-Text and sent to the server in text format.

[0757] Step 3:

[0758] The server analyzes the user's emotional state and interests based on facial recognition data and text data converted from speech. This analysis uses a computational algorithm implemented in Python. The output is structured data indicating the user's emotional state and interests.

[0759] Step 4:

[0760] The server uses an AI model to generate information best suited to the user based on structured data. Prompts are used to input information into the AI ​​model, which then generates and outputs information as text data tailored to the customer's emotions and interests.

[0761] Step 5:

[0762] The terminal receives the generated text data and presents it to the customer as audio or visual information through a speech synthesis system. The output is the audio or screen information provided to the customer.

[0763] Step 6:

[0764] The user provides feedback on the information provided, and the device sends that feedback data to the server. The server stores this data for use in retraining machine learning models.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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."

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0786] The following is further disclosed regarding the embodiments described above.

[0787] (Claim 1)

[0788] [Means for analyzing user preferences based on initial settings information and browsing history obtained from the user,

[0789] [A means of receiving user inquiries, searching for relevant information using generation AI, and obtaining the latest data,

[0790] [Methods for customizing information based on the user's individual information and preferences and presenting it in a chat format,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, which receives user feedback and retrains a machine learning model to use it to improve the service.

[0794] (Claim 3)

[0795] [The system according to claim 1, which provides region-specific public service information and economic lifestyle options based on the user's place of residence and economic circumstances.

[0796] "Example 1"

[0797] (Claim 1)

[0798] [Methods for analyzing user preferences based on basic settings information and viewing history obtained from users,

[0799] [A means of searching for relevant information using generative AI technology in response to user inquiries and obtaining fresh data,

[0800] [Means of tailoring information based on the user's individual information and preferences and presenting it in a conversational format,

[0801] [A means of displaying information in an intuitively understandable way through results presented in a conversational format,

[0802] [Means for updating collected information in real time based on user profiles,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, which receives user feedback and retrains a learning model to use it for service improvement.

[0806] (Claim 3)

[0807] [The system according to claim 1, which provides localized public information services and economic options based on the user's place of residence and economic background.

[0808] "Application Example 1"

[0809] (Claim 1)

[0810] [A means for analyzing user preferences based on initial setup information and browsing history obtained from the user, and selecting relevant product information,

[0811] [A means of receiving user inquiries, using a generative AI model to search for product information and promotional information in real time, and obtaining the latest data,]

[0812] [Means of customizing the information provided based on the user's individual information and preferences, and presenting it through an interactive interface,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] [The system according to claim 1, which receives user feedback and retrains a machine learning algorithm to improve the service.

[0816] (Claim 3)

[0817] [The system according to claim 1, which provides public service information or economic options for a specific region based on the user's location and purchasing power.

[0818] "Example 2 of combining an emotion engine"

[0819] (Claim 1)

[0820] [Means for analyzing user interests based on initial setup data, visit history, and emotional state obtained from the user,

[0821] [A means of receiving user inquiries, searching for relevant information using a generative model, and obtaining the latest information,

[0822] [A means of customizing information in a tone that matches the user's interests based on emotional state data and providing it to an information display terminal,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, which takes in user response data and implements machine learning techniques to utilize it for service improvement and updating of learning models.

[0826] (Claim 3)

[0827] [The system according to claim 1, which selects and provides relaxation methods and emotional stabilization information based on the user's situation and emotional state.

[0828] "Application example 2 when combining with an emotional engine"

[0829] (Claim 1)

[0830] [Means for analyzing user preferences based on initial settings information and browsing history obtained from the user,

[0831] [A means of receiving user inquiries, using a generation AI to search for relevant information, and obtaining the latest information,

[0832] [Means for customizing information based on the user's individual information and preferences and presenting it through audio and visual devices,

[0833] [A means of understanding the user's emotional state in real time and making appropriate tone and product suggestions,

[0834] A system that includes this.

[0835] (Claim 2)

[0836] The system according to claim 1, which receives user feedback and retrains a machine learning model to use it to improve the service.

[0837] (Claim 3)

[0838] [The system according to claim 1, which provides region-specific social service information and economic lifestyle choices based on the user's residential area and economic situation. [Explanation of Symbols]

[0839] 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 for analyzing user preferences based on initial settings information and browsing history obtained from the user, A means of receiving user inquiries, searching for relevant information using generation AI, and obtaining the latest data, A means of customizing information based on the user's individual information and preferences and presenting it in a chat format, A system that includes this.

2. The system according to claim 1, which receives user feedback and retrains a machine learning model to use it to improve the service.

3. The system according to claim 1, which provides region-specific public service information and economic lifestyle options based on the user's place of residence and economic circumstances.

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