system

Abstract

Provide a system. 【Solution means】 Means for recording the user's actions in a virtual space, Means for generating a user profile based on the action data, Means for providing information to the user via a virtual agent generated based on the profile, Means for analyzing the user's real-time actions and reactions and adjusting the response content of the virtual agent, Means for analyzing user data and proposing improvement points for marketing strategies, Means for presenting relevant products to users accessing a virtual store via a communication device and assisting the user's purchase decision, Means for displaying a virtual agent on a visual display device and interacting with the user, A system including.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional online marketing methods, there are problems that the interaction with users is limited and customer engagement is likely to decline. Also, it is difficult to analyze customer feedback in real time and optimize marketing strategies, and there is a problem that it takes a great deal of cost and time to improve the effect of campaigns. The purpose of this invention is to solve these problems and realize efficient marketing activities.

Means for Solving the Problems

[0005] This invention provides a system that includes means for recording user behavior in a virtual space and means for generating a user profile based on the recorded data. Furthermore, it includes means for providing information and interacting with the user through a virtual agent generated based on this profile, means for analyzing user behavior in real time and adjusting responses, and means for suggesting improvements to marketing strategies. This configuration makes it possible to enhance two-way communication with users and optimize marketing activities.

[0006] A "virtual space" is a virtual environment created using computer graphics and virtual reality technologies, allowing users to experience a digital world.

[0007] "Means for recording user behavior" refers to software or hardware systems for collecting and recording behavioral data such as operations and purchasing activities performed by users in a virtual space.

[0008] A "user profile" is a collection of personal digital information generated by analyzing collected user behavior data, including their interests, preferences, and purchase history.

[0009] A "virtual agent" is a digital character that uses generated AI to interact with users in a virtual space, providing product information and answering questions.

[0010] "Real-time analysis methods" refer to technologies that instantly process user behavior and reactions, and adjust the virtual agent's responses on the spot based on the analysis results.

[0011] "Means of suggesting improvements to marketing strategies" refer to algorithms and systems that analyze the effectiveness of current marketing activities based on user behavior data and suggest necessary modifications or new measures. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Also, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] 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).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is provided as a system for improving the user's interactive experience in a virtual space. Specific embodiments for carrying out the invention are described below.

[0034] 1. Data Collection

[0035] The device verifies login information and initiates a session when a user accesses the virtual space. It continuously records user behavior data such as content viewed, product selections, click history, and time spent in the virtual space.

[0036] The server receives behavioral data transmitted from the terminal and stores it in a database. This data is used for later analysis.

[0037] 2. Generating a user profile

[0038] The server analyzes the accumulated data and extracts user interests and behavioral patterns. This generates user profiles.

[0039] User profiles include information such as the user's purchase history, areas of interest, and past events attended, reflecting the user's personal preferences.

[0040] 3. Providing interactive virtual agents

[0041] The server generates a virtual agent using a generative AI based on the user profile. This agent provides product descriptions and recommendations through natural conversations with the user.

[0042] The terminal visually presents the generated virtual agent to the user and provides an interface for interaction.

[0043] 4. Real-time behavioral analysis and optimization of marketing strategies

[0044] The server analyzes user behavior data in real time during interactions and adjusts the agent's responses accordingly. This enables the provision of information tailored to the user's interests.

[0045] The server integrates all user data and extracts areas for improvement in marketing strategies. These improvements are presented to administrators and used as needed to develop campaigns or new initiatives.

[0046] For example, if a user spends time browsing a particular fashion item and is hesitant to purchase it, a virtual agent can introduce the product's features and past reviews, and even suggest related styling options. This can help the user make a purchasing decision.

[0047] In this way, users can obtain a personalized shopping experience in a virtual space, and companies can achieve efficient marketing activities.

[0048] The following describes the processing flow.

[0049] Step 1:

[0050] The device initiates a session when the user logs into the virtual space and begins monitoring the user's behavior. This includes data such as the content the user views, product clicks, selections, and scrolling.

[0051] Step 2:

[0052] The server receives user behavior data transmitted from the terminal and immediately records it in the database. This allows for the continuous accumulation of all user actions.

[0053] Step 3:

[0054] The server analyzes the accumulated data and uses a specific algorithm to analyze user behavior patterns. Based on this analysis, a user profile is generated. The profile includes information such as product categories of interest and purchase history.

[0055] Step 4:

[0056] The server uses generative AI to create a customized virtual agent based on the analyzed user profile. This agent is then ready to provide personalized recommendations with conversational content tailored to the user.

[0057] Step 5:

[0058] The terminal presents the generated virtual agent to the user and initiates an interactive dialogue. The user can have a natural conversation with the agent, obtain product information, and ask questions.

[0059] Step 6:

[0060] The server analyzes user responses in real time during interactions between the user and the virtual agent, and adjusts the response content as needed. This enables the provision of information tailored to the user's interests.

[0061] Step 7:

[0062] The server comprehensively evaluates all user behavior data and identifies areas for improvement in the company's marketing strategy. It generates new suggestions and presents them to administrators to optimize campaign effectiveness.

[0063] (Example 1)

[0064] 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."

[0065] In today's virtual space, there is a demand for personalized and dynamic experiences for users. However, conventional systems cannot fully utilize user behavior data, making it difficult to provide timely information and optimize sales strategies. As a result of these challenges, users do not receive satisfactory experiences, and companies are limited in their ability to conduct efficient marketing activities.

[0066] 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.

[0067] In this invention, the server includes a device for recording user actions, a device for generating a profile based on the action data, and a device for generating a virtual agent based on the profile and providing information. This enables real-time personalized information provision and optimization of sales strategies for users.

[0068] A "user" refers to an individual who accesses a virtual space and has an interactive experience through the system.

[0069] "Actions" refer to actions and operations performed by users within the virtual space, such as clicking, browsing, and selecting products.

[0070] "Action data" refers to data that records information about a user's actions and is used to analyze behavioral patterns and interests.

[0071] A "profile" refers to a collection of information generated based on behavioral data that reflects the user's interests and preferences.

[0072] A "virtual agent" is an interactive character or software based on a generated AI model that interacts with the user and provides information.

[0073] "Information provision" refers to the presentation of information such as product descriptions and recommendations to users via a virtual agent.

[0074] A "sales strategy" refers to a plan or policy that a company uses to efficiently provide its products and services and maximize sales.

[0075] This invention is designed to allow users to have a personalized experience within a virtual space. The following describes in detail how the invention is implemented.

[0076] Data collection and profile generation

[0077] When a user logs into the system, the device records the user's actions in real time, such as content browsing history and click actions. This is done using standard web browsers and mobile applications.

[0078] The server receives behavioral data transmitted from the terminal and stores it in a database. The server analyzes this data, extracts user behavior patterns, and generates a user profile. Data analysis software and machine learning algorithms are used for this analysis.

[0079] Provision of virtual agents

[0080] The server generates a virtual agent using a generative AI model based on the generated user profile. The generative AI model uses natural language processing technology to interact with the user naturally.

[0081] The terminal displays a generated virtual agent as a user interaction interface. Through interaction with the agent, the user can obtain product descriptions and recommendations.

[0082] Real-time analysis and response optimization

[0083] During interactions with the user, the server analyzes new user behavior data in real time and adjusts the agent's responses accordingly. This functionality enables the provision of information tailored to the user's interests.

[0084] Optimizing marketing strategy

[0085] The server integrates all user data and extracts areas for improvement in sales strategies. Based on these results, companies can adjust campaign content and new initiatives in real time.

[0086] Specific examples and prompt statements

[0087] For example, a user looking to purchase a fashion item might spend a long time browsing a product page. In this case, a virtual agent could introduce the product's features and user reviews, and suggest related styling options. This would allow the user to make a better decision.

[0088] An example of a prompt message for a generative AI model can be entered as follows:

[0089] "Based on the user's purchase history and currently viewed items, please generate recommended fashion items and styling suggestions."

[0090] In this way, the system provides users with a highly personalized experience and becomes an important tool for businesses to dynamically optimize their sales strategies.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Start of data collection

[0094] The user logs into the virtual space and begins accessing the system.

[0095] The terminal requests user authentication information and initiates a session. After login, it immediately begins recording user activity data. The input is the user's login information, and the output is a confirmation of session initiation.

[0096] Step 2:

[0097] Collection and transmission of behavioral data

[0098] The device records detailed user activity, including content viewed, product selections, click history, and time spent on pages.

[0099] The collected data is sent to the server in real time. The input is a series of data about the user's actions, and the output is the transmission of this data to the server.

[0100] Step 3:

[0101] Storage and analysis in the database

[0102] The server receives behavioral data sent from the terminal and securely stores it in a database.

[0103] Subsequently, analysis is performed based on the accumulated data to extract user interests and behavioral patterns. This analysis utilizes machine learning algorithms and data mining. The input is behavioral data sent to the server, and the output is the analysis results of user interests and patterns.

[0104] Step 4:

[0105] User Profile Generation

[0106] The server generates user profiles based on the analyzed data, reflecting the user's preferences.

[0107] The profile includes purchase history and categories of interests. The input is analyzed behavioral data, and the output is a detailed user profile.

[0108] Step 5:

[0109] Virtual agent generation

[0110] The server uses a generative AI model to generate a virtual agent suitable for the user profile. This process is performed based on prompt statements.

[0111] The terminal displays the generated agent on the user's screen and provides an interface that enables interaction. The input consists of the user profile and prompt text, and the output is a visually represented agent.

[0112] Step 6:

[0113] Real-time behavioral analysis and response optimization

[0114] The server analyzes new actions that occur during the user-agent interaction in real time.

[0115] Based on this, the agent's response is dynamically adjusted to present the user with the most relevant information. The input is the user's real-time actions during the interaction, and the output is the adjusted agent's response.

[0116] Step 7:

[0117] Optimizing marketing strategy

[0118] The server analyzes integrated data from all users to identify areas for improvement in sales strategies.

[0119] This allows companies to instantly adjust campaign content and new initiatives. The input is integrated user data, and the output is an analytical report for strategic improvement.

[0120] (Application Example 1)

[0121] 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."

[0122] The shopping experience in modern virtual stores is still limited, and there is a need for flexible and interactive responses tailored to individual users. Furthermore, optimizing marketing strategies to respond immediately to user behavior is currently difficult. Therefore, the challenges to be addressed here are providing a customized shopping experience for each user and improving marketing strategies in real time.

[0123] 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.

[0124] In this invention, the server includes means for providing information based on user profiles using a virtual agent, means for analyzing the user's real-time behavior and reactions and adjusting the response content, and means for presenting relevant products to the user via communication devices and providing purchase support. As a result, users can enjoy a personalized and interactive shopping experience, and companies can improve their marketing strategies in real time.

[0125] A "virtual space" is a virtual environment created by a computer that users can access via the internet.

[0126] "User behavior" refers to information such as user actions, browsing history, choices, and time spent in a virtual space.

[0127] A "user profile" is a data structure generated based on a user's past behavioral data, and it reflects the user's interests and preferences.

[0128] A "virtual agent" is a virtual conversational software created using generative AI based on a user profile, and its role is to provide information to the user.

[0129] "Real-time actions and responses" refer to the immediate response to choices, actions, and inputs that users make during ongoing interactions.

[0130] "Communication equipment" refers to devices used by users to access virtual spaces and send and receive information.

[0131] A "visual display device" refers to a display device used to present virtual spaces or virtual agents to users, and includes monitors such as those found on smartphones and personal computers.

[0132] To implement this invention, several key elements are required to realize a personalized shopping experience for users on a virtual store system. This system primarily operates using servers, terminals, and generative AI models.

[0133] First, users access the virtual space through devices such as smartphones or personal computers. Here, user activity data, such as operations and browsing history, is recorded and transmitted to the server in real time.

[0134] The server uses this behavioral data to generate user profiles and, based on the accumulated information, creates a virtual agent suited to that user using an AI model. This agent provides information tailored to the user's preferences and is displayed on a visual display device via communication equipment.

[0135] Furthermore, the server analyzes the user's real-time behavior and reactions, and adjusts the agent's responses as needed. This ensures that users always receive the latest and most relevant information, allowing them to enjoy a shopping experience tailored to their needs.

[0136] As a concrete example, if a user interested in fashion is browsing jackets, a virtual agent will suggest related products and styling to that user. In this case, the server uses a generative AI model to generate the agent's response via a prompt message, for example, "This user is interested in street style. Please suggest jackets and styling that would suit him."

[0137] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0138] Step 1:

[0139] The terminal verifies login information when a user accesses the virtual space and records user behavior data such as operations and browsing history. Input data consists of user identification information and initial operation history. This information is used to construct behavior data, which is then sent to the server. Output data is behavior data with the user identification information attached.

[0140] Step 2:

[0141] The server receives the transmitted behavioral data and stores it in a database. The input is user behavioral data, and the output is the raw data stored in the database. The server uses this data to generate user profiles and analyze user interests and purchasing trends. Statistical analysis and machine learning algorithms are used for data processing, and the output is the user profile.

[0142] Step 3:

[0143] The server uses a generative AI model to generate a virtual agent based on the user profile. At this stage, the user profile is the input, and prompt statements are provided to the generative AI model. For example, the prompt might be, "This user is highly interested in electronic devices. Create an agent that describes related products." The output is the generated virtual agent.

[0144] Step 4:

[0145] The server further monitors user behavior data in real time and adjusts the virtual agent's responses based on that data. The input is the current user session data, and the output is the adjusted agent response. This adjustment is performed using natural language processing and control routines.

[0146] Step 5:

[0147] The terminal presents the generated virtual agent to the user on a visual display device. The input is the agent's interface information sent from the server, and the output is a dynamic display of the agent that the user visually perceives. This provides the user with a visual and interactive experience.

[0148] 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.

[0149] This invention is provided as an interactive system that combines an emotion engine that recognizes user emotions. Specific embodiments for carrying out the invention are described below.

[0150] 1. Collection of emotional data

[0151] The device activates its camera and microphone as soon as the user logs into the virtual space, capturing facial expressions and voice. This allows the system to obtain data about the user's emotions.

[0152] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This analysis identifies the user's emotional state.

[0153] 2. Combining user profiles and sentiment information

[0154] The server integrates previously accumulated user profile information with real-time sentiment information to update the profile. This generates a more refined user model.

[0155] User profiles include recent emotional information and past emotional history, reflecting the user's potential needs and preferences.

[0156] 3. Providing interactive virtual agents

[0157] The server uses generative AI to adjust the virtual agent's responses based on the user's emotional information. The agent is then ready to engage in conversation optimized for the user's current emotional state.

[0158] The device displays a virtual agent to the user that responds with consideration for emotions, and initiates a conversation. The agent engages in emotionally sensitive communication, such as offering words of encouragement or introducing specific products.

[0159] 4. Real-time analysis and optimization of marketing strategies

[0160] The server detects changes in the user's emotions in real time during the interaction and adjusts the virtual agent's speech and actions accordingly.

[0161] The server uses sentiment analysis data to identify areas for improvement in marketing strategies. Based on this data, suggestions are provided to administrators to help develop new campaigns.

[0162] For example, if a user exhibits a negative emotional reaction, the virtual agent can present calming video content or recommend products that match their mood. This improves the user experience and allows companies to conduct more effective marketing activities.

[0163] The following describes the processing flow.

[0164] Step 1:

[0165] The device activates its camera and microphone when the user logs into the virtual space and starts a session. This allows it to begin capturing the user's facial expressions and voice data.

[0166] Step 2:

[0167] The server receives facial expression and voice data transmitted from the terminal. It uses an emotion engine to analyze this data and perform a process to identify the user's emotional state.

[0168] Step 3:

[0169] The server integrates the analyzed sentiment data with the existing user profile and updates the profile to the latest state. This profile includes the user's current sentiment and its history.

[0170] Step 4:

[0171] Based on the updated user profile, the server uses generative AI to generate a virtual agent that provides emotionally appropriate responses. The agent prepares utterances and actions that match the user's emotions.

[0172] Step 5:

[0173] The terminal presents the generated virtual agent to the user and initiates interaction with the user. By interacting with the agent, the user can gain a deeper understanding of products and information that match their interests and needs.

[0174] Step 6:

[0175] The server monitors the user's emotional changes in real time during interaction and adjusts the agent's response accordingly, providing a more personalized experience.

[0176] Step 7:

[0177] The server analyzes all of the user's emotional and behavioral data and suggests improvements to the marketing strategy. These suggestions are provided to administrators and used to optimize campaigns and promotions.

[0178] (Example 2)

[0179] 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".

[0180] In today's virtual space, there is a need for interactive systems that can effectively recognize user emotions and enable communication tailored to individual needs. Traditional methods have been limited to creating superficial profiles based solely on user behavior data, failing to adequately consider users' underlying emotions and preferences. As a result, the quality of the user experience is compromised, and optimizing marketing strategies becomes difficult.

[0181] 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.

[0182] In this invention, the server includes means for acquiring facial and voice data using a communication device in order to collect the user's emotions; means for performing emotion analysis on a computing device and determining the emotional state in order to process the acquired emotion data; and means for integrating and updating new emotion information with existing user information using the results of the emotion analysis. This enables advanced interaction based on the user's emotions, leading to improved user satisfaction and the development of more sophisticated marketing strategies.

[0183] "User emotions" refer to the temporary mental state a user experiences in response to a particular stimulus or situation.

[0184] "Facial and voice data" refers to information indicating emotional states, obtained through the user's facial appearance and voice.

[0185] A "communication device" refers to equipment used to send and receive data between other systems.

[0186] "Computing equipment" refers to hardware and software used for processing and analyzing data.

[0187] "Sentiment analysis" refers to the process of identifying a user's emotions from collected data.

[0188] "User information" refers to a series of data about a user that is used to build their profile.

[0189] An "artificial intelligence model" refers to an algorithm or program that uses data to mimic human thought and decision-making.

[0190] "Market strategy" refers to plans and tactics for promoting the sale of products or services.

[0191] "Mutual exchange" refers to the exchange of information and two-way communication among multiple parties.

[0192] This invention relates to a system that recognizes user emotions and uses that data to optimize interaction with a virtual agent. This system collects and analyzes user emotions in real time when a user participates in a virtual space, and provides responses based on that analysis, thereby achieving sophisticated communication tailored to individual needs.

[0193] Hardware and software environment

[0194] When a user logs into a virtual space, the device activates its built-in camera and high-sensitivity microphone to capture the user's facial expressions and voice. Ideally, a camera with a resolution of 2 megapixels or higher and a microphone with noise-canceling capabilities should be used.

[0195] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This emotion engine may utilize OpenFace or speech recognition software (e.g., speech-to-text technology). Based on the analysis, the user's emotional state is determined, and this information is updated in the user profile.

[0196] The server further utilizes generative AI models to generate responses based on the user's emotional information. For example, a large-scale language model might suggest, "Shall we try a simple way to relax?" if it identifies that the user is feeling stressed.

[0197] Usage example

[0198] As a concrete example of its use, suppose a user says, "Today was a tough day." In this case, the server analyzes the data indicating negative emotions, and the generative AI model generates a response such as, "That sounds tough. Shall I play some soothing music to help you relax?" This response is emotionally sensitive and enables communication tailored to the user's needs.

[0199] Example of a prompt

[0200] In generative AI models, prompts such as "How can we provide reassurance when a user is feeling anxious?" are used.

[0201] In this way, responses that resonate with the user's emotions are generated, enabling more natural and effective interactions.

[0202] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0203] Step 1:

[0204] The device activates its camera and microphone simultaneously with the user logging into the virtual space. This initiates the real-time capture of the user's facial expressions and voice. Inputs include video data from the camera and audio data from the microphone. Based on this data, an output is obtained in which initial raw data is collected.

[0205] Step 2:

[0206] The terminal transmits the collected video and audio data to the server. The server receives this data and temporarily stores it in a database. As input, the user's raw data is sent to the server, and as output, it is stored in the database, enabling subsequent processing.

[0207] Step 3:

[0208] The server passes the stored data to emotion analysis software to analyze the user's emotional state. This data processing captures changes in facial expressions and voice tone, and the emotion engine identifies emotions such as joy, sadness, and anger. The input is raw data, and the output is the analysis results.

[0209] Step 4:

[0210] The server updates user information using the acquired sentiment analysis results. Calculations are performed to integrate the new sentiment data into the user profile. The input is the analysis results, and the output is the generated updated user profile.

[0211] Step 5:

[0212] The server uses a generative AI model to generate the optimal response to present to the user. The generative AI model creates a response tailored to the user's emotional state based on the prompt. For example, a prompt might ask, "What kind of encouragement should you offer when the user is feeling anxious?" The input consists of an updated user profile and a prompt, and the output is a response appropriate for the user.

[0213] Step 6:

[0214] The terminal presents the user with the response sent from the server. A virtual agent then begins interacting with the user using voice and animation. The input is the response data sent from the server, and the output is the interactive experience provided to the user.

[0215] Step 7:

[0216] The server continuously monitors the user's emotional data during the interaction and adjusts the virtual agent's response as needed. This enables real-time adaptation. The input is continuously monitored data, and the output is dynamically adjusted dialogue content.

[0217] This series of processes enables personalized responses based on the user's emotions, resulting in high-quality virtual interactions.

[0218] (Application Example 2)

[0219] 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".

[0220] Modern consumers demand personalized service not only in the digital space but also in physical stores. Meeting this demand requires recognizing user emotions in real time and providing appropriate responses. However, conventional technologies struggle to optimize responses based on user emotions, resulting in a lack of improved user experience. Furthermore, systems capable of providing real-time, emotion-based product recommendations are limited. These are the challenges that exist.

[0221] 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.

[0222] In this invention, the server includes means for recognizing the user's emotional state and optimizing the response, means for making real-time emotion-based product recommendations, and means for analyzing user data and suggesting improvements to the marketing strategy. This makes it possible to provide appropriate services tailored to the user's emotions, which is expected to improve customer satisfaction and boost sales.

[0223] A "virtual space" is a simulated environment of the real world generated by a computer.

[0224] "User behavior" refers to the choices and statements made by the user when interacting with the virtual agent.

[0225] A "user profile" is a set of individual attribute information built based on a user's behavior and past interactions.

[0226] A "virtual agent" is a virtual artificial intelligence agent developed for the purpose of interacting with users.

[0227] "Means for adjusting response content" refers to methods for optimizing the content of interactions in accordance with the user's actions and emotions.

[0228] "Areas for improvement in marketing strategy" refer to points for optimizing promotional activities extracted from user data and interaction results.

[0229] "Real-time, emotion-based product recommendations" is a method of instantly suggesting appropriate products and services based on the user's emotional state at that moment.

[0230] "Means of recognizing a user's emotional state" refers to technologies that analyze data obtained from facial expressions, voice, etc., to identify the user's emotions.

[0231] This invention is a system that recognizes user emotions in real time and improves the user experience through a virtual agent. The server receives user facial expressions and voice data and uses OpenCV, Google® Cloud Speech-to-Text API, and TENSORFLOW® as software for analysis. This identifies the user's emotional state and generates an AI-optimized response.

[0232] The device is equipped with a camera and microphone, and its role is to capture the user's facial expressions and voice in real time. The acquired data is sent to a server, and based on the results of the analysis, products and services tailored to the user's emotions are recommended.

[0233] Users can interact with virtual agents via smartphones or head-mounted displays. They can enjoy personalized services tailored to their emotions; for example, if they are feeling negative, they may be offered products that promote relaxation.

[0234] As a concrete example, suppose a user is browsing products in a store via their smartphone and makes an expression indicating interest. At this point, the server performs facial analysis, and if a positive emotion is detected, a virtual agent can immediately introduce information about related products. This allows the user to quickly and appropriately obtain information that meets their needs, which is expected to increase their willingness to purchase.

[0235] An example of a prompt to input into a generative AI model is, "Identify emotions from the user's facial expressions and voice, and adjust the response accordingly."

[0236] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0237] Step 1:

[0238] The device activates its camera and microphone to capture the user's facial expressions and voice in real time. Input consists of video data from the camera and audio data from the microphone. Output consists of sending the collected facial video and audio files to the server. Specifically, the device continuously acquires camera frames and records audio.

[0239] Step 2:

[0240] The server analyzes received facial expression data using OpenCV and identifies emotional states using a TensorFlow model. It receives facial expression image data from the terminal as input, preprocesses the images to extract facial features, and inputs them into the emotion model. The output is the identified emotional state. Specifically, the server applies a face detection algorithm to convert facial expression information into emotion labels.

[0241] Step 3:

[0242] The server converts the received audio data into text using the Google Cloud Speech-to-Text API, analyzes the audio features, and obtains complementary sentiment information. The audio data is the input, and the text data for sentiment identification is the output. Specifically, the server sends the audio data to the API, and the returned text data is analyzed by the sentiment recognition algorithm.

[0243] Step 4:

[0244] The server determines the user's overall emotional state based on the facial expression and voice data obtained as analysis results. The emotional state label is the input, and the basic data for generating the response to display to the user is the output. Specifically, the server integrates multiple emotional indicators to generate a single emotional evaluation.

[0245] Step 5:

[0246] The server inputs prompt sentences into a generative AI model and generates the optimal response for the virtual agent based on the user's emotional state. The input consists of an emotional state evaluation and a prompt sentence, while the output is the content of the virtual agent's utterance. Specifically, the server inputs prompt sentences containing emotional indicators into the generative AI model and constructs the resulting text response.

[0247] Step 6:

[0248] The terminal presents the user with the response from the virtual agent sent from the server, either as audio or text. The input is the response text from the server, and the output is the information presented to the user. Specifically, the terminal either synthesizes the text information into speech or displays it directly on the screen.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] [Second Embodiment]

[0253] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0254] 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.

[0255] 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).

[0256] 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.

[0257] 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.

[0258] 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).

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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".

[0265] This invention is provided as a system for improving the user's interactive experience in a virtual space. Specific embodiments for carrying out the invention are described below.

[0266] 1. Data Collection

[0267] The device verifies login information and initiates a session when a user accesses the virtual space. It continuously records user behavior data such as content viewed, product selections, click history, and time spent in the virtual space.

[0268] The server receives behavioral data transmitted from the terminal and stores it in a database. This data is used for later analysis.

[0269] 2. Generating a user profile

[0270] The server analyzes the accumulated data and extracts user interests and behavioral patterns. This generates user profiles.

[0271] User profiles include information such as the user's purchase history, areas of interest, and past events attended, reflecting the user's personal preferences.

[0272] 3. Providing interactive virtual agents

[0273] The server generates a virtual agent using a generative AI based on the user profile. This agent provides product descriptions and recommendations through natural conversations with the user.

[0274] The terminal visually presents the generated virtual agent to the user and provides an interface for interaction.

[0275] 4. Real-time behavioral analysis and optimization of marketing strategies

[0276] The server analyzes user behavior data in real time during interactions and adjusts the agent's responses accordingly. This enables the provision of information tailored to the user's interests.

[0277] The server integrates all user data and extracts areas for improvement in marketing strategies. These improvements are presented to administrators and used as needed to develop campaigns or new initiatives.

[0278] As a specific example, when a user spends time browsing a specific fashion item and is hesitant to purchase, the virtual agent introduces the features of the product and past reviews, and further provides proposals for related styling. This can assist the user in making a purchase decision.

[0279] In this way, the user can obtain an individualized shopping experience within the virtual space, and the enterprise can achieve efficient marketing activities.

[0280] The following describes the processing flow.

[0281] Step 1:

[0282] When the user logs in to the virtual space, the terminal starts a session and begins to monitor the user's actions. This includes data such as the content the user browses, clicks on products, selections, and scrolls.

[0283] Step 2:

[0284] The server receives the user's action data transmitted from the terminal and immediately records it in the database. In this way, all the user's actions are continuously accumulated.

[0285] Step 3:

[0286] The server analyzes the accumulated data and analyzes the user's action patterns using a specific algorithm. Based on this analysis, a user profile is generated. The profile includes product categories of interest and purchase history, etc.

[0287] Step 4:

[0288] Based on the analyzed user profile, the server creates a customized virtual agent using generative AI. This agent has dialogue content suitable for the user and is ready to provide individualized recommendations.

[0289] Step 5:

[0290] The terminal presents the generated virtual agent to the user and initiates an interactive dialogue. The user can have a natural conversation with the agent, obtain product information, and ask questions.

[0291] Step 6:

[0292] The server analyzes user responses in real time during interactions between the user and the virtual agent, and adjusts the response content as needed. This enables the provision of information tailored to the user's interests.

[0293] Step 7:

[0294] The server comprehensively evaluates all user behavior data and identifies areas for improvement in the company's marketing strategy. It generates new suggestions and presents them to administrators to optimize campaign effectiveness.

[0295] (Example 1)

[0296] 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".

[0297] In today's virtual space, there is a demand for personalized and dynamic experiences for users. However, conventional systems cannot fully utilize user behavior data, making it difficult to provide timely information and optimize sales strategies. As a result of these challenges, users do not receive satisfactory experiences, and companies are limited in their ability to conduct efficient marketing activities.

[0298] 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.

[0299] In this invention, the server includes a device for recording the actions of users, a device for generating a profile based on the action data, and a device for generating a virtual agent based on the profile to provide information. This enables real-time personalized information provision and optimization of sales strategies for users.

[0300] "User" refers to an individual who accesses the virtual space and experiences interactively through the system.

[0301] "Action" refers to the actions and operations performed by the user within the virtual space, such as clicks, browsing, product selection, etc.

[0302] "Action data" is data that records information related to the actions of users and is used to analyze behavior patterns and interests.

[0303] "Profile" refers to a collection of information that reflects the interests and preferences of users, generated based on action data.

[0304] "Virtual agent" is an interactive character or software based on the generated AI model, which interacts with users and provides information.

[0305] "Information provision" refers to the presentation of information such as product descriptions and recommendations to users through virtual agents.

[0306] "Sales strategy" refers to the plan and policy for a company to efficiently provide products and services and maximize sales.

[0307] This invention is designed to enable users to obtain a personalized experience within the virtual space. The following specifically describes how to implement the invention.

[0308] Data collection and profile generation

[0309] When a user logs into the system, the device records the user's actions in real time, such as content browsing history and click actions. This is done using standard web browsers and mobile applications.

[0310] The server receives behavioral data transmitted from the terminal and stores it in a database. The server analyzes this data, extracts user behavior patterns, and generates a user profile. Data analysis software and machine learning algorithms are used for this analysis.

[0311] Provision of virtual agents

[0312] The server generates a virtual agent using a generative AI model based on the generated user profile. The generative AI model uses natural language processing technology to interact with the user naturally.

[0313] The terminal displays a generated virtual agent as a user interaction interface. Through interaction with the agent, the user can obtain product descriptions and recommendations.

[0314] Real-time analysis and response optimization

[0315] During interactions with the user, the server analyzes new user behavior data in real time and adjusts the agent's responses accordingly. This functionality enables the provision of information tailored to the user's interests.

[0316] Optimizing marketing strategy

[0317] The server integrates all user data and extracts areas for improvement in sales strategies. Based on these results, companies can adjust campaign content and new initiatives in real time.

[0318] Specific examples and prompt statements

[0319] For example, a user looking to purchase a fashion item might spend a long time browsing a product page. In this case, a virtual agent could introduce the product's features and user reviews, and suggest related styling options. This would allow the user to make a better decision.

[0320] An example of a prompt message for a generative AI model can be entered as follows:

[0321] "Based on the user's purchase history and currently viewed items, please generate recommended fashion items and styling suggestions."

[0322] In this way, the system provides users with a highly personalized experience and becomes an important tool for businesses to dynamically optimize their sales strategies.

[0323] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0324] Step 1:

[0325] Start of data collection

[0326] The user logs into the virtual space and begins accessing the system.

[0327] The terminal requests user authentication information and initiates a session. After login, it immediately begins recording user activity data. The input is the user's login information, and the output is a confirmation of session initiation.

[0328] Step 2:

[0329] Collection and transmission of behavioral data

[0330] The device records detailed user activity, including content viewed, product selections, click history, and time spent on pages.

[0331] The collected data is sent to the server in real time. The input is a series of data about the user's actions, and the output is the transmission of this data to the server.

[0332] Step 3:

[0333] Storage and analysis in the database

[0334] The server receives behavioral data sent from the terminal and securely stores it in a database.

[0335] Subsequently, analysis is performed based on the accumulated data to extract user interests and behavioral patterns. This analysis utilizes machine learning algorithms and data mining. The input is behavioral data sent to the server, and the output is the analysis results of user interests and patterns.

[0336] Step 4:

[0337] User Profile Generation

[0338] The server generates user profiles based on the analyzed data, reflecting the user's preferences.

[0339] The profile includes purchase history and categories of interests. The input is analyzed behavioral data, and the output is a detailed user profile.

[0340] Step 5:

[0341] Virtual agent generation

[0342] The server uses a generative AI model to generate a virtual agent suitable for the user profile. This process is performed based on prompt statements.

[0343] The terminal displays the generated agent on the user's screen and provides an interface that enables interaction. The input consists of the user profile and prompt text, and the output is a visually represented agent.

[0344] Step 6:

[0345] Real-time behavioral analysis and response optimization

[0346] The server analyzes new actions that occur during the user-agent interaction in real time.

[0347] Based on this, the agent's response is dynamically adjusted to present the user with the most relevant information. The input is the user's real-time actions during the interaction, and the output is the adjusted agent's response.

[0348] Step 7:

[0349] Optimizing marketing strategy

[0350] The server analyzes integrated data from all users to identify areas for improvement in sales strategies.

[0351] This allows companies to instantly adjust campaign content and new initiatives. The input is integrated user data, and the output is an analytical report for strategic improvement.

[0352] (Application Example 1)

[0353] 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."

[0354] The shopping experience in modern virtual stores is still limited, and there is a need for flexible and interactive responses tailored to individual users. Furthermore, optimizing marketing strategies to respond immediately to user behavior is currently difficult. Therefore, the challenges to be addressed here are providing a customized shopping experience for each user and improving marketing strategies in real time.

[0355] 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.

[0356] In this invention, the server includes means for providing information based on user profiles using a virtual agent, means for analyzing the user's real-time behavior and reactions and adjusting the response content, and means for presenting relevant products to the user via communication devices and providing purchase support. As a result, users can enjoy a personalized and interactive shopping experience, and companies can improve their marketing strategies in real time.

[0357] A "virtual space" is a virtual environment created by a computer that users can access via the internet.

[0358] "User behavior" refers to information such as user actions, browsing history, choices, and time spent in a virtual space.

[0359] A "user profile" is a data structure generated based on a user's past behavioral data, and it reflects the user's interests and preferences.

[0360] A "virtual agent" is a virtual conversational software created using generative AI based on a user profile, and its role is to provide information to the user.

[0361] "Real-time actions and responses" refer to the immediate response to choices, actions, and inputs that users make during ongoing interactions.

[0362] "Communication equipment" refers to devices used by users to access virtual spaces and send and receive information.

[0363] A "visual display device" refers to a display device used to present virtual spaces or virtual agents to users, and includes monitors such as those found on smartphones and personal computers.

[0364] To implement this invention, several key elements are required to realize a personalized shopping experience for users on a virtual store system. This system primarily operates using servers, terminals, and generative AI models.

[0365] First, users access the virtual space through devices such as smartphones or personal computers. Here, user activity data, such as operations and browsing history, is recorded and transmitted to the server in real time.

[0366] The server uses this behavioral data to generate user profiles and, based on the accumulated information, creates a virtual agent suited to that user using an AI model. This agent provides information tailored to the user's preferences and is displayed on a visual display device via communication equipment.

[0367] Furthermore, the server analyzes the user's real-time behavior and reactions, and adjusts the agent's responses as needed. This ensures that users always receive the latest and most relevant information, allowing them to enjoy a shopping experience tailored to their needs.

[0368] As a concrete example, if a user interested in fashion is browsing jackets, a virtual agent will suggest related products and styling to that user. In this case, the server uses a generative AI model to generate the agent's response via a prompt message, for example, "This user is interested in street style. Please suggest jackets and styling that would suit him."

[0369] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0370] Step 1:

[0371] The terminal verifies login information when a user accesses the virtual space and records user behavior data such as operations and browsing history. Input data consists of user identification information and initial operation history. This information is used to construct behavior data, which is then sent to the server. Output data is behavior data with the user identification information attached.

[0372] Step 2:

[0373] The server receives the transmitted behavioral data and stores it in a database. The input is user behavioral data, and the output is the raw data stored in the database. The server uses this data to generate user profiles and analyze user interests and purchasing trends. Statistical analysis and machine learning algorithms are used for data processing, and the output is the user profile.

[0374] Step 3:

[0375] The server uses a generative AI model to generate a virtual agent based on the user profile. At this stage, the user profile is the input, and prompt statements are provided to the generative AI model. For example, the prompt might be, "This user is highly interested in electronic devices. Create an agent that describes related products." The output is the generated virtual agent.

[0376] Step 4:

[0377] The server further monitors user behavior data in real time and adjusts the virtual agent's responses based on that data. The input is the current user session data, and the output is the adjusted agent response. This adjustment is performed using natural language processing and control routines.

[0378] Step 5:

[0379] The terminal presents the generated virtual agent to the user on a visual display device. The input is the agent's interface information sent from the server, and the output is a dynamic display of the agent that the user visually perceives. This provides the user with a visual and interactive experience.

[0380] 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.

[0381] This invention is provided as an interactive system that combines an emotion engine that recognizes user emotions. Specific embodiments for carrying out the invention are described below.

[0382] 1. Collection of emotional data

[0383] The device activates its camera and microphone as soon as the user logs into the virtual space, capturing facial expressions and voice. This allows the system to obtain data about the user's emotions.

[0384] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This analysis identifies the user's emotional state.

[0385] 2. Combining user profiles and sentiment information

[0386] The server integrates previously accumulated user profile information with real-time sentiment information to update the profile. This generates a more refined user model.

[0387] User profiles include recent emotional information and past emotional history, reflecting the user's potential needs and preferences.

[0388] 3. Providing interactive virtual agents

[0389] The server uses generative AI to adjust the virtual agent's responses based on the user's emotional information. The agent is then ready to engage in conversation optimized for the user's current emotional state.

[0390] The device displays a virtual agent to the user that responds with consideration for emotions, and initiates a conversation. The agent engages in emotionally sensitive communication, such as offering words of encouragement or introducing specific products.

[0391] 4. Real-time analysis and optimization of marketing strategies

[0392] The server detects changes in the user's emotions in real time during the interaction and adjusts the virtual agent's speech and actions accordingly.

[0393] The server uses sentiment analysis data to identify areas for improvement in marketing strategies. Based on this data, suggestions are provided to administrators to help develop new campaigns.

[0394] For example, if a user exhibits a negative emotional reaction, the virtual agent can present calming video content or recommend products that match their mood. This improves the user experience and allows companies to conduct more effective marketing activities.

[0395] The following describes the processing flow.

[0396] Step 1:

[0397] The device activates its camera and microphone when the user logs into the virtual space and starts a session. This allows it to begin capturing the user's facial expressions and voice data.

[0398] Step 2:

[0399] The server receives facial expression and voice data transmitted from the terminal. It uses an emotion engine to analyze this data and perform a process to identify the user's emotional state.

[0400] Step 3:

[0401] The server integrates the analyzed sentiment data with the existing user profile and updates the profile to the latest state. This profile includes the user's current sentiment and its history.

[0402] Step 4:

[0403] Based on the updated user profile, the server uses generative AI to generate a virtual agent that provides emotionally appropriate responses. The agent prepares utterances and actions that match the user's emotions.

[0404] Step 5:

[0405] The terminal presents the generated virtual agent to the user and initiates interaction with the user. By interacting with the agent, the user can gain a deeper understanding of products and information that match their interests and needs.

[0406] Step 6:

[0407] The server monitors the user's emotional changes in real time during interaction and adjusts the agent's response accordingly, providing a more personalized experience.

[0408] Step 7:

[0409] The server analyzes all of the user's emotional and behavioral data and suggests improvements to the marketing strategy. These suggestions are provided to administrators and used to optimize campaigns and promotions.

[0410] (Example 2)

[0411] 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".

[0412] In today's virtual space, there is a need for interactive systems that can effectively recognize user emotions and enable communication tailored to individual needs. Traditional methods have been limited to creating superficial profiles based solely on user behavior data, failing to adequately consider users' underlying emotions and preferences. As a result, the quality of the user experience is compromised, and optimizing marketing strategies becomes difficult.

[0413] 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.

[0414] In this invention, the server includes means for acquiring facial and voice data using a communication device in order to collect the user's emotions; means for performing emotion analysis on a computing device and determining the emotional state in order to process the acquired emotion data; and means for integrating and updating new emotion information with existing user information using the results of the emotion analysis. This enables advanced interaction based on the user's emotions, leading to improved user satisfaction and the development of more sophisticated marketing strategies.

[0415] "User emotions" refer to the temporary mental state a user experiences in response to a particular stimulus or situation.

[0416] "Facial and voice data" refers to information indicating emotional states, obtained through the user's facial appearance and voice.

[0417] A "communication device" refers to equipment used to send and receive data between other systems.

[0418] "Computing equipment" refers to hardware and software used for processing and analyzing data.

[0419] "Sentiment analysis" refers to the process of identifying a user's emotions from collected data.

[0420] "User information" refers to a series of data about a user that is used to build their profile.

[0421] An "artificial intelligence model" refers to an algorithm or program that uses data to mimic human thought and decision-making.

[0422] "Market strategy" refers to plans and tactics for promoting the sale of products or services.

[0423] "Mutual exchange" refers to the exchange of information and two-way communication among multiple parties.

[0424] This invention relates to a system that recognizes user emotions and uses that data to optimize interaction with a virtual agent. This system collects and analyzes user emotions in real time when a user participates in a virtual space, and provides responses based on that analysis, thereby achieving sophisticated communication tailored to individual needs.

[0425] Hardware and software environment

[0426] When a user logs into a virtual space, the device activates its built-in camera and high-sensitivity microphone to capture the user's facial expressions and voice. Ideally, a camera with a resolution of 2 megapixels or higher and a microphone with noise-canceling capabilities should be used.

[0427] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This emotion engine may utilize OpenFace or speech recognition software (e.g., speech-to-text technology). Based on the analysis, the user's emotional state is determined, and this information is updated in the user profile.

[0428] The server further utilizes generative AI models to generate responses based on the user's emotional information. For example, a large-scale language model might suggest, "Shall we try a simple way to relax?" if it identifies that the user is feeling stressed.

[0429] Usage example

[0430] As a concrete example of its use, suppose a user says, "Today was a tough day." In this case, the server analyzes the data indicating negative emotions, and the generative AI model generates a response such as, "That sounds tough. Shall I play some soothing music to help you relax?" This response is emotionally sensitive and enables communication tailored to the user's needs.

[0431] Example of a prompt

[0432] In generative AI models, prompts such as "How can we provide reassurance when a user is feeling anxious?" are used.

[0433] In this way, responses that resonate with the user's emotions are generated, enabling more natural and effective interactions.

[0434] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0435] Step 1:

[0436] The device activates its camera and microphone simultaneously with the user logging into the virtual space. This initiates the real-time capture of the user's facial expressions and voice. Inputs include video data from the camera and audio data from the microphone. Based on this data, an output is obtained in which initial raw data is collected.

[0437] Step 2:

[0438] The terminal transmits the collected video and audio data to the server. The server receives this data and temporarily stores it in a database. As input, the user's raw data is sent to the server, and as output, it is stored in the database, enabling subsequent processing.

[0439] Step 3:

[0440] The server passes the stored data to emotion analysis software to analyze the user's emotional state. This data processing captures changes in facial expressions and voice tone, and the emotion engine identifies emotions such as joy, sadness, and anger. The input is raw data, and the output is the analysis results.

[0441] Step 4:

[0442] The server updates user information using the acquired sentiment analysis results. Calculations are performed to integrate the new sentiment data into the user profile. The input is the analysis results, and the output is the generated updated user profile.

[0443] Step 5:

[0444] The server uses a generative AI model to generate the optimal response to present to the user. The generative AI model creates a response tailored to the user's emotional state based on the prompt. For example, a prompt might ask, "What kind of encouragement should you offer when the user is feeling anxious?" The input consists of an updated user profile and a prompt, and the output is a response appropriate for the user.

[0445] Step 6:

[0446] The terminal presents the user with the response sent from the server. A virtual agent then begins interacting with the user using voice and animation. The input is the response data sent from the server, and the output is the interactive experience provided to the user.

[0447] Step 7:

[0448] The server continuously monitors the user's emotional data during the interaction and adjusts the virtual agent's response as needed. This enables real-time adaptation. The input is continuously monitored data, and the output is dynamically adjusted dialogue content.

[0449] This series of processes enables personalized responses based on the user's emotions, resulting in high-quality virtual interactions.

[0450] (Application Example 2)

[0451] 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."

[0452] Modern consumers demand personalized service not only in the digital space but also in physical stores. Meeting this demand requires recognizing user emotions in real time and providing appropriate responses. However, conventional technologies struggle to optimize responses based on user emotions, resulting in a lack of improved user experience. Furthermore, systems capable of providing real-time, emotion-based product recommendations are limited. These are the challenges that exist.

[0453] 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.

[0454] In this invention, the server includes means for recognizing the user's emotional state and optimizing the response, means for making real-time emotion-based product recommendations, and means for analyzing user data and suggesting improvements to the marketing strategy. This makes it possible to provide appropriate services tailored to the user's emotions, which is expected to improve customer satisfaction and boost sales.

[0455] A "virtual space" is a simulated environment of the real world generated by a computer.

[0456] "User behavior" refers to the choices and statements made by the user when interacting with the virtual agent.

[0457] A "user profile" is a set of individual attribute information built based on a user's behavior and past interactions.

[0458] A "virtual agent" is a virtual artificial intelligence agent developed for the purpose of interacting with users.

[0459] "Means for adjusting response content" refers to methods for optimizing the content of interactions in accordance with the user's actions and emotions.

[0460] "Areas for improvement in marketing strategy" refer to points for optimizing promotional activities extracted from user data and interaction results.

[0461] "Real-time, emotion-based product recommendations" is a method of instantly suggesting appropriate products and services based on the user's emotional state at that moment.

[0462] "Means of recognizing a user's emotional state" refers to technologies that analyze data obtained from facial expressions, voice, etc., to identify the user's emotions.

[0463] This invention is a system that recognizes user emotions in real time and improves the user experience through a virtual agent. The server receives user facial expressions and voice data and uses OpenCV, Google Cloud Speech-to-Text API, and TensorFlow as software to analyze this data. This identifies the user's emotional state and generates an AI-optimized response.

[0464] The device is equipped with a camera and microphone, and its role is to capture the user's facial expressions and voice in real time. The acquired data is sent to a server, and based on the results of the analysis, products and services tailored to the user's emotions are recommended.

[0465] Users can interact with virtual agents via smartphones or head-mounted displays. They can enjoy personalized services tailored to their emotions; for example, if they are feeling negative, they may be offered products that promote relaxation.

[0466] As a concrete example, suppose a user is browsing products in a store via their smartphone and makes an expression indicating interest. At this point, the server performs facial analysis, and if a positive emotion is detected, a virtual agent can immediately introduce information about related products. This allows the user to quickly and appropriately obtain information that meets their needs, which is expected to increase their willingness to purchase.

[0467] An example of a prompt to input into a generative AI model is, "Identify emotions from the user's facial expressions and voice, and adjust the response accordingly."

[0468] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0469] Step 1:

[0470] The device activates its camera and microphone to capture the user's facial expressions and voice in real time. Input consists of video data from the camera and audio data from the microphone. Output consists of sending the collected facial video and audio files to the server. Specifically, the device continuously acquires camera frames and records audio.

[0471] Step 2:

[0472] The server analyzes received facial expression data using OpenCV and identifies emotional states using a TensorFlow model. It receives facial expression image data from the terminal as input, preprocesses the images to extract facial features, and inputs them into the emotion model. The output is the identified emotional state. Specifically, the server applies a face detection algorithm to convert facial expression information into emotion labels.

[0473] Step 3:

[0474] The server converts the received audio data into text using the Google Cloud Speech-to-Text API, analyzes the audio features, and obtains complementary sentiment information. The audio data is the input, and the text data for sentiment identification is the output. Specifically, the server sends the audio data to the API, and the returned text data is analyzed by the sentiment recognition algorithm.

[0475] Step 4:

[0476] The server determines the user's overall emotional state based on the facial expression and voice data obtained as analysis results. The emotional state label is the input, and the basic data for generating the response to display to the user is the output. Specifically, the server integrates multiple emotional indicators to generate a single emotional evaluation.

[0477] Step 5:

[0478] The server inputs prompt sentences into a generative AI model and generates the optimal response for the virtual agent based on the user's emotional state. The input consists of an emotional state evaluation and a prompt sentence, while the output is the content of the virtual agent's utterance. Specifically, the server inputs prompt sentences containing emotional indicators into the generative AI model and constructs the resulting text response.

[0479] Step 6:

[0480] The terminal presents the user with the response from the virtual agent sent from the server, either as audio or text. The input is the response text from the server, and the output is the information presented to the user. Specifically, the terminal either synthesizes the text information into speech or displays it directly on the screen.

[0481] 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.

[0482] 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.

[0483] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0484] [Third Embodiment]

[0485] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0486] 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.

[0487] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0488] The 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.

[0489] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0490] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0491] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0492] Figure 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.

[0493] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0494] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0495] In the 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.

[0496] 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".

[0497] This invention is provided as a system for improving the user's interactive experience in a virtual space. Specific embodiments for carrying out the invention are described below.

[0498] 1. Data Collection

[0499] The device verifies login information and initiates a session when a user accesses the virtual space. It continuously records user behavior data such as content viewed, product selections, click history, and time spent in the virtual space.

[0500] The server receives behavioral data transmitted from the terminal and stores it in a database. This data is used for later analysis.

[0501] 2. Generating a user profile

[0502] The server analyzes the accumulated data and extracts user interests and behavioral patterns. This generates user profiles.

[0503] User profiles include information such as the user's purchase history, areas of interest, and past events attended, reflecting the user's personal preferences.

[0504] 3. Providing interactive virtual agents

[0505] The server generates a virtual agent using a generative AI based on the user profile. This agent provides product descriptions and recommendations through natural conversations with the user.

[0506] The terminal visually presents the generated virtual agent to the user and provides an interface for interaction.

[0507] 4. Real-time behavioral analysis and optimization of marketing strategies

[0508] The server analyzes user behavior data in real time during interactions and adjusts the agent's responses accordingly. This enables the provision of information tailored to the user's interests.

[0509] The server integrates all user data and extracts areas for improvement in marketing strategies. These improvements are presented to administrators and used as needed to develop campaigns or new initiatives.

[0510] For example, if a user spends time browsing a particular fashion item and is hesitant to purchase it, a virtual agent can introduce the product's features and past reviews, and even suggest related styling options. This can help the user make a purchasing decision.

[0511] In this way, users can obtain a personalized shopping experience in a virtual space, and companies can achieve efficient marketing activities.

[0512] The following describes the processing flow.

[0513] Step 1:

[0514] The device initiates a session when the user logs into the virtual space and begins monitoring the user's behavior. This includes data such as the content the user views, product clicks, selections, and scrolling.

[0515] Step 2:

[0516] The server receives user behavior data transmitted from the terminal and immediately records it in the database. This allows for the continuous accumulation of all user actions.

[0517] Step 3:

[0518] The server analyzes the accumulated data and uses a specific algorithm to analyze user behavior patterns. Based on this analysis, a user profile is generated. The profile includes information such as product categories of interest and purchase history.

[0519] Step 4:

[0520] The server uses generative AI to create a customized virtual agent based on the analyzed user profile. This agent is then ready to provide personalized recommendations with conversational content tailored to the user.

[0521] Step 5:

[0522] The terminal presents the generated virtual agent to the user and initiates an interactive dialogue. The user can have a natural conversation with the agent, obtain product information, and ask questions.

[0523] Step 6:

[0524] The server analyzes user responses in real time during interactions between the user and the virtual agent, and adjusts the response content as needed. This enables the provision of information tailored to the user's interests.

[0525] Step 7:

[0526] The server comprehensively evaluates all user behavior data and identifies areas for improvement in the company's marketing strategy. It generates new suggestions and presents them to administrators to optimize campaign effectiveness.

[0527] (Example 1)

[0528] 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."

[0529] In today's virtual space, there is a demand for personalized and dynamic experiences for users. However, conventional systems cannot fully utilize user behavior data, making it difficult to provide timely information and optimize sales strategies. As a result of these challenges, users do not receive satisfactory experiences, and companies are limited in their ability to conduct efficient marketing activities.

[0530] 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.

[0531] In this invention, the server includes a device for recording user actions, a device for generating a profile based on the action data, and a device for generating a virtual agent based on the profile and providing information. This enables real-time personalized information provision and optimization of sales strategies for users.

[0532] A "user" refers to an individual who accesses a virtual space and has an interactive experience through the system.

[0533] "Actions" refer to actions and operations performed by users within the virtual space, such as clicking, browsing, and selecting products.

[0534] "Action data" refers to data that records information about a user's actions and is used to analyze behavioral patterns and interests.

[0535] A "profile" refers to a collection of information generated based on behavioral data that reflects the user's interests and preferences.

[0536] A "virtual agent" is an interactive character or software based on a generated AI model that interacts with the user and provides information.

[0537] "Information provision" refers to the presentation of information such as product descriptions and recommendations to users via a virtual agent.

[0538] A "sales strategy" refers to a plan or policy that a company uses to efficiently provide its products and services and maximize sales.

[0539] This invention is designed to allow users to have a personalized experience within a virtual space. The following describes in detail how the invention is implemented.

[0540] Data collection and profile generation

[0541] When a user logs into the system, the device records the user's actions in real time, such as content browsing history and click actions. This is done using standard web browsers and mobile applications.

[0542] The server receives behavioral data transmitted from the terminal and stores it in a database. The server analyzes this data, extracts user behavior patterns, and generates a user profile. Data analysis software and machine learning algorithms are used for this analysis.

[0543] Provision of virtual agents

[0544] The server generates a virtual agent using a generative AI model based on the generated user profile. The generative AI model uses natural language processing technology to interact with the user naturally.

[0545] The terminal displays a generated virtual agent as a user interaction interface. Through interaction with the agent, the user can obtain product descriptions and recommendations.

[0546] Real-time analysis and response optimization

[0547] During interactions with the user, the server analyzes new user behavior data in real time and adjusts the agent's responses accordingly. This functionality enables the provision of information tailored to the user's interests.

[0548] Optimizing marketing strategy

[0549] The server integrates all user data and extracts areas for improvement in sales strategies. Based on these results, companies can adjust campaign content and new initiatives in real time.

[0550] Specific examples and prompt statements

[0551] For example, a user looking to purchase a fashion item might spend a long time browsing a product page. In this case, a virtual agent could introduce the product's features and user reviews, and suggest related styling options. This would allow the user to make a better decision.

[0552] An example of a prompt message for a generative AI model can be entered as follows:

[0553] "Based on the user's purchase history and currently viewed items, please generate recommended fashion items and styling suggestions."

[0554] In this way, the system provides users with a highly personalized experience and becomes an important tool for businesses to dynamically optimize their sales strategies.

[0555] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0556] Step 1:

[0557] Start of data collection

[0558] The user logs into the virtual space and begins accessing the system.

[0559] The terminal requests user authentication information and initiates a session. After login, it immediately begins recording user activity data. The input is the user's login information, and the output is a confirmation of session initiation.

[0560] Step 2:

[0561] Collection and transmission of behavioral data

[0562] The device records detailed user activity, including content viewed, product selections, click history, and time spent on pages.

[0563] The collected data is sent to the server in real time. The input is a series of data about the user's actions, and the output is the transmission of this data to the server.

[0564] Step 3:

[0565] Storage and analysis in the database

[0566] The server receives behavioral data sent from the terminal and securely stores it in a database.

[0567] Subsequently, analysis is performed based on the accumulated data to extract user interests and behavioral patterns. This analysis utilizes machine learning algorithms and data mining. The input is behavioral data sent to the server, and the output is the analysis results of user interests and patterns.

[0568] Step 4:

[0569] User Profile Generation

[0570] The server generates user profiles based on the analyzed data, reflecting the user's preferences.

[0571] The profile includes purchase history and categories of interests. The input is analyzed behavioral data, and the output is a detailed user profile.

[0572] Step 5:

[0573] Virtual agent generation

[0574] The server uses a generative AI model to generate a virtual agent suitable for the user profile. This process is performed based on prompt statements.

[0575] The terminal displays the generated agent on the user's screen and provides an interface that enables interaction. The input consists of the user profile and prompt text, and the output is a visually represented agent.

[0576] Step 6:

[0577] Real-time behavioral analysis and response optimization

[0578] The server analyzes new actions that occur during the user-agent interaction in real time.

[0579] Based on this, the agent's response is dynamically adjusted to present the user with the most relevant information. The input is the user's real-time actions during the interaction, and the output is the adjusted agent's response.

[0580] Step 7:

[0581] Optimizing marketing strategy

[0582] The server analyzes integrated data from all users to identify areas for improvement in sales strategies.

[0583] This allows companies to instantly adjust campaign content and new initiatives. The input is integrated user data, and the output is an analytical report for strategic improvement.

[0584] (Application Example 1)

[0585] 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."

[0586] The shopping experience in modern virtual stores is still limited, and there is a need for flexible and interactive responses tailored to individual users. Furthermore, optimizing marketing strategies to respond immediately to user behavior is currently difficult. Therefore, the challenges to be addressed here are providing a customized shopping experience for each user and improving marketing strategies in real time.

[0587] 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.

[0588] In this invention, the server includes means for providing information based on user profiles using a virtual agent, means for analyzing the user's real-time behavior and reactions and adjusting the response content, and means for presenting relevant products to the user via communication devices and providing purchase support. As a result, users can enjoy a personalized and interactive shopping experience, and companies can improve their marketing strategies in real time.

[0589] A "virtual space" is a virtual environment created by a computer that users can access via the internet.

[0590] "User behavior" refers to information such as user actions, browsing history, choices, and time spent in a virtual space.

[0591] A "user profile" is a data structure generated based on a user's past behavioral data, and it reflects the user's interests and preferences.

[0592] A "virtual agent" is a virtual conversational software created using generative AI based on a user profile, and its role is to provide information to the user.

[0593] "Real-time actions and responses" refer to the immediate response to choices, actions, and inputs that users make during ongoing interactions.

[0594] "Communication equipment" refers to devices used by users to access virtual spaces and send and receive information.

[0595] A "visual display device" refers to a display device used to present virtual spaces or virtual agents to users, and includes monitors such as those found on smartphones and personal computers.

[0596] To implement this invention, several key elements are required to realize a personalized shopping experience for users on a virtual store system. This system primarily operates using servers, terminals, and generative AI models.

[0597] First, users access the virtual space through devices such as smartphones or personal computers. Here, user activity data, such as operations and browsing history, is recorded and transmitted to the server in real time.

[0598] The server uses this behavioral data to generate user profiles and, based on the accumulated information, creates a virtual agent suited to that user using an AI model. This agent provides information tailored to the user's preferences and is displayed on a visual display device via communication equipment.

[0599] Furthermore, the server analyzes the user's real-time behavior and reactions, and adjusts the agent's responses as needed. This ensures that users always receive the latest and most relevant information, allowing them to enjoy a shopping experience tailored to their needs.

[0600] As a concrete example, if a user interested in fashion is browsing jackets, a virtual agent will suggest related products and styling to that user. In this case, the server uses a generative AI model to generate the agent's response via a prompt message, for example, "This user is interested in street style. Please suggest jackets and styling that would suit him."

[0601] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0602] Step 1:

[0603] The terminal verifies login information when a user accesses the virtual space and records user behavior data such as operations and browsing history. Input data consists of user identification information and initial operation history. This information is used to construct behavior data, which is then sent to the server. Output data is behavior data with the user identification information attached.

[0604] Step 2:

[0605] The server receives the transmitted behavioral data and stores it in a database. The input is user behavioral data, and the output is the raw data stored in the database. The server uses this data to generate user profiles and analyze user interests and purchasing trends. Statistical analysis and machine learning algorithms are used for data processing, and the output is the user profile.

[0606] Step 3:

[0607] The server uses a generative AI model to generate a virtual agent based on the user profile. At this stage, the user profile is the input, and prompt statements are provided to the generative AI model. For example, the prompt might be, "This user is highly interested in electronic devices. Create an agent that describes related products." The output is the generated virtual agent.

[0608] Step 4:

[0609] The server further monitors user behavior data in real time and adjusts the virtual agent's responses based on that data. The input is the current user session data, and the output is the adjusted agent response. This adjustment is performed using natural language processing and control routines.

[0610] Step 5:

[0611] The terminal presents the generated virtual agent to the user on a visual display device. The input is the agent's interface information sent from the server, and the output is a dynamic display of the agent that the user visually perceives. This provides the user with a visual and interactive experience.

[0612] 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.

[0613] This invention is provided as an interactive system that combines an emotion engine that recognizes user emotions. Specific embodiments for carrying out the invention are described below.

[0614] 1. Collection of emotional data

[0615] The device activates its camera and microphone as soon as the user logs into the virtual space, capturing facial expressions and voice. This allows the system to obtain data about the user's emotions.

[0616] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This analysis identifies the user's emotional state.

[0617] 2. Combining user profiles and sentiment information

[0618] The server integrates previously accumulated user profile information with real-time sentiment information to update the profile. This generates a more refined user model.

[0619] User profiles include recent emotional information and past emotional history, reflecting the user's potential needs and preferences.

[0620] 3. Providing interactive virtual agents

[0621] The server uses generative AI to adjust the virtual agent's responses based on the user's emotional information. The agent is then ready to engage in conversation optimized for the user's current emotional state.

[0622] The device displays a virtual agent to the user that responds with consideration for emotions, and initiates a conversation. The agent engages in emotionally sensitive communication, such as offering words of encouragement or introducing specific products.

[0623] 4. Real-time analysis and optimization of marketing strategies

[0624] The server detects changes in the user's emotions in real time during the interaction and adjusts the virtual agent's speech and actions accordingly.

[0625] The server uses sentiment analysis data to identify areas for improvement in marketing strategies. Based on this data, suggestions are provided to administrators to help develop new campaigns.

[0626] For example, if a user exhibits a negative emotional reaction, the virtual agent can present calming video content or recommend products that match their mood. This improves the user experience and allows companies to conduct more effective marketing activities.

[0627] The following describes the processing flow.

[0628] Step 1:

[0629] The device activates its camera and microphone when the user logs into the virtual space and starts a session. This allows it to begin capturing the user's facial expressions and voice data.

[0630] Step 2:

[0631] The server receives facial expression and voice data transmitted from the terminal. It uses an emotion engine to analyze this data and perform a process to identify the user's emotional state.

[0632] Step 3:

[0633] The server integrates the analyzed sentiment data with the existing user profile and updates the profile to the latest state. This profile includes the user's current sentiment and its history.

[0634] Step 4:

[0635] Based on the updated user profile, the server uses generative AI to generate a virtual agent that provides emotionally appropriate responses. The agent prepares utterances and actions that match the user's emotions.

[0636] Step 5:

[0637] The terminal presents the generated virtual agent to the user and initiates interaction with the user. By interacting with the agent, the user can gain a deeper understanding of products and information that match their interests and needs.

[0638] Step 6:

[0639] The server monitors the user's emotional changes in real time during interaction and adjusts the agent's response accordingly, providing a more personalized experience.

[0640] Step 7:

[0641] The server analyzes all of the user's emotional and behavioral data and suggests improvements to the marketing strategy. These suggestions are provided to administrators and used to optimize campaigns and promotions.

[0642] (Example 2)

[0643] 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."

[0644] In today's virtual space, there is a need for interactive systems that can effectively recognize user emotions and enable communication tailored to individual needs. Traditional methods have been limited to creating superficial profiles based solely on user behavior data, failing to adequately consider users' underlying emotions and preferences. As a result, the quality of the user experience is compromised, and optimizing marketing strategies becomes difficult.

[0645] 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.

[0646] In this invention, the server includes means for acquiring facial and voice data using a communication device in order to collect the user's emotions; means for performing emotion analysis on a computing device and determining the emotional state in order to process the acquired emotion data; and means for integrating and updating new emotion information with existing user information using the results of the emotion analysis. This enables advanced interaction based on the user's emotions, leading to improved user satisfaction and the development of more sophisticated marketing strategies.

[0647] "User emotions" refer to the temporary mental state a user experiences in response to a particular stimulus or situation.

[0648] "Facial and voice data" refers to information indicating emotional states, obtained through the user's facial appearance and voice.

[0649] A "communication device" refers to equipment used to send and receive data between other systems.

[0650] "Computing equipment" refers to hardware and software used for processing and analyzing data.

[0651] "Sentiment analysis" refers to the process of identifying a user's emotions from collected data.

[0652] "User information" refers to a series of data about a user that is used to build their profile.

[0653] An "artificial intelligence model" refers to an algorithm or program that uses data to mimic human thought and decision-making.

[0654] "Market strategy" refers to plans and tactics for promoting the sale of products or services.

[0655] "Mutual exchange" refers to the exchange of information and two-way communication among multiple parties.

[0656] This invention relates to a system that recognizes user emotions and uses that data to optimize interaction with a virtual agent. This system collects and analyzes user emotions in real time when a user participates in a virtual space, and provides responses based on that analysis, thereby achieving sophisticated communication tailored to individual needs.

[0657] Hardware and software environment

[0658] When a user logs into a virtual space, the device activates its built-in camera and high-sensitivity microphone to capture the user's facial expressions and voice. Ideally, a camera with a resolution of 2 megapixels or higher and a microphone with noise-canceling capabilities should be used.

[0659] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This emotion engine may utilize OpenFace or speech recognition software (e.g., speech-to-text technology). Based on the analysis, the user's emotional state is determined, and this information is updated in the user profile.

[0660] The server further utilizes generative AI models to generate responses based on the user's emotional information. For example, a large-scale language model might suggest, "Shall we try a simple way to relax?" if it identifies that the user is feeling stressed.

[0661] Usage example

[0662] As a concrete example of its use, suppose a user says, "Today was a tough day." In this case, the server analyzes the data indicating negative emotions, and the generative AI model generates a response such as, "That sounds tough. Shall I play some soothing music to help you relax?" This response is emotionally sensitive and enables communication tailored to the user's needs.

[0663] Example of a prompt

[0664] In generative AI models, prompts such as "How can we provide reassurance when a user is feeling anxious?" are used.

[0665] In this way, responses that resonate with the user's emotions are generated, enabling more natural and effective interactions.

[0666] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0667] Step 1:

[0668] The device activates its camera and microphone simultaneously with the user logging into the virtual space. This initiates the real-time capture of the user's facial expressions and voice. Inputs include video data from the camera and audio data from the microphone. Based on this data, an output is obtained in which initial raw data is collected.

[0669] Step 2:

[0670] The terminal transmits the collected video and audio data to the server. The server receives this data and temporarily stores it in a database. As input, the user's raw data is sent to the server, and as output, it is stored in the database, enabling subsequent processing.

[0671] Step 3:

[0672] The server passes the stored data to emotion analysis software to analyze the user's emotional state. This data processing captures changes in facial expressions and voice tone, and the emotion engine identifies emotions such as joy, sadness, and anger. The input is raw data, and the output is the analysis results.

[0673] Step 4:

[0674] The server updates user information using the acquired sentiment analysis results. Calculations are performed to integrate the new sentiment data into the user profile. The input is the analysis results, and the output is the generated updated user profile.

[0675] Step 5:

[0676] The server uses a generative AI model to generate the optimal response to present to the user. The generative AI model creates a response tailored to the user's emotional state based on the prompt. For example, a prompt might ask, "What kind of encouragement should you offer when the user is feeling anxious?" The input consists of an updated user profile and a prompt, and the output is a response appropriate for the user.

[0677] Step 6:

[0678] The terminal presents the user with the response sent from the server. A virtual agent then begins interacting with the user using voice and animation. The input is the response data sent from the server, and the output is the interactive experience provided to the user.

[0679] Step 7:

[0680] The server continuously monitors the user's emotional data during the interaction and adjusts the virtual agent's response as needed. This enables real-time adaptation. The input is continuously monitored data, and the output is dynamically adjusted dialogue content.

[0681] This series of processes enables personalized responses based on the user's emotions, resulting in high-quality virtual interactions.

[0682] (Application Example 2)

[0683] 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."

[0684] Modern consumers demand personalized service not only in the digital space but also in physical stores. Meeting this demand requires recognizing user emotions in real time and providing appropriate responses. However, conventional technologies struggle to optimize responses based on user emotions, resulting in a lack of improved user experience. Furthermore, systems capable of providing real-time, emotion-based product recommendations are limited. These are the challenges that exist.

[0685] 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.

[0686] In this invention, the server includes means for recognizing the user's emotional state and optimizing the response, means for making real-time emotion-based product recommendations, and means for analyzing user data and suggesting improvements to the marketing strategy. This makes it possible to provide appropriate services tailored to the user's emotions, which is expected to improve customer satisfaction and boost sales.

[0687] A "virtual space" is a simulated environment of the real world generated by a computer.

[0688] "User behavior" refers to the choices and statements made by the user when interacting with the virtual agent.

[0689] A "user profile" is a set of individual attribute information built based on a user's behavior and past interactions.

[0690] A "virtual agent" is a virtual artificial intelligence agent developed for the purpose of interacting with users.

[0691] "Means for adjusting response content" refers to methods for optimizing the content of interactions in accordance with the user's actions and emotions.

[0692] "Areas for improvement in marketing strategy" refer to points for optimizing promotional activities extracted from user data and interaction results.

[0693] "Real-time, emotion-based product recommendations" is a method of instantly suggesting appropriate products and services based on the user's emotional state at that moment.

[0694] "Means of recognizing a user's emotional state" refers to technologies that analyze data obtained from facial expressions, voice, etc., to identify the user's emotions.

[0695] This invention is a system that recognizes user emotions in real time and improves the user experience through a virtual agent. The server receives user facial expressions and voice data and uses OpenCV, Google Cloud Speech-to-Text API, and TensorFlow as software to analyze this data. This identifies the user's emotional state and generates an AI-optimized response.

[0696] The device is equipped with a camera and microphone, and its role is to capture the user's facial expressions and voice in real time. The acquired data is sent to a server, and based on the results of the analysis, products and services tailored to the user's emotions are recommended.

[0697] Users can interact with virtual agents via smartphones or head-mounted displays. They can enjoy personalized services tailored to their emotions; for example, if they are feeling negative, they may be offered products that promote relaxation.

[0698] As a concrete example, suppose a user is browsing products in a store via their smartphone and makes an expression indicating interest. At this point, the server performs facial analysis, and if a positive emotion is detected, a virtual agent can immediately introduce information about related products. This allows the user to quickly and appropriately obtain information that meets their needs, which is expected to increase their willingness to purchase.

[0699] An example of a prompt to input into a generative AI model is, "Identify emotions from the user's facial expressions and voice, and adjust the response accordingly."

[0700] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0701] Step 1:

[0702] The device activates its camera and microphone to capture the user's facial expressions and voice in real time. Input consists of video data from the camera and audio data from the microphone. Output consists of sending the collected facial video and audio files to the server. Specifically, the device continuously acquires camera frames and records audio.

[0703] Step 2:

[0704] The server analyzes received facial expression data using OpenCV and identifies emotional states using a TensorFlow model. It receives facial expression image data from the terminal as input, preprocesses the images to extract facial features, and inputs them into the emotion model. The output is the identified emotional state. Specifically, the server applies a face detection algorithm to convert facial expression information into emotion labels.

[0705] Step 3:

[0706] The server converts the received audio data into text using the Google Cloud Speech-to-Text API, analyzes the audio features, and obtains complementary sentiment information. The audio data is the input, and the text data for sentiment identification is the output. Specifically, the server sends the audio data to the API, and the returned text data is analyzed by the sentiment recognition algorithm.

[0707] Step 4:

[0708] The server determines the user's overall emotional state based on the facial expression and voice data obtained as analysis results. The emotional state label is the input, and the basic data for generating the response to display to the user is the output. Specifically, the server integrates multiple emotional indicators to generate a single emotional evaluation.

[0709] Step 5:

[0710] The server inputs prompt sentences into a generative AI model and generates the optimal response for the virtual agent based on the user's emotional state. The input consists of an emotional state evaluation and a prompt sentence, while the output is the content of the virtual agent's utterance. Specifically, the server inputs prompt sentences containing emotional indicators into the generative AI model and constructs the resulting text response.

[0711] Step 6:

[0712] The terminal presents the user with the response from the virtual agent sent from the server, either as audio or text. The input is the response text from the server, and the output is the information presented to the user. Specifically, the terminal either synthesizes the text information into speech or displays it directly on the screen.

[0713] 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.

[0714] 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.

[0715] 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.

[0716] [Fourth Embodiment]

[0717] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0718] 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.

[0719] 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).

[0720] 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.

[0721] 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.

[0722] 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).

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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".

[0730] This invention is provided as a system for improving the user's interactive experience in a virtual space. Specific embodiments for carrying out the invention are described below.

[0731] 1. Data Collection

[0732] The device verifies login information and initiates a session when a user accesses the virtual space. It continuously records user behavior data such as content viewed, product selections, click history, and time spent in the virtual space.

[0733] The server receives behavioral data transmitted from the terminal and stores it in a database. This data is used for later analysis.

[0734] 2. Generating a user profile

[0735] The server analyzes the accumulated data and extracts user interests and behavioral patterns. This generates user profiles.

[0736] User profiles include information such as the user's purchase history, areas of interest, and past events attended, reflecting the user's personal preferences.

[0737] 3. Providing interactive virtual agents

[0738] The server generates a virtual agent using a generative AI based on the user profile. This agent provides product descriptions and recommendations through natural conversations with the user.

[0739] The terminal visually presents the generated virtual agent to the user and provides an interface for interaction.

[0740] 4. Real-time behavioral analysis and optimization of marketing strategies

[0741] The server analyzes user behavior data in real time during interactions and adjusts the agent's responses accordingly. This enables the provision of information tailored to the user's interests.

[0742] The server integrates all user data and extracts areas for improvement in marketing strategies. These improvements are presented to administrators and used as needed to develop campaigns or new initiatives.

[0743] For example, if a user spends time browsing a particular fashion item and is hesitant to purchase it, a virtual agent can introduce the product's features and past reviews, and even suggest related styling options. This can help the user make a purchasing decision.

[0744] In this way, users can obtain a personalized shopping experience in a virtual space, and companies can achieve efficient marketing activities.

[0745] The following describes the processing flow.

[0746] Step 1:

[0747] The device initiates a session when the user logs into the virtual space and begins monitoring the user's behavior. This includes data such as the content the user views, product clicks, selections, and scrolling.

[0748] Step 2:

[0749] The server receives user behavior data transmitted from the terminal and immediately records it in the database. This allows for the continuous accumulation of all user actions.

[0750] Step 3:

[0751] The server analyzes the accumulated data and uses a specific algorithm to analyze user behavior patterns. Based on this analysis, a user profile is generated. The profile includes information such as product categories of interest and purchase history.

[0752] Step 4:

[0753] The server uses generative AI to create a customized virtual agent based on the analyzed user profile. This agent is then ready to provide personalized recommendations with conversational content tailored to the user.

[0754] Step 5:

[0755] The terminal presents the generated virtual agent to the user and initiates an interactive dialogue. The user can have a natural conversation with the agent, obtain product information, and ask questions.

[0756] Step 6:

[0757] The server analyzes user responses in real time during interactions between the user and the virtual agent, and adjusts the response content as needed. This enables the provision of information tailored to the user's interests.

[0758] Step 7:

[0759] The server comprehensively evaluates all user behavior data and identifies areas for improvement in the company's marketing strategy. It generates new suggestions and presents them to administrators to optimize campaign effectiveness.

[0760] (Example 1)

[0761] 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".

[0762] In today's virtual space, there is a demand for personalized and dynamic experiences for users. However, conventional systems cannot fully utilize user behavior data, making it difficult to provide timely information and optimize sales strategies. As a result of these challenges, users do not receive satisfactory experiences, and companies are limited in their ability to conduct efficient marketing activities.

[0763] 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.

[0764] In this invention, the server includes a device for recording user actions, a device for generating a profile based on the action data, and a device for generating a virtual agent based on the profile and providing information. This enables real-time personalized information provision and optimization of sales strategies for users.

[0765] A "user" refers to an individual who accesses a virtual space and has an interactive experience through the system.

[0766] "Actions" refer to actions and operations performed by users within the virtual space, such as clicking, browsing, and selecting products.

[0767] "Action data" refers to data that records information about a user's actions and is used to analyze behavioral patterns and interests.

[0768] A "profile" refers to a collection of information generated based on behavioral data that reflects the user's interests and preferences.

[0769] A "virtual agent" is an interactive character or software based on a generated AI model that interacts with the user and provides information.

[0770] "Information provision" refers to the presentation of information such as product descriptions and recommendations to users via a virtual agent.

[0771] A "sales strategy" refers to a plan or policy that a company uses to efficiently provide its products and services and maximize sales.

[0772] This invention is designed to allow users to have a personalized experience within a virtual space. The following describes in detail how the invention is implemented.

[0773] Data collection and profile generation

[0774] When a user logs into the system, the device records the user's actions in real time, such as content browsing history and click actions. This is done using standard web browsers and mobile applications.

[0775] The server receives behavioral data transmitted from the terminal and stores it in a database. The server analyzes this data, extracts user behavior patterns, and generates a user profile. Data analysis software and machine learning algorithms are used for this analysis.

[0776] Provision of virtual agents

[0777] The server generates a virtual agent using a generative AI model based on the generated user profile. The generative AI model uses natural language processing technology to interact with the user naturally.

[0778] The terminal displays a generated virtual agent as a user interaction interface. Through interaction with the agent, the user can obtain product descriptions and recommendations.

[0779] Real-time analysis and response optimization

[0780] During interactions with the user, the server analyzes new user behavior data in real time and adjusts the agent's responses accordingly. This functionality enables the provision of information tailored to the user's interests.

[0781] Optimizing marketing strategy

[0782] The server integrates all user data and extracts areas for improvement in sales strategies. Based on these results, companies can adjust campaign content and new initiatives in real time.

[0783] Specific examples and prompt statements

[0784] For example, a user looking to purchase a fashion item might spend a long time browsing a product page. In this case, a virtual agent could introduce the product's features and user reviews, and suggest related styling options. This would allow the user to make a better decision.

[0785] An example of a prompt message for a generative AI model can be entered as follows:

[0786] "Based on the user's purchase history and currently viewed items, please generate recommended fashion items and styling suggestions."

[0787] In this way, the system provides users with a highly personalized experience and becomes an important tool for businesses to dynamically optimize their sales strategies.

[0788] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0789] Step 1:

[0790] Start of data collection

[0791] The user logs into the virtual space and begins accessing the system.

[0792] The terminal requests user authentication information and initiates a session. After login, it immediately begins recording user activity data. The input is the user's login information, and the output is a confirmation of session initiation.

[0793] Step 2:

[0794] Collection and transmission of behavioral data

[0795] The device records detailed user activity, including content viewed, product selections, click history, and time spent on pages.

[0796] The collected data is sent to the server in real time. The input is a series of data about the user's actions, and the output is the transmission of this data to the server.

[0797] Step 3:

[0798] Storage and analysis in the database

[0799] The server receives behavioral data sent from the terminal and securely stores it in a database.

[0800] Subsequently, analysis is performed based on the accumulated data to extract user interests and behavioral patterns. This analysis utilizes machine learning algorithms and data mining. The input is behavioral data sent to the server, and the output is the analysis results of user interests and patterns.

[0801] Step 4:

[0802] User Profile Generation

[0803] The server generates user profiles based on the analyzed data, reflecting the user's preferences.

[0804] The profile includes purchase history and categories of interests. The input is analyzed behavioral data, and the output is a detailed user profile.

[0805] Step 5:

[0806] Virtual agent generation

[0807] The server uses a generative AI model to generate a virtual agent suitable for the user profile. This process is performed based on prompt statements.

[0808] The terminal displays the generated agent on the user's screen and provides an interface that enables interaction. The input consists of the user profile and prompt text, and the output is a visually represented agent.

[0809] Step 6:

[0810] Real-time behavioral analysis and response optimization

[0811] The server analyzes new actions that occur during the user-agent interaction in real time.

[0812] Based on this, the agent's response is dynamically adjusted to present the user with the most relevant information. The input is the user's real-time actions during the interaction, and the output is the adjusted agent's response.

[0813] Step 7:

[0814] Optimizing marketing strategy

[0815] The server analyzes integrated data from all users to identify areas for improvement in sales strategies.

[0816] This allows companies to instantly adjust campaign content and new initiatives. The input is integrated user data, and the output is an analytical report for strategic improvement.

[0817] (Application Example 1)

[0818] 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".

[0819] The shopping experience in modern virtual stores is still limited, and there is a need for flexible and interactive responses tailored to individual users. Furthermore, optimizing marketing strategies to respond immediately to user behavior is currently difficult. Therefore, the challenges to be addressed here are providing a customized shopping experience for each user and improving marketing strategies in real time.

[0820] 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.

[0821] In this invention, the server includes means for providing information based on user profiles using a virtual agent, means for analyzing the user's real-time behavior and reactions and adjusting the response content, and means for presenting relevant products to the user via communication devices and providing purchase support. As a result, users can enjoy a personalized and interactive shopping experience, and companies can improve their marketing strategies in real time.

[0822] A "virtual space" is a virtual environment created by a computer that users can access via the internet.

[0823] "User behavior" refers to information such as user actions, browsing history, choices, and time spent in a virtual space.

[0824] A "user profile" is a data structure generated based on a user's past behavioral data, and it reflects the user's interests and preferences.

[0825] A "virtual agent" is a virtual conversational software created using generative AI based on a user profile, and its role is to provide information to the user.

[0826] "Real-time actions and responses" refer to the immediate response to choices, actions, and inputs that users make during ongoing interactions.

[0827] "Communication equipment" refers to devices used by users to access virtual spaces and send and receive information.

[0828] A "visual display device" refers to a display device used to present virtual spaces or virtual agents to users, and includes monitors such as those found on smartphones and personal computers.

[0829] To implement this invention, several key elements are required to realize a personalized shopping experience for users on a virtual store system. This system primarily operates using servers, terminals, and generative AI models.

[0830] First, users access the virtual space through devices such as smartphones or personal computers. Here, user activity data, such as operations and browsing history, is recorded and transmitted to the server in real time.

[0831] The server uses this behavioral data to generate user profiles and, based on the accumulated information, creates a virtual agent suited to that user using an AI model. This agent provides information tailored to the user's preferences and is displayed on a visual display device via communication equipment.

[0832] Furthermore, the server analyzes the user's real-time behavior and reactions, and adjusts the agent's responses as needed. This ensures that users always receive the latest and most relevant information, allowing them to enjoy a shopping experience tailored to their needs.

[0833] As a concrete example, if a user interested in fashion is browsing jackets, a virtual agent will suggest related products and styling to that user. In this case, the server uses a generative AI model to generate the agent's response via a prompt message, for example, "This user is interested in street style. Please suggest jackets and styling that would suit him."

[0834] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0835] Step 1:

[0836] The terminal verifies login information when a user accesses the virtual space and records user behavior data such as operations and browsing history. Input data consists of user identification information and initial operation history. This information is used to construct behavior data, which is then sent to the server. Output data is behavior data with the user identification information attached.

[0837] Step 2:

[0838] The server receives the transmitted behavioral data and stores it in a database. The input is user behavioral data, and the output is the raw data stored in the database. The server uses this data to generate user profiles and analyze user interests and purchasing trends. Statistical analysis and machine learning algorithms are used for data processing, and the output is the user profile.

[0839] Step 3:

[0840] The server uses a generative AI model to generate a virtual agent based on the user profile. At this stage, the user profile is the input, and prompt statements are provided to the generative AI model. For example, the prompt might be, "This user is highly interested in electronic devices. Create an agent that describes related products." The output is the generated virtual agent.

[0841] Step 4:

[0842] The server further monitors user behavior data in real time and adjusts the virtual agent's responses based on that data. The input is the current user session data, and the output is the adjusted agent response. This adjustment is performed using natural language processing and control routines.

[0843] Step 5:

[0844] The terminal presents the generated virtual agent to the user on a visual display device. The input is the agent's interface information sent from the server, and the output is a dynamic display of the agent that the user visually perceives. This provides the user with a visual and interactive experience.

[0845] 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.

[0846] This invention is provided as an interactive system that combines an emotion engine that recognizes user emotions. Specific embodiments for carrying out the invention are described below.

[0847] 1. Collection of emotional data

[0848] The device activates its camera and microphone as soon as the user logs into the virtual space, capturing facial expressions and voice. This allows the system to obtain data about the user's emotions.

[0849] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This analysis identifies the user's emotional state.

[0850] 2. Combining user profiles and sentiment information

[0851] The server integrates previously accumulated user profile information with real-time sentiment information to update the profile. This generates a more refined user model.

[0852] User profiles include recent emotional information and past emotional history, reflecting the user's potential needs and preferences.

[0853] 3. Providing interactive virtual agents

[0854] The server uses generative AI to adjust the virtual agent's responses based on the user's emotional information. The agent is then ready to engage in conversation optimized for the user's current emotional state.

[0855] The device displays a virtual agent to the user that responds with consideration for emotions, and initiates a conversation. The agent engages in emotionally sensitive communication, such as offering words of encouragement or introducing specific products.

[0856] 4. Real-time analysis and optimization of marketing strategies

[0857] The server detects changes in the user's emotions in real time during the interaction and adjusts the virtual agent's speech and actions accordingly.

[0858] The server uses sentiment analysis data to identify areas for improvement in marketing strategies. Based on this data, suggestions are provided to administrators to help develop new campaigns.

[0859] For example, if a user exhibits a negative emotional reaction, the virtual agent can present calming video content or recommend products that match their mood. This improves the user experience and allows companies to conduct more effective marketing activities.

[0860] The following describes the processing flow.

[0861] Step 1:

[0862] The device activates its camera and microphone when the user logs into the virtual space and starts a session. This allows it to begin capturing the user's facial expressions and voice data.

[0863] Step 2:

[0864] The server receives facial expression and voice data transmitted from the terminal. It uses an emotion engine to analyze this data and perform a process to identify the user's emotional state.

[0865] Step 3:

[0866] The server integrates the analyzed sentiment data with the existing user profile and updates the profile to the latest state. This profile includes the user's current sentiment and its history.

[0867] Step 4:

[0868] Based on the updated user profile, the server uses generative AI to generate a virtual agent that provides emotionally appropriate responses. The agent prepares utterances and actions that match the user's emotions.

[0869] Step 5:

[0870] The terminal presents the generated virtual agent to the user and initiates interaction with the user. By interacting with the agent, the user can gain a deeper understanding of products and information that match their interests and needs.

[0871] Step 6:

[0872] The server monitors the user's emotional changes in real time during interaction and adjusts the agent's response accordingly, providing a more personalized experience.

[0873] Step 7:

[0874] The server analyzes all of the user's emotional and behavioral data and suggests improvements to the marketing strategy. These suggestions are provided to administrators and used to optimize campaigns and promotions.

[0875] (Example 2)

[0876] 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".

[0877] In today's virtual space, there is a need for interactive systems that can effectively recognize user emotions and enable communication tailored to individual needs. Traditional methods have been limited to creating superficial profiles based solely on user behavior data, failing to adequately consider users' underlying emotions and preferences. As a result, the quality of the user experience is compromised, and optimizing marketing strategies becomes difficult.

[0878] 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.

[0879] In this invention, the server includes means for acquiring facial and voice data using a communication device in order to collect the user's emotions; means for performing emotion analysis on a computing device and determining the emotional state in order to process the acquired emotion data; and means for integrating and updating new emotion information with existing user information using the results of the emotion analysis. This enables advanced interaction based on the user's emotions, leading to improved user satisfaction and the development of more sophisticated marketing strategies.

[0880] "User emotions" refer to the temporary mental state a user experiences in response to a particular stimulus or situation.

[0881] "Facial and voice data" refers to information indicating emotional states, obtained through the user's facial appearance and voice.

[0882] A "communication device" refers to equipment used to send and receive data between other systems.

[0883] "Computing equipment" refers to hardware and software used for processing and analyzing data.

[0884] "Sentiment analysis" refers to the process of identifying a user's emotions from collected data.

[0885] "User information" refers to a series of data about a user that is used to build their profile.

[0886] An "artificial intelligence model" refers to an algorithm or program that uses data to mimic human thought and decision-making.

[0887] "Market strategy" refers to plans and tactics for promoting the sale of products or services.

[0888] "Mutual exchange" refers to the exchange of information and two-way communication among multiple parties.

[0889] This invention relates to a system that recognizes user emotions and uses that data to optimize interaction with a virtual agent. This system collects and analyzes user emotions in real time when a user participates in a virtual space, and provides responses based on that analysis, thereby achieving sophisticated communication tailored to individual needs.

[0890] Hardware and software environment

[0891] When a user logs into a virtual space, the device activates its built-in camera and high-sensitivity microphone to capture the user's facial expressions and voice. Ideally, a camera with a resolution of 2 megapixels or higher and a microphone with noise-canceling capabilities should be used.

[0892] The server receives facial and voice data sent from the terminal and analyzes it using an emotion engine. This emotion engine may utilize OpenFace or speech recognition software (e.g., speech-to-text technology). Based on the analysis, the user's emotional state is determined, and this information is updated in the user profile.

[0893] The server further utilizes generative AI models to generate responses based on the user's emotional information. For example, a large-scale language model might suggest, "Shall we try a simple way to relax?" if it identifies that the user is feeling stressed.

[0894] Usage example

[0895] As a concrete example of its use, suppose a user says, "Today was a tough day." In this case, the server analyzes the data indicating negative emotions, and the generative AI model generates a response such as, "That sounds tough. Shall I play some soothing music to help you relax?" This response is emotionally sensitive and enables communication tailored to the user's needs.

[0896] Example of a prompt

[0897] In generative AI models, prompts such as "How can we provide reassurance when a user is feeling anxious?" are used.

[0898] In this way, responses that resonate with the user's emotions are generated, enabling more natural and effective interactions.

[0899] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0900] Step 1:

[0901] The device activates its camera and microphone simultaneously with the user logging into the virtual space. This initiates the real-time capture of the user's facial expressions and voice. Inputs include video data from the camera and audio data from the microphone. Based on this data, an output is obtained in which initial raw data is collected.

[0902] Step 2:

[0903] The terminal transmits the collected video and audio data to the server. The server receives this data and temporarily stores it in a database. As input, the user's raw data is sent to the server, and as output, it is stored in the database, enabling subsequent processing.

[0904] Step 3:

[0905] The server passes the stored data to emotion analysis software to analyze the user's emotional state. This data processing captures changes in facial expressions and voice tone, and the emotion engine identifies emotions such as joy, sadness, and anger. The input is raw data, and the output is the analysis results.

[0906] Step 4:

[0907] The server updates user information using the acquired sentiment analysis results. Calculations are performed to integrate the new sentiment data into the user profile. The input is the analysis results, and the output is the generated updated user profile.

[0908] Step 5:

[0909] The server uses a generative AI model to generate the optimal response to present to the user. The generative AI model creates a response tailored to the user's emotional state based on the prompt. For example, a prompt might ask, "What kind of encouragement should you offer when the user is feeling anxious?" The input consists of an updated user profile and a prompt, and the output is a response appropriate for the user.

[0910] Step 6:

[0911] The terminal presents the user with the response sent from the server. A virtual agent then begins interacting with the user using voice and animation. The input is the response data sent from the server, and the output is the interactive experience provided to the user.

[0912] Step 7:

[0913] The server continuously monitors the user's emotional data during the interaction and adjusts the virtual agent's response as needed. This enables real-time adaptation. The input is continuously monitored data, and the output is dynamically adjusted dialogue content.

[0914] This series of processes enables personalized responses based on the user's emotions, resulting in high-quality virtual interactions.

[0915] (Application Example 2)

[0916] 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".

[0917] Modern consumers demand personalized service not only in the digital space but also in physical stores. Meeting this demand requires recognizing user emotions in real time and providing appropriate responses. However, conventional technologies struggle to optimize responses based on user emotions, resulting in a lack of improved user experience. Furthermore, systems capable of providing real-time, emotion-based product recommendations are limited. These are the challenges that exist.

[0918] 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.

[0919] In this invention, the server includes means for recognizing the user's emotional state and optimizing the response, means for making real-time emotion-based product recommendations, and means for analyzing user data and suggesting improvements to the marketing strategy. This makes it possible to provide appropriate services tailored to the user's emotions, which is expected to improve customer satisfaction and boost sales.

[0920] A "virtual space" is a simulated environment of the real world generated by a computer.

[0921] "User behavior" refers to the choices and statements made by the user when interacting with the virtual agent.

[0922] A "user profile" is a set of individual attribute information built based on a user's behavior and past interactions.

[0923] A "virtual agent" is a virtual artificial intelligence agent developed for the purpose of interacting with users.

[0924] "Means for adjusting response content" refers to methods for optimizing the content of interactions in accordance with the user's actions and emotions.

[0925] "Areas for improvement in marketing strategy" refer to points for optimizing promotional activities extracted from user data and interaction results.

[0926] "Real-time, emotion-based product recommendations" is a method of instantly suggesting appropriate products and services based on the user's emotional state at that moment.

[0927] "Means of recognizing a user's emotional state" refers to technologies that analyze data obtained from facial expressions, voice, etc., to identify the user's emotions.

[0928] This invention is a system that recognizes user emotions in real time and improves the user experience through a virtual agent. The server receives user facial expressions and voice data and uses OpenCV, Google Cloud Speech-to-Text API, and TensorFlow as software to analyze this data. This identifies the user's emotional state and generates an AI-optimized response.

[0929] The device is equipped with a camera and microphone, and its role is to capture the user's facial expressions and voice in real time. The acquired data is sent to a server, and based on the results of the analysis, products and services tailored to the user's emotions are recommended.

[0930] Users can interact with virtual agents via smartphones or head-mounted displays. They can enjoy personalized services tailored to their emotions; for example, if they are feeling negative, they may be offered products that promote relaxation.

[0931] As a concrete example, suppose a user is browsing products in a store via their smartphone and makes an expression indicating interest. At this point, the server performs facial analysis, and if a positive emotion is detected, a virtual agent can immediately introduce information about related products. This allows the user to quickly and appropriately obtain information that meets their needs, which is expected to increase their willingness to purchase.

[0932] An example of a prompt to input into a generative AI model is, "Identify emotions from the user's facial expressions and voice, and adjust the response accordingly."

[0933] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0934] Step 1:

[0935] The device activates its camera and microphone to capture the user's facial expressions and voice in real time. Input consists of video data from the camera and audio data from the microphone. Output consists of sending the collected facial video and audio files to the server. Specifically, the device continuously acquires camera frames and records audio.

[0936] Step 2:

[0937] The server analyzes received facial expression data using OpenCV and identifies emotional states using a TensorFlow model. It receives facial expression image data from the terminal as input, preprocesses the images to extract facial features, and inputs them into the emotion model. The output is the identified emotional state. Specifically, the server applies a face detection algorithm to convert facial expression information into emotion labels.

[0938] Step 3:

[0939] The server converts the received audio data into text using the Google Cloud Speech-to-Text API, analyzes the audio features, and obtains complementary sentiment information. The audio data is the input, and the text data for sentiment identification is the output. Specifically, the server sends the audio data to the API, and the returned text data is analyzed by the sentiment recognition algorithm.

[0940] Step 4:

[0941] The server determines the user's overall emotional state based on the facial expression and voice data obtained as analysis results. The emotional state label is the input, and the basic data for generating the response to display to the user is the output. Specifically, the server integrates multiple emotional indicators to generate a single emotional evaluation.

[0942] Step 5:

[0943] The server inputs prompt sentences into a generative AI model and generates the optimal response for the virtual agent based on the user's emotional state. The input consists of an emotional state evaluation and a prompt sentence, while the output is the content of the virtual agent's utterance. Specifically, the server inputs prompt sentences containing emotional indicators into the generative AI model and constructs the resulting text response.

[0944] Step 6:

[0945] The terminal presents the user with the response from the virtual agent sent from the server, either as audio or text. The input is the response text from the server, and the output is the information presented to the user. Specifically, the terminal either synthesizes the text information into speech or displays it directly on the screen.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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."

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] 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.

[0965] 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.

[0966] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0967] The following is further disclosed regarding the embodiments described above.

[0968] (Claim 1)

[0969] In a virtual space, a means of recording user behavior,

[0970] Means for generating a user profile based on the aforementioned behavioral data,

[0971] A means for providing information to the user via a virtual agent generated based on the aforementioned profile,

[0972] A means for analyzing the user's real-time behavior and reactions and adjusting the virtual agent's responses,

[0973] A system that includes means to analyze user data and suggest improvements to marketing strategies.

[0974] (Claim 2)

[0975] The system according to claim 1, which includes means for the virtual agent to host live events and Q&A sessions and enable two-way communication with users.

[0976] (Claim 3)

[0977] The system according to claim 1, comprising means for providing improvements to the marketing strategy in real time based on user behavior data.

[0978] "Example 1"

[0979] (Claim 1)

[0980] A device that records the user's actions,

[0981] A device that generates a user profile based on the aforementioned operational data,

[0982] A device that provides information to a user via a virtual agent generated based on the aforementioned profile,

[0983] A device that analyzes the user's real-time actions and reactions and adjusts the response content of the virtual agent,

[0984] A device that includes equipment for analyzing operational data and suggesting improvements to sales strategies.

[0985] (Claim 2)

[0986] The apparatus according to claim 1, which includes a device in which the virtual agent performs direct communication events and question-and-answer sessions to realize two-way dialogue with a user.

[0987] (Claim 3)

[0988] The apparatus according to claim 1, which includes a device that provides improvements to the sales strategy in real time based on user behavior data.

[0989] "Application Example 1"

[0990] (Claim 1)

[0991] In a virtual space, a means of recording user behavior,

[0992] Means for generating a user profile based on the aforementioned behavioral data,

[0993] A means for providing information to the user via a virtual agent generated based on the aforementioned profile,

[0994] A means for analyzing the user's real-time behavior and reactions and adjusting the virtual agent's responses,

[0995] A means of analyzing user data and suggesting improvements to marketing strategies,

[0996] A means of presenting relevant products to users who access a virtual store via communication devices and supporting the user's purchase decision,

[0997] A means of displaying a virtual agent on a visual display device and interacting with the user.

[0998] A system that includes this.

[0999] (Claim 2)

[1000] The system according to claim 1, wherein the virtual agent recommends products based on the user's past purchase history and interests, thereby supporting a personalized shopping experience.

[1001] (Claim 3)

[1002] The system according to claim 1, wherein improvements to the marketing strategy are provided in real time based on user behavior data, and the agent's response is adaptively adjusted.

[1003] "Example 2 of combining an emotion engine"

[1004] (Claim 1)

[1005] A means for acquiring facial and voice data using a communication device in order to collect user emotions,

[1006] To process the acquired emotional data, a means is provided to perform emotional analysis using a computing device and determine the emotional state.

[1007] A means for integrating and updating new emotional information with existing user information using the aforementioned emotion analysis results,

[1008] An information provision means that utilizes an artificial intelligence model generated using a computing device to determine a response based on emotional information,

[1009] A response control means that monitors and adjusts emotional changes in real time through a dialogue device for interacting with the user, and adaptively modifies the content of the information provided.

[1010] A system that includes optimization suggestion methods to support the improvement of market strategies through various acquired data.

[1011] (Claim 2)

[1012] The system according to claim 1, wherein the information provision means enables interaction with users through complex events and question-and-answer sessions.

[1013] (Claim 3)

[1014] The system according to claim 1, wherein the optimization proposal for the market strategy is supplied in real time based on emotional data and user behavior data.

[1015] "Application example 2 when combining with an emotional engine"

[1016] (Claim 1)

[1017] In a virtual space, a means of recording user behavior,

[1018] Means for generating a user profile based on the aforementioned behavioral data,

[1019] A means for providing information to the user via a virtual agent generated based on the aforementioned profile,

[1020] A means for analyzing the user's real-time behavior and reactions and adjusting the virtual agent's responses,

[1021] A means of analyzing user data and suggesting improvements to marketing strategies,

[1022] A means of recognizing the user's emotional state and optimizing the response,

[1023] A system that includes means for providing real-time, emotion-based product recommendations.

[1024] (Claim 2)

[1025] The system according to claim 1, which includes means for the virtual agent to host live events and Q&A sessions and enable two-way communication with users.

[1026] (Claim 3)

[1027] The system according to claim 1, comprising means for providing improvements to the marketing strategy in real time based on user behavior data and sentiment data. [Explanation of symbols]

[1028] 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. In a virtual space, a means of recording user behavior, Means for generating a user profile based on the aforementioned behavioral data, A means for providing information to the user via a virtual agent generated based on the aforementioned profile, A means for analyzing the user's real-time behavior and reactions and adjusting the virtual agent's responses, A means of analyzing user data and suggesting improvements to marketing strategies, A means of presenting relevant products to users who access a virtual store via communication devices and supporting the user's purchase decision, A means of displaying a virtual agent on a visual display device and interacting with the user. A system that includes this.

2. The system according to claim 1, wherein the virtual agent recommends products based on the user's past purchase history and interests, thereby supporting a personalized shopping experience.

3. The system according to claim 1, wherein improvements to the marketing strategy are provided in real time based on user behavior data, and the agent's response is adaptively adjusted.

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