system

Abstract

We provide the system. [Solution] Means of collecting information based on past life experiences, A means of analyzing collected information and generating visual components, A means of structuring a virtual space based on the generated visual components, A means of collecting human sensory responses in a virtual space, A means of modifying the virtual space based on collected human sensory responses, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the operation of virtual events, it is required to provide an immersive experience that responds to the needs of participants and viewers in real time beyond physical constraints and limitations. In addition, it is difficult to customize according to the individual preferences of viewers, and there is a problem that it is difficult to improve the satisfaction of the viewing experience by conventional methods. Furthermore, it is also difficult to immediately utilize the feedback collected during the event and dynamically adjust the virtual space, so there is a problem that rapid response cannot be achieved and the satisfaction of participants decreases.

Means for Solving the Problems

[0005] This invention provides a means for generating visual elements by collecting information based on past life experiences and analyzing that information. This makes it possible to construct an environment tailored to each individual participant within a virtual space. Furthermore, by using a means to collect human sensory responses in the virtual space in real time and modify the virtual space based on those responses, it is possible to immediately reflect viewer feedback and dynamically adjust the virtual environment. Moreover, by generating individual virtual spaces based on multiple life experiences and integrating them, it is possible to provide a more diverse and immersive experience. This improves the quality of the viewing experience and increases participant satisfaction.

[0006] "Information based on past life experiences" refers to data related to past experiences and activities, including the preferences and behavioral history of participants and viewers.

[0007] A "virtual space" refers to a three-dimensional simulated environment constructed within a computer using digital technology, a space that participants can visually experience.

[0008] "Visual elements" refer to design elements such as color, shape, and movement that are represented in a virtual space and that influence the user experience.

[0009] "Human sensory responses" refer to the psychological and physiological reactions that viewers and participants exhibit to the virtual space, and are data that can be collected and analyzed in real time.

[0010] "Means of modification" refers to methods and processes for changing and optimizing the virtual space and its components based on collected feedback.

[0011] "Dynamic adjustment" refers to continuously and in real time changing the virtual environment according to the situation, making it possible to immediately reflect viewer feedback.

[0012] "Means of integration" refers to methods for combining and coordinating multiple individually generated virtual spaces into a single, coherent system. [Brief explanation of the drawing]

[0013] [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]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0017] 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.

[0018] 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 disk (e.g., hard disk), or magnetic tape, etc.

[0019] 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).

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] As an embodiment of this invention, a system for automatically generating and customizing virtual spaces consists mainly of a program that includes functions for data collection, data analysis, arena generation, and real-time response.

[0035] First, the server collects information based on the past life experiences of event participants and viewers. This includes past competition data from participants, preferences shown by viewers, and feedback data from social media. This allows the server to understand users' past trends and preferences.

[0036] Next, the server uses an AI agent to analyze the collected information. Based on the results of this analysis, it generates visual elements and designs a prototype of a virtual space that is visually appealing and suitable for the participants. This virtual space is dynamically constructed, taking into account design elements such as color, shape, and visual effects.

[0037] The generated virtual space is stored in the cloud and delivered to participants via their devices. Users join the virtual space via a provided access link, and personalization is automatically performed according to their environment and device. This allows each user to freely experience the event beyond physical limitations.

[0038] During a real-time event, the terminal continuously collects feedback from viewers and sends it to the server. The server analyzes this real-time data and dynamically adjusts the visual elements and layout of the virtual space as the event progresses. For example, if it is found that many viewers prefer a particular element, the server can instantly adjust the virtual space to emphasize that element.

[0039] As a concrete example, when an international gaming tournament is held, a more personalized experience can be provided by changing the design according to the nationality of the participants and viewers, and by providing content that is compatible with different time zones. Also, when a large audience is enjoying a particular battle scene, it is possible to further enhance immersion by strengthening the visual effects and adjusting the sound.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects information based on the past life experiences of event participants and viewers. Specifically, it retrieves past competition results, viewing history, and feedback from social media. This reveals user preferences and trends.

[0043] Step 2:

[0044] The server passes the collected data to an AI agent for analysis. The AI ​​agent identifies viewer preferences and participants' playing styles from the provided data and generates visual elements. Based on these results, it designs the virtual space.

[0045] Step 3:

[0046] The server constructs a virtual arena based on the generated visual elements. The virtual arena includes colors, shapes, layouts, and visual effects, which are optimized according to the participants' preferences.

[0047] Step 4:

[0048] The server saves the completed virtual arena to the cloud and generates an access link. Users use this link to access the virtual arena. Upon access, the device personalizes the virtual space according to the user's environment.

[0049] Step 5:

[0050] During the event, the device collects real-time feedback from viewers. This may include evaluations of visual effects and the event's flow. The collected data is sent to a server.

[0051] Step 6:

[0052] The server analyzes real-time feedback to determine if adjustments are needed to the virtual arena. If necessary, it instantly modifies the visual elements and layout of the virtual space, making changes as the event progresses.

[0053] Step 7:

[0054] The device receives correction instructions sent from the server and reflects the changes in the virtual arena. Through this process, viewers can always enjoy the latest and most optimized experience.

[0055] (Example 1)

[0056] 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."

[0057] In generating and customizing virtual spaces, there is a need for systems that can provide personalized experiences by dynamically utilizing users' past life experiences and real-time perceptual information. However, current systems struggle to effectively collect, analyze, and adapt this data, making it difficult to provide users with attractive and personalized virtual spaces.

[0058] 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.

[0059] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information using an analysis algorithm to generate visual components, and means for distributing the virtual space via a network environment and personalizing it according to the user environment. This makes it possible to provide a virtual space that is individualized and dynamically optimized for the user experience.

[0060] "Information based on past life experiences" is a general term for data related to a user's past behavior, preferences, digital feedback, etc., and is used to generate personalized experiences in virtual space.

[0061] An "analysis algorithm" is a mathematical method or computer program used to process collected data and extract patterns or trends.

[0062] "Visual elements" refer to the collective visual elements involved in the design of a virtual space, such as color, shape, effects, and layout.

[0063] A "virtual space" is a digital environment created through computer simulation, a space that users can experience interactively.

[0064] A "network environment" is a general term for the internet and other communication infrastructure used to send and receive digital information, and is the environment used to connect users and servers.

[0065] "Personalizing based on user environment" refers to the process of customizing digital content and interfaces according to the user's device characteristics and individual preferences.

[0066] "Perceptual information" is a general term for data related to senses such as sight, hearing, and touch that a user obtains within a virtual space.

[0067] "Dynamic modification" is the process of instantly adjusting and changing the components and layout of a virtual space based on information the system collects in real time.

[0068] This invention relates to a system for dynamically generating and customizing virtual spaces. The system consists of a combination of servers, terminals, and user interaction technologies for collecting a user's past life experiences, analyzing that data, and generating visual elements.

[0069] The server uses APIs and databases to collect past competition data, social media feedback data, and preference data from participants and viewers. Based on this information, an AI agent uses analytical algorithms to analyze the data and identify patterns and key visual elements that should be applied to the virtual space. The server uses generative AI models to dynamically generate visual components and designs a prototype of the virtual space using real-time rendering technology.

[0070] The designed virtual space is stored in the cloud and delivered to the user via the device. The user experiences the virtual space via the provided access link. The device detects the user's device characteristics and provides an optimized virtual space by personalizing the visual and operational elements accordingly.

[0071] During a real-time event, the terminal collects perceptual information from viewers and sends it to the server. The server analyzes the real-time data and dynamically adjusts the elements and layout of the virtual space. For example, if many viewers prefer a particular visual effect, the server will make adjustments to emphasize that element.

[0072] For example, when hosting international gaming tournaments, it's possible to provide a more adaptive virtual experience for individual participants by customizing the design according to the nationalities of participants and viewers, and by providing content suitable for different time zones. Furthermore, when a large number of viewers are enjoying a particular action scene, immersion can be further enhanced by strengthening visual effects and adjusting sound effects.

[0073] An example of a prompt message is, "How will you customize the virtual space for viewers with diverse cultural backgrounds in an international gaming tournament?" This allows for dynamic adjustments to be requested.

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

[0075] Step 1:

[0076] The server uses APIs and database access to collect information based on the user's past life experiences. It receives user athletic data, preference data, and feedback from social media as input, and stores this data digitally. Each piece of information collected in the database is output as foundational data for building the user's profile.

[0077] Step 2:

[0078] The server analyzes the collected data using the AI ​​agent's analysis algorithm. From the input user data, it extracts visual preferences and experience trends using pattern recognition technology. As a result, it outputs a list of visual components to be implemented in the virtual space, which serves as a guideline for designing the virtual space prototype.

[0079] Step 3:

[0080] The server uses a generative AI model to generate visual elements and design a prototype of the virtual space. Using the analysis results from step 2 as input, a graphics engine generates a 3D model. Real-time rendering technology is also utilized to add more effective visual effects and presentation. As output, the designed virtual space prototype is saved to the cloud.

[0081] Step 4:

[0082] The device receives a prototype of the virtual space from the cloud and delivers it to the user. The input is virtual space data received via the network, which is personalized based on the user's device characteristics (resolution, processing power, etc.). The output is an optimized rendering of the virtual space, which the user can experience through the device.

[0083] Step 5:

[0084] The device collects user perception information in real time and sends it to the server. It accepts user feedback as input via survey forms and reaction buttons. This data is sent to the server during the event and output for real-time analysis.

[0085] Step 6:

[0086] The server analyzes the perceptual information received in real time and dynamically adjusts the visual elements of the virtual space. Input data includes user feedback collected in step 5. Based on this, the server changes the saturation and contrast of the virtual space, making adjustments according to the user's preferences. The output is the adjusted virtual space provided to the user, resulting in a more personalized experience.

[0087] (Application Example 1)

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

[0089] In the modern digital content field, providing users with personalized and immersive virtual experiences is a challenge. However, systems that dynamically adjust virtual spaces and optimize visual and auditory effects based on user preferences and on-the-spot feedback are limited. Therefore, there is a need for more real-time, personalized virtual experiences.

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

[0091] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information and generating visual components, and means for structuring a virtual space based on the generated visual components. This makes it possible to provide a personalized virtual space to the user and to dynamically adjust the virtual space in real time based on the user's response.

[0092] "Information based on past life experiences" refers to data related to the user's past experiences and preferences.

[0093] "Means for analyzing information and generating visual components" refers to a device or program that executes a process of designing the visual elements necessary for a virtual space based on collected data.

[0094] "Means of structuring virtual space" refers to a method or system that uses generated visual components to form a three-dimensional digital environment experienced by the user.

[0095] "Means for obtaining user responses" refers to systems that collect user input and reactions within a virtual space, and this includes sensors and user interfaces.

[0096] "Means for dynamically adjusting a virtual space" refers to a device or program that performs a process of changing elements such as visuals and sounds within a virtual space in real time based on the user's responses obtained.

[0097] To implement this invention, a system is constructed to automatically generate a virtual space and customize and adjust it in real time according to the user. The server collects the user's past life experiences and preferences from a database and analyzes them using an AI agent. This analysis generates visual elements and designs a prototype of the virtual space. The server saves the generated digital space to a cloud server and delivers it to the user's device. This delivery requires an internet connection, and the device used can be a smartphone or a head-mounted display.

[0098] Users enter a virtual space using a provided access link and can enjoy a personalized experience based on their preferences. Feedback is sent to the server in real time through the device, and the virtual space is dynamically adjusted based on the user's responses. This adjustment includes visual and auditory effects such as graphics and sound.

[0099] For example, if a user requests a virtual space with a nature theme, an environment themed around forests or oceans will be generated accordingly, with sounds like birdsong and waves emphasized. If the user's response is positive, the effects will be adjusted. This system provides users with a real-time virtual experience based on their interests and preferences.

[0100] The generation AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The server collects information from a database based on the user's past life experiences. This information includes the user's past viewing history and preference data. The user ID is used as input, and the user's preference data is obtained as output. The server prepares this as input data for data analysis.

[0104] Step 2:

[0105] The server uses an AI agent to analyze data based on collected user preference information. Using preference data as input, visual elements of a virtual space are generated as output. In this process, a machine learning model analyzes user preference patterns and identifies optimal design elements.

[0106] Step 3:

[0107] The server structures the virtual space based on the generated visual components. Using the visual components as input, the concrete virtual space design is stored in the cloud as output. The server prepares for real-time events through this design.

[0108] Step 4:

[0109] Users join the virtual space using an access link provided via their device. The access link is used as input, and a personalized virtual experience is displayed on the device as output. The device recognizes the user's environment and provides optimal visual settings.

[0110] Step 5:

[0111] The device transmits user responses within the virtual space to the server in real time. User actions and comments are taken as input, and feedback data is provided to the server as output. Through this, the device maintains an interactive experience.

[0112] Step 6:

[0113] The server analyzes the received feedback data and dynamically adjusts the visual elements and sound effects of the virtual space. Using the feedback data as input, it generates updated virtual space settings as output. The server immediately reflects these changes, providing the user with a continuous experience. In this process, the generative AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0114] 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.

[0115] One embodiment of this invention is a system that recognizes the user's emotions and customizes the virtual space. By combining this system with an emotion engine, the system can analyze the user's emotional state in real time and flexibly adjust the design and presentation of the virtual space.

[0116] First, the server collects information about the user's past life experiences. This collected information includes data on the user's past event participation, social media activity history, and feedback. Based on this, the server understands the user's basic preferences and patterns.

[0117] Next, the server uses an emotion engine to analyze the user's real-time emotions within the virtual space. The emotion engine infers emotions using various input data, such as the user's facial expressions, tone of voice, and behavioral patterns.

[0118] Based on the analysis results, the server dynamically adjusts the elements of the virtual space, changing the design to match the user's emotions. For example, if the server determines that the user is excited, it will make the colors more vibrant and enhance the sound effects to provide a stimulating environment.

[0119] The device personalizes the virtual space according to the user's device environment, ensuring accessibility. Users enter the virtual space via a provided access link. Visual changes that respond to the user's emotions are instantly reflected through the device, providing an immersive experience.

[0120] As a concrete example, in esports tournaments, servers can monitor the emotional states of players and spectators, dynamically changing lighting and visual effects during matches. This allows users to enjoy the event in an environment synchronized with their own emotions.

[0121] In this way, by incorporating an emotion engine, virtual spaces can go beyond mere visual displays and deepen the user experience in real time by interacting with the user's emotions.

[0122] The following describes the processing flow.

[0123] Step 1:

[0124] The server collects information about the user's past life experiences, including their competition participation history, viewing patterns, and social media activity feedback. This reveals the user's preferences and tendencies.

[0125] Step 2:

[0126] The server uses an emotion engine to receive emotional data from users in real time. The emotion engine analyzes the user's facial expressions, voice tone, and behavioral patterns to determine their current emotional state. This analysis continues even after the user accesses the virtual space.

[0127] Step 3:

[0128] The server dynamically adjusts the visual elements within the virtual space based on the analysis results of the emotion engine. For example, if the server determines that the user is experiencing stress, it will provide a relaxing environment by changing the color scheme to a calming tone and the sound to a more soothing one.

[0129] Step 4:

[0130] The server updates the virtual arena after the adjustments and delivers them to the user via the terminal. The terminal immediately reflects the changes and displays them in the virtual space in a way that is optimized for the user's device environment.

[0131] Step 5:

[0132] Users continue their experience within the updated virtual space, enjoying a natural sense of immersion without their emotional changes being detected. If emotional changes occur, feedback is sent back to the server, and the virtual space is updated accordingly.

[0133] (Example 2)

[0134] 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".

[0135] In modern society, virtual environments are used in many fields, but they are primarily static and uniform in configuration, making it difficult to dynamically adjust them to the user's emotions and individual experiences. There is a demand to achieve greater immersion and personalization by reflecting the user's emotional state in real time and providing a virtual environment that responds accordingly.

[0136] 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.

[0137] In this invention, the server includes means for collecting information based on past events, means for analyzing the collected information and generating visual elements, and means for analyzing human emotions in real time using an emotion analysis engine. This enables dynamic adjustment of the virtual environment in response to the user's emotions.

[0138] "Past events" refers to the history of various events and occurrences that the user has experienced up to that point. This includes information on events attended and records of online activities.

[0139] "Means of collecting information" refers to the methods or functions that a server uses to obtain data about a user. This includes processes such as accessing databases and retrieving data from external sources.

[0140] "Means of generating visual elements" refers to the process of creating visible parts within a virtual environment based on collected data. Design tools and graphics engines may fall into this category.

[0141] A "virtual environment" refers to a virtual space or world created using computer technology. This can be experienced by users through computer devices.

[0142] "Means of collecting human emotional responses" refers to methods or functions that collect data to understand a user's emotional state. This includes facial recognition technology and voice analysis technology.

[0143] "Means of dynamic modification" refers to processes that enable the virtual environment to change in response to the user's real-time state and requests.

[0144] An "emotion analysis engine" refers to an algorithm or program that analyzes and interprets a user's emotional state. This engine can perform detailed analysis of facial expressions and voice tone.

[0145] A "generative AI model" refers to a model that uses artificial intelligence technology to generate output based on input data. This model has the ability to optimize its prediction and generation through learning.

[0146] A "prompt" refers to a text-based input used to provide specific instructions or information to a generative AI model. This serves as a guideline for the model to generate the desired output.

[0147] This invention is a system that analyzes user emotions in real time and dynamically adjusts the virtual environment based on those emotions. The system mainly consists of a server, terminals, and users.

[0148] The server collects information based on past events. This collection includes accessing databases and analyzing the user's online activity history. The server also uses an emotion analysis engine to analyze the user's emotions in real time. This process utilizes facial recognition software and voice analysis technology to identify emotions based on the user's facial expressions and tone of voice.

[0149] When a user accesses a virtual environment using their device, the device displays a virtual environment adapted to the user's device environment. Based on sentiment analysis results received from the server, the device adjusts the visual and acoustic components of the virtual environment. For example, if the user is surprised, the device increases the brightness of the virtual environment and dramatically changes the music to create an appropriate atmosphere.

[0150] Furthermore, the server uses a generative AI model to generate emotion-responsive prompts. These prompts are used to provide specific instructions on how the system should adjust the virtual environment. An example of a prompt might be, "Generate instructions to select background music suitable for when the user is relaxed."

[0151] This configuration allows the system to flexibly and in real time adjust the virtual environment according to the user's emotional state, aiming to provide a more personalized and immersive experience.

[0152] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0153] Step 1:

[0154] The server collects information based on the user's past experiences. It retrieves past event participation data and online activity history from databases and external sources as input. This allows it to analyze the user's preference patterns and output them as a base dataset. Specifically, it uses SQL queries to extract the necessary data from the database.

[0155] Step 2:

[0156] The server analyzes the collected base dataset and generates visual elements. The data from Step 1 is used as input, and an algorithm uses this data to create a virtual environment design tailored to the user's preferences. The generated visual elements are obtained as output. Specifically, graphic templates are generated by design software.

[0157] Step 3:

[0158] The server uses an emotion analysis engine to analyze the user's emotions in real time. It acquires user facial and voice data from the camera and microphone as input. This data is analyzed using a machine learning model to identify the user's emotional state. The output is an emotion classification result. Specific operations include the use of facial recognition APIs and voice tone analysis tools.

[0159] Step 4:

[0160] The server dynamically adjusts the virtual environment based on the analyzed emotion results. It uses the visual components generated in step 2 and the emotion classification data obtained in step 3 as input. This data is processed to adjust the composition of the virtual environment, including its colors and sound effects. The output is the design of the adjusted virtual environment. Specifically, real-time rendering is performed by the graphics engine.

[0161] Step 5:

[0162] The terminal displays a pre-configured virtual environment received from the server to the user. It receives virtual environment data from the server as input, and based on this, a visual and auditory experience is prepared on the terminal. As output, the user is provided with an immersive, interactive virtual experience. Specifically, the device's GPU is used to render the image.

[0163] (Application Example 2)

[0164] 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".

[0165] Traditional virtual environments have been unable to reflect users' past experiences or real-time emotions, making it difficult to provide personalized experiences. Furthermore, they lack dynamic content adjustments based on user emotions, and there is a particular need for ways to improve the satisfaction of the purchasing experience, especially in virtual stores. Therefore, technology is needed that effectively changes the virtual environment in response to user emotions, enabling personalized experiences and product recommendations.

[0166] 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.

[0167] In this invention, the server includes means for collecting information based on past experience, means for analyzing the collected information and generating visual components, means for structuring a virtual environment based on the generated visual components, means for acquiring the user's emotions in the virtual environment, means for dynamically adjusting the virtual environment based on the emotion analysis results, and means for personalizing sales activities using emotion recognition technology. This makes it possible to provide visuals and experiences that match the user's emotions and to recommend personalized products suitable for specific sales situations.

[0168] "Means of collecting information based on past experience" refers to technologies that collect data on users' past activity history and preferences.

[0169] "Means for generating visual elements" refers to technologies that create visual elements in a virtual space based on collected data.

[0170] "Means of structuring virtual environments" are technologies that combine generated visual components to form virtual spaces.

[0171] "Means of acquiring emotions" refers to technologies that recognize and acquire information about a user's emotions from their facial expressions, voice, and actions.

[0172] "Methods for dynamically adjusting the virtual environment based on emotion analysis results" refers to technologies that change the design and configuration of the virtual environment in real time based on acquired emotion data.

[0173] "Methods for personalizing sales activities using emotion recognition technology" refers to technologies that recognize the emotions of users and provide product recommendations and promotions tailored to their individual needs.

[0174] The system required to implement this application includes the following elements.

[0175] First, the server collects information based on the user's past experiences. This includes data reflecting past activity history, purchase history, and online preferences. This information is stored in a database and used to understand the user's basic preferences.

[0176] Next, the server generates visual elements based on the collected information. This utilizes data analysis tools using Python and machine learning models. For example, by analyzing user behavior patterns using TENSORFLOW®, data is generated to customize the design of the virtual environment.

[0177] Furthermore, the server uses libraries such as OpenCV to perform facial recognition and emotion analysis in order to acquire the user's emotions. The user's emotional state is recognized in real time, and the virtual environment is dynamically adjusted accordingly.

[0178] The device runs a smartphone application using React Native to provide this virtual environment to the user. The virtual environment provided by the device automatically changes in response to the user's emotions, providing a personalized experience.

[0179] This system allows, for example, a virtual store to run bright and vibrant promotions when it determines that a user is happy. On the other hand, when a user is undecided, it can provide detailed product information in a timely manner.

[0180] A concrete example of a prompt message is, "Instruct the generation AI how to generate a list of recommended products in the virtual store when the user is smiling." This ensures that the entire system works together to provide an appropriate virtual experience that responds to the user's emotions.

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The server collects information based on the user's past experiences. The input data includes the user's activity history and preferences. This data is stored in a database and organized into a format that allows for analysis of user trends. Specifically, the user's past purchase history and online activity logs are used as input data.

[0184] Step 2:

[0185] The server generates visual elements based on collected information. It uses previously collected data as input. This data is analyzed using Python data analysis tools and machine learning algorithms to create the visual elements necessary for the virtual environment. The output consists of visual elements tailored to the user's preferences.

[0186] Step 3:

[0187] The server acquires the user's emotions. It uses real-time captured image data of the user's face as input. This data is processed by an emotion analysis model using OpenCV and TensorFlow, and the output is the user's emotional state (e.g., joy, confusion). Specifically, emotions are estimated by analyzing the movement of the user's facial muscles.

[0188] Step 4:

[0189] The server dynamically adjusts the virtual environment based on the emotion analysis results. It uses the emotion state obtained in step 3 as input. Based on this emotion state, it generates instructions to modify the UI of the application built with React Native. The output is configuration information for the virtual environment that reflects the user's emotions.

[0190] Step 5:

[0191] The device provides the user with a virtual environment based on configuration information received from the server. It receives configuration information from the server as input and renders it using a React Native application. Finally, the virtual environment is displayed on the user's device, providing the experience. Specifically, dynamic visual effects are displayed when the user is excited, and calming colors are used when the user is relaxed.

[0192] Step 6:

[0193] The user experiences a customized virtual environment. As input, they perceive the virtual environment displayed on their device through their five senses and perform specific actions (e.g., purchasing a product) based on this perception. As output, the user's next action is generated, and further data is fed back to the server. Specific actions include clicking on a product of interest and viewing detailed information.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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".

[0210] As an embodiment of this invention, a system for automatically generating and customizing virtual spaces consists mainly of a program that includes functions for data collection, data analysis, arena generation, and real-time response.

[0211] First, the server collects information based on the past life experiences of event participants and viewers. This includes past competition data from participants, preferences shown by viewers, and feedback data from social media. This allows the server to understand users' past trends and preferences.

[0212] Next, the server uses an AI agent to analyze the collected information. Based on the results of this analysis, it generates visual elements and designs a prototype of a virtual space that is visually appealing and suitable for the participants. This virtual space is dynamically constructed, taking into account design elements such as color, shape, and visual effects.

[0213] The generated virtual space is stored in the cloud and delivered to participants via their devices. Users join the virtual space via a provided access link, and personalization is automatically performed according to their environment and device. This allows each user to freely experience the event beyond physical limitations.

[0214] During a real-time event, the terminal continuously collects feedback from viewers and sends it to the server. The server analyzes this real-time data and dynamically adjusts the visual elements and layout of the virtual space as the event progresses. For example, if it is found that many viewers prefer a particular element, the server can instantly adjust the virtual space to emphasize that element.

[0215] As a concrete example, when an international gaming tournament is held, a more personalized experience can be provided by changing the design according to the nationality of the participants and viewers, and by providing content that is compatible with different time zones. Also, when a large audience is enjoying a particular battle scene, it is possible to further enhance immersion by strengthening the visual effects and adjusting the sound.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The server collects information based on the past life experiences of event participants and viewers. Specifically, it retrieves past competition results, viewing history, and feedback from social media. This reveals user preferences and trends.

[0219] Step 2:

[0220] The server passes the collected data to an AI agent for analysis. The AI ​​agent identifies viewer preferences and participants' playing styles from the provided data and generates visual elements. Based on these results, it designs the virtual space.

[0221] Step 3:

[0222] The server constructs a virtual arena based on the generated visual elements. The virtual arena includes colors, shapes, layouts, and visual effects, which are optimized according to the participants' preferences.

[0223] Step 4:

[0224] The server saves the completed virtual arena to the cloud and generates an access link. Users use this link to access the virtual arena. Upon access, the device personalizes the virtual space according to the user's environment.

[0225] Step 5:

[0226] During the event, the device collects real-time feedback from viewers. This may include evaluations of visual effects and the event's flow. The collected data is sent to a server.

[0227] Step 6:

[0228] The server analyzes real-time feedback to determine if adjustments are needed to the virtual arena. If necessary, it instantly modifies the visual elements and layout of the virtual space, making changes as the event progresses.

[0229] Step 7:

[0230] The device receives correction instructions sent from the server and reflects the changes in the virtual arena. Through this process, viewers can always enjoy the latest and most optimized experience.

[0231] (Example 1)

[0232] 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."

[0233] In generating and customizing virtual spaces, there is a need for systems that can provide personalized experiences by dynamically utilizing users' past life experiences and real-time perceptual information. However, current systems struggle to effectively collect, analyze, and adapt this data, making it difficult to provide users with attractive and personalized virtual spaces.

[0234] 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.

[0235] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information using an analysis algorithm to generate visual components, and means for distributing the virtual space via a network environment and personalizing it according to the user environment. This makes it possible to provide a virtual space that is individualized and dynamically optimized for the user experience.

[0236] "Information based on past life experiences" is a general term for data related to a user's past behavior, preferences, digital feedback, etc., and is used to generate personalized experiences in virtual space.

[0237] An "analysis algorithm" is a mathematical method or computer program used to process collected data and extract patterns or trends.

[0238] "Visual elements" refer to the collective visual elements involved in the design of a virtual space, such as color, shape, effects, and layout.

[0239] A "virtual space" is a digital environment created through computer simulation, a space that users can experience interactively.

[0240] A "network environment" is a general term for the internet and other communication infrastructure used to send and receive digital information, and is the environment used to connect users and servers.

[0241] "Personalizing based on user environment" refers to the process of customizing digital content and interfaces according to the user's device characteristics and individual preferences.

[0242] "Perceptual information" is a general term for data related to senses such as sight, hearing, and touch that a user obtains within a virtual space.

[0243] "Dynamic modification" is the process of instantly adjusting and changing the components and layout of a virtual space based on information the system collects in real time.

[0244] This invention relates to a system for dynamically generating and customizing virtual spaces. The system consists of a combination of servers, terminals, and user interaction technologies for collecting a user's past life experiences, analyzing that data, and generating visual elements.

[0245] The server uses APIs and databases to collect past competition data, social media feedback data, and preference data from participants and viewers. Based on this information, an AI agent uses analytical algorithms to analyze the data and identify patterns and key visual elements that should be applied to the virtual space. The server uses generative AI models to dynamically generate visual components and designs a prototype of the virtual space using real-time rendering technology.

[0246] The designed virtual space is stored in the cloud and delivered to the user via the device. The user experiences the virtual space via the provided access link. The device detects the user's device characteristics and provides an optimized virtual space by personalizing the visual and operational elements accordingly.

[0247] During a real-time event, the terminal collects perceptual information from viewers and sends it to the server. The server analyzes the real-time data and dynamically adjusts the elements and layout of the virtual space. For example, if many viewers prefer a particular visual effect, the server will make adjustments to emphasize that element.

[0248] For example, when hosting international gaming tournaments, it's possible to provide a more adaptive virtual experience for individual participants by customizing the design according to the nationalities of participants and viewers, and by providing content suitable for different time zones. Furthermore, when a large number of viewers are enjoying a particular action scene, immersion can be further enhanced by strengthening visual effects and adjusting sound effects.

[0249] An example of a prompt message is, "How will you customize the virtual space for viewers with diverse cultural backgrounds in an international gaming tournament?" This allows for dynamic adjustments to be requested.

[0250] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0251] Step 1:

[0252] The server uses APIs and database access to collect information based on the user's past life experiences. It receives user athletic data, preference data, and feedback from social media as input, and stores this data digitally. Each piece of information collected in the database is output as foundational data for building the user's profile.

[0253] Step 2:

[0254] The server analyzes the collected data using the AI ​​agent's analysis algorithm. From the input user data, it extracts visual preferences and experience trends using pattern recognition technology. As a result, it outputs a list of visual components to be implemented in the virtual space, which serves as a guideline for designing the virtual space prototype.

[0255] Step 3:

[0256] The server uses a generative AI model to generate visual elements and design a prototype of the virtual space. Using the analysis results from step 2 as input, a graphics engine generates a 3D model. Real-time rendering technology is also utilized to add more effective visual effects and presentation. As output, the designed virtual space prototype is saved to the cloud.

[0257] Step 4:

[0258] The device receives a prototype of the virtual space from the cloud and delivers it to the user. The input is virtual space data received via the network, which is personalized based on the user's device characteristics (resolution, processing power, etc.). The output is an optimized rendering of the virtual space, which the user can experience through the device.

[0259] Step 5:

[0260] The device collects user perception information in real time and sends it to the server. It accepts user feedback as input via survey forms and reaction buttons. This data is sent to the server during the event and output for real-time analysis.

[0261] Step 6:

[0262] The server analyzes the perceptual information received in real time and dynamically adjusts the visual elements of the virtual space. Input data includes user feedback collected in step 5. Based on this, the server changes the saturation and contrast of the virtual space, making adjustments according to the user's preferences. The output is the adjusted virtual space provided to the user, resulting in a more personalized experience.

[0263] (Application Example 1)

[0264] 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."

[0265] In the modern digital content field, providing users with personalized and immersive virtual experiences is a challenge. However, systems that dynamically adjust virtual spaces and optimize visual and auditory effects based on user preferences and on-the-spot feedback are limited. Therefore, there is a need for more real-time, personalized virtual experiences.

[0266] 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.

[0267] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information and generating visual components, and means for structuring a virtual space based on the generated visual components. This makes it possible to provide a personalized virtual space to the user and to dynamically adjust the virtual space in real time based on the user's response.

[0268] "Information based on past life experiences" refers to data related to the user's past experiences and preferences.

[0269] "Means for analyzing information and generating visual components" refers to a device or program that executes a process of designing the visual elements necessary for a virtual space based on collected data.

[0270] "Means of structuring virtual space" refers to a method or system that uses generated visual components to form a three-dimensional digital environment experienced by the user.

[0271] "Means for obtaining user responses" refers to systems that collect user input and reactions within a virtual space, and this includes sensors and user interfaces.

[0272] "Means for dynamically adjusting a virtual space" refers to a device or program that performs a process of changing elements such as visuals and sounds within a virtual space in real time based on the user's responses obtained.

[0273] To implement this invention, a system is constructed to automatically generate a virtual space and customize and adjust it in real time according to the user. The server collects the user's past life experiences and preferences from a database and analyzes them using an AI agent. This analysis generates visual elements and designs a prototype of the virtual space. The server saves the generated digital space to a cloud server and delivers it to the user's device. This delivery requires an internet connection, and the device used can be a smartphone or a head-mounted display.

[0274] Users enter a virtual space using a provided access link and can enjoy a personalized experience based on their preferences. Feedback is sent to the server in real time through the device, and the virtual space is dynamically adjusted based on the user's responses. This adjustment includes visual and auditory effects such as graphics and sound.

[0275] For example, if a user requests a virtual space with a nature theme, an environment themed around forests or oceans will be generated accordingly, with sounds like birdsong and waves emphasized. If the user's response is positive, the effects will be adjusted. This system provides users with a real-time virtual experience based on their interests and preferences.

[0276] The generation AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] The server collects information based on the user's past life experiences from the database. This information includes the user's past viewing history and preference data. The user ID is used as input, and the user's preference information data is obtained as output. The server prepares this as input data for data analysis.

[0280] Step 2:

[0281] The server performs data analysis using an AI agent based on the collected user preference information data. The preference information data is used as input, and visual components of the virtual space are generated as output. In this process, the machine learning model analyzes the user's preference patterns and identifies the optimal design elements.

[0282] Step 3:

[0283] The server structures the virtual space based on the generated visual components. The visual components are used as input, and the specific virtual space design is saved to the cloud as output. The server prepares for real-time events through this design.

[0284] Step 4:

[0285] The user uses the access link provided using the terminal to participate in the virtual space. The access link is used as input, and a personalized virtual experience is displayed on the terminal as output. The terminal performs device recognition according to the user's environment and provides an optimal visual setting.

[0286] Step 5:

[0287] The terminal transmits the user's responses in the virtual space to the server in real time. The user's actions and feelings are captured as input, and feedback data is provided to the server as output. The terminal maintains an interactive experience through this.

[0288] Step 6:

[0289] The server analyzes the received feedback data and dynamically adjusts the visual elements and sound effects of the virtual space. Using the feedback data as input, it generates updated virtual space settings as output. The server immediately reflects these changes, providing the user with a continuous experience. In this process, the generative AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0290] 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.

[0291] One embodiment of this invention is a system that recognizes the user's emotions and customizes the virtual space. By combining this system with an emotion engine, the system can analyze the user's emotional state in real time and flexibly adjust the design and presentation of the virtual space.

[0292] First, the server collects information about the user's past life experiences. This collected information includes data on the user's past event participation, social media activity history, and feedback. Based on this, the server understands the user's basic preferences and patterns.

[0293] Next, the server uses an emotion engine to analyze the user's real-time emotions within the virtual space. The emotion engine infers emotions using various input data, such as the user's facial expressions, tone of voice, and behavioral patterns.

[0294] Based on the analysis results, the server dynamically adjusts the elements of the virtual space, changing the design to match the user's emotions. For example, if the server determines that the user is excited, it will make the colors more vibrant and enhance the sound effects to provide a stimulating environment.

[0295] The device personalizes the virtual space according to the user's device environment, ensuring accessibility. Users enter the virtual space via a provided access link. Visual changes that respond to the user's emotions are instantly reflected through the device, providing an immersive experience.

[0296] As a concrete example, in esports tournaments, servers can monitor the emotional states of players and spectators, dynamically changing lighting and visual effects during matches. This allows users to enjoy the event in an environment synchronized with their own emotions.

[0297] In this way, by incorporating an emotion engine, virtual spaces can go beyond mere visual displays and deepen the user experience in real time while interacting with the user's emotions.

[0298] The following describes the processing flow.

[0299] Step 1:

[0300] The server collects information about the user's past life experiences, including their competition participation history, viewing patterns, and social media activity feedback. This reveals the user's preferences and tendencies.

[0301] Step 2:

[0302] The server uses an emotion engine to receive emotional data from users in real time. The emotion engine analyzes the user's facial expressions, voice tone, and behavioral patterns to determine their current emotional state. This analysis continues even after the user accesses the virtual space.

[0303] Step 3:

[0304] Based on the analysis results of the emotion engine, the server dynamically adjusts the visual components in the virtual space. For example, if the user is determined to be feeling stressed, the server provides a relaxing environment by making the color tone calming and changing the acoustics to be gentle.

[0305] Step 4:

[0306] The server updates the adjusted virtual arena and distributes it to the user through the terminal. The terminal immediately reflects the changes and displays them in the virtual space in a form optimized for the user's device environment.

[0307] Step 5:

[0308] The user continues to experience in the updated virtual space and enjoys the immersion in a natural environment without perceiving changes in emotions. If there are changes in emotions, feedback is sent to the server again, and the virtual space is updated as appropriate.

[0309] (Example 2)

[0310] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In modern society, virtual environments are being utilized in many fields, but there is an issue that they are centered around static and uniform configurations, and it is difficult to make dynamic adjustments according to the emotions and individual experiences of users. There is a desire to achieve a greater sense of immersion and personalization by reflecting the user's emotional state in real time and providing a corresponding virtual environment.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0313] In this invention, the server includes means for collecting information based on past events, means for analyzing the collected information and generating visual elements, and means for analyzing human emotions in real time using an emotion analysis engine. This enables dynamic adjustment of the virtual environment in response to the user's emotions.

[0314] "Past events" refers to the history of various events and occurrences that the user has experienced up to that point. This includes information on events attended and records of online activities.

[0315] "Means of collecting information" refers to the methods or functions that a server uses to obtain data about a user. This includes processes such as accessing databases or retrieving data from external sources.

[0316] "Means of generating visual elements" refers to the process of creating visible parts within a virtual environment based on collected data. Design tools and graphics engines may fall into this category.

[0317] A "virtual environment" refers to a virtual space or world created using computer technology. This can be experienced by users through computer devices.

[0318] "Means of collecting human emotional responses" refers to methods or functions that collect data to understand a user's emotional state. This includes facial recognition technology and voice analysis technology.

[0319] "Means of dynamic modification" refers to processes that enable the virtual environment to change in response to the user's real-time state and requests.

[0320] An "emotion analysis engine" refers to an algorithm or program that analyzes and interprets a user's emotional state. This engine can perform detailed analysis of facial expressions and voice tone.

[0321] A "generative AI model" refers to a model that uses artificial intelligence technology to generate output based on input data. This model has the ability to optimize its prediction and generation through learning.

[0322] A "prompt" refers to a text-based input used to provide specific instructions or information to a generative AI model. This serves as a guideline for the model to generate the desired output.

[0323] This invention is a system that analyzes user emotions in real time and dynamically adjusts the virtual environment based on those emotions. The system mainly consists of a server, terminals, and users.

[0324] The server collects information based on past events. This collection includes accessing databases and analyzing the user's online activity history. The server also uses an emotion analysis engine to analyze the user's emotions in real time. This process utilizes facial recognition software and voice analysis technology to identify emotions based on the user's facial expressions and tone of voice.

[0325] When a user accesses a virtual environment using their device, the device displays a virtual environment adapted to the user's device environment. Based on sentiment analysis results received from the server, the device adjusts the visual and acoustic components of the virtual environment. For example, if the user is surprised, the device increases the brightness of the virtual environment and dramatically changes the music to create an appropriate atmosphere.

[0326] Furthermore, the server uses a generative AI model to generate emotion-responsive prompts. These prompts are used to provide specific instructions on how the system should adjust the virtual environment. An example of a prompt might be, "Generate instructions to select background music suitable for when the user is relaxed."

[0327] This configuration allows the system to flexibly and in real time adjust the virtual environment according to the user's emotional state, aiming to provide a more personalized and immersive experience.

[0328] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0329] Step 1:

[0330] The server collects information based on the user's past experiences. It retrieves past event participation data and online activity history from databases and external sources as input. This allows it to analyze the user's preference patterns and output them as a base dataset. Specifically, it uses SQL queries to extract the necessary data from the database.

[0331] Step 2:

[0332] The server analyzes the collected base dataset and generates visual elements. The data from Step 1 is used as input, and an algorithm uses this data to create a virtual environment design tailored to the user's preferences. The generated visual elements are obtained as output. Specifically, graphic templates are generated by design software.

[0333] Step 3:

[0334] The server uses an emotion analysis engine to analyze the user's emotions in real time. It acquires user facial and voice data from the camera and microphone as input. This data is analyzed using a machine learning model to identify the user's emotional state. The output is an emotion classification result. Specific operations include the use of facial recognition APIs and voice tone analysis tools.

[0335] Step 4:

[0336] The server dynamically adjusts the virtual environment based on the analyzed emotion results. It uses the visual components generated in step 2 and the emotion classification data obtained in step 3 as input. This data is processed to adjust the composition of the virtual environment, including its colors and sound effects. The output is the design of the adjusted virtual environment. Specifically, real-time rendering is performed by the graphics engine.

[0337] Step 5:

[0338] The terminal displays a pre-configured virtual environment received from the server to the user. It receives virtual environment data from the server as input, and based on this, a visual and auditory experience is prepared on the terminal. As output, the user is provided with an immersive, interactive virtual experience. Specifically, the device's GPU is used to render the image.

[0339] (Application Example 2)

[0340] 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."

[0341] Traditional virtual environments have been unable to reflect users' past experiences or real-time emotions, making it difficult to provide personalized experiences. Furthermore, they lack dynamic content adjustments based on user emotions, and there is a particular need for ways to improve the satisfaction of the purchasing experience, especially in virtual stores. Therefore, technology is needed that effectively changes the virtual environment in response to user emotions, enabling personalized experiences and product recommendations.

[0342] 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.

[0343] In this invention, the server includes means for collecting information based on past experience, means for analyzing the collected information and generating visual components, means for structuring a virtual environment based on the generated visual components, means for acquiring the user's emotions in the virtual environment, means for dynamically adjusting the virtual environment based on the emotion analysis results, and means for personalizing sales activities using emotion recognition technology. This makes it possible to provide visuals and experiences that match the user's emotions and to recommend personalized products suitable for specific sales situations.

[0344] "Means of collecting information based on past experience" refers to technologies that collect data on users' past activity history and preferences.

[0345] "Means for generating visual elements" refers to technologies that create visual elements in a virtual space based on collected data.

[0346] "Means of structuring virtual environments" are technologies that combine generated visual components to form virtual spaces.

[0347] "Means of acquiring emotions" refers to technologies that recognize and acquire information about a user's emotions from their facial expressions, voice, and actions.

[0348] "Methods for dynamically adjusting the virtual environment based on emotion analysis results" refers to technologies that change the design and configuration of the virtual environment in real time based on acquired emotion data.

[0349] "Methods for personalizing sales activities using emotion recognition technology" refers to technologies that recognize the emotions of users and provide product recommendations and promotions tailored to their individual needs.

[0350] The system required to implement this application includes the following elements.

[0351] First, the server collects information based on the user's past experiences. This includes data reflecting past activity history, purchase history, and online preferences. This information is stored in a database and used to understand the user's basic preferences.

[0352] Next, the server generates visual components based on the collected information. This utilizes data analysis tools using Python and machine learning models. For example, by analyzing user behavior patterns using TensorFlow, data is generated to customize the design of the virtual environment.

[0353] Furthermore, the server uses libraries such as OpenCV to perform facial recognition and emotion analysis in order to acquire the user's emotions. The user's emotional state is recognized in real time, and the virtual environment is dynamically adjusted accordingly.

[0354] The device runs a smartphone application using React Native to provide this virtual environment to the user. The virtual environment provided by the device automatically changes in response to the user's emotions, providing a personalized experience.

[0355] This system allows, for example, a virtual store to run bright and vibrant promotions when it determines that a user is happy. On the other hand, when a user is undecided, it can provide detailed product information in a timely manner.

[0356] A concrete example of a prompt message is, "Instruct the generation AI how to generate a list of recommended products in the virtual store when the user is smiling." This ensures that the entire system works together to provide an appropriate virtual experience that responds to the user's emotions.

[0357] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0358] Step 1:

[0359] The server collects information based on the user's past experiences. The input data includes the user's activity history and preferences. This data is stored in a database and organized into a format that allows for analysis of user trends. Specifically, the user's past purchase history and online activity logs are used as input data.

[0360] Step 2:

[0361] The server generates visual elements based on collected information. It uses previously collected data as input. This data is analyzed using Python data analysis tools and machine learning algorithms to create the visual elements necessary for the virtual environment. The output consists of visual elements tailored to the user's preferences.

[0362] Step 3:

[0363] The server acquires the user's emotions. It uses real-time captured image data of the user's face as input. This data is processed by an emotion analysis model using OpenCV and TensorFlow, and the output is the user's emotional state (e.g., joy, confusion). Specifically, emotions are estimated by analyzing the movement of the user's facial muscles.

[0364] Step 4:

[0365] The server dynamically adjusts the virtual environment based on the emotion analysis results. It uses the emotion state obtained in step 3 as input. Based on this emotion state, it generates instructions to modify the UI of the application built with React Native. The output is configuration information for the virtual environment that reflects the user's emotions.

[0366] Step 5:

[0367] The device provides the user with a virtual environment based on configuration information received from the server. It receives configuration information from the server as input and renders it using a React Native application. Finally, the virtual environment is displayed on the user's device, providing the experience. Specifically, dynamic visual effects are displayed when the user is excited, and calming colors are used when the user is relaxed.

[0368] Step 6:

[0369] The user experiences a customized virtual environment. As input, they perceive the virtual environment displayed on their device through their five senses and perform specific actions (e.g., purchasing a product) based on this perception. As output, the user's next action is generated, and further data is fed back to the server. Specific actions include clicking on a product of interest and viewing detailed information.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] [Third Embodiment]

[0374] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0375] 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.

[0376] 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).

[0377] 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.

[0378] 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.

[0379] 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).

[0380] 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.

[0381] 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.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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".

[0386] As an embodiment of this invention, a system for automatically generating and customizing virtual spaces consists mainly of a program that includes functions for data collection, data analysis, arena generation, and real-time response.

[0387] First, the server collects information based on the past life experiences of event participants and viewers. This includes past competition data from participants, preferences shown by viewers, and feedback data from social media. This allows the server to understand users' past trends and preferences.

[0388] Next, the server uses an AI agent to analyze the collected information. Based on the results of this analysis, it generates visual elements and designs a prototype of a virtual space that is visually appealing and suitable for the participants. This virtual space is dynamically constructed, taking into account design elements such as color, shape, and visual effects.

[0389] The generated virtual space is stored in the cloud and delivered to participants via their devices. Users join the virtual space via a provided access link, and personalization is automatically performed according to their environment and device. This allows each user to freely experience the event beyond physical limitations.

[0390] During a real-time event, the terminal continuously collects feedback from viewers and sends it to the server. The server analyzes this real-time data and dynamically adjusts the visual elements and layout of the virtual space as the event progresses. For example, if it is found that many viewers prefer a particular element, the server can instantly adjust the virtual space to emphasize that element.

[0391] As a concrete example, when an international gaming tournament is held, a more personalized experience can be provided by changing the design according to the nationality of the participants and viewers, and by providing content that is compatible with different time zones. Also, when a large audience is enjoying a particular battle scene, it is possible to further enhance immersion by strengthening the visual effects and adjusting the sound.

[0392] The following describes the processing flow.

[0393] Step 1:

[0394] The server collects information based on the past life experiences of event participants and viewers. Specifically, it retrieves past competition results, viewing history, and feedback from social media. This reveals user preferences and trends.

[0395] Step 2:

[0396] The server passes the collected data to an AI agent for analysis. The AI ​​agent identifies viewer preferences and participants' playing styles from the provided data and generates visual elements. Based on these results, it designs the virtual space.

[0397] Step 3:

[0398] The server constructs a virtual arena based on the generated visual elements. The virtual arena includes colors, shapes, layouts, and visual effects, which are optimized according to the participants' preferences.

[0399] Step 4:

[0400] The server saves the completed virtual arena to the cloud and generates an access link. Users use this link to access the virtual arena. Upon access, the device personalizes the virtual space according to the user's environment.

[0401] Step 5:

[0402] During the event, the device collects real-time feedback from viewers. This may include evaluations of visual effects and the event's flow. The collected data is sent to a server.

[0403] Step 6:

[0404] The server analyzes real-time feedback to determine if adjustments are needed to the virtual arena. If necessary, it instantly modifies the visual elements and layout of the virtual space, making changes as the event progresses.

[0405] Step 7:

[0406] The device receives correction instructions sent from the server and reflects the changes in the virtual arena. Through this process, viewers can always enjoy the latest and most optimized experience.

[0407] (Example 1)

[0408] 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."

[0409] In generating and customizing virtual spaces, there is a need for systems that can provide personalized experiences by dynamically utilizing users' past life experiences and real-time perceptual information. However, current systems struggle to effectively collect, analyze, and adapt this data, making it difficult to provide users with attractive and personalized virtual spaces.

[0410] 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.

[0411] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information using an analysis algorithm to generate visual components, and means for distributing the virtual space via a network environment and personalizing it according to the user environment. This makes it possible to provide a virtual space that is individualized and dynamically optimized for the user experience.

[0412] "Information based on past life experiences" is a general term for data related to a user's past behavior, preferences, digital feedback, etc., and is used to generate personalized experiences in virtual space.

[0413] An "analysis algorithm" is a mathematical method or computer program used to process collected data and extract patterns or trends.

[0414] "Visual elements" refer to the collective visual elements involved in the design of a virtual space, such as color, shape, effects, and layout.

[0415] A "virtual space" is a digital environment created through computer simulation, a space that users can experience interactively.

[0416] A "network environment" is a general term for the internet and other communication infrastructure used to send and receive digital information, and is the environment used to connect users and servers.

[0417] "Personalizing based on user environment" refers to the process of customizing digital content and interfaces according to the user's device characteristics and individual preferences.

[0418] "Perceptual information" is a general term for data related to senses such as sight, hearing, and touch that a user obtains within a virtual space.

[0419] "Dynamic modification" is the process of instantly adjusting and changing the components and layout of a virtual space based on information the system collects in real time.

[0420] This invention relates to a system for dynamically generating and customizing virtual spaces. The system consists of a combination of servers, terminals, and user interaction technologies for collecting a user's past life experiences, analyzing that data, and generating visual elements.

[0421] The server uses APIs and databases to collect past competition data, social media feedback data, and preference data from participants and viewers. Based on this information, an AI agent uses analytical algorithms to analyze the data and identify patterns and key visual elements that should be applied to the virtual space. The server uses generative AI models to dynamically generate visual components and designs a prototype of the virtual space using real-time rendering technology.

[0422] The designed virtual space is stored in the cloud and delivered to the user via the device. The user experiences the virtual space via the provided access link. The device detects the user's device characteristics and provides an optimized virtual space by personalizing the visual and operational elements accordingly.

[0423] During a real-time event, the terminal collects perceptual information from viewers and sends it to the server. The server analyzes the real-time data and dynamically adjusts the elements and layout of the virtual space. For example, if many viewers prefer a particular visual effect, the server will make adjustments to emphasize that element.

[0424] For example, when hosting international gaming tournaments, it's possible to provide a more adaptive virtual experience for individual participants by customizing the design according to the nationalities of participants and viewers, and by providing content suitable for different time zones. Furthermore, when a large number of viewers are enjoying a particular action scene, immersion can be further enhanced by strengthening visual effects and adjusting sound effects.

[0425] An example of a prompt message is, "How will you customize the virtual space for viewers with diverse cultural backgrounds in an international gaming tournament?" This allows for dynamic adjustments to be requested.

[0426] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0427] Step 1:

[0428] The server uses APIs and database access to collect information based on the user's past life experiences. It receives user athletic data, preference data, and feedback from social media as input, and stores this data digitally. Each piece of information collected in the database is output as foundational data for building the user's profile.

[0429] Step 2:

[0430] The server analyzes the collected data using the AI ​​agent's analysis algorithm. From the input user data, it extracts visual preferences and experience trends using pattern recognition technology. As a result, it outputs a list of visual components to be implemented in the virtual space, which serves as a guideline for designing the virtual space prototype.

[0431] Step 3:

[0432] The server uses a generative AI model to generate visual elements and design a prototype of the virtual space. Using the analysis results from step 2 as input, a graphics engine generates a 3D model. Real-time rendering technology is also utilized to add more effective visual effects and presentation. As output, the designed virtual space prototype is saved to the cloud.

[0433] Step 4:

[0434] The device receives a prototype of the virtual space from the cloud and delivers it to the user. The input is virtual space data received via the network, which is personalized based on the user's device characteristics (resolution, processing power, etc.). The output is an optimized rendering of the virtual space, which the user can experience through the device.

[0435] Step 5:

[0436] The device collects user perception information in real time and sends it to the server. It accepts user feedback as input via survey forms and reaction buttons. This data is sent to the server during the event and output for real-time analysis.

[0437] Step 6:

[0438] The server analyzes the perceptual information received in real time and dynamically adjusts the visual elements of the virtual space. Input data includes user feedback collected in step 5. Based on this, the server changes the saturation and contrast of the virtual space, making adjustments according to the user's preferences. The output is the adjusted virtual space provided to the user, resulting in a more personalized experience.

[0439] (Application Example 1)

[0440] 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."

[0441] In the modern digital content field, providing users with personalized and immersive virtual experiences is a challenge. However, systems that dynamically adjust virtual spaces and optimize visual and auditory effects based on user preferences and on-the-spot feedback are limited. Therefore, there is a need for more real-time, personalized virtual experiences.

[0442] 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.

[0443] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information and generating visual components, and means for structuring a virtual space based on the generated visual components. This makes it possible to provide a personalized virtual space to the user and to dynamically adjust the virtual space in real time based on the user's response.

[0444] "Information based on past life experiences" refers to data related to the user's past experiences and preferences.

[0445] "Means for analyzing information and generating visual components" refers to a device or program that executes a process of designing the visual elements necessary for a virtual space based on collected data.

[0446] "Means of structuring virtual space" refers to a method or system that uses generated visual components to form a three-dimensional digital environment experienced by the user.

[0447] "Means for obtaining user responses" refers to systems that collect user input and reactions within a virtual space, and this includes sensors and user interfaces.

[0448] "Means for dynamically adjusting a virtual space" refers to a device or program that performs a process of changing elements such as visuals and sounds within a virtual space in real time based on the user's responses obtained.

[0449] To implement this invention, a system is constructed to automatically generate a virtual space and customize and adjust it in real time according to the user. The server collects the user's past life experiences and preferences from a database and analyzes them using an AI agent. This analysis generates visual elements and designs a prototype of the virtual space. The server saves the generated digital space to a cloud server and delivers it to the user's device. This delivery requires an internet connection, and the device used can be a smartphone or a head-mounted display.

[0450] Users enter a virtual space using a provided access link and can enjoy a personalized experience based on their preferences. Feedback is sent to the server in real time through the device, and the virtual space is dynamically adjusted based on the user's responses. This adjustment includes visual and auditory effects such as graphics and sound.

[0451] For example, if a user requests a virtual space with a nature theme, an environment themed around forests or oceans will be generated accordingly, with sounds like birdsong and waves emphasized. If the user's response is positive, the effects will be adjusted. This system provides users with a real-time virtual experience based on their interests and preferences.

[0452] The generation AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0454] Step 1:

[0455] The server collects information from a database based on the user's past life experiences. This information includes the user's past viewing history and preference data. The user ID is used as input, and the user's preference data is obtained as output. The server prepares this as input data for data analysis.

[0456] Step 2:

[0457] The server uses an AI agent to analyze data based on collected user preference information. Using preference data as input, visual elements of a virtual space are generated as output. In this process, a machine learning model analyzes user preference patterns and identifies optimal design elements.

[0458] Step 3:

[0459] The server structures the virtual space based on the generated visual components. Using visual components as input, the concrete virtual space design is saved to the cloud as output. The server prepares for real-time events through this design.

[0460] Step 4:

[0461] Users join the virtual space using an access link provided via their device. The access link is used as input, and a personalized virtual experience is displayed on the device as output. The device recognizes the user's environment and provides optimal visual settings.

[0462] Step 5:

[0463] The device transmits user responses within the virtual space to the server in real time. User actions and comments are taken as input, and feedback data is provided to the server as output. Through this, the device maintains an interactive experience.

[0464] Step 6:

[0465] The server analyzes the received feedback data and dynamically adjusts the visual elements and sound effects of the virtual space. Using the feedback data as input, it generates updated virtual space settings as output. The server immediately reflects these changes, providing the user with a continuous experience. In this process, the generative AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0466] 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.

[0467] One embodiment of this invention is a system that recognizes the user's emotions and customizes the virtual space. By combining this system with an emotion engine, the system can analyze the user's emotional state in real time and flexibly adjust the design and presentation of the virtual space.

[0468] First, the server collects information about the user's past life experiences. This collected information includes data on the user's past event participation, social media activity history, and feedback. Based on this, the server understands the user's basic preferences and patterns.

[0469] Next, the server uses an emotion engine to analyze the user's real-time emotions within the virtual space. The emotion engine infers emotions using various input data, such as the user's facial expressions, tone of voice, and behavioral patterns.

[0470] Based on the analysis results, the server dynamically adjusts the elements of the virtual space, changing the design to match the user's emotions. For example, if the server determines that the user is excited, it will make the colors more vibrant and enhance the sound effects to provide a stimulating environment.

[0471] The device personalizes the virtual space according to the user's device environment, ensuring accessibility. Users enter the virtual space via a provided access link. Visual changes that respond to the user's emotions are instantly reflected through the device, providing an immersive experience.

[0472] As a concrete example, in esports tournaments, servers can monitor the emotional states of players and spectators, dynamically changing lighting and visual effects during matches. This allows users to enjoy the event in an environment synchronized with their own emotions.

[0473] In this way, by incorporating an emotion engine, virtual spaces can go beyond mere visual displays and deepen the user experience in real time while interacting with the user's emotions.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The server collects information about the user's past life experiences, including their competition participation history, viewing patterns, and social media activity feedback. This reveals the user's preferences and tendencies.

[0477] Step 2:

[0478] The server uses an emotion engine to receive emotional data from users in real time. The emotion engine analyzes the user's facial expressions, voice tone, and behavioral patterns to determine their current emotional state. This analysis continues even after the user accesses the virtual space.

[0479] Step 3:

[0480] The server dynamically adjusts the visual elements within the virtual space based on the analysis results of the emotion engine. For example, if the server determines that the user is experiencing stress, it will provide a relaxing environment by changing the color scheme to a calming tone and the sound to a more soothing one.

[0481] Step 4:

[0482] The server updates the virtual arena after the adjustments and delivers them to the user via the terminal. The terminal immediately reflects the changes and displays them in the virtual space in a way that is optimized for the user's device environment.

[0483] Step 5:

[0484] Users continue their experience within the updated virtual space, enjoying a natural sense of immersion without their emotional changes being detected. If emotional changes occur, feedback is sent back to the server, and the virtual space is updated accordingly.

[0485] (Example 2)

[0486] 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."

[0487] In modern society, virtual environments are used in many fields, but they are primarily static and uniform in configuration, making it difficult to dynamically adjust them to the user's emotions and individual experiences. There is a demand to achieve greater immersion and personalization by reflecting the user's emotional state in real time and providing a virtual environment that responds accordingly.

[0488] 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.

[0489] In this invention, the server includes means for collecting information based on past events, means for analyzing the collected information and generating visual elements, and means for analyzing human emotions in real time using an emotion analysis engine. This enables dynamic adjustment of the virtual environment in response to the user's emotions.

[0490] "Past events" refers to the history of various events and occurrences that the user has experienced up to that point. This includes information on events attended and records of online activities.

[0491] "Means of collecting information" refers to the methods or functions that a server uses to obtain data about a user. This includes processes such as accessing databases or retrieving data from external sources.

[0492] "Means of generating visual elements" refers to the process of creating visible parts within a virtual environment based on collected data. Design tools and graphics engines may fall into this category.

[0493] A "virtual environment" refers to a virtual space or world created using computer technology. This can be experienced by users through computer devices.

[0494] "Means of collecting human emotional responses" refers to methods or functions that collect data to understand a user's emotional state. This includes facial recognition technology and voice analysis technology.

[0495] "Means of dynamic modification" refers to processes that enable the virtual environment to change in response to the user's real-time state and requests.

[0496] An "emotion analysis engine" refers to an algorithm or program that analyzes and interprets a user's emotional state. This engine can perform detailed analysis of facial expressions and voice tone.

[0497] A "generative AI model" refers to a model that uses artificial intelligence technology to generate output based on input data. This model has the ability to optimize its prediction and generation through learning.

[0498] A "prompt" refers to a text-based input used to provide specific instructions or information to a generative AI model. This serves as a guideline for the model to generate the desired output.

[0499] This invention is a system that analyzes user emotions in real time and dynamically adjusts the virtual environment based on those emotions. The system mainly consists of a server, terminals, and users.

[0500] The server collects information based on past events. This collection includes accessing databases and analyzing the user's online activity history. The server also uses an emotion analysis engine to analyze the user's emotions in real time. This process utilizes facial recognition software and voice analysis technology to identify emotions based on the user's facial expressions and tone of voice.

[0501] When a user accesses a virtual environment using their device, the device displays a virtual environment adapted to the user's device environment. Based on sentiment analysis results received from the server, the device adjusts the visual and acoustic components of the virtual environment. For example, if the user is surprised, the device increases the brightness of the virtual environment and dramatically changes the music to create an appropriate atmosphere.

[0502] Furthermore, the server uses a generative AI model to generate emotion-responsive prompts. These prompts are used to provide specific instructions on how the system should adjust the virtual environment. An example of a prompt might be, "Generate instructions to select background music suitable for when the user is relaxed."

[0503] This configuration allows the system to flexibly and in real time adjust the virtual environment according to the user's emotional state, aiming to provide a more personalized and immersive experience.

[0504] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0505] Step 1:

[0506] The server collects information based on the user's past experiences. It retrieves past event participation data and online activity history from databases and external sources as input. This allows it to analyze the user's preference patterns and output them as a base dataset. Specifically, it uses SQL queries to extract the necessary data from the database.

[0507] Step 2:

[0508] The server analyzes the collected base dataset and generates visual elements. The data from Step 1 is used as input, and an algorithm uses this data to create a virtual environment design tailored to the user's preferences. The generated visual elements are obtained as output. Specifically, graphic templates are generated by design software.

[0509] Step 3:

[0510] The server uses an emotion analysis engine to analyze the user's emotions in real time. It acquires user facial and voice data from the camera and microphone as input. This data is analyzed using a machine learning model to identify the user's emotional state. The output is an emotion classification result. Specific operations include the use of facial recognition APIs and voice tone analysis tools.

[0511] Step 4:

[0512] The server dynamically adjusts the virtual environment based on the analyzed emotion results. It uses the visual components generated in step 2 and the emotion classification data obtained in step 3 as input. This data is processed to adjust the composition of the virtual environment, including its colors and sound effects. The output is the design of the adjusted virtual environment. Specifically, real-time rendering is performed by the graphics engine.

[0513] Step 5:

[0514] The terminal displays a pre-configured virtual environment received from the server to the user. It receives virtual environment data from the server as input, and based on this, a visual and auditory experience is prepared on the terminal. As output, the user is provided with an immersive, interactive virtual experience. Specifically, the device's GPU is used to render the image.

[0515] (Application Example 2)

[0516] 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."

[0517] Traditional virtual environments have been unable to reflect users' past experiences or real-time emotions, making it difficult to provide personalized experiences. Furthermore, they lack dynamic content adjustments based on user emotions, and there is a particular need for ways to improve the satisfaction of the purchasing experience, especially in virtual stores. Therefore, technology is needed that effectively changes the virtual environment in response to user emotions, enabling personalized experiences and product recommendations.

[0518] 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.

[0519] In this invention, the server includes means for collecting information based on past experience, means for analyzing the collected information and generating visual components, means for structuring a virtual environment based on the generated visual components, means for acquiring the user's emotions in the virtual environment, means for dynamically adjusting the virtual environment based on the emotion analysis results, and means for personalizing sales activities using emotion recognition technology. This makes it possible to provide visuals and experiences that match the user's emotions and to recommend personalized products suitable for specific sales situations.

[0520] "Means of collecting information based on past experience" refers to technologies that collect data on users' past activity history and preferences.

[0521] "Means for generating visual elements" refers to technologies that create visual elements in a virtual space based on collected data.

[0522] "Means of structuring virtual environments" are technologies that combine generated visual components to form virtual spaces.

[0523] "Means of acquiring emotions" refers to technologies that recognize and acquire information about a user's emotions from their facial expressions, voice, and actions.

[0524] "Methods for dynamically adjusting the virtual environment based on emotion analysis results" refers to technologies that change the design and configuration of the virtual environment in real time based on acquired emotion data.

[0525] "Methods for personalizing sales activities using emotion recognition technology" refers to technologies that recognize the emotions of users and provide product recommendations and promotions tailored to their individual needs.

[0526] The system required to implement this application includes the following elements.

[0527] First, the server collects information based on the user's past experiences. This includes data reflecting past activity history, purchase history, and online preferences. This information is stored in a database and used to understand the user's basic preferences.

[0528] Next, the server generates visual components based on the collected information. This utilizes data analysis tools using Python and machine learning models. For example, by analyzing user behavior patterns using TensorFlow, data is generated to customize the design of the virtual environment.

[0529] Furthermore, the server uses libraries such as OpenCV to perform facial recognition and emotion analysis in order to acquire the user's emotions. The user's emotional state is recognized in real time, and the virtual environment is dynamically adjusted accordingly.

[0530] The device runs a smartphone application using React Native to provide this virtual environment to the user. The virtual environment provided by the device automatically changes in response to the user's emotions, providing a personalized experience.

[0531] This system allows, for example, a virtual store to run bright and vibrant promotions when it determines that a user is happy. On the other hand, when a user is undecided, it can provide detailed product information in a timely manner.

[0532] A concrete example of a prompt message is, "Instruct the generation AI how to generate a list of recommended products in the virtual store when the user is smiling." This ensures that the entire system works together to provide an appropriate virtual experience that responds to the user's emotions.

[0533] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0534] Step 1:

[0535] The server collects information based on the user's past experiences. The input data includes the user's activity history and preferences. This data is stored in a database and organized into a format that allows for analysis of user trends. Specifically, the user's past purchase history and online activity logs are used as input data.

[0536] Step 2:

[0537] The server generates visual elements based on collected information. It uses previously collected data as input. This data is analyzed using Python data analysis tools and machine learning algorithms to create the visual elements necessary for the virtual environment. The output consists of visual elements tailored to the user's preferences.

[0538] Step 3:

[0539] The server acquires the user's emotions. It uses real-time captured image data of the user's face as input. This data is processed by an emotion analysis model using OpenCV and TensorFlow, and the output is the user's emotional state (e.g., joy, confusion). Specifically, emotions are estimated by analyzing the movement of the user's facial muscles.

[0540] Step 4:

[0541] The server dynamically adjusts the virtual environment based on the emotion analysis results. It uses the emotion state obtained in step 3 as input. Based on this emotion state, it generates instructions to modify the UI of the application built with React Native. The output is configuration information for the virtual environment that reflects the user's emotions.

[0542] Step 5:

[0543] The device provides the user with a virtual environment based on configuration information received from the server. It receives configuration information from the server as input and renders it using a React Native application. Finally, the virtual environment is displayed on the user's device, providing the experience. Specifically, dynamic visual effects are displayed when the user is excited, and calming colors are used when the user is relaxed.

[0544] Step 6:

[0545] The user experiences a customized virtual environment. As input, they perceive the virtual environment displayed on their device through their five senses and perform specific actions (e.g., purchasing a product) based on this perception. As output, the user's next action is generated, and further data is fed back to the server. Specific actions include clicking on a product of interest and viewing detailed information.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] [Fourth Embodiment]

[0550] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0551] 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.

[0552] 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).

[0553] 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.

[0554] 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.

[0555] 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).

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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".

[0563] As an embodiment of this invention, a system for automatically generating and customizing virtual spaces consists mainly of a program that includes functions for data collection, data analysis, arena generation, and real-time response.

[0564] First, the server collects information based on the past life experiences of event participants and viewers. This includes past competition data from participants, preferences shown by viewers, and feedback data from social media. This allows the server to understand users' past trends and preferences.

[0565] Next, the server uses an AI agent to analyze the collected information. Based on the results of this analysis, it generates visual elements and designs a prototype of a virtual space that is visually appealing and suitable for the participants. This virtual space is dynamically constructed, taking into account design elements such as color, shape, and visual effects.

[0566] The generated virtual space is stored in the cloud and delivered to participants via their devices. Users join the virtual space via a provided access link, and personalization is automatically performed according to their environment and device. This allows each user to freely experience the event beyond physical limitations.

[0567] During a real-time event, the terminal continuously collects feedback from viewers and sends it to the server. The server analyzes this real-time data and dynamically adjusts the visual elements and layout of the virtual space as the event progresses. For example, if it is found that many viewers prefer a particular element, the server can instantly adjust the virtual space to emphasize that element.

[0568] As a concrete example, when an international gaming tournament is held, a more personalized experience can be provided by changing the design according to the nationality of the participants and viewers, and by providing content that is compatible with different time zones. Also, when a large audience is enjoying a particular battle scene, it is possible to further enhance immersion by strengthening the visual effects and adjusting the sound.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] The server collects information based on the past life experiences of event participants and viewers. Specifically, it retrieves past competition results, viewing history, and feedback from social media. This reveals user preferences and trends.

[0572] Step 2:

[0573] The server passes the collected data to an AI agent for analysis. The AI ​​agent identifies viewer preferences and participants' playing styles from the provided data and generates visual elements. Based on these results, it designs the virtual space.

[0574] Step 3:

[0575] The server constructs a virtual arena based on the generated visual elements. The virtual arena includes colors, shapes, layouts, and visual effects, which are optimized according to the participants' preferences.

[0576] Step 4:

[0577] The server saves the completed virtual arena to the cloud and generates an access link. Users use this link to access the virtual arena. Upon access, the device personalizes the virtual space according to the user's environment.

[0578] Step 5:

[0579] During the event, the device collects real-time feedback from viewers. This may include evaluations of visual effects and the event's flow. The collected data is sent to a server.

[0580] Step 6:

[0581] The server analyzes real-time feedback to determine if adjustments are needed to the virtual arena. If necessary, it instantly modifies the visual elements and layout of the virtual space, making changes as the event progresses.

[0582] Step 7:

[0583] The device receives correction instructions sent from the server and reflects the changes in the virtual arena. Through this process, viewers can always enjoy the latest and most optimized experience.

[0584] (Example 1)

[0585] 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".

[0586] In generating and customizing virtual spaces, there is a need for systems that can provide personalized experiences by dynamically utilizing users' past life experiences and real-time perceptual information. However, current systems struggle to effectively collect, analyze, and adapt this data, making it difficult to provide users with attractive and personalized virtual spaces.

[0587] 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.

[0588] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information using an analysis algorithm to generate visual components, and means for distributing the virtual space via a network environment and personalizing it according to the user environment. This makes it possible to provide a virtual space that is individualized and dynamically optimized for the user experience.

[0589] "Information based on past life experiences" is a general term for data related to a user's past behavior, preferences, digital feedback, etc., and is used to generate personalized experiences in virtual space.

[0590] An "analysis algorithm" is a mathematical method or computer program used to process collected data and extract patterns or trends.

[0591] "Visual elements" refer to the collective visual elements involved in the design of a virtual space, such as color, shape, effects, and layout.

[0592] A "virtual space" is a digital environment created through computer simulation, a space that users can experience interactively.

[0593] A "network environment" is a general term for the internet and other communication infrastructure used to send and receive digital information, and is the environment used to connect users and servers.

[0594] "Personalizing based on user environment" refers to the process of customizing digital content and interfaces according to the user's device characteristics and individual preferences.

[0595] "Perceptual information" is a general term for data related to senses such as sight, hearing, and touch that a user obtains within a virtual space.

[0596] "Dynamic modification" is the process of instantly adjusting and changing the components and layout of a virtual space based on information the system collects in real time.

[0597] This invention relates to a system for dynamically generating and customizing virtual spaces. The system consists of a combination of servers, terminals, and user interaction technologies for collecting a user's past life experiences, analyzing that data, and generating visual elements.

[0598] The server uses APIs and databases to collect past competition data, social media feedback data, and preference data from participants and viewers. Based on this information, an AI agent uses analytical algorithms to analyze the data and identify patterns and key visual elements that should be applied to the virtual space. The server uses generative AI models to dynamically generate visual components and designs a prototype of the virtual space using real-time rendering technology.

[0599] The designed virtual space is stored in the cloud and delivered to the user via the device. The user experiences the virtual space via the provided access link. The device detects the user's device characteristics and provides an optimized virtual space by personalizing the visual and operational elements accordingly.

[0600] During a real-time event, the terminal collects perceptual information from viewers and sends it to the server. The server analyzes the real-time data and dynamically adjusts the elements and layout of the virtual space. For example, if many viewers prefer a particular visual effect, the server will make adjustments to emphasize that element.

[0601] For example, when hosting international gaming tournaments, it's possible to provide a more adaptive virtual experience for individual participants by customizing the design according to the nationalities of participants and viewers, and by providing content suitable for different time zones. Furthermore, when a large number of viewers are enjoying a particular action scene, immersion can be further enhanced by strengthening visual effects and adjusting sound effects.

[0602] An example of a prompt message is, "How will you customize the virtual space for viewers with diverse cultural backgrounds in an international gaming tournament?" This allows for dynamic adjustments to be requested.

[0603] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0604] Step 1:

[0605] The server uses APIs and database access to collect information based on the user's past life experiences. It receives user athletic data, preference data, and feedback from social media as input, and stores this data digitally. Each piece of information collected in the database is output as foundational data for building the user's profile.

[0606] Step 2:

[0607] The server analyzes the collected data using the AI ​​agent's analysis algorithm. From the input user data, it extracts visual preferences and experience trends using pattern recognition technology. As a result, it outputs a list of visual components to be implemented in the virtual space, which serves as a guideline for designing the virtual space prototype.

[0608] Step 3:

[0609] The server uses a generative AI model to generate visual elements and design a prototype of the virtual space. Using the analysis results from step 2 as input, a graphics engine generates a 3D model. Real-time rendering technology is also utilized to add more effective visual effects and presentation. As output, the designed virtual space prototype is saved to the cloud.

[0610] Step 4:

[0611] The device receives a prototype of the virtual space from the cloud and delivers it to the user. The input is virtual space data received via the network, which is personalized based on the user's device characteristics (resolution, processing power, etc.). The output is an optimized rendering of the virtual space, which the user can experience through the device.

[0612] Step 5:

[0613] The device collects user perception information in real time and sends it to the server. It accepts user feedback as input via survey forms and reaction buttons. This data is sent to the server during the event and output for real-time analysis.

[0614] Step 6:

[0615] The server analyzes the perceptual information received in real time and dynamically adjusts the visual elements of the virtual space. Input data includes user feedback collected in step 5. Based on this, the server changes the saturation and contrast of the virtual space, making adjustments according to the user's preferences. The output is the adjusted virtual space provided to the user, resulting in a more personalized experience.

[0616] (Application Example 1)

[0617] 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".

[0618] In the modern digital content field, providing users with personalized and immersive virtual experiences is a challenge. However, systems that dynamically adjust virtual spaces and optimize visual and auditory effects based on user preferences and on-the-spot feedback are limited. Therefore, there is a need for more real-time, personalized virtual experiences.

[0619] 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.

[0620] In this invention, the server includes means for collecting information based on past life experiences, means for analyzing the collected information and generating visual components, and means for structuring a virtual space based on the generated visual components. This makes it possible to provide a personalized virtual space to the user and to dynamically adjust the virtual space in real time based on the user's response.

[0621] "Information based on past life experiences" refers to data related to the user's past experiences and preferences.

[0622] "Means for analyzing information and generating visual components" refers to a device or program that executes a process of designing the visual elements necessary for a virtual space based on collected data.

[0623] "Means of structuring virtual space" refers to a method or system that uses generated visual components to form a three-dimensional digital environment experienced by the user.

[0624] "Means for obtaining user responses" refers to systems that collect user input and reactions within a virtual space, and this includes sensors and user interfaces.

[0625] "Means for dynamically adjusting a virtual space" refers to a device or program that performs a process of changing elements such as visuals and sounds within a virtual space in real time based on the user's responses obtained.

[0626] To implement this invention, a system is constructed to automatically generate a virtual space and customize and adjust it in real time according to the user. The server collects the user's past life experiences and preferences from a database and analyzes them using an AI agent. This analysis generates visual elements and designs a prototype of the virtual space. The server saves the generated digital space to a cloud server and delivers it to the user's device. This delivery requires an internet connection, and the device used can be a smartphone or a head-mounted display.

[0627] Users enter a virtual space using a provided access link and can enjoy a personalized experience based on their preferences. Feedback is sent to the server in real time through the device, and the virtual space is dynamically adjusted based on the user's responses. This adjustment includes visual and auditory effects such as graphics and sound.

[0628] For example, if a user requests a virtual space with a nature theme, an environment themed around forests or oceans will be generated accordingly, with sounds like birdsong and waves emphasized. If the user's response is positive, the effects will be adjusted. This system provides users with a real-time virtual experience based on their interests and preferences.

[0629] The generation AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0630] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0631] Step 1:

[0632] The server collects information from a database based on the user's past life experiences. This information includes the user's past viewing history and preference data. The user ID is used as input, and the user's preference data is obtained as output. The server prepares this as input data for data analysis.

[0633] Step 2:

[0634] The server uses an AI agent to analyze data based on collected user preference information. Using preference data as input, visual elements of a virtual space are generated as output. In this process, a machine learning model analyzes user preference patterns and identifies optimal design elements.

[0635] Step 3:

[0636] The server structures the virtual space based on the generated visual components. Using visual components as input, the concrete virtual space design is saved to the cloud as output. The server prepares for real-time events through this design.

[0637] Step 4:

[0638] Users join the virtual space using an access link provided via their device. The access link is used as input, and a personalized virtual experience is displayed on the device as output. The device recognizes the user's environment and provides optimal visual settings.

[0639] Step 5:

[0640] The device transmits user responses within the virtual space to the server in real time. User actions and comments are taken as input, and feedback data is provided to the server as output. Through this, the device maintains an interactive experience.

[0641] Step 6:

[0642] The server analyzes the received feedback data and dynamically adjusts the visual elements and sound effects of the virtual space. Using the feedback data as input, it generates updated virtual space settings as output. The server immediately reflects these changes, providing the user with a continuous experience. In this process, the generative AI model uses the prompt message, "Generate a virtual space based on user preferences and adjust it in real time."

[0643] 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.

[0644] One embodiment of this invention is a system that recognizes the user's emotions and customizes the virtual space. By combining this system with an emotion engine, the system can analyze the user's emotional state in real time and flexibly adjust the design and presentation of the virtual space.

[0645] First, the server collects information about the user's past life experiences. This collected information includes data on the user's past event participation, social media activity history, and feedback. Based on this, the server understands the user's basic preferences and patterns.

[0646] Next, the server uses an emotion engine to analyze the user's real-time emotions within the virtual space. The emotion engine infers emotions using various input data, such as the user's facial expressions, tone of voice, and behavioral patterns.

[0647] Based on the analysis results, the server dynamically adjusts the elements of the virtual space, changing the design to match the user's emotions. For example, if the server determines that the user is excited, it will make the colors more vibrant and enhance the sound effects to provide a stimulating environment.

[0648] The device personalizes the virtual space according to the user's device environment, ensuring accessibility. Users enter the virtual space via a provided access link. Visual changes that respond to the user's emotions are instantly reflected through the device, providing an immersive experience.

[0649] As a concrete example, in esports tournaments, servers can monitor the emotional states of players and spectators, dynamically changing lighting and visual effects during matches. This allows users to enjoy the event in an environment synchronized with their own emotions.

[0650] In this way, by incorporating an emotion engine, virtual spaces can go beyond mere visual displays and deepen the user experience in real time while interacting with the user's emotions.

[0651] The following describes the processing flow.

[0652] Step 1:

[0653] The server collects information about the user's past life experiences, including their competition participation history, viewing patterns, and social media activity feedback. This reveals the user's preferences and tendencies.

[0654] Step 2:

[0655] The server uses an emotion engine to receive emotional data from users in real time. The emotion engine analyzes the user's facial expressions, voice tone, and behavioral patterns to determine their current emotional state. This analysis continues even after the user accesses the virtual space.

[0656] Step 3:

[0657] The server dynamically adjusts the visual elements within the virtual space based on the analysis results of the emotion engine. For example, if the server determines that the user is experiencing stress, it will provide a relaxing environment by changing the color scheme to a calming tone and the sound to a more soothing one.

[0658] Step 4:

[0659] The server updates the virtual arena after the adjustments and delivers them to the user via the terminal. The terminal immediately reflects the changes and displays them in the virtual space in a way that is optimized for the user's device environment.

[0660] Step 5:

[0661] Users continue their experience within the updated virtual space, enjoying a natural sense of immersion without their emotional changes being detected. If emotional changes occur, feedback is sent back to the server, and the virtual space is updated accordingly.

[0662] (Example 2)

[0663] 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".

[0664] In modern society, virtual environments are used in many fields, but they are primarily static and uniform in configuration, making it difficult to dynamically adjust them to the user's emotions and individual experiences. There is a demand to achieve greater immersion and personalization by reflecting the user's emotional state in real time and providing a virtual environment that responds accordingly.

[0665] 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.

[0666] In this invention, the server includes means for collecting information based on past events, means for analyzing the collected information and generating visual elements, and means for analyzing human emotions in real time using an emotion analysis engine. This enables dynamic adjustment of the virtual environment in response to the user's emotions.

[0667] "Past events" refers to the history of various events and occurrences that the user has experienced up to that point. This includes information on events attended and records of online activities.

[0668] "Means of collecting information" refers to the methods or functions that a server uses to obtain data about a user. This includes processes such as accessing databases or retrieving data from external sources.

[0669] "Means of generating visual elements" refers to the process of creating visible parts within a virtual environment based on collected data. Design tools and graphics engines may fall into this category.

[0670] A "virtual environment" refers to a virtual space or world created using computer technology. This can be experienced by users through computer devices.

[0671] "Means of collecting human emotional responses" refers to methods or functions that collect data to understand a user's emotional state. This includes facial recognition technology and voice analysis technology.

[0672] "Means of dynamic modification" refers to processes that enable the virtual environment to change in response to the user's real-time state and requests.

[0673] An "emotion analysis engine" refers to an algorithm or program that analyzes and interprets a user's emotional state. This engine can perform detailed analysis of facial expressions and voice tone.

[0674] A "generative AI model" refers to a model that uses artificial intelligence technology to generate output based on input data. This model has the ability to optimize its prediction and generation through learning.

[0675] A "prompt" refers to a text-based input used to provide specific instructions or information to a generative AI model. This serves as a guideline for the model to generate the desired output.

[0676] This invention is a system that analyzes user emotions in real time and dynamically adjusts the virtual environment based on those emotions. The system mainly consists of a server, terminals, and users.

[0677] The server collects information based on past events. This collection includes accessing databases and analyzing the user's online activity history. The server also uses an emotion analysis engine to analyze the user's emotions in real time. This process utilizes facial recognition software and voice analysis technology to identify emotions based on the user's facial expressions and tone of voice.

[0678] When a user accesses a virtual environment using their device, the device displays a virtual environment adapted to the user's device environment. Based on sentiment analysis results received from the server, the device adjusts the visual and acoustic components of the virtual environment. For example, if the user is surprised, the device increases the brightness of the virtual environment and dramatically changes the music to create an appropriate atmosphere.

[0679] Furthermore, the server uses a generative AI model to generate emotion-responsive prompts. These prompts are used to provide specific instructions on how the system should adjust the virtual environment. An example of a prompt might be, "Generate instructions to select background music suitable for when the user is relaxed."

[0680] This configuration allows the system to flexibly and in real time adjust the virtual environment according to the user's emotional state, aiming to provide a more personalized and immersive experience.

[0681] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0682] Step 1:

[0683] The server collects information based on the user's past experiences. It retrieves past event participation data and online activity history from databases and external sources as input. This allows it to analyze the user's preference patterns and output them as a base dataset. Specifically, it uses SQL queries to extract the necessary data from the database.

[0684] Step 2:

[0685] The server analyzes the collected base dataset and generates visual elements. The data from Step 1 is used as input, and an algorithm uses this data to create a virtual environment design tailored to the user's preferences. The generated visual elements are obtained as output. Specifically, graphic templates are generated by design software.

[0686] Step 3:

[0687] The server uses an emotion analysis engine to analyze the user's emotions in real time. It acquires user facial and voice data from the camera and microphone as input. This data is analyzed using a machine learning model to identify the user's emotional state. The output is an emotion classification result. Specific operations include the use of facial recognition APIs and voice tone analysis tools.

[0688] Step 4:

[0689] The server dynamically adjusts the virtual environment based on the analyzed emotion results. It uses the visual components generated in step 2 and the emotion classification data obtained in step 3 as input. This data is processed to adjust the composition of the virtual environment, including its colors and sound effects. The output is the design of the adjusted virtual environment. Specifically, real-time rendering is performed by the graphics engine.

[0690] Step 5:

[0691] The terminal displays a pre-configured virtual environment received from the server to the user. It receives virtual environment data from the server as input, and based on this, a visual and auditory experience is prepared on the terminal. As output, the user is provided with an immersive, interactive virtual experience. Specifically, the device's GPU is used to render the image.

[0692] (Application Example 2)

[0693] 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".

[0694] Traditional virtual environments have been unable to reflect users' past experiences or real-time emotions, making it difficult to provide personalized experiences. Furthermore, they lack dynamic content adjustments based on user emotions, and there is a particular need for ways to improve the satisfaction of the purchasing experience, especially in virtual stores. Therefore, technology is needed that effectively changes the virtual environment in response to user emotions, enabling personalized experiences and product recommendations.

[0695] 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.

[0696] In this invention, the server includes means for collecting information based on past experience, means for analyzing the collected information and generating visual components, means for structuring a virtual environment based on the generated visual components, means for acquiring the user's emotions in the virtual environment, means for dynamically adjusting the virtual environment based on the emotion analysis results, and means for personalizing sales activities using emotion recognition technology. This makes it possible to provide visuals and experiences that match the user's emotions and to recommend personalized products suitable for specific sales situations.

[0697] "Means of collecting information based on past experience" refers to technologies that collect data on users' past activity history and preferences.

[0698] "Means for generating visual elements" refers to technologies that create visual elements in a virtual space based on collected data.

[0699] "Means of structuring virtual environments" are technologies that combine generated visual components to form virtual spaces.

[0700] "Means of acquiring emotions" refers to technologies that recognize and acquire information about a user's emotions from their facial expressions, voice, and actions.

[0701] "Methods for dynamically adjusting the virtual environment based on emotion analysis results" refers to technologies that change the design and configuration of the virtual environment in real time based on acquired emotion data.

[0702] "Methods for personalizing sales activities using emotion recognition technology" refers to technologies that recognize the emotions of users and provide product recommendations and promotions tailored to their individual needs.

[0703] The system required to implement this application includes the following elements.

[0704] First, the server collects information based on the user's past experiences. This includes data reflecting past activity history, purchase history, and online preferences. This information is stored in a database and used to understand the user's basic preferences.

[0705] Next, the server generates visual components based on the collected information. This utilizes data analysis tools using Python and machine learning models. For example, by analyzing user behavior patterns using TensorFlow, data is generated to customize the design of the virtual environment.

[0706] Furthermore, the server uses libraries such as OpenCV to perform facial recognition and emotion analysis in order to acquire the user's emotions. The user's emotional state is recognized in real time, and the virtual environment is dynamically adjusted accordingly.

[0707] The device runs a smartphone application using React Native to provide this virtual environment to the user. The virtual environment provided by the device automatically changes in response to the user's emotions, providing a personalized experience.

[0708] This system allows, for example, a virtual store to run bright and vibrant promotions when it determines that a user is happy. On the other hand, when a user is undecided, it can provide detailed product information in a timely manner.

[0709] A concrete example of a prompt message is, "Instruct the generation AI how to generate a list of recommended products in the virtual store when the user is smiling." This ensures that the entire system works together to provide an appropriate virtual experience that responds to the user's emotions.

[0710] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0711] Step 1:

[0712] The server collects information based on the user's past experiences. The input data includes the user's activity history and preferences. This data is stored in a database and organized into a format that allows for analysis of user trends. Specifically, the user's past purchase history and online activity logs are used as input data.

[0713] Step 2:

[0714] The server generates visual elements based on collected information. It uses previously collected data as input. This data is analyzed using Python data analysis tools and machine learning algorithms to create the visual elements necessary for the virtual environment. The output consists of visual elements tailored to the user's preferences.

[0715] Step 3:

[0716] The server acquires the user's emotions. It uses real-time captured image data of the user's face as input. This data is processed by an emotion analysis model using OpenCV and TensorFlow, and the output is the user's emotional state (e.g., joy, confusion). Specifically, emotions are estimated by analyzing the movement of the user's facial muscles.

[0717] Step 4:

[0718] The server dynamically adjusts the virtual environment based on the emotion analysis results. It uses the emotion state obtained in step 3 as input. Based on this emotion state, it generates instructions to modify the UI of the application built with React Native. The output is configuration information for the virtual environment that reflects the user's emotions.

[0719] Step 5:

[0720] The device provides the user with a virtual environment based on configuration information received from the server. It receives configuration information from the server as input and renders it using a React Native application. Finally, the virtual environment is displayed on the user's device, providing the experience. Specifically, dynamic visual effects are displayed when the user is excited, and calming colors are used when the user is relaxed.

[0721] Step 6:

[0722] The user experiences a customized virtual environment. As input, they perceive the virtual environment displayed on their device through their five senses and perform specific actions (e.g., purchasing a product) based on this perception. As output, the user's next action is generated, and further data is fed back to the server. Specific actions include clicking on a product of interest and viewing detailed information.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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.

[0729] 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.

[0730] 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.

[0731] 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."

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] 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.

[0743] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0744] The following is further disclosed regarding the embodiments described above.

[0745] (Claim 1)

[0746] Means of collecting information based on past life experiences,

[0747] A means of analyzing collected information and generating visual components,

[0748] A means of structuring a virtual space based on the generated visual components,

[0749] A means of collecting human sensory responses in a virtual space,

[0750] A means of modifying the virtual space based on collected human sensory responses,

[0751] A system that includes this.

[0752] (Claim 2)

[0753] The system according to claim 1, which dynamically adjusts visual elements in a virtual space using human sensory responses.

[0754] (Claim 3)

[0755] The system according to claim 1, comprising means for generating individual virtual spaces based on multiple life experiences and integrating them.

[0756] "Example 1"

[0757] (Claim 1)

[0758] Means of collecting information based on past life experiences,

[0759] A means for analyzing collected information using an analysis algorithm and generating visual components,

[0760] A means of designing a virtual space based on the generated visual components,

[0761] A means of delivering a virtual space via a network environment and personalizing it according to the user's environment,

[0762] A means of collecting perceptual information in a virtual space in real time,

[0763] A means for dynamically modifying the components within a virtual space based on collected perceptual information,

[0764] A system that includes this.

[0765] (Claim 2)

[0766] The system according to claim 1, which adjusts visual components in a virtual space in real time using perceptual information.

[0767] (Claim 3)

[0768] The system according to claim 1, which has means for constructing individual virtual spaces based on diverse life experiences and integrating them.

[0769] "Application Example 1"

[0770] (Claim 1)

[0771] Means of collecting information based on past life experiences,

[0772] A means of analyzing collected information and generating visual components,

[0773] A means of structuring a virtual space based on the generated visual components,

[0774] A means of obtaining user responses in a virtual space,

[0775] A means for dynamically adjusting the virtual space based on the user's response,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, which dynamically adjusts visual and auditory effects in a virtual space using user responses.

[0779] (Claim 3)

[0780] The system according to claim 1, comprising means for generating individual virtual spaces based on multiple life experiences and integrating them.

[0781] "Example 2 of combining an emotion engine"

[0782] (Claim 1)

[0783] Means of collecting information based on past events,

[0784] A means of analyzing collected information and generating visual components,

[0785] A means of structuring a virtual environment based on the generated visual components,

[0786] A means of collecting human emotional responses in a virtual environment,

[0787] A means of dynamically modifying the virtual environment based on collected human emotional responses,

[0788] A means of analyzing human emotions in real time using an emotion analysis engine,

[0789] A means for dynamically adjusting the configuration of the virtual environment based on the analysis results,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, which dynamically adjusts visual and auditory components in a virtual environment using human emotional responses acquired in real time.

[0793] (Claim 3)

[0794] The system according to claim 1, which has means for generating individual virtual environments based on multiple event experiences and integrating them, and further generates prompt sentences using a generative AI model.

[0795] "Application example 2 when combining with an emotional engine"

[0796] (Claim 1)

[0797] Means of collecting information based on past experience,

[0798] A means of analyzing collected information and generating visual components,

[0799] A means of structuring a virtual environment based on the generated visual components,

[0800] A means of acquiring the emotions of a user in a virtual environment,

[0801] A means of dynamically adjusting the virtual environment based on the results of emotion analysis,

[0802] A means of personalizing sales activities using emotion recognition technology,

[0803] A system that includes this.

[0804] (Claim 2)

[0805] The system according to claim 1, which dynamically modifies the visual elements within a virtual environment in accordance with the user's emotions.

[0806] (Claim 3)

[0807] The system according to claim 1, which has means for generating individual virtual environments based on multiple experiences and integrating them, and for recommending products that are suitable for the user's emotional state in a particular sales situation. [Explanation of symbols]

[0808] 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. Means of collecting information based on past life experiences, A means of analyzing collected information and generating visual components, A means of structuring a virtual space based on the generated visual components, A means of collecting human sensory responses in a virtual space, A means of modifying the virtual space based on collected human sensory responses, A system that includes this.

2. The system according to claim 1, which dynamically adjusts the visual components in a virtual space using human sensory responses.

3. The system according to claim 1, comprising means for generating individual virtual spaces based on multiple life experiences and integrating them.

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