system

Abstract

Provide a system. 【Solution means】 Means for collecting information on competitive events, Means for analyzing the structure of space based on the viewer's interest data and the situation of the event, Means for automatically generating space in a virtual environment based on the analysis results, Means for analyzing the opinions of viewers during the event and dynamically adjusting the staging of space, Means for proposing an optimal virtual environment based on the user's past viewing data and preferences, Means for automatically adjusting visual and acoustic effects by utilizing a generative AI model using prompt sentences, Means for providing the user with an experience of an individually configured virtual stadium through a smart device, A system including the above.

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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the large-scale development of modern competitive events, physical venue constraints, inconvenience of access, and lack of customization have become problems. As a result, the satisfaction of participants and spectators has decreased, and it has become difficult to provide new experiences. In addition, it is difficult to conduct flexible event progress based on real-time viewer feedback by conventional methods. To solve these problems, new technical means are required.

Means for Solving the Problems

[0005] This invention provides means for collecting information on a sporting event, means for analyzing the structure of an arena based on viewer preference data and event conditions, means for automatically generating an arena in a virtual space based on the analysis results, and means for analyzing viewer feedback during the event and dynamically adjusting the arena's presentation. This provides a flexible event environment that is not dependent on the physical space and enables dynamic, real-time, viewer-participatory presentations.

[0006] A "competitive event" is an event in which participants compete in a specific skill or ability, and spectators watch the proceedings.

[0007] "Means for collecting information" refers to an apparatus or method for acquiring specific data and carrying out the process of storing or analyzing it.

[0008] "Viewer preference data" refers to information that indicates specific elements that viewers like and the reactions they tend to give.

[0009] "Means for analyzing arena structure based on event conditions" refers to a method or apparatus for considering an appropriate virtual arena structure based on the progress and theme of an event.

[0010] "Means for automatically generating arenas in virtual space" refers to a method or apparatus that uses computer technology to automatically design and generate a virtual venue.

[0011] "Means for analyzing viewer feedback during an event and dynamically adjusting the arena's presentation" refers to a method or device that analyzes real-time reactions from viewers and changes the event's presentation accordingly. [Brief explanation of the drawing]

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

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The automatically generated virtual esports arena system based on the present invention is realized through the interaction of a server, a terminal, and a user. The main embodiments thereof are shown below.

[0034] Method of data collection

[0035] Before the event begins, the server collects information related to the competition. Specifically, it retrieves basic information such as the type of competition, the number of participants, and the schedule from the event management platform. Meanwhile, the terminal collects viewer preference data by obtaining personal interests and past viewing history from social media and participant registration forms. In addition, direct feedback and reactions provided by users when they participate in the event are also collected.

[0036] Data analysis embodiment

[0037] The collected data is analyzed by an AI agent on the server. The AI ​​agent analyzes viewer preferences and participant data, and executes algorithms to select the optimal arena theme and structure. If necessary, it performs comparative analysis with past event data to extract successful patterns.

[0038] Arena generation embodiment

[0039] Based on the analyzed data, the server automatically generates an arena in a virtual space. The generation process includes setting up visual representations, sound effects, and interactive elements. For example, if the audience prefers a fantasy style, the AI ​​agent will generate elements such as castles and dragons themed after medieval Europe and incorporate them into the arena.

[0040] Real-time adjustment embodiment

[0041] Throughout the event, the server continuously monitors viewer reactions. When viewers use reaction buttons or comment functions through their devices, that information is sent to the server in real time. The server uses this information to dynamically adjust the arena's presentation, for example, by changing the lighting or sound effects. If the audience becomes enthusiastic, the server provides entertainment that responds to their reactions, such as enhancing the visual effects of the audience seating.

[0042] In this way, the present invention enables a real-time, dynamic, user-participatory competitive experience that is not bound by physical constraints.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The device receives login information from viewers and simultaneously collects preference data through social media and survey functions. This information is immediately sent to the server for real-time analysis.

[0046] Step 2:

[0047] The server uses an AI agent to begin analysis based on acquired preference data and initial event-related information (e.g., sports and schedule). The AI ​​agent compares the viewer's past viewing history and trend data to select the optimal arena theme and design.

[0048] Step 3:

[0049] The server automatically generates a virtual arena design based on analysis results from the AI ​​agent. Utilizing 3D modeling technology, it incorporates visual elements and sound effects to create an engaging virtual space. Parameters related to the placement, color, and movement of each structural element are set.

[0050] Step 4:

[0051] The server continuously monitors viewer feedback at the start and throughout the event. This data is used to fine-tune visual and auditory effects in real time, enhancing audience engagement and satisfaction. Feedback provided via terminals is immediately reflected in changes to the arena's production.

[0052] Step 5:

[0053] After the event ends, the server saves all viewer feedback and competition data to a database. This saved data is used to train and improve the AI ​​agent for future events, and to serve as reference material for future arena design and production.

[0054] (Example 1)

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

[0056] Traditional virtual competition spaces have the challenge of limiting the user experience because they are not able to dynamically change according to viewer preferences or the circumstances of the event. Furthermore, it has been difficult to generate arenas based on specific themes or characteristics, and to create real-time effects based on participant and viewer interactions.

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

[0058] In this invention, the server includes means for collecting competition information, means for analyzing the characteristics of the space based on viewer preference information and the status of the competition, and means for automatically generating the space in a virtual environment based on the analysis results. This makes it possible to provide a dynamic and immersive competition experience that responds to viewer preferences and real-time reactions.

[0059] A "competition" refers to a competitive event among participants that is conducted according to specific rules and norms.

[0060] "Viewer preference information" refers to data used to identify the items that viewers have shown interest in or concern about in the past.

[0061] A "virtual environment" refers to a digital world that includes a three-dimensional space created by a computer.

[0062] A "generative AI model" refers to a model that uses artificial intelligence technology to generate structures and presentations based on specific conditions.

[0063] "Means of analyzing spatial characteristics" refers to the process of determining what structure or theme is optimal within a virtual environment based on collected data.

[0064] "Special effects" refer to special events or effects that are triggered by the actions of participants or the reactions of viewers.

[0065] "Real-time reactions" refer to the dynamic opinions and actions that viewers immediately express in response to an event.

[0066] Embodiments of the present invention are systems that dynamically generate virtual competition environments and provide participants and viewers with an interactive and immersive experience. This system is realized through the interaction of servers, terminals, and users.

[0067] The server is the hardware device responsible for primary data processing, collecting basic event information and viewer preference data. This is done using software such as event management platforms and social media APIs. The collected information is stored in a database and analyzed by an AI agent. Based on this analysis, the generative AI model selects the optimal arena theme and structure. Specifically, it can generate virtual environments incorporating elements such as medieval European castles or fantasy elements based on themes that have shown high viewer interest.

[0068] The device collects data from users and provides information to identify their interests. It uses social media and event registration forms to obtain users' past viewing history and feedback. This data is transmitted to the server in real time and used to dynamically adjust the virtual environment.

[0069] Users can send real-time feedback through an interface provided during the event. This involves using reaction buttons and comment functions via their device. This feedback is analyzed on the server and used to adjust the lighting and sound effects of the virtual environment.

[0070] As a concrete example, in a virtual tennis tournament held online, the server uses a 3D rendering engine to generate a virtual arena based on tennis-related data. The terminals can track which player viewers are supporting, and the server can change the sound effects in real time based on this information.

[0071] An example of a prompt sentence to input into the generative AI model is, "Suggest an arena theme that will please the audience and incorporate new visual effects." Based on this prompt sentence, the AI ​​model will derive appropriate themes and effects, enriching the viewer experience.

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

[0073] Step 1:

[0074] The server collects basic competition information from the event management platform. This information includes competition events, participant information, and schedules, and is obtained via API. The input is an API call to the event management platform, and the output is a dataset containing detailed competition information. This data is used to design the virtual environment.

[0075] Step 2:

[0076] The device collects viewer preference information from social media and participant registration forms. This includes the user's past viewing history and reaction data such as "likes." Inputs are social media APIs and participant registration information, and output is a dataset showing viewer interests. The device sends this to the server in real time, which is used to identify viewer preferences.

[0077] Step 3:

[0078] The server analyzes the collected data using an AI agent. It utilizes a generative AI model to select the optimal arena theme based on viewer preferences and event conditions. Inputs include viewer preference information and detailed event information, while output is configuration data specifying the optimal theme and structure. This configuration data is then used to design the virtual space.

[0079] Step 4:

[0080] The server generates a virtual arena based on the analysis results. A 3D rendering engine is used to construct visuals and sound effects that align with the theme. The input is the configuration data derived by the AI ​​model, and the output is the completed virtual arena. The generation process includes the creation of visual effects and sound design.

[0081] Step 5:

[0082] During the event, users provide real-time feedback using reaction buttons and comment functions via their devices. This includes active cheering and writing comments; the input is reaction data from the user, and the output is feedback information sent to the server. This feedback is then reflected in the arena's presentation.

[0083] Step 6:

[0084] The server analyzes real-time feedback and dynamically adjusts the visuals within the virtual environment. The input is user feedback information, and the output is the adjusted visual and sound effect settings. This enhances the arena's presentation as the competition progresses, providing viewers with a greater sense of immersion.

[0085] (Application Example 1)

[0086] 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."

[0087] In modern sporting events, there is a need to provide an interactive and personalized viewing experience while meeting the expectations of viewers with diverse interests and preferences. However, conventional systems have faced challenges in responding to viewer reactions in real time and dynamically adjusting the virtual environment.

[0088] 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.

[0089] In this invention, the server includes means for collecting information on sporting events, means for analyzing the spatial structure based on viewer interest data and the status of the event, and means for automatically generating a space in a virtual environment based on the analysis results. This makes it possible to suggest an optimal virtual environment based on the viewer's past viewing data and preferences, and to automatically adjust visual and sound effects using a generation AI model with prompt messages.

[0090] - A "competitive event" is an event in which participants compete in skills and abilities based on specific rules, and includes sports and games.

[0091] "Means of collecting information" refers to methods and devices for gathering data related to an event, and includes data collection from event management platforms and social media.

[0092] "Viewer interest data" refers to information that shows what genres and themes the audience prefers, and includes past viewing history and preferences.

[0093] "Means for analyzing the structure of space" refer to methods and devices for considering appropriate placement and design in a virtual space based on collected data.

[0094] A "virtual environment" refers to a digital space created by a computer system that users can experience through interaction.

[0095] "Means of automatic generation" refers to methods or devices that use AI technology or similar methods to autonomously create digital content based on pre-set conditions.

[0096] "Viewer feedback" refers to the feedback and reactions provided by the audience during the event, which allows for continuous evaluation.

[0097] "Means of dynamic adjustment" refers to methods or devices that allow the system to automatically change the direction or settings based on real-time data from viewers.

[0098] A "prompt message" is a text input that gives instructions to an AI model to generate a specific result.

[0099] The system that realizes this invention is mainly composed of server, terminal, and user elements.

[0100] The server is responsible for gathering information about the competition, automatically retrieving necessary data from event management platforms and other databases. The server also analyzes viewer interest data using AI models and designs the virtual environment based on the results. Viewer feedback is analyzed in real time, and the visual and auditory effects within the virtual space are dynamically adjusted accordingly. The software used by the server includes AI models built in Python and Unity or Unreal Engine for generating the virtual space.

[0101] The device functions as an interface with the viewer. Through a smartphone or head-mounted display, the viewer accesses a self-configured virtual stadium and obtains a personalized viewing experience. In this process, prompt messages are supplied to a generating AI model, which provides an optimal virtual experience based on the viewer's preferences and real-time feedback.

[0102] Users select events that align with their interests and participate through their devices, providing feedback along the way. This feedback is sent to the server and used to further optimize the virtual environment.

[0103] As a concrete example, consider a scenario where a user is using their smartphone to watch a match of their favorite esports team in a virtual arena themed after medieval Europe. If the AI ​​detects the user's enthusiastic support during viewing, it adjusts the lighting and effects to provide a more interactive environment.

[0104] An example of a prompt to a generative AI model is: "Based on past viewing data, please suggest the most suitable virtual arena theme and visuals for the user. Please also modify the arena's visuals in response to real-time feedback."

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

[0106] Step 1:

[0107] The server collects information about the sporting event from the event management platform. The database information accessed as input includes sporting events, participant numbers, and schedule information. Using this data, it efficiently outputs data that provides an overview of the entire event.

[0108] Step 2:

[0109] The device collects viewer interest data from social media and participant registration forms. Input includes viewing history and topics of interest, which is then processed to generate individual viewer preference profiles. These profiles are then sent to the server.

[0110] Step 3:

[0111] The server uses an AI model to analyze the optimal structure of the virtual space based on collected competition event information and viewer preference profiles. Input data includes viewer profiles and event information, which the AI ​​algorithm analyzes and outputs a proposed design for the virtual environment.

[0112] Step 4:

[0113] The server automatically generates a virtual environment using Unity or Unreal Engine based on the analysis results. The input includes AI analysis results, and the output is an environment that appropriately places visual and auditory elements within the virtual space to enhance interactivity.

[0114] Step 5:

[0115] Users access the virtual stadium through their device and begin their experience. The input is an access request from the user, which causes the device to deliver a personalized virtual environment to the user.

[0116] Step 6:

[0117] During the event, user reaction data is sent from the terminal to the server in real time. The input includes comments and reaction button information, which the server analyzes to generate output that dynamically adjusts the visuals within the virtual space.

[0118] Step 7:

[0119] The server utilizes a generative AI model and repeatedly optimizes the virtual environment's presentation using prompt messages. Real-time feedback information is used as input, and adjustments are output accordingly, continuously improving the virtual experience.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] The virtual esports arena system incorporating the emotion engine based on this invention is a technology that analyzes user emotions and feedback during competitive events and dynamically adapts the arena's presentation. This system mainly consists of a server, terminals, and users, and is realized through the exchange of data between them.

[0122] Embodiments of emotional data collection and analysis

[0123] The device collects data on how viewers express their emotions towards the performance through their digital devices. This data is obtained from user facial recognition, voice tone analysis, or the use of reaction buttons. This emotional data is immediately transmitted to the server.

[0124] The server analyzes the received emotional data using an emotion engine. The emotion engine has algorithms to identify different emotional patterns (joy, excitement, tension, etc.) and understands the overall emotional trends of the audience.

[0125] Arena design embodiment

[0126] Based on analyzed emotional data, the server automatically generates and adjusts the atmosphere and design of the virtual arena. For example, if viewers are excited, the arena's color scheme brightens and the visual presentation is enhanced. When the emotion engine detects a change in viewers' emotions, the server immediately uses AI to adjust the arena's presentation to maintain viewer engagement.

[0127] Implementation of special effects

[0128] During the event, the server evaluates participant performance and audience emotional data in real time. This triggers special effects (e.g., music changes, added special effects) to provide viewers with a deeper sense of immersion. The emotional engine also reacts instantly to moments of audience emotion, introducing new effects to enhance the participants' engagement.

[0129] Thus, by utilizing an emotion engine, the present invention makes it possible to provide innovative visual and auditory experiences that respond to viewers' real-time emotions and feedback, making competitive events more engaging and interactive.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects real-time emotional data provided by viewers through digital devices (e.g., smartphones and PCs). This emotional data is obtained through facial recognition sensors and voice tone analysis from microphones. Presses of emotional reaction buttons are also included in this data.

[0133] Step 2:

[0134] The server instantly analyzes the emotional data received from the terminal. The emotion engine uses algorithms to determine each viewer's emotion and understand the overall emotional trend of the audience. The analysis results clearly show what emotional state the viewer is currently in (e.g., joy, excitement, tension, boredom).

[0135] Step 3:

[0136] The server instantly adjusts the atmosphere of the virtual arena based on analyzed emotion data. For example, if viewers are excited, the virtual arena's color scheme becomes more vibrant and the lighting effects are enhanced. Sound effects are also adjusted, and background music is selected to match the viewers' emotional state.

[0137] Step 4:

[0138] The server monitors participant performance data and triggers special effects when viewers' emotions show a specific shift. For example, when a participant delivers an outstanding performance, the emotion engine detects the viewers' excitement and immediately deploys effects such as fireworks or a special light show.

[0139] Step 5:

[0140] Based on user feedback, the server will save data after the event ends to help improve the emotion engine and virtual space for future events. The saved data will be used as training material for the AI ​​system, contributing to the creation of more sophisticated visual and auditory experiences in future events.

[0141] (Example 2)

[0142] 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".

[0143] Traditional virtual event productions have not fully utilized audience emotions and reactions, and their interaction with participant performances has been limited. Therefore, maximizing audience engagement and emotional impact has been difficult. Furthermore, proposing and optimizing new production methods required complex manual work.

[0144] 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.

[0145] In this invention, the server includes means for a device that recognizes and acquires data on the emotions of an observer; means for a computing device that analyzes the collected emotion data and identifies specific emotional states; means for generating and adapting environmental effects in a virtual space based on the analysis results; means for monitoring the observer's emotional responses in real time and dynamically adjusting the environmental effects; trigger means for activating special effects based on the behavior of participants and the emotional responses of the observer; and means for utilizing a generative AI model to propose and optimize effects based on external input. This enables more interactive and dynamic effects that reflect the emotions of viewers in real time.

[0146] "Observer emotions" refers to the emotional expressions that viewers show through digital devices, including facial expressions, tone of voice, and reaction manipulation.

[0147] "Devices for acquiring data" refers to various devices and sensors used to collect viewers' emotions and behaviors.

[0148] "Collected sentiment data" refers to a dataset containing information about the emotional state of the observer.

[0149] A "processing unit" refers to a server or data processing unit used to analyze emotional data and identify specific emotional states.

[0150] "Means for generating and adapting environmental effects" refers to technologies and processes for dynamically changing visual and auditory elements within a virtual space.

[0151] "Means of monitoring emotional responses in real time and dynamically adjusting the environmental presentation" refers to technology that instantly adjusts the environmental presentation when the viewer's emotions change.

[0152] "A trigger mechanism for activating special effects based on participants' behavior and observers' emotional responses" refers to a system for setting conditions and monitoring them to trigger additional effects when specific conditions are met.

[0153] "Means for proposing and optimizing performances based on external input using generative AI models" refers to technologies that use machine learning and artificial intelligence to propose new performance ideas and optimize events.

[0154] This invention is a system that utilizes observer emotion data in real time to dynamically adjust the presentation in a virtual event space. The system mainly consists of a server, terminals, and users, and is realized through the cooperation of each element.

[0155] As a user's digital device, the terminal uses sensors such as a webcam and microphone to capture the observer's facial expressions and voice tone. Furthermore, emotional data is collected through reaction buttons operated on the terminal. This information is transmitted to the server quickly and efficiently.

[0156] Upon receiving data, the server analyzes it using a specialized emotion engine. This emotion engine implements machine learning algorithms and can identify the diverse emotional states of the observer. Based on this analysis, the server issues instructions to generate and adapt the virtual environment's presentation. It tracks emotional patterns in real time and dynamically changes the representation as needed.

[0157] This invention utilizes a generative AI model. The AI ​​model plays a role in suggesting and optimizing new effects based on external input. For example, the AI ​​model can be queried for effect suggestions using a prompt such as, "Suggest the best visual effect when the viewer is surprised."

[0158] As a concrete example, imagine a scenario where a user is watching a competition at a virtual event. In this scenario, the user's emotions, such as cheering or being surprised, are captured by sensors and analyzed by a server. Subsequently, the color tone and sound within the virtual space are adjusted according to the viewer's reactions, making it possible to provide a deeper sense of immersion.

[0159] This invention aims to provide a new interactive experience and enhance the level of excitement for both participants and observers by utilizing the emotions of observers in the production of virtual events.

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

[0161] Step 1:

[0162] Users watch the sporting event through a digital device. The webcam and microphone built into the device continuously capture the user's facial expressions and voice tone as input. This input data is collected along with button operation data triggered by the user's reactions and prepared as emotion data. Through this process, the device obtains various raw data that indicates the user's emotions.

[0163] Step 2:

[0164] The device immediately transmits the collected emotional data to the server via the network. Using the emotional data received as input, the server activates an emotion engine and analyzes each data point. This analysis employs machine learning algorithms to identify the user's emotional state from the input data. The identified emotional state, such as "joy" or "surprise," is output as the analysis result.

[0165] Step 3:

[0166] Based on the analyzed emotional state, the server uses that data as input to determine the environmental effects of the virtual arena. If the emotional state is "excited," it generates commands to brighten the lighting and add dynamic effects. These commands are sent to the virtual space's environment control system, which then changes the visual effects in real time as output.

[0167] Step 4:

[0168] The server uses a generative AI model to process external input prompts. For example, when a prompt such as "Suggest the best visual effect when the viewer is surprised" is input, the AI ​​model analyzes it and provides new visual effect ideas as a suggestion. This suggestion is then used to further optimize the presentation of the virtual arena.

[0169] Step 5:

[0170] The server evaluates observer emotional responses and participant behavior in real time during the virtual event. It analyzes participant performance data and observer emotional data as input, and if certain conditions are met, it issues commands to trigger special effects as output. For example, it might activate a fireworks effect and play a special music track at the moment of a goal to enhance the user experience.

[0171] (Application Example 2)

[0172] 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".

[0173] In modern competitive events and virtual retail experiences, a challenge lies in the lack of dynamic presentations and product displays that adequately reflect the emotions of viewers and users in real time, resulting in a lack of immersion and engagement among participants and consumers. Therefore, there is a need for technologies that enable deeper engagement and provide personalized experiences for viewers and users.

[0174] 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.

[0175] In this invention, the server includes means for collecting information on a competitive event, means for acquiring and analyzing viewer emotion data, means for automatically generating an arena in a virtual space based on the analysis results, means for analyzing viewer feedback during the event and dynamically adjusting the arena's presentation, and means for dynamically displaying information within a virtual store according to the viewer's emotions. This enables viewers and users to enjoy an engaging and personalized experience in real time that is tailored to their emotions.

[0176] A "competitive event" is a situation in which participants compete in performance based on specific rules, and spectators can enjoy watching it.

[0177] "Viewer emotion data" refers to information that quantifies the emotions expressed by viewers through their facial expressions, tone of voice, and actions.

[0178] "Analysis results" refer to insights and indicators obtained after analyzing collected data using algorithms and methods.

[0179] A "virtual space" is an artificial and visually represented environment created within a computer using digital technology.

[0180] "Viewer feedback" refers to the reactions and evaluations that viewers give to the progress of an event, and includes emotional reactions and specific opinions.

[0181] A "virtual store" is a sales platform established on the internet that provides goods and services through virtual means.

[0182] "Dynamic display" means changing and adjusting the displayed content in real time according to conditions or input.

[0183] In embodiments of the present invention, a server, a terminal, and a user collaborate to provide an emotion-responsive virtual store experience. The server is responsible for acquiring and analyzing emotion data from viewers in real time. This emotion data is collected through the terminal's camera and microphone. Specifically, the emotional state of the viewer is identified by combining facial recognition using the terminal's camera and voice tone analysis using the microphone. Image and speech recognition libraries such as OpenCV and Google® Cloud Vision API can be used for this purpose.

[0184] The server dynamically adjusts the placement and advertising of products within the virtual store in a virtual space based on the analyzed sentiment data. This allows users to access products and information that best suit their current emotions. By using a cloud-based platform (e.g., Firebase or AWS®), large amounts of data are processed efficiently, and real-time feedback is provided.

[0185] For example, when a user accesses a virtual store via their smartphone, the server can pick up on the user's excitement and interest and present relevant products via push notifications. By enhancing the user experience in this way, it is possible to expect increased purchase intent and satisfaction.

[0186] As a concrete example, when an AI model is given the prompt, "Please suggest ways to display products and customize the store interface in a virtual store according to the user's emotions," the AI ​​dynamically optimizes and provides the order of product displays and advertisements. This allows viewers and users to receive a personalized experience that responds to their emotions. In this way, the present invention provides a system that realizes emotion-responsive, personalized product delivery and improves the user's purchasing experience.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The device uses a camera and microphone to capture the user's facial expressions and voice data when they access a virtual store. The input here is raw data representing the user's real-time emotions, and the output is captured as digital data necessary for emotion analysis.

[0190] Step 2:

[0191] The device uses the acquired facial and audio data to perform emotion recognition, utilizing OpenCV and the Google Cloud Vision API. The input is the digital data obtained in step 1, and the output is analyzed data indicating the emotional state. This analyzed data includes emotional categories such as joy, surprise, and interest.

[0192] Step 3:

[0193] The server receives emotion analysis data sent from the terminal and uses this to understand the user's emotional state. It receives emotion category data sent from the terminal as input and generates instructions for adjusting the display in the virtual space as output.

[0194] Step 4:

[0195] The server dynamically adjusts the layout of product displays and promotional content within the virtual store based on the received sentiment data. It uses the analyzed sentiment data as input and generates updated product lists and advertisement displays as output. Specifically, it performs actions such as prioritizing the display of products of interest, making them appear larger on the screen.

[0196] Step 5:

[0197] Users view updated store layouts on their smartphones or digital devices based on instructions from the server. The system receives display update data from the server as input and visually displays personalized product suggestions as output.

[0198] 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.

[0199] Data generation model 58 is a type of 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] The automatically generated virtual esports arena system based on the present invention is realized through the interaction of a server, a terminal, and a user. The main embodiments thereof are shown below.

[0215] Method of data collection

[0216] Before the event begins, the server collects information related to the competition. Specifically, it retrieves basic information such as the type of competition, the number of participants, and the schedule from the event management platform. Meanwhile, the terminal collects viewer preference data by obtaining personal interests and past viewing history from social media and participant registration forms. In addition, direct feedback and reactions provided by users when they participate in the event are also collected.

[0217] Data analysis embodiment

[0218] The collected data is analyzed by an AI agent on the server. The AI ​​agent analyzes viewer preferences and participant data, and executes algorithms to select the optimal arena theme and structure. If necessary, it performs comparative analysis with past event data to extract successful patterns.

[0219] Arena generation embodiment

[0220] Based on the analyzed data, the server automatically generates an arena in a virtual space. The generation process includes setting up visual representations, sound effects, and interactive elements. For example, if the audience prefers a fantasy style, the AI ​​agent will generate elements such as castles and dragons themed after medieval Europe and incorporate them into the arena.

[0221] Real-time adjustment embodiment

[0222] Throughout the event, the server continuously monitors viewer reactions. When viewers use reaction buttons or comment functions through their devices, that information is sent to the server in real time. The server uses this information to dynamically adjust the arena's presentation, for example, by changing the lighting or sound effects. If the audience becomes enthusiastic, the server provides entertainment that responds to their reactions, such as enhancing the visual effects of the audience seating.

[0223] In this way, the present invention enables a real-time, dynamic, user-participatory competitive experience that is not bound by physical constraints.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The device receives login information from viewers and simultaneously collects preference data through social media and survey functions. This information is immediately sent to the server for real-time analysis.

[0227] Step 2:

[0228] The server uses an AI agent to begin analysis based on acquired preference data and initial event-related information (e.g., sports and schedule). The AI ​​agent compares the viewer's past viewing history and trend data to select the optimal arena theme and design.

[0229] Step 3:

[0230] The server automatically generates a virtual arena design based on analysis results from the AI ​​agent. Utilizing 3D modeling technology, it incorporates visual elements and sound effects to create an engaging virtual space. Parameters related to the placement, color, and movement of each structural element are set.

[0231] Step 4:

[0232] The server continuously monitors viewer feedback at the start and throughout the event. This data is used to fine-tune visual and auditory effects in real time, enhancing audience engagement and satisfaction. Feedback provided via terminals is immediately reflected in changes to the arena's production.

[0233] Step 5:

[0234] After the event ends, the server saves all viewer feedback and competition data to a database. This saved data is used to train and improve the AI ​​agent for future events, and to serve as reference material for future arena design and production.

[0235] (Example 1)

[0236] 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."

[0237] Traditional virtual competition spaces have the challenge of limiting the user experience because they are not able to dynamically change according to viewer preferences or the circumstances of the event. Furthermore, it has been difficult to generate arenas based on specific themes or characteristics, and to create real-time effects based on participant and viewer interactions.

[0238] 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.

[0239] In this invention, the server includes means for collecting competition information, means for analyzing the characteristics of the space based on viewer preference information and the status of the competition, and means for automatically generating the space in a virtual environment based on the analysis results. This makes it possible to provide a dynamic and immersive competition experience that responds to viewer preferences and real-time reactions.

[0240] A "competition" refers to a competitive event among participants that is conducted according to specific rules and norms.

[0241] "Viewer preference information" refers to data used to identify the items that viewers have shown interest in or concern about in the past.

[0242] A "virtual environment" refers to a digital world that includes a three-dimensional space created by a computer.

[0243] A "generative AI model" refers to a model that uses artificial intelligence technology to generate structures and presentations based on specific conditions.

[0244] "Means of analyzing spatial characteristics" refers to the process of determining what structure or theme is optimal within a virtual environment based on collected data.

[0245] "Special effects" refer to special events or effects that are triggered by the actions of participants or the reactions of viewers.

[0246] "Real-time reactions" refer to the dynamic opinions and actions that viewers immediately express in response to an event.

[0247] Embodiments of the present invention are systems that dynamically generate virtual competition environments and provide participants and viewers with an interactive and immersive experience. This system is realized through the interaction of servers, terminals, and users.

[0248] The server is the hardware device responsible for primary data processing, collecting basic event information and viewer preference data. This is done using software such as event management platforms and social media APIs. The collected information is stored in a database and analyzed by an AI agent. Based on this analysis, the generative AI model selects the optimal arena theme and structure. Specifically, it can generate virtual environments incorporating elements such as medieval European castles or fantasy elements based on themes that have shown high viewer interest.

[0249] The device collects data from users and provides information to identify their interests. It uses social media and event registration forms to obtain users' past viewing history and feedback. This data is transmitted to the server in real time and used to dynamically adjust the virtual environment.

[0250] Users can send real-time feedback through an interface provided during the event. This involves using reaction buttons and comment functions via their device. This feedback is analyzed on the server and used to adjust the lighting and sound effects of the virtual environment.

[0251] As a concrete example, in a virtual tennis tournament held online, the server uses a 3D rendering engine to generate a virtual arena based on tennis-related data. The terminals can track which player viewers are supporting, and the server can change the sound effects in real time based on this information.

[0252] An example of a prompt sentence to input into the generative AI model is, "Suggest an arena theme that will please the audience and incorporate new visual effects." Based on this prompt sentence, the AI ​​model will derive appropriate themes and effects, enriching the viewer experience.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The server collects basic competition information from the event management platform. This information includes competition events, participant information, and schedules, and is obtained via API. The input is an API call to the event management platform, and the output is a dataset containing detailed competition information. This data is used to design the virtual environment.

[0256] Step 2:

[0257] The device collects viewer preference information from social media and participant registration forms. This includes the user's past viewing history and reaction data such as "likes." Inputs are social media APIs and participant registration information, and output is a dataset showing viewer interests. The device sends this to the server in real time, which is used to identify viewer preferences.

[0258] Step 3:

[0259] The server analyzes the collected data using an AI agent. It utilizes a generative AI model to select the optimal arena theme based on viewer preferences and event conditions. Inputs include viewer preference information and detailed event information, while output is configuration data specifying the optimal theme and structure. This configuration data is then used to design the virtual space.

[0260] Step 4:

[0261] The server generates a virtual arena based on the analysis results. A 3D rendering engine is used to construct visuals and sound effects that align with the theme. The input is the configuration data derived by the AI ​​model, and the output is the completed virtual arena. The generation process includes the creation of visual effects and sound design.

[0262] Step 5:

[0263] During the event, users provide real-time feedback using reaction buttons and comment functions via their devices. This includes active cheering and writing comments; the input is reaction data from the user, and the output is feedback information sent to the server. This feedback is then reflected in the arena's presentation.

[0264] Step 6:

[0265] The server analyzes real-time feedback and dynamically adjusts the visuals within the virtual environment. The input is user feedback information, and the output is the adjusted visual and sound effect settings. This enhances the arena's presentation as the competition progresses, providing viewers with a greater sense of immersion.

[0266] (Application Example 1)

[0267] 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."

[0268] In modern sporting events, there is a need to provide an interactive and personalized viewing experience while meeting the expectations of viewers with diverse interests and preferences. However, conventional systems have faced challenges in responding to viewer reactions in real time and dynamically adjusting the virtual environment.

[0269] 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.

[0270] In this invention, the server includes means for collecting information on sporting events, means for analyzing the spatial structure based on viewer interest data and the status of the event, and means for automatically generating a space in a virtual environment based on the analysis results. This makes it possible to suggest an optimal virtual environment based on the viewer's past viewing data and preferences, and to automatically adjust visual and sound effects using a generation AI model with prompt messages.

[0271] - A "competitive event" is an event in which participants compete in skills and abilities based on specific rules, and includes sports and games.

[0272] "Means of collecting information" refers to methods and devices for gathering data related to an event, and includes data collection from event management platforms and social media.

[0273] "Viewer interest data" refers to information that shows what genres and themes the audience prefers, and includes past viewing history and preferences.

[0274] "Means for analyzing the structure of space" refer to methods and devices for considering appropriate placement and design in a virtual space based on collected data.

[0275] A "virtual environment" refers to a digital space created by a computer system that users can experience through interaction.

[0276] "Means of automatic generation" refers to methods or devices that use AI technology or similar methods to autonomously create digital content based on pre-set conditions.

[0277] "Viewer feedback" refers to the feedback and reactions provided by the audience during the event, which allows for continuous evaluation.

[0278] "Means of dynamic adjustment" refers to methods or devices that allow the system to automatically change the direction or settings based on real-time data from viewers.

[0279] A "prompt message" is a text input that gives instructions to an AI model to generate a specific result.

[0280] The system that realizes this invention is mainly composed of server, terminal, and user elements.

[0281] The server has the role of collecting information on competitive events and automatically extracts the necessary data from the event management platform and other databases. In addition, the server analyzes the viewers' interest data using an AI model and designs the virtual environment based on the obtained results. The viewers' opinions are analyzed in real time, and based on this, the visual and acoustic effects in the virtual space are dynamically adjusted. The software used by the server includes an AI model built in Python and Unity or Unreal Engine for generating the virtual space.

[0282] The terminal functions as an interface with the viewers. Through smartphones and head-mounted displays, viewers can access the virtual stadium they have set and obtain an individualized viewing experience. At this time, prompt texts are supplied to the generation AI model, and an optimal virtual experience based on the viewers' preferences and real-time feedback is provided.

[0283] The user selects an event according to their own interests and participates through the terminal, thus playing the role of providing feedback. This feedback is sent to the server and used for further optimization of the virtual environment.

[0284] As a specific example, for instance, consider the situation where a user is using a smartphone to watch a game of their favorite e-sports team in a virtual arena themed on medieval Europe. If the user's enthusiastic support is detected during the viewing, the AI adjusts the lighting and effects to provide a more interactive environment.

[0285] An example of a prompt text to the generation AI model is "Based on past viewing data, please propose the theme and effects of an optimal virtual arena for the user. Also, please change the arena effects according to real-time feedback."

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The server collects information about the sporting event from the event management platform. The database information accessed as input includes sporting events, participant numbers, and schedule information. Using this data, it efficiently outputs data that provides an overview of the entire event.

[0289] Step 2:

[0290] The device collects viewer interest data from social media and participant registration forms. Input includes viewing history and topics of interest, which is then processed to generate individual viewer preference profiles. These profiles are then sent to the server.

[0291] Step 3:

[0292] The server uses an AI model to analyze the optimal structure of the virtual space based on collected competition event information and viewer preference profiles. Input data includes viewer profiles and event information, which the AI ​​algorithm analyzes and outputs a proposed design for the virtual environment.

[0293] Step 4:

[0294] The server automatically generates a virtual environment using Unity or Unreal Engine based on the analysis results. The input includes AI analysis results, and the output is an environment that appropriately places visual and auditory elements within the virtual space to enhance interactivity.

[0295] Step 5:

[0296] Users access the virtual stadium through their device and begin their experience. The input is an access request from the user, which causes the device to deliver a personalized virtual environment to the user.

[0297] Step 6:

[0298] During the event, user reaction data is sent from the terminal to the server in real time. The input includes comments and reaction button information, which the server analyzes to generate output that dynamically adjusts the visuals within the virtual space.

[0299] Step 7:

[0300] The server utilizes a generative AI model and repeatedly optimizes the virtual environment's presentation using prompt messages. Real-time feedback information is used as input, and adjustments are output accordingly, continuously improving the virtual experience.

[0301] 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.

[0302] The virtual esports arena system incorporating the emotion engine based on this invention is a technology that analyzes user emotions and feedback during competitive events and dynamically adapts the arena's presentation. This system mainly consists of a server, terminals, and users, and is realized through the exchange of data between them.

[0303] Embodiments of emotional data collection and analysis

[0304] The device collects data on how viewers express their emotions towards the performance through their digital devices. This data is obtained from user facial recognition, voice tone analysis, or the use of reaction buttons. This emotional data is immediately transmitted to the server.

[0305] The server analyzes the received emotional data using an emotion engine. The emotion engine has algorithms to identify different emotional patterns (joy, excitement, tension, etc.) and understands the overall emotional trends of the audience.

[0306] Arena design embodiment

[0307] Based on the analyzed emotional data, the server automatically generates and adjusts the atmosphere and design of the virtual arena. For example, if the viewer is in an excited state, the server brightens the color tone of the arena and enhances the visual effects. When the emotion engine detects a change in the viewer's emotion, the server immediately uses AI to adjust the effects within the arena to maintain the viewer's engagement.

[0308] Embodiments of Special Effects

[0309] During the event, the server evaluates the participants' performances and the viewers' emotional data in real time. As a result, special effects (e.g., music changes, addition of special effects) are triggered to provide the viewers with a deeper sense of immersion. Also, the emotion engine immediately responds at the moment the audience is touched and introduces new effects to enhance the charm of the participants.

[0310] In this way, by utilizing the emotion engine, the present invention can provide an innovative visual and auditory experience according to the real-time emotions and feedback of the viewers, making the competitive event more attractive and interactive.

[0311] The following describes the processing flow.

[0312] Step 1:

[0313] The terminal collects the real-time emotional data provided by the viewer through a digital device (e.g., smartphone or PC). The emotional data is obtained by facial recognition sensors and voice tone analysis from a microphone. Pressing the reaction button related to emotions is also included in this data.

[0314] Step 2:

[0315] The server instantly analyzes the emotional data received from the terminal. The emotion engine uses algorithms to determine each viewer's emotion and understand the overall emotional trend of the audience. The analysis results clearly show what emotional state the viewer is currently in (e.g., joy, excitement, tension, boredom).

[0316] Step 3:

[0317] The server instantly adjusts the atmosphere of the virtual arena based on analyzed emotion data. For example, if viewers are excited, the virtual arena's color scheme becomes more vibrant and the lighting effects are enhanced. Sound effects are also adjusted, and background music is selected to match the viewers' emotional state.

[0318] Step 4:

[0319] The server monitors participant performance data and triggers special effects when viewers' emotions show a specific shift. For example, when a participant delivers an outstanding performance, the emotion engine detects the viewers' excitement and immediately deploys effects such as fireworks or a special light show.

[0320] Step 5:

[0321] Based on user feedback, the server will save data after the event ends to help improve the emotion engine and virtual space for future events. The saved data will be used as training material for the AI ​​system, contributing to the creation of more sophisticated visual and auditory experiences in future events.

[0322] (Example 2)

[0323] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0324] Traditional virtual event productions have not fully utilized audience emotions and reactions, and their interaction with participant performances has been limited. Therefore, maximizing audience engagement and emotional impact has been difficult. Furthermore, proposing and optimizing new production methods required complex manual work.

[0325] 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.

[0326] In this invention, the server includes means for a device that recognizes and acquires data on the emotions of an observer; means for a computing device that analyzes the collected emotion data and identifies specific emotional states; means for generating and adapting environmental effects in a virtual space based on the analysis results; means for monitoring the observer's emotional responses in real time and dynamically adjusting the environmental effects; trigger means for activating special effects based on the behavior of participants and the emotional responses of the observer; and means for utilizing a generative AI model to propose and optimize effects based on external input. This enables more interactive and dynamic effects that reflect the emotions of viewers in real time.

[0327] "Observer emotions" refers to the emotional expressions that viewers show through digital devices, including facial expressions, tone of voice, and reaction manipulation.

[0328] "Devices for acquiring data" refers to various devices and sensors used to collect viewers' emotions and behaviors.

[0329] "Collected sentiment data" refers to a dataset containing information about the emotional state of the observer.

[0330] A "processing unit" refers to a server or data processing unit used to analyze emotional data and identify specific emotional states.

[0331] "Means for generating and adapting environmental effects" refers to technologies and processes for dynamically changing visual and auditory elements within a virtual space.

[0332] "Means of monitoring emotional responses in real time and dynamically adjusting the environmental presentation" refers to technology that instantly adjusts the environmental presentation when the viewer's emotions change.

[0333] "A trigger mechanism for activating special effects based on participants' behavior and observers' emotional responses" refers to a system for setting conditions and monitoring them to trigger additional effects when specific conditions are met.

[0334] "Means for proposing and optimizing performances based on external input using generative AI models" refers to technologies that use machine learning and artificial intelligence to propose new performance ideas and optimize events.

[0335] This invention is a system that utilizes observer emotion data in real time to dynamically adjust the presentation in a virtual event space. The system mainly consists of a server, terminals, and users, and is realized through the cooperation of each element.

[0336] As a user's digital device, the terminal uses sensors such as a webcam and microphone to capture the observer's facial expressions and voice tone. Furthermore, emotional data is collected through reaction buttons operated on the terminal. This information is transmitted to the server quickly and efficiently.

[0337] Upon receiving data, the server analyzes it using a specialized emotion engine. This emotion engine implements machine learning algorithms and can identify the diverse emotional states of the observer. Based on this analysis, the server issues instructions to generate and adapt the virtual environment's presentation. It tracks emotional patterns in real time and dynamically changes the representation as needed.

[0338] This invention utilizes a generative AI model. The AI ​​model plays a role in suggesting and optimizing new effects based on external input. For example, the AI ​​model can be queried for effect suggestions using a prompt such as, "Suggest the best visual effect when the viewer is surprised."

[0339] As a concrete example, imagine a scenario where a user is watching a competition at a virtual event. In this scenario, the user's emotions, such as cheering or being surprised, are captured by sensors and analyzed by a server. Subsequently, the color tone and sound within the virtual space are adjusted according to the viewer's reactions, making it possible to provide a deeper sense of immersion.

[0340] This invention aims to provide a new interactive experience and enhance the level of excitement for both participants and observers by utilizing the emotions of observers in the production of virtual events.

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

[0342] Step 1:

[0343] Users watch the sporting event through a digital device. The webcam and microphone built into the device continuously capture the user's facial expressions and voice tone as input. This input data is collected along with button operation data triggered by the user's reactions and prepared as emotion data. Through this process, the device obtains various raw data that indicates the user's emotions.

[0344] Step 2:

[0345] The device immediately transmits the collected emotional data to the server via the network. Using the emotional data received as input, the server activates an emotion engine and analyzes each data point. This analysis employs machine learning algorithms to identify the user's emotional state from the input data. The identified emotional state, such as "joy" or "surprise," is output as the analysis result.

[0346] Step 3:

[0347] Based on the analyzed emotional state, the server uses that data as input to determine the environmental effects of the virtual arena. If the emotional state is "excited," it generates commands to brighten the lighting and add dynamic effects. These commands are sent to the virtual space's environment control system, which then changes the visual effects in real time as output.

[0348] Step 4:

[0349] The server uses a generative AI model to process external input prompts. For example, when a prompt such as "Suggest the best visual effect when the viewer is surprised" is input, the AI ​​model analyzes it and provides new visual effect ideas as a suggestion. This suggestion is then used to further optimize the presentation of the virtual arena.

[0350] Step 5:

[0351] The server evaluates observer emotional responses and participant behavior in real time during the virtual event. It analyzes participant performance data and observer emotional data as input, and if certain conditions are met, it issues commands to trigger special effects as output. For example, it might activate a fireworks effect and play a special music track at the moment of a goal to enhance the user experience.

[0352] (Application Example 2)

[0353] 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."

[0354] In modern competitive events and virtual retail experiences, a challenge lies in the lack of dynamic presentations and product displays that adequately reflect the emotions of viewers and users in real time, resulting in a lack of immersion and engagement among participants and consumers. Therefore, there is a need for technologies that enable deeper engagement and provide personalized experiences for viewers and users.

[0355] 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.

[0356] In this invention, the server includes means for collecting information on a competitive event, means for acquiring and analyzing viewer emotion data, means for automatically generating an arena in a virtual space based on the analysis results, means for analyzing viewer feedback during the event and dynamically adjusting the arena's presentation, and means for dynamically displaying information within a virtual store according to the viewer's emotions. This enables viewers and users to enjoy an engaging and personalized experience in real time that is tailored to their emotions.

[0357] A "competitive event" is a situation in which participants compete in performance based on specific rules, and spectators can enjoy watching it.

[0358] "Viewer emotion data" refers to information that quantifies the emotions expressed by viewers through their facial expressions, tone of voice, and actions.

[0359] "Analysis results" refer to insights and indicators obtained after analyzing collected data using algorithms and methods.

[0360] A "virtual space" is an artificial and visually represented environment created within a computer using digital technology.

[0361] "Viewer feedback" refers to the reactions and evaluations that viewers give to the progress of an event, and includes emotional reactions and specific opinions.

[0362] A "virtual store" is a sales platform established on the internet that provides goods and services through virtual means.

[0363] "Dynamic display" means changing and adjusting the displayed content in real time according to conditions or input.

[0364] In embodiments of the present invention, a server, a terminal, and a user collaborate to provide an emotion-responsive virtual store experience. The server is responsible for acquiring and analyzing emotion data from viewers in real time. This emotion data is collected through the terminal's camera and microphone. Specifically, the emotional state of the viewer is identified by combining facial recognition using the terminal's camera and voice tone analysis using the microphone. Image and speech recognition libraries such as OpenCV and Google Cloud Vision API can be used for this purpose.

[0365] The server dynamically adjusts the placement and advertising of products within the virtual store in a virtual space based on the analyzed sentiment data. This allows users to access products and information that best suit their current emotions. By using a cloud-based platform (e.g., Firebase or AWS), large amounts of data can be processed efficiently, and real-time feedback can be provided.

[0366] For example, when a user accesses a virtual store via their smartphone, the server can pick up on the user's excitement and interest and present relevant products via push notifications. By enhancing the user experience in this way, it is possible to expect increased purchase intent and satisfaction.

[0367] As a concrete example, when an AI model is given the prompt, "Please suggest ways to display products and customize the store interface in a virtual store according to the user's emotions," the AI ​​dynamically optimizes and provides the order of product displays and advertisements. This allows viewers and users to receive a personalized experience that responds to their emotions. In this way, the present invention provides a system that realizes emotion-responsive, personalized product delivery and improves the user's purchasing experience.

[0368] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0369] Step 1:

[0370] The device uses a camera and microphone to capture the user's facial expressions and voice data when they access a virtual store. The input here is raw data representing the user's real-time emotions, and the output is captured as digital data necessary for emotion analysis.

[0371] Step 2:

[0372] The device uses the acquired facial and audio data to perform emotion recognition, utilizing OpenCV and the Google Cloud Vision API. The input is the digital data obtained in step 1, and the output is analyzed data indicating the emotional state. This analyzed data includes emotional categories such as joy, surprise, and interest.

[0373] Step 3:

[0374] The server receives emotion analysis data sent from the terminal and uses this to understand the user's emotional state. It receives emotion category data sent from the terminal as input and generates instructions for adjusting the display in the virtual space as output.

[0375] Step 4:

[0376] The server dynamically adjusts the layout of product displays and promotional content within the virtual store based on the received sentiment data. It uses the analyzed sentiment data as input and generates updated product lists and advertisement displays as output. Specifically, it performs actions such as prioritizing the display of products of interest, making them appear larger on the screen.

[0377] Step 5:

[0378] Users view updated store layouts on their smartphones or digital devices based on instructions from the server. The system receives display update data from the server as input and visually displays personalized product suggestions as output.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] [Third Embodiment]

[0383] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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".

[0395] The automatically generated virtual esports arena system based on the present invention is realized through the interaction of a server, a terminal, and a user. The main embodiments thereof are shown below.

[0396] Method of data collection

[0397] Before the event begins, the server collects information related to the competition. Specifically, it retrieves basic information such as the type of competition, the number of participants, and the schedule from the event management platform. Meanwhile, the terminal collects viewer preference data by obtaining personal interests and past viewing history from social media and participant registration forms. In addition, direct feedback and reactions provided by users when they participate in the event are also collected.

[0398] Data analysis embodiment

[0399] The collected data is analyzed by an AI agent on the server. The AI ​​agent analyzes viewer preferences and participant data, and executes algorithms to select the optimal arena theme and structure. If necessary, it performs comparative analysis with past event data to extract successful patterns.

[0400] Arena generation embodiment

[0401] Based on the analyzed data, the server automatically generates an arena in a virtual space. The generation process includes setting up visual representations, sound effects, and interactive elements. For example, if the audience prefers a fantasy style, the AI ​​agent will generate elements such as castles and dragons themed after medieval Europe and incorporate them into the arena.

[0402] Real-time adjustment embodiment

[0403] Throughout the event, the server continuously monitors viewer reactions. When viewers use reaction buttons or comment functions through their devices, that information is sent to the server in real time. The server uses this information to dynamically adjust the arena's presentation, for example, by changing the lighting or sound effects. If the audience becomes enthusiastic, the server provides entertainment that responds to their reactions, such as enhancing the visual effects of the audience seating.

[0404] In this way, the present invention enables a real-time, dynamic, user-participatory competitive experience that is not bound by physical constraints.

[0405] The following describes the processing flow.

[0406] Step 1:

[0407] The device receives login information from viewers and simultaneously collects preference data through social media and survey functions. This information is immediately sent to the server for real-time analysis.

[0408] Step 2:

[0409] The server uses an AI agent to begin analysis based on acquired preference data and initial event-related information (e.g., sports and schedule). The AI ​​agent compares the viewer's past viewing history and trend data to select the optimal arena theme and design.

[0410] Step 3:

[0411] The server automatically generates a virtual arena design based on analysis results from the AI ​​agent. Utilizing 3D modeling technology, it incorporates visual elements and sound effects to create an engaging virtual space. Parameters related to the placement, color, and movement of each structural element are set.

[0412] Step 4:

[0413] The server continuously monitors viewer feedback at the start and throughout the event. This data is used to fine-tune visual and auditory effects in real time, enhancing audience engagement and satisfaction. Feedback provided via terminals is immediately reflected in changes to the arena's production.

[0414] Step 5:

[0415] After the event ends, the server saves all viewer feedback and competition data to a database. This saved data is used to train and improve the AI ​​agent for future events, and to serve as reference material for future arena design and production.

[0416] (Example 1)

[0417] 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."

[0418] Traditional virtual competition spaces have the challenge of limiting the user experience because they are not able to dynamically change according to viewer preferences or the circumstances of the event. Furthermore, it has been difficult to generate arenas based on specific themes or characteristics, and to create real-time effects based on participant and viewer interactions.

[0419] 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.

[0420] In this invention, the server includes means for collecting competition information, means for analyzing the characteristics of the space based on viewer preference information and the status of the competition, and means for automatically generating the space in a virtual environment based on the analysis results. This makes it possible to provide a dynamic and immersive competition experience that responds to viewer preferences and real-time reactions.

[0421] A "competition" refers to a competitive event among participants that is conducted according to specific rules and norms.

[0422] "Viewer preference information" refers to data used to identify the items that viewers have shown interest in or concern about in the past.

[0423] A "virtual environment" refers to a digital world that includes a three-dimensional space created by a computer.

[0424] A "generative AI model" refers to a model that uses artificial intelligence technology to generate structures and presentations based on specific conditions.

[0425] "Means of analyzing spatial characteristics" refers to the process of determining what structure or theme is optimal within a virtual environment based on collected data.

[0426] "Special effects" refer to special events or effects that are triggered by the actions of participants or the reactions of viewers.

[0427] "Real-time reactions" refer to the dynamic opinions and actions that viewers immediately express in response to an event.

[0428] Embodiments of the present invention are systems that dynamically generate virtual competition environments and provide participants and viewers with an interactive and immersive experience. This system is realized through the interaction of servers, terminals, and users.

[0429] The server is the hardware device responsible for primary data processing, collecting basic event information and viewer preference data. This is done using software such as event management platforms and social media APIs. The collected information is stored in a database and analyzed by an AI agent. Based on this analysis, the generative AI model selects the optimal arena theme and structure. Specifically, it can generate virtual environments incorporating elements such as medieval European castles or fantasy elements based on themes that have shown high viewer interest.

[0430] The device collects data from users and provides information to identify their interests. It uses social media and event registration forms to obtain users' past viewing history and feedback. This data is transmitted to the server in real time and used to dynamically adjust the virtual environment.

[0431] Users can send real-time feedback through an interface provided during the event. This involves using reaction buttons and comment functions via their device. This feedback is analyzed on the server and used to adjust the lighting and sound effects of the virtual environment.

[0432] As a concrete example, in a virtual tennis tournament held online, the server uses a 3D rendering engine to generate a virtual arena based on tennis-related data. The terminals can track which player viewers are supporting, and the server can change the sound effects in real time based on this information.

[0433] An example of a prompt sentence to input into the generative AI model is, "Suggest an arena theme that will please the audience and incorporate new visual effects." Based on this prompt sentence, the AI ​​model will derive appropriate themes and effects, enriching the viewer experience.

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The server collects basic competition information from the event management platform. This information includes competition events, participant information, and schedules, and is obtained via API. The input is an API call to the event management platform, and the output is a dataset containing detailed competition information. This data is used to design the virtual environment.

[0437] Step 2:

[0438] The device collects viewer preference information from social media and participant registration forms. This includes the user's past viewing history and reaction data such as "likes." Inputs are social media APIs and participant registration information, and output is a dataset showing viewer interests. The device sends this to the server in real time, which is used to identify viewer preferences.

[0439] Step 3:

[0440] The server analyzes the collected data using an AI agent. It utilizes a generative AI model to select the optimal arena theme based on viewer preferences and event conditions. Inputs include viewer preference information and detailed event information, while output is configuration data specifying the optimal theme and structure. This configuration data is then used to design the virtual space.

[0441] Step 4:

[0442] The server generates a virtual arena based on the analysis results. A 3D rendering engine is used to construct visuals and sound effects that align with the theme. The input is the configuration data derived by the AI ​​model, and the output is the completed virtual arena. The generation process includes the creation of visual effects and sound design.

[0443] Step 5:

[0444] During the event, users provide real-time feedback using reaction buttons and comment functions via their devices. This includes active cheering and writing comments; the input is reaction data from the user, and the output is feedback information sent to the server. This feedback is then reflected in the arena's presentation.

[0445] Step 6:

[0446] The server analyzes real-time feedback and dynamically adjusts the visuals within the virtual environment. The input is user feedback information, and the output is the adjusted visual and sound effect settings. This enhances the arena's presentation as the competition progresses, providing viewers with a greater sense of immersion.

[0447] (Application Example 1)

[0448] 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."

[0449] In modern sporting events, there is a need to provide an interactive and personalized viewing experience while meeting the expectations of viewers with diverse interests and preferences. However, conventional systems have faced challenges in responding to viewer reactions in real time and dynamically adjusting the virtual environment.

[0450] 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.

[0451] In this invention, the server includes means for collecting information on sporting events, means for analyzing the spatial structure based on viewer interest data and the status of the event, and means for automatically generating a space in a virtual environment based on the analysis results. This makes it possible to suggest an optimal virtual environment based on the viewer's past viewing data and preferences, and to automatically adjust visual and sound effects using a generation AI model with prompt messages.

[0452] - A "competitive event" is an event in which participants compete in skills and abilities based on specific rules, and includes sports and games.

[0453] "Means of collecting information" refers to methods and devices for gathering data related to an event, and includes data collection from event management platforms and social media.

[0454] "Viewer interest data" refers to information that shows what genres and themes the audience prefers, and includes past viewing history and preferences.

[0455] "Means for analyzing the structure of space" refer to methods and devices for considering appropriate placement and design in a virtual space based on collected data.

[0456] A "virtual environment" refers to a digital space created by a computer system that users can experience through interaction.

[0457] "Means of automatic generation" refers to methods or devices that use AI technology or similar methods to autonomously create digital content based on pre-set conditions.

[0458] "Viewer feedback" refers to the feedback and reactions provided by the audience during the event, which allows for continuous evaluation.

[0459] "Means of dynamic adjustment" refers to methods or devices that allow the system to automatically change the direction or settings based on real-time data from viewers.

[0460] A "prompt message" is a text input that gives instructions to an AI model to generate a specific result.

[0461] The system that realizes this invention is mainly composed of server, terminal, and user elements.

[0462] The server is responsible for gathering information about the competition, automatically retrieving necessary data from event management platforms and other databases. The server also analyzes viewer interest data using AI models and designs the virtual environment based on the results. Viewer feedback is analyzed in real time, and the visual and auditory effects within the virtual space are dynamically adjusted accordingly. The software used by the server includes AI models built in Python and Unity or Unreal Engine for generating the virtual space.

[0463] The device functions as an interface with the viewer. Through a smartphone or head-mounted display, the viewer accesses a self-configured virtual stadium and obtains a personalized viewing experience. In this process, prompt messages are supplied to a generating AI model, which provides an optimal virtual experience based on the viewer's preferences and real-time feedback.

[0464] Users select events that align with their interests and participate through their devices, providing feedback along the way. This feedback is sent to the server and used to further optimize the virtual environment.

[0465] As a concrete example, consider a scenario where a user is using their smartphone to watch a match of their favorite esports team in a virtual arena themed after medieval Europe. If the AI ​​detects the user's enthusiastic support during viewing, it adjusts the lighting and effects to provide a more interactive environment.

[0466] An example of a prompt to a generative AI model is: "Based on past viewing data, please suggest the most suitable virtual arena theme and visuals for the user. Please also modify the arena's visuals in response to real-time feedback."

[0467] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0468] Step 1:

[0469] The server collects information about the sporting event from the event management platform. The database information accessed as input includes sporting events, participant numbers, and schedule information. Using this data, it efficiently outputs data that provides an overview of the entire event.

[0470] Step 2:

[0471] The device collects viewer interest data from social media and participant registration forms. Input includes viewing history and topics of interest, which is then processed to generate individual viewer preference profiles. These profiles are then sent to the server.

[0472] Step 3:

[0473] The server uses an AI model to analyze the optimal structure of the virtual space based on collected competition event information and viewer preference profiles. Input data includes viewer profiles and event information, which the AI ​​algorithm analyzes and outputs a proposed design for the virtual environment.

[0474] Step 4:

[0475] The server automatically generates a virtual environment using Unity or Unreal Engine based on the analysis results. The input includes AI analysis results, and the output is an environment that appropriately places visual and auditory elements within the virtual space to enhance interactivity.

[0476] Step 5:

[0477] Users access the virtual stadium through their device and begin their experience. The input is an access request from the user, which causes the device to deliver a personalized virtual environment to the user.

[0478] Step 6:

[0479] During the event, user reaction data is sent from the terminal to the server in real time. The input includes comments and reaction button information, which the server analyzes to generate output that dynamically adjusts the visuals within the virtual space.

[0480] Step 7:

[0481] The server utilizes a generative AI model and repeatedly optimizes the virtual environment's presentation using prompt messages. Real-time feedback information is used as input, and adjustments are output accordingly, continuously improving the virtual experience.

[0482] 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.

[0483] The virtual esports arena system incorporating the emotion engine based on this invention is a technology that analyzes user emotions and feedback during competitive events and dynamically adapts the arena's presentation. This system mainly consists of a server, terminals, and users, and is realized through the exchange of data between them.

[0484] Embodiments of emotional data collection and analysis

[0485] The device collects data on how viewers express their emotions towards the performance through their digital devices. This data is obtained from user facial recognition, voice tone analysis, or the use of reaction buttons. This emotional data is immediately transmitted to the server.

[0486] The server analyzes the received emotional data using an emotion engine. The emotion engine has algorithms to identify different emotional patterns (joy, excitement, tension, etc.) and understands the overall emotional trends of the audience.

[0487] Arena design embodiment

[0488] Based on analyzed emotional data, the server automatically generates and adjusts the atmosphere and design of the virtual arena. For example, if viewers are excited, the arena's color scheme brightens and the visual presentation is enhanced. When the emotion engine detects a change in viewers' emotions, the server immediately uses AI to adjust the arena's presentation to maintain viewer engagement.

[0489] Implementation of special effects

[0490] During the event, the server evaluates participant performance and audience emotional data in real time. This triggers special effects (e.g., music changes, added special effects) to provide viewers with a deeper sense of immersion. The emotional engine also reacts instantly to moments of audience emotion, introducing new effects to enhance the participants' engagement.

[0491] Thus, by utilizing an emotion engine, the present invention makes it possible to provide innovative visual and auditory experiences that respond to viewers' real-time emotions and feedback, making competitive events more engaging and interactive.

[0492] The following describes the processing flow.

[0493] Step 1:

[0494] The device collects real-time emotional data provided by viewers through digital devices (e.g., smartphones and PCs). This emotional data is obtained through facial recognition sensors and voice tone analysis from microphones. Presses of emotional reaction buttons are also included in this data.

[0495] Step 2:

[0496] The server instantly analyzes the emotional data received from the terminal. The emotion engine uses algorithms to determine each viewer's emotion and understand the overall emotional trend of the audience. The analysis results clearly show what emotional state the viewer is currently in (e.g., joy, excitement, tension, boredom).

[0497] Step 3:

[0498] The server instantly adjusts the atmosphere of the virtual arena based on analyzed emotion data. For example, if viewers are excited, the virtual arena's color scheme becomes more vibrant and the lighting effects are enhanced. Sound effects are also adjusted, and background music is selected to match the viewers' emotional state.

[0499] Step 4:

[0500] The server monitors participant performance data and triggers special effects when viewers' emotions show a specific shift. For example, when a participant delivers an outstanding performance, the emotion engine detects the viewers' excitement and immediately deploys effects such as fireworks or a special light show.

[0501] Step 5:

[0502] Based on user feedback, the server will save data after the event ends to help improve the emotion engine and virtual space for future events. The saved data will be used as training material for the AI ​​system, contributing to the creation of more sophisticated visual and auditory experiences in future events.

[0503] (Example 2)

[0504] 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."

[0505] Traditional virtual event productions have not fully utilized audience emotions and reactions, and their interaction with participant performances has been limited. Therefore, maximizing audience engagement and emotional impact has been difficult. Furthermore, proposing and optimizing new production methods required complex manual work.

[0506] 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.

[0507] In this invention, the server includes means for a device that recognizes and acquires data on the emotions of an observer; means for a computing device that analyzes the collected emotion data and identifies specific emotional states; means for generating and adapting environmental effects in a virtual space based on the analysis results; means for monitoring the observer's emotional responses in real time and dynamically adjusting the environmental effects; trigger means for activating special effects based on the behavior of participants and the emotional responses of the observer; and means for utilizing a generative AI model to propose and optimize effects based on external input. This enables more interactive and dynamic effects that reflect the emotions of viewers in real time.

[0508] "Observer emotions" refers to the emotional expressions that viewers show through digital devices, including facial expressions, tone of voice, and reaction manipulation.

[0509] "Devices for acquiring data" refers to various devices and sensors used to collect viewers' emotions and behaviors.

[0510] "Collected sentiment data" refers to a dataset containing information about the emotional state of the observer.

[0511] A "processing unit" refers to a server or data processing unit used to analyze emotional data and identify specific emotional states.

[0512] "Means for generating and adapting environmental effects" refers to technologies and processes for dynamically changing visual and auditory elements within a virtual space.

[0513] "Means of monitoring emotional responses in real time and dynamically adjusting the environmental presentation" refers to technology that instantly adjusts the environmental presentation when the viewer's emotions change.

[0514] "A trigger mechanism for activating special effects based on participants' behavior and observers' emotional responses" refers to a system for setting conditions and monitoring them to trigger additional effects when specific conditions are met.

[0515] "Means for proposing and optimizing performances based on external input using generative AI models" refers to technologies that use machine learning and artificial intelligence to propose new performance ideas and optimize events.

[0516] This invention is a system that utilizes observer emotion data in real time to dynamically adjust the presentation in a virtual event space. The system mainly consists of a server, terminals, and users, and is realized through the cooperation of each element.

[0517] As a user's digital device, the terminal uses sensors such as a webcam and microphone to capture the observer's facial expressions and voice tone. Furthermore, emotional data is collected through reaction buttons operated on the terminal. This information is transmitted to the server quickly and efficiently.

[0518] Upon receiving data, the server analyzes it using a specialized emotion engine. This emotion engine implements machine learning algorithms and can identify the diverse emotional states of the observer. Based on this analysis, the server issues instructions to generate and adapt the virtual environment's presentation. It tracks emotional patterns in real time and dynamically changes the representation as needed.

[0519] This invention utilizes a generative AI model. The AI ​​model plays a role in suggesting and optimizing new effects based on external input. For example, the AI ​​model can be queried for effect suggestions using a prompt such as, "Suggest the best visual effect when the viewer is surprised."

[0520] As a concrete example, imagine a scenario where a user is watching a competition at a virtual event. In this scenario, the user's emotions, such as cheering or being surprised, are captured by sensors and analyzed by a server. Subsequently, the color tone and sound within the virtual space are adjusted according to the viewer's reactions, making it possible to provide a deeper sense of immersion.

[0521] This invention aims to provide a new interactive experience and enhance the level of excitement for both participants and observers by utilizing the emotions of observers in the production of virtual events.

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] Users watch the sporting event through a digital device. The webcam and microphone built into the device continuously capture the user's facial expressions and voice tone as input. This input data is collected along with button operation data triggered by the user's reactions and prepared as emotion data. Through this process, the device obtains various raw data that indicates the user's emotions.

[0525] Step 2:

[0526] The device immediately transmits the collected emotional data to the server via the network. Using the emotional data received as input, the server activates an emotion engine and analyzes each data point. This analysis employs machine learning algorithms to identify the user's emotional state from the input data. The identified emotional state, such as "joy" or "surprise," is output as the analysis result.

[0527] Step 3:

[0528] Based on the analyzed emotional state, the server uses that data as input to determine the environmental effects of the virtual arena. If the emotional state is "excited," it generates commands to brighten the lighting and add dynamic effects. These commands are sent to the virtual space's environment control system, which then changes the visual effects in real time as output.

[0529] Step 4:

[0530] The server uses a generative AI model to process external input prompts. For example, when a prompt such as "Suggest the best visual effect when the viewer is surprised" is input, the AI ​​model analyzes it and provides new visual effect ideas as a suggestion. This suggestion is then used to further optimize the presentation of the virtual arena.

[0531] Step 5:

[0532] The server evaluates observer emotional responses and participant behavior in real time during the virtual event. It analyzes participant performance data and observer emotional data as input, and if certain conditions are met, it issues commands to trigger special effects as output. For example, it might activate a fireworks effect and play a special music track at the moment of a goal to enhance the user experience.

[0533] (Application Example 2)

[0534] 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."

[0535] In modern competitive events and virtual retail experiences, a challenge lies in the lack of dynamic presentations and product displays that adequately reflect the emotions of viewers and users in real time, resulting in a lack of immersion and engagement among participants and consumers. Therefore, there is a need for technologies that enable deeper engagement and provide personalized experiences for viewers and users.

[0536] 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.

[0537] In this invention, the server includes means for collecting information on a competitive event, means for acquiring and analyzing viewer emotion data, means for automatically generating an arena in a virtual space based on the analysis results, means for analyzing viewer feedback during the event and dynamically adjusting the arena's presentation, and means for dynamically displaying information within a virtual store according to the viewer's emotions. This enables viewers and users to enjoy an engaging and personalized experience in real time that is tailored to their emotions.

[0538] A "competitive event" is a situation in which participants compete in performance based on specific rules, and spectators can enjoy watching it.

[0539] "Viewer emotion data" refers to information that quantifies the emotions expressed by viewers through their facial expressions, tone of voice, and actions.

[0540] "Analysis results" refer to insights and indicators obtained after analyzing collected data using algorithms and methods.

[0541] A "virtual space" is an artificial and visually represented environment created within a computer using digital technology.

[0542] "Viewer feedback" refers to the reactions and evaluations that viewers give to the progress of an event, and includes emotional reactions and specific opinions.

[0543] A "virtual store" is a sales platform established on the internet that provides goods and services through virtual means.

[0544] "Dynamic display" means changing and adjusting the displayed content in real time according to conditions or input.

[0545] In embodiments of the present invention, a server, a terminal, and a user collaborate to provide an emotion-responsive virtual store experience. The server is responsible for acquiring and analyzing emotion data from viewers in real time. This emotion data is collected through the terminal's camera and microphone. Specifically, the emotional state of the viewer is identified by combining facial recognition using the terminal's camera and voice tone analysis using the microphone. Image and speech recognition libraries such as OpenCV and Google Cloud Vision API can be used for this purpose.

[0546] The server dynamically adjusts the placement and advertising of products within the virtual store in a virtual space based on the analyzed sentiment data. This allows users to access products and information that best suit their current emotions. By using a cloud-based platform (e.g., Firebase or AWS), large amounts of data can be processed efficiently, and real-time feedback can be provided.

[0547] For example, when a user accesses a virtual store via their smartphone, the server can pick up on the user's excitement and interest and present relevant products via push notifications. By enhancing the user experience in this way, it is possible to expect increased purchase intent and satisfaction.

[0548] As a concrete example, when an AI model is given the prompt, "Please suggest ways to display products and customize the store interface in a virtual store according to the user's emotions," the AI ​​dynamically optimizes and provides the order of product displays and advertisements. This allows viewers and users to receive a personalized experience that responds to their emotions. In this way, the present invention provides a system that realizes emotion-responsive, personalized product delivery and improves the user's purchasing experience.

[0549] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0550] Step 1:

[0551] The device uses a camera and microphone to capture the user's facial expressions and voice data when they access a virtual store. The input here is raw data representing the user's real-time emotions, and the output is captured as digital data necessary for emotion analysis.

[0552] Step 2:

[0553] The device uses the acquired facial and audio data to perform emotion recognition, utilizing OpenCV and the Google Cloud Vision API. The input is the digital data obtained in step 1, and the output is analyzed data indicating the emotional state. This analyzed data includes emotional categories such as joy, surprise, and interest.

[0554] Step 3:

[0555] The server receives emotion analysis data sent from the terminal and uses this to understand the user's emotional state. It receives emotion category data sent from the terminal as input and generates instructions for adjusting the display in the virtual space as output.

[0556] Step 4:

[0557] The server dynamically adjusts the layout of product displays and promotional content within the virtual store based on the received sentiment data. It uses the analyzed sentiment data as input and generates updated product lists and advertisement displays as output. Specifically, it performs actions such as prioritizing the display of products of interest, making them appear larger on the screen.

[0558] Step 5:

[0559] Users view updated store layouts on their smartphones or digital devices based on instructions from the server. The system receives display update data from the server as input and visually displays personalized product suggestions as output.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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".

[0577] The automatically generated virtual esports arena system based on the present invention is realized through the interaction of a server, a terminal, and a user. The main embodiments thereof are shown below.

[0578] Method of data collection

[0579] Before the event begins, the server collects information related to the competition. Specifically, it retrieves basic information such as the type of competition, the number of participants, and the schedule from the event management platform. Meanwhile, the terminal collects viewer preference data by obtaining personal interests and past viewing history from social media and participant registration forms. In addition, direct feedback and reactions provided by users when they participate in the event are also collected.

[0580] Data analysis embodiment

[0581] The collected data is analyzed by an AI agent on the server. The AI ​​agent analyzes viewer preferences and participant data, and executes algorithms to select the optimal arena theme and structure. If necessary, it performs comparative analysis with past event data to extract successful patterns.

[0582] Arena generation embodiment

[0583] Based on the analyzed data, the server automatically generates an arena in a virtual space. The generation process includes setting up visual representations, sound effects, and interactive elements. For example, if the audience prefers a fantasy style, the AI ​​agent will generate elements such as castles and dragons themed after medieval Europe and incorporate them into the arena.

[0584] Real-time adjustment embodiment

[0585] Throughout the event, the server continuously monitors viewer reactions. When viewers use reaction buttons or comment functions through their devices, that information is sent to the server in real time. The server uses this information to dynamically adjust the arena's presentation, for example, by changing the lighting or sound effects. If the audience becomes enthusiastic, the server provides entertainment that responds to their reactions, such as enhancing the visual effects of the audience seating.

[0586] In this way, the present invention enables a real-time, dynamic, user-participatory competitive experience that is not bound by physical constraints.

[0587] The following describes the processing flow.

[0588] Step 1:

[0589] The device receives login information from viewers and simultaneously collects preference data through social media and survey functions. This information is immediately sent to the server for real-time analysis.

[0590] Step 2:

[0591] The server uses an AI agent to begin analysis based on acquired preference data and initial event-related information (e.g., sports and schedule). The AI ​​agent compares the viewer's past viewing history and trend data to select the optimal arena theme and design.

[0592] Step 3:

[0593] The server automatically generates a virtual arena design based on analysis results from the AI ​​agent. Utilizing 3D modeling technology, it incorporates visual elements and sound effects to create an engaging virtual space. Parameters related to the placement, color, and movement of each structural element are set.

[0594] Step 4:

[0595] The server continuously monitors viewer feedback at the start and throughout the event. This data is used to fine-tune visual and auditory effects in real time, enhancing audience engagement and satisfaction. Feedback provided via terminals is immediately reflected in changes to the arena's production.

[0596] Step 5:

[0597] After the event ends, the server saves all viewer feedback and competition data to a database. This saved data is used to train and improve the AI ​​agent for future events, and to serve as reference material for future arena design and production.

[0598] (Example 1)

[0599] 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".

[0600] Traditional virtual competition spaces have the challenge of limiting the user experience because they are not able to dynamically change according to viewer preferences or the circumstances of the event. Furthermore, it has been difficult to generate arenas based on specific themes or characteristics, and to create real-time effects based on participant and viewer interactions.

[0601] 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.

[0602] In this invention, the server includes means for collecting competition information, means for analyzing the characteristics of the space based on viewer preference information and the status of the competition, and means for automatically generating the space in a virtual environment based on the analysis results. This makes it possible to provide a dynamic and immersive competition experience that responds to viewer preferences and real-time reactions.

[0603] A "competition" refers to a competitive event among participants that is conducted according to specific rules and norms.

[0604] "Viewer preference information" refers to data used to identify the items that viewers have shown interest in or concern about in the past.

[0605] A "virtual environment" refers to a digital world that includes a three-dimensional space created by a computer.

[0606] A "generative AI model" refers to a model that uses artificial intelligence technology to generate structures and presentations based on specific conditions.

[0607] "Means of analyzing spatial characteristics" refers to the process of determining what structure or theme is optimal within a virtual environment based on collected data.

[0608] "Special effects" refer to special events or effects that are triggered by the actions of participants or the reactions of viewers.

[0609] "Real-time reactions" refer to the dynamic opinions and actions that viewers immediately express in response to an event.

[0610] Embodiments of the present invention are systems that dynamically generate virtual competition environments and provide participants and viewers with an interactive and immersive experience. This system is realized through the interaction of servers, terminals, and users.

[0611] The server is the hardware device responsible for primary data processing, collecting basic event information and viewer preference data. This is done using software such as event management platforms and social media APIs. The collected information is stored in a database and analyzed by an AI agent. Based on this analysis, the generative AI model selects the optimal arena theme and structure. Specifically, it can generate virtual environments incorporating elements such as medieval European castles or fantasy elements based on themes that have shown high viewer interest.

[0612] The device collects data from users and provides information to identify their interests. It uses social media and event registration forms to obtain users' past viewing history and feedback. This data is transmitted to the server in real time and used to dynamically adjust the virtual environment.

[0613] Users can send real-time feedback through an interface provided during the event. This involves using reaction buttons and comment functions via their device. This feedback is analyzed on the server and used to adjust the lighting and sound effects of the virtual environment.

[0614] As a concrete example, in a virtual tennis tournament held online, the server uses a 3D rendering engine to generate a virtual arena based on tennis-related data. The terminals can track which player viewers are supporting, and the server can change the sound effects in real time based on this information.

[0615] An example of a prompt sentence to input into the generative AI model is, "Suggest an arena theme that will please the audience and incorporate new visual effects." Based on this prompt sentence, the AI ​​model will derive appropriate themes and effects, enriching the viewer experience.

[0616] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0617] Step 1:

[0618] The server collects basic competition information from the event management platform. This information includes competition events, participant information, and schedules, and is obtained via API. The input is an API call to the event management platform, and the output is a dataset containing detailed competition information. This data is used to design the virtual environment.

[0619] Step 2:

[0620] The device collects viewer preference information from social media and participant registration forms. This includes the user's past viewing history and reaction data such as "likes." Inputs are social media APIs and participant registration information, and output is a dataset showing viewer interests. The device sends this to the server in real time, which is used to identify viewer preferences.

[0621] Step 3:

[0622] The server analyzes the collected data using an AI agent. It utilizes a generative AI model to select the optimal arena theme based on viewer preferences and event conditions. Inputs include viewer preference information and detailed event information, while output is configuration data specifying the optimal theme and structure. This configuration data is then used to design the virtual space.

[0623] Step 4:

[0624] The server generates a virtual arena based on the analysis results. A 3D rendering engine is used to construct visuals and sound effects that align with the theme. The input is the configuration data derived by the AI ​​model, and the output is the completed virtual arena. The generation process includes the creation of visual effects and sound design.

[0625] Step 5:

[0626] During the event, users provide real-time feedback using reaction buttons and comment functions via their devices. This includes active cheering and writing comments; the input is reaction data from the user, and the output is feedback information sent to the server. This feedback is then reflected in the arena's presentation.

[0627] Step 6:

[0628] The server analyzes real-time feedback and dynamically adjusts the visuals within the virtual environment. The input is user feedback information, and the output is the adjusted visual and sound effect settings. This enhances the arena's presentation as the competition progresses, providing viewers with a greater sense of immersion.

[0629] (Application Example 1)

[0630] 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".

[0631] In modern sporting events, there is a need to provide an interactive and personalized viewing experience while meeting the expectations of viewers with diverse interests and preferences. However, conventional systems have faced challenges in responding to viewer reactions in real time and dynamically adjusting the virtual environment.

[0632] 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.

[0633] In this invention, the server includes means for collecting information on sporting events, means for analyzing the spatial structure based on viewer interest data and the status of the event, and means for automatically generating a space in a virtual environment based on the analysis results. This makes it possible to suggest an optimal virtual environment based on the viewer's past viewing data and preferences, and to automatically adjust visual and sound effects using a generation AI model with prompt messages.

[0634] - A "competitive event" is an event in which participants compete in skills and abilities based on specific rules, and includes sports and games.

[0635] "Means of collecting information" refers to methods and devices for gathering data related to an event, and includes data collection from event management platforms and social media.

[0636] "Viewer interest data" refers to information that shows what genres and themes the audience prefers, and includes past viewing history and preferences.

[0637] "Means for analyzing the structure of space" refer to methods and devices for considering appropriate placement and design in a virtual space based on collected data.

[0638] A "virtual environment" refers to a digital space created by a computer system that users can experience through interaction.

[0639] "Means of automatic generation" refers to methods or devices that use AI technology or similar methods to autonomously create digital content based on pre-set conditions.

[0640] "Viewer feedback" refers to the feedback and reactions provided by the audience during the event, which allows for continuous evaluation.

[0641] "Means of dynamic adjustment" refers to methods or devices that allow the system to automatically change the direction or settings based on real-time data from viewers.

[0642] A "prompt message" is a text input that gives instructions to an AI model to generate a specific result.

[0643] The system that realizes this invention is mainly composed of server, terminal, and user elements.

[0644] The server is responsible for gathering information about the competition, automatically retrieving necessary data from event management platforms and other databases. The server also analyzes viewer interest data using AI models and designs the virtual environment based on the results. Viewer feedback is analyzed in real time, and the visual and auditory effects within the virtual space are dynamically adjusted accordingly. The software used by the server includes AI models built in Python and Unity or Unreal Engine for generating the virtual space.

[0645] The device functions as an interface with the viewer. Through a smartphone or head-mounted display, the viewer accesses a self-configured virtual stadium and obtains a personalized viewing experience. In this process, prompt messages are supplied to a generating AI model, which provides an optimal virtual experience based on the viewer's preferences and real-time feedback.

[0646] Users select events that align with their interests and participate through their devices, providing feedback along the way. This feedback is sent to the server and used to further optimize the virtual environment.

[0647] As a concrete example, consider a scenario where a user is using their smartphone to watch a match of their favorite esports team in a virtual arena themed after medieval Europe. If the AI ​​detects the user's enthusiastic support during viewing, it adjusts the lighting and effects to provide a more interactive environment.

[0648] An example of a prompt to a generative AI model is: "Based on past viewing data, please suggest the most suitable virtual arena theme and visuals for the user. Please also modify the arena's visuals in response to real-time feedback."

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The server collects information about the sporting event from the event management platform. The database information accessed as input includes sporting events, participant numbers, and schedule information. Using this data, it efficiently outputs data that provides an overview of the entire event.

[0652] Step 2:

[0653] The device collects viewer interest data from social media and participant registration forms. Input includes viewing history and topics of interest, which is then processed to generate individual viewer preference profiles. These profiles are then sent to the server.

[0654] Step 3:

[0655] The server uses an AI model to analyze the optimal structure of the virtual space based on collected competition event information and viewer preference profiles. Input data includes viewer profiles and event information, which the AI ​​algorithm analyzes and outputs a proposed design for the virtual environment.

[0656] Step 4:

[0657] The server automatically generates a virtual environment using Unity or Unreal Engine based on the analysis results. The input includes AI analysis results, and the output is an environment that appropriately places visual and auditory elements within the virtual space to enhance interactivity.

[0658] Step 5:

[0659] Users access the virtual stadium through their device and begin their experience. The input is an access request from the user, which causes the device to deliver a personalized virtual environment to the user.

[0660] Step 6:

[0661] During the event, user reaction data is sent from the terminal to the server in real time. The input includes comments and reaction button information, which the server analyzes to generate output that dynamically adjusts the visuals within the virtual space.

[0662] Step 7:

[0663] The server utilizes a generative AI model and repeatedly optimizes the virtual environment's presentation using prompt messages. Real-time feedback information is used as input, and adjustments are output accordingly, continuously improving the virtual experience.

[0664] 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.

[0665] The virtual esports arena system incorporating the emotion engine based on this invention is a technology that analyzes user emotions and feedback during competitive events and dynamically adapts the arena's presentation. This system mainly consists of a server, terminals, and users, and is realized through the exchange of data between them.

[0666] Embodiments of emotional data collection and analysis

[0667] The device collects data on how viewers express their emotions towards the performance through their digital devices. This data is obtained from user facial recognition, voice tone analysis, or the use of reaction buttons. This emotional data is immediately transmitted to the server.

[0668] The server analyzes the received emotional data using an emotion engine. The emotion engine has algorithms to identify different emotional patterns (joy, excitement, tension, etc.) and understands the overall emotional trends of the audience.

[0669] Arena design embodiment

[0670] Based on analyzed emotional data, the server automatically generates and adjusts the atmosphere and design of the virtual arena. For example, if viewers are excited, the arena's color scheme brightens and the visual presentation is enhanced. When the emotion engine detects a change in viewers' emotions, the server immediately uses AI to adjust the arena's presentation to maintain viewer engagement.

[0671] Implementation of special effects

[0672] During the event, the server evaluates participant performance and audience emotional data in real time. This triggers special effects (e.g., music changes, added special effects) to provide viewers with a deeper sense of immersion. The emotional engine also reacts instantly to moments of audience emotion, introducing new effects to enhance the participants' engagement.

[0673] Thus, by utilizing an emotion engine, the present invention makes it possible to provide innovative visual and auditory experiences that respond to viewers' real-time emotions and feedback, making competitive events more engaging and interactive.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The device collects real-time emotional data provided by viewers through digital devices (e.g., smartphones and PCs). This emotional data is obtained through facial recognition sensors and voice tone analysis from microphones. Presses of emotional reaction buttons are also included in this data.

[0677] Step 2:

[0678] The server instantly analyzes the emotional data received from the terminal. The emotion engine uses algorithms to determine each viewer's emotion and understand the overall emotional trend of the audience. The analysis results clearly show what emotional state the viewer is currently in (e.g., joy, excitement, tension, boredom).

[0679] Step 3:

[0680] The server instantly adjusts the atmosphere of the virtual arena based on analyzed emotion data. For example, if viewers are excited, the virtual arena's color scheme becomes more vibrant and the lighting effects are enhanced. Sound effects are also adjusted, and background music is selected to match the viewers' emotional state.

[0681] Step 4:

[0682] The server monitors participant performance data and triggers special effects when viewers' emotions show a specific shift. For example, when a participant delivers an outstanding performance, the emotion engine detects the viewers' excitement and immediately deploys effects such as fireworks or a special light show.

[0683] Step 5:

[0684] Based on user feedback, the server will save data after the event ends to help improve the emotion engine and virtual space for future events. The saved data will be used as training material for the AI ​​system, contributing to the creation of more sophisticated visual and auditory experiences in future events.

[0685] (Example 2)

[0686] 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".

[0687] Traditional virtual event productions have not fully utilized audience emotions and reactions, and their interaction with participant performances has been limited. Therefore, maximizing audience engagement and emotional impact has been difficult. Furthermore, proposing and optimizing new production methods required complex manual work.

[0688] 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.

[0689] In this invention, the server includes means for a device that recognizes and acquires data on the emotions of an observer; means for a computing device that analyzes the collected emotion data and identifies specific emotional states; means for generating and adapting environmental effects in a virtual space based on the analysis results; means for monitoring the observer's emotional responses in real time and dynamically adjusting the environmental effects; trigger means for activating special effects based on the behavior of participants and the emotional responses of the observer; and means for utilizing a generative AI model to propose and optimize effects based on external input. This enables more interactive and dynamic effects that reflect the emotions of viewers in real time.

[0690] "Observer emotions" refers to the emotional expressions that viewers show through digital devices, including facial expressions, tone of voice, and reaction manipulation.

[0691] "Devices for acquiring data" refers to various devices and sensors used to collect viewers' emotions and behaviors.

[0692] "Collected sentiment data" refers to a dataset containing information about the emotional state of the observer.

[0693] A "processing unit" refers to a server or data processing unit used to analyze emotional data and identify specific emotional states.

[0694] "Means for generating and adapting environmental effects" refers to technologies and processes for dynamically changing visual and auditory elements within a virtual space.

[0695] "Means of monitoring emotional responses in real time and dynamically adjusting the environmental presentation" refers to technology that instantly adjusts the environmental presentation when the viewer's emotions change.

[0696] "A trigger mechanism for activating special effects based on participants' behavior and observers' emotional responses" refers to a system for setting conditions and monitoring them to trigger additional effects when specific conditions are met.

[0697] "Means for proposing and optimizing performances based on external input using generative AI models" refers to technologies that use machine learning and artificial intelligence to propose new performance ideas and optimize events.

[0698] This invention is a system that utilizes observer emotion data in real time to dynamically adjust the presentation in a virtual event space. The system mainly consists of a server, terminals, and users, and is realized through the cooperation of each element.

[0699] As a user's digital device, the terminal uses sensors such as a webcam and microphone to capture the observer's facial expressions and voice tone. Furthermore, emotional data is collected through reaction buttons operated on the terminal. This information is transmitted to the server quickly and efficiently.

[0700] Upon receiving data, the server analyzes it using a specialized emotion engine. This emotion engine implements machine learning algorithms and can identify the diverse emotional states of the observer. Based on this analysis, the server issues instructions to generate and adapt the virtual environment's presentation. It tracks emotional patterns in real time and dynamically changes the representation as needed.

[0701] This invention utilizes a generative AI model. The AI ​​model plays a role in suggesting and optimizing new effects based on external input. For example, the AI ​​model can be queried for effect suggestions using a prompt such as, "Suggest the best visual effect when the viewer is surprised."

[0702] As a concrete example, imagine a scenario where a user is watching a competition at a virtual event. In this scenario, the user's emotions, such as cheering or being surprised, are captured by sensors and analyzed by a server. Subsequently, the color tone and sound within the virtual space are adjusted according to the viewer's reactions, making it possible to provide a deeper sense of immersion.

[0703] This invention aims to provide a new interactive experience and enhance the level of excitement for both participants and observers by utilizing the emotions of observers in the production of virtual events.

[0704] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0705] Step 1:

[0706] Users watch the sporting event through a digital device. The webcam and microphone built into the device continuously capture the user's facial expressions and voice tone as input. This input data is collected along with button operation data triggered by the user's reactions and prepared as emotion data. Through this process, the device obtains various raw data that indicates the user's emotions.

[0707] Step 2:

[0708] The device immediately transmits the collected emotional data to the server via the network. Using the emotional data received as input, the server activates an emotion engine and analyzes each data point. This analysis employs machine learning algorithms to identify the user's emotional state from the input data. The identified emotional state, such as "joy" or "surprise," is output as the analysis result.

[0709] Step 3:

[0710] Based on the analyzed emotional state, the server uses that data as input to determine the environmental effects of the virtual arena. If the emotional state is "excited," it generates commands to brighten the lighting and add dynamic effects. These commands are sent to the virtual space's environment control system, which then changes the visual effects in real time as output.

[0711] Step 4:

[0712] The server uses a generative AI model to process external input prompts. For example, when a prompt such as "Suggest the best visual effect when the viewer is surprised" is input, the AI ​​model analyzes it and provides new visual effect ideas as a suggestion. This suggestion is then used to further optimize the presentation of the virtual arena.

[0713] Step 5:

[0714] The server evaluates observer emotional responses and participant behavior in real time during the virtual event. It analyzes participant performance data and observer emotional data as input, and if certain conditions are met, it issues commands to trigger special effects as output. For example, it might activate a fireworks effect and play a special music track at the moment of a goal to enhance the user experience.

[0715] (Application Example 2)

[0716] 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".

[0717] In modern competitive events and virtual retail experiences, a challenge lies in the lack of dynamic presentations and product displays that adequately reflect the emotions of viewers and users in real time, resulting in a lack of immersion and engagement among participants and consumers. Therefore, there is a need for technologies that enable deeper engagement and provide personalized experiences for viewers and users.

[0718] 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.

[0719] In this invention, the server includes means for collecting information on a competitive event, means for acquiring and analyzing viewer emotion data, means for automatically generating an arena in a virtual space based on the analysis results, means for analyzing viewer feedback during the event and dynamically adjusting the arena's presentation, and means for dynamically displaying information within a virtual store according to the viewer's emotions. This enables viewers and users to enjoy an engaging and personalized experience in real time that is tailored to their emotions.

[0720] A "competitive event" is a situation in which participants compete in performance based on specific rules, and spectators can enjoy watching it.

[0721] "Viewer emotion data" refers to information that quantifies the emotions expressed by viewers through their facial expressions, tone of voice, and actions.

[0722] "Analysis results" refer to insights and indicators obtained after analyzing collected data using algorithms and methods.

[0723] A "virtual space" is an artificial and visually represented environment created within a computer using digital technology.

[0724] "Viewer feedback" refers to the reactions and evaluations that viewers give to the progress of an event, and includes emotional reactions and specific opinions.

[0725] A "virtual store" is a sales platform established on the internet that provides goods and services through virtual means.

[0726] "Dynamic display" means changing and adjusting the displayed content in real time according to conditions or input.

[0727] In embodiments of the present invention, a server, a terminal, and a user collaborate to provide an emotion-responsive virtual store experience. The server is responsible for acquiring and analyzing emotion data from viewers in real time. This emotion data is collected through the terminal's camera and microphone. Specifically, the emotional state of the viewer is identified by combining facial recognition using the terminal's camera and voice tone analysis using the microphone. Image and speech recognition libraries such as OpenCV and Google Cloud Vision API can be used for this purpose.

[0728] The server dynamically adjusts the placement and advertising of products within the virtual store in a virtual space based on the analyzed sentiment data. This allows users to access products and information that best suit their current emotions. By using a cloud-based platform (e.g., Firebase or AWS), large amounts of data can be processed efficiently, and real-time feedback can be provided.

[0729] For example, when a user accesses a virtual store via their smartphone, the server can pick up on the user's excitement and interest and present relevant products via push notifications. By enhancing the user experience in this way, it is possible to expect increased purchase intent and satisfaction.

[0730] As a concrete example, when an AI model is given the prompt, "Please suggest ways to display products and customize the store interface in a virtual store according to the user's emotions," the AI ​​dynamically optimizes and provides the order of product displays and advertisements. This allows viewers and users to receive a personalized experience that responds to their emotions. In this way, the present invention provides a system that realizes emotion-responsive, personalized product delivery and improves the user's purchasing experience.

[0731] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0732] Step 1:

[0733] The device uses a camera and microphone to capture the user's facial expressions and voice data when they access a virtual store. The input here is raw data representing the user's real-time emotions, and the output is captured as digital data necessary for emotion analysis.

[0734] Step 2:

[0735] The device uses the acquired facial and audio data to perform emotion recognition, utilizing OpenCV and the Google Cloud Vision API. The input is the digital data obtained in step 1, and the output is analyzed data indicating the emotional state. This analyzed data includes emotional categories such as joy, surprise, and interest.

[0736] Step 3:

[0737] The server receives emotion analysis data sent from the terminal and uses this to understand the user's emotional state. It receives emotion category data sent from the terminal as input and generates instructions for adjusting the display in the virtual space as output.

[0738] Step 4:

[0739] The server dynamically adjusts the layout of product displays and promotional content within the virtual store based on the received sentiment data. It uses the analyzed sentiment data as input and generates updated product lists and advertisement displays as output. Specifically, it performs actions such as prioritizing the display of products of interest, making them appear larger on the screen.

[0740] Step 5:

[0741] Users view updated store layouts on their smartphones or digital devices based on instructions from the server. The system receives display update data from the server as input and visually displays personalized product suggestions as output.

[0742] 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.

[0743] 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.

[0744] 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 robot 414.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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."

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0763] The following is further disclosed regarding the embodiments described above.

[0764] (Claim 1)

[0765] Means of collecting information on competitive events,

[0766] A means of analyzing the arena structure based on viewer preference data and event circumstances,

[0767] A means for automatically generating an arena in a virtual space based on the analysis results,

[0768] A means of analyzing viewer feedback during the event and dynamically adjusting the arena's presentation,

[0769] A system that includes this.

[0770] (Claim 2)

[0771] The system according to claim 1, comprising means for setting visual elements of a virtual space generated based on the theme of a competitive event.

[0772] (Claim 3)

[0773] The system according to claim 1, comprising means for triggering special effects based on participant performance and viewer reactions.

[0774] "Example 1"

[0775] (Claim 1)

[0776] Means of gathering information about competitions,

[0777] A means for analyzing the characteristics of a space based on viewer preference information and the status of the competition,

[0778] A means for automatically generating a space in a virtual environment based on the analysis results,

[0779] A means of dynamically adjusting the spatial presentation by analyzing viewer reaction data during the competition,

[0780] A means of using a generative AI model for designing visual expressions and sound effects,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, comprising means for setting the characteristics of a virtual environment generated based on the theme of a competition.

[0784] (Claim 3)

[0785] The system according to claim 1, further comprising means for initiating a special performance based on the skills of the participants and the reactions of the viewers.

[0786] "Application Example 1"

[0787] (Claim 1)

[0788] Means of collecting information on athletic events,

[0789] A means of analyzing the spatial structure based on viewer interest data and the status of events,

[0790] A means for automatically generating a space in a virtual environment based on the analysis results,

[0791] A means of analyzing viewer opinions during the event and dynamically adjusting the spatial presentation,

[0792] A means of suggesting the optimal virtual environment based on the user's past viewing data and preferences,

[0793] A method for automatically adjusting visual and auditory effects by utilizing a generative AI model with prompt text,

[0794] A means of providing users with a personalized virtual stadium experience through smart devices,

[0795] A system that includes this.

[0796] (Claim 2)

[0797] The system according to claim 1, comprising means for setting the visual characteristics of a virtual environment generated based on the theme of a competitive event.

[0798] (Claim 3)

[0799] The system according to claim 1, comprising means for activating special effects based on the activities of participants and the reactions of viewers.

[0800] "Example 2 of combining an emotion engine"

[0801] (Claim 1)

[0802] Means including a device for recognizing the emotions of an observer and acquiring data,

[0803] A means including a computational processing unit for analyzing collected emotional data and identifying specific emotional states,

[0804] A means for generating and adapting environmental effects within a virtual space based on the analysis results,

[0805] A means of monitoring the observer's emotional response in real time and dynamically adjusting the environmental presentation,

[0806] A trigger mechanism for activating special effects based on the behavior of participants and the emotional responses of observers,

[0807] A system that includes this.

[0808] (Claim 2)

[0809] The system according to claim 1, comprising means for setting performance elements generated using a specific combat engine in a virtual space.

[0810] (Claim 3)

[0811] The system according to claim 1, comprising means for proposing and optimizing performance based on external input, utilizing a generative AI model.

[0812] "Application example 2 when combining with an emotional engine"

[0813] (Claim 1)

[0814] Means of collecting information on competitive events,

[0815] A means of acquiring and analyzing viewer emotional data,

[0816] A means for automatically generating an arena in a virtual space based on the analysis results,

[0817] A means of analyzing viewer feedback during the event and dynamically adjusting the arena's presentation,

[0818] A means of dynamically displaying information within a virtual store in response to the viewer's emotions,

[0819] A system that includes this.

[0820] (Claim 2)

[0821] The system according to claim 1, comprising means for setting visual elements of a virtual space generated based on the theme of a competitive event.

[0822] (Claim 3)

[0823] The system according to claim 1, comprising means for triggering special effects based on participant performance and viewer reactions. [Explanation of Symbols]

[0824] 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 on athletic events, A means of analyzing the spatial structure based on viewer interest data and the status of events, A means for automatically generating a space in a virtual environment based on the analysis results, A means of analyzing viewer opinions during the event and dynamically adjusting the spatial presentation, A means of suggesting the optimal virtual environment based on the user's past viewing data and preferences, A method for automatically adjusting visual and auditory effects by utilizing a generative AI model with prompt text, A means of providing users with a personalized virtual stadium experience through smart devices, A system that includes this.

2. The system according to claim 1, comprising means for setting the visual characteristics of a virtual environment generated based on the theme of a competitive event.

3. The system according to claim 1, comprising means for activating special effects based on the activities of participants and the reactions of viewers.

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