system

Abstract

We provide the system. [Solution] Means of collecting past match data, A method for analyzing collected match data and calculating player performance indicators, A means for generating avatar data that mimics the abilities of a player based on the analysis results, A means of storing the generated avatar data in a database, A means of providing users with an interactive competition simulation using player avatar data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional sports games, the abilities of players and the flow of the game deviate from the actual performance, lacking realism, so it has been difficult for users to obtain an immersive experience. Also, the abilities of game players are often fixed and cannot reflect the latest player data in many cases. As a result, there is a problem that the needs of sports fans and game players cannot be fully satisfied.

Means for Solving the Problems

[0005] This invention provides a system that generates avatar data that mimics the abilities of actual professional athletes by collecting past match data, analyzing that data, and calculating player performance indicators. The generated avatar data is stored in a database, and users can select and manipulate it in the game to experience a realistic competition simulation. This allows users to enjoy a realistic sports game experience based on the latest player data.

[0006] "Past match data" refers to record information of matches played in the past in professional sports, including details such as player statistics and match results.

[0007] "Means of collection" refers to methods and devices for acquiring specific data and making it available for storage.

[0008] "Means for analyzing and calculating player performance indicators" refers to methods and devices that perform the process of analyzing collected data and quantifying players' abilities and characteristics.

[0009] "Avatar data" refers to virtual player information generated by mimicking actual players, and is digital data that represents the player's abilities and characteristics.

[0010] "Means of storing in a database" refers to methods and devices for systematically managing and storing generated or acquired data.

[0011] "Means of providing users with interactive competition simulations" refers to methods and devices that create an environment in which users can operate themselves and experience a simulated competition in real time. [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, a 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, a 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, a 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, a 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 applied 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] In the system implementing this invention, the server, terminal, and user each play a specific role and work together in cooperation.

[0034] Server Functions

[0035] The server first accesses the official databases of professional sports leagues such as NPB and MLB, and periodically collects historical game data. This is extensive data, including individual player statistics and detailed game results. Next, a data processing algorithm using machine learning is applied to analyze this collected data. In this analysis process, the server quantifies each player's abilities and characteristics and calculates performance indicators. Subsequently, it generates avatar data based on the analysis results and stores this data in a highly efficient database. The server keeps the specified avatar data ready to be sent in response to access requests from users.

[0036] Device functions

[0037] When a user launches the game, the device communicates with the server to download the necessary game data and displays the interface. Here, the user can select players and teams, and the device retrieves corresponding avatar data from the server based on their selections. Based on the avatar data, the device generates player characters in the game and accurately recreates the match environment. The movements of the players controlled by the user and the flow of the match are processed in real time, providing a realistic simulation based on the characteristics of actual players.

[0038] User actions

[0039] At the start of the game, users can choose their favorite players and teams. After selecting players, the game begins, and users can perform various actions using their chosen players. For example, if a pitcher is selected, the user can give detailed instructions on pitching control and pitch type; if a batter is selected, the user can adjust batting timing and swing direction. The flow of the game is realistically reproduced based on the characteristics of the players, and users will develop strategies accordingly. After the game ends, users can review player statistics and game highlights, and use this information to improve their strategies for the next game.

[0040] Thus, the system based on the present invention provides users with a realistic sports experience, bringing a level of immersion and enjoyment not found in conventional sports games.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server connects to the official databases of professional sports leagues and collects match data for players from the past 10 years. This data includes match dates and times, player statistics, opponents, and match results.

[0044] Step 2:

[0045] The server cleanses the collected data, removing missing and outlier values ​​to prepare it for analysis. This process also verifies data integrity and performs optimization.

[0046] Step 3:

[0047] The server uses machine learning models to calculate player performance metrics based on the organized data. The models quantify the players' abilities such as speed, power, and technique, and create profiles.

[0048] Step 4:

[0049] The server generates player avatar data based on calculated performance metrics. This data virtually reproduces the player's characteristics and abilities.

[0050] Step 5:

[0051] The server saves the generated avatar data to a database so that users can access it later. The saved data is indexed by player ID.

[0052] Step 6:

[0053] When the game is launched, the device connects to the server, retrieves information about currently available players and teams, and displays it to the user.

[0054] Step 7:

[0055] The user selects the players or teams they want to challenge in the game. The selected information is sent to the server as a request via the device.

[0056] Step 8:

[0057] The server sends player avatar data corresponding to the user's request to the terminal. The terminal generates a player character based on the received data and displays it on the game screen.

[0058] Step 9:

[0059] Users control in-game matches that take place in real time. Player actions are realistically reproduced on the device based on their characteristics, and the system reacts instantly to every action the user takes.

[0060] Step 10:

[0061] The terminal displays player data and match statistics to the user after the match ends. This information can be used to develop strategies for the next match.

[0062] (Example 1)

[0063] 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."

[0064] Conventional competition simulation systems have struggled to efficiently collect past competition data and conduct detailed analysis. Furthermore, providing users with a realistic, real-time competition experience that reflects participant characteristics has been difficult. Additionally, there has been a lack of technical means to perform more advanced analysis and simulations by incorporating generative AI models into the information gathering process.

[0065] 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.

[0066] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating performance indicators for participants, and means for generating virtual data that mimics the characteristics of the participants based on the analysis results. This enables users to have an interactive competition simulation experience and allows for advanced, real-time simulations based on real-world data.

[0067] "Past competition data" refers to information about results, records, and participant performance from previously held competition events.

[0068] "Means of collection" refers to the methods and technologies used to accumulate and preserve specific information.

[0069] "Means of analysis" refers to methods and techniques for analyzing data to derive regularities and characteristics.

[0070] "Participant performance indicators" refer to numerical values ​​or indicators that represent the technical skills and abilities of individual participants in a competition.

[0071] "Virtual data" refers to data created through calculations and simulations based on actual data.

[0072] An "information base" refers to a system or structured data storage area used to aggregate and manage various types of data and information.

[0073] An "interactive competition simulation experience" refers to a virtual environment where users can interactively experience a competition from the perspective of a participant.

[0074] "Generative AI models" refer to artificial intelligence technologies that learn from large amounts of data to automate or optimize specific tasks.

[0075] A "prompt" refers to a text instruction entered into a generative AI model to cause it to perform a specific action or task.

[0076] The system for implementing this invention involves a server, terminal, and user each playing a specific role and working in cooperation. The server first accesses a database related to past competition events via a network connection and collects competition data. For data collection, it uses HTTP requests, for example, with the "requests" library.

[0077] The server analyzes the collected data using machine learning algorithms. Libraries such as "scikit-learn" and "TENSORFLOW®" are used for the analysis. This analysis allows for the calculation of participant performance metrics, making it possible to accurately quantify the characteristics of each participant.

[0078] Next, the server generates virtual data, or avatar data, that mimics the characteristics of the participants based on the analysis results. This avatar data is stored in a database such as PostgreSQL so that it can be accessed quickly later.

[0079] The terminal downloads necessary data from the server when the user launches the game. A GUI toolkit such as "PyQt" is used for the data interface. The terminal generates a virtual environment with the user's selected participants and teams, providing an interactive simulated game experience. Player characters and match situations are reproduced in real time, allowing users to enjoy an interactive experience.

[0080] As a concrete example, the server inputs the prompt message "Use data from over 200 games to evaluate batting performance using a machine learning model and predict the success rate of each participant" into the generating AI model, enabling more accurate analysis. This allows the user to experience a realistic simulation of the game.

[0081] Through this system, users can develop competitive strategies based on statistical information and performance analysis, and then apply them to their next gameplay.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server accesses a database of past competitions via the network. It uses an API to retrieve data related to past competition events. In this step, queries to the database are input, and competition data is retrieved as output. The Python library "requests" is used to perform the specific operation of collecting match data in JSON format.

[0085] Step 2:

[0086] The server applies machine learning algorithms to analyze the collected competition data. The input to this analysis is the collected competition data, and the output is quantified data of the players' performance metrics and characteristics. Using "TensorFlow" and "scikit-learn," the server performs the specific operation of training a model that evaluates the participants' hitting and defensive performance based on the data.

[0087] Step 3:

[0088] The server generates virtual data that mimics the characteristics of the participants based on the analysis results. The input is the analysis results from the previous step, and the output is avatar data used in the game. The server then performs the specific action of saving the generated avatar data to a database system such as PostgreSQL.

[0089] Step 4:

[0090] The device downloads the necessary game data from the server when the user launches the game. It takes the user's selection (participants or team) as input and outputs the corresponding avatar data. The specific operation of displaying the user interface is performed using "PyQt".

[0091] Step 5:

[0092] The device generates a player character within the game based on acquired avatar data and performs a simulation. The input is avatar data, and the output is an interactive game simulation experience. It uses a 3D modeling engine to perform specific actions that reproduce the game situation in real time.

[0093] Step 6:

[0094] Users control players and teams within the game to advance the match. The match progression is output based on user input (e.g., selection of actions and strategies). Users use a controller to perform actions in real time, executing specific actions to strategically advance the match.

[0095] Step 7:

[0096] After the match ends, the server displays match statistics and performance analysis to the user. Match data is the input, and statistics and analysis results are provided to the user as output. The server retrieves match results from the database and performs specific actions such as displaying highlights and analysis results on the user interface.

[0097] (Application Example 1)

[0098] 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."

[0099] In recent years, there has been a growing demand for more immersive and interactive sports experiences. However, traditional simulation games and virtual experiences lack realism and fail to meet user expectations. Furthermore, the limited visualization of product trials in virtual space makes it difficult for users to deeply understand product characteristics and applicability. This invention overcomes these shortcomings of conventional systems and provides an environment that supports purchasing decisions with a high degree of realism.

[0100] 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.

[0101] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating participant performance indicators, means for generating avatar data that mimics the participant's abilities based on the analysis results, means for storing the generated avatar data in an information storage device, means for providing users with an interactive competition simulation experience, and means for visualizing a simulated product usage experience within a virtual store. This enables users to obtain a detailed and realistic experience in real time, realizing a new type of virtual experience that integrates sports experience and product trial.

[0102] "Competition data" refers to a collection of information about past matches and performances in professional sports leagues.

[0103] "Participant avatar data" refers to the data of a virtual character generated based on the analyzed abilities and characteristics of the participants.

[0104] An "information storage device" is a physical or virtual device used to store and manage digital information.

[0105] An "interactive competition simulation experience" is a two-way system that allows users to control and experience a competition in a virtual space in real time.

[0106] A "virtual store" is a platform for electronic buying and selling that exists on the internet.

[0107] "Simulated product usage experience" is a method of visualizing a user's experience using a product in a virtual environment as if they were actually using it.

[0108] The system of this invention achieves its functionality through the coordinated efforts of the server, terminal, and user components.

[0109] The server first automatically collects competition data from official APIs of professional sports leagues and other sources, and periodically imports it into a database. Next, the collected data is processed by data analysis algorithms using Python libraries (e.g., NumPy, Pandas, Scikit-Learn) to quantify participants' performance indicators and abilities. Based on these analysis results, participant avatar data is generated and stored efficiently in the information storage device. The server also provides this avatar data to users upon request.

[0110] The device uses a game engine such as Unity to visualize avatar data and provide users with an interactive, simulated game experience. When a user launches the game using the device, it communicates with a server to retrieve necessary data and generates a 3D environment in real time. Within this environment, users can visually see their avatar trying out products in a virtual store. This process is also performed and visualized through smartphones and smart glasses.

[0111] Users can select specific participants or groups to simulate product trials in a virtual store. This selection is made intuitively using a terminal interface. For example, a user who wants to try out new basketball shoes can apply player avatar data and see how they jump and move in the shoes in a virtual environment.

[0112] For example, if the server receives the prompt "Visualize in 3D a player avatar wearing the latest model of soccer cleats and scoring a goal in the virtual sports store app," it will generate a simulation based on the specified data. This experience helps users understand product characteristics better and serves as a reference for purchasing decisions.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server accesses the official APIs of professional sports leagues to retrieve competition data. This input data mainly consists of past match results and player performance information. The retrieved data is temporarily stored and used for later data analysis. This process is performed periodically, and new data is continuously added.

[0116] Step 2:

[0117] The server analyzes the acquired competition data using Python data analysis libraries (NumPy, Pandas, Scikit-Learn). In this step, player abilities and performance indicators are extracted as numerical data. For example, players' batting averages and pitching speeds are calculated. These analysis results form the basis for avatar generation later on.

[0118] Step 3:

[0119] The server generates participant avatar data based on the analysis results. This avatar is a virtual character that mimics the characteristics of the player and is converted into a data format based on the analysis results. The generated avatar data is stored in a highly efficient information storage device, ready for subsequent access requests.

[0120] Step 4:

[0121] The device retrieves avatar data for the player or organization selected by the user from the server. In this step, the selection is made through the application interface, and the corresponding data is downloaded. Based on this input, the device prepares to visualize the player avatar.

[0122] Step 5:

[0123] The device uses the Unity game engine to generate a 3D environment in real time, providing users with an interactive simulated competitive experience. In this process, the virtual environment is designed based on acquired avatar data, and the user interacts with it through the device. Dynamic visual effects are rendered in response to user input.

[0124] Step 6:

[0125] Users access a virtual store through their device and experience a simulated product usage. In this processing step, a combination of product information and player avatar data is visually presented. For example, a player avatar wearing new sneakers and dribbling is displayed on the screen, serving to support the user's purchase decision.

[0126] 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.

[0127] This invention provides a system that combines player avatar data and user emotional states in real time in professional sports games. This system functions through the coordinated operation of a server, terminal, and user.

[0128] Server Functions

[0129] The server first collects historical professional sports match data and applies machine learning algorithms to analyze player abilities. The analyzed data is generated as player avatar data and stored in a database. Furthermore, the server also manages an emotion engine database and integrates sensors and AI models that can receive and analyze the user's emotional state in real time.

[0130] Device functions

[0131] The device communicates with the server when the user launches the game, retrieves player avatar data, and presents it to the user. When the user selects a player or team, the corresponding avatar data is reflected in the game. Furthermore, the device is equipped with an emotion engine that can infer emotions in real time from the user's voice and facial expression data. The device sends this emotion data to the server, which is used for simulations within the game.

[0132] User actions

[0133] During gameplay, users control player avatars to progress through the competition. An emotion engine detects the user's emotions during gameplay (e.g., joy, tension, frustration), and this data is analyzed in real time. Based on the analysis results, the in-game audio, graphics, and even the difficulty of the match are dynamically adjusted. This process provides a personalized gaming experience tailored to the user's emotions.

[0134] For example, if a user is in a state of excitement after winning an MLB game and hitting many home runs, the system will sense this excitement and react by increasing the volume of the crowd's cheers or adding effects to the graphics. In this way, by dynamically changing the game experience in response to the user's emotional changes, it becomes possible to provide a more immersive and realistic sports simulation.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server collects historical data from professional sports matches and performs data cleansing to prepare the data for analysis.

[0138] Step 2:

[0139] The server uses machine learning to analyze collected match data and calculate player performance metrics. This generates detailed avatar data tailored to each player.

[0140] Step 3:

[0141] The server stores the generated player avatar data in a database, preparing it for future access and searching.

[0142] Step 4:

[0143] The device connects to the server when the game starts and retrieves avatar data based on the user's player and team selections.

[0144] Step 5:

[0145] The device uses acquired avatar data to realistically recreate player characters within the game. The players and teams selected by the user are reflected on the game screen.

[0146] Step 6:

[0147] The emotion engine installed in the device monitors the user's voice and facial expression data in real time, inferring and analyzing their emotional state.

[0148] Step 7:

[0149] During a match, users control player avatars to perform actions such as throwing and hitting the ball.

[0150] Step 8:

[0151] The device uses an emotion engine to analyze the emotions the user expresses during a match and sends that information to the server.

[0152] Step 9:

[0153] The server dynamically adjusts the difficulty and presentation of the match (for example, audience reactions and sound effects) based on the transmitted emotion data.

[0154] Step 10:

[0155] The device changes the visual and auditory elements within the game based on instructions from the server, providing the user with an emotionally responsive gaming experience.

[0156] Step 11:

[0157] After a match ends, users can check statistics and match highlights on their devices to plan their next gameplay.

[0158] (Example 2)

[0159] 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".

[0160] Existing professional sports game systems do not adequately implement real-time interaction that takes into account the user's emotional state. As a result, users often lack immersion during gameplay, and the game experience tends to become monotonous. To solve this problem, there is a need for technology that can detect user emotions in real time and dynamically adjust the game environment accordingly.

[0161] 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.

[0162] In this invention, the server includes means for collecting and analyzing past match data, means for acquiring and analyzing the user's emotional state in real time, and means for dynamically adjusting the game simulation based on the analyzed emotional data. This enables a dynamic and interactive game experience that responds to the user's emotions.

[0163] "Past match data" refers to information related to the results of various matches in sports competitions, as well as the performance of individuals and teams.

[0164] "Avatar data" refers to a collection of digital information used to imitate and represent athletes in the digital space.

[0165] "User emotional state" refers to information that indicates the user's psychological or emotional state during gameplay.

[0166] "Means of acquiring and analyzing data in real time" refers to a system or method for instantly acquiring user emotional data and performing analysis based on that information.

[0167] "Means of dynamic adjustment" refers to methods and technologies for adjusting the operation and display content of a system on the fly based on the user's emotional state and other data.

[0168] "Competitive simulation" refers to a digital experience that simulates the conditions of a sports competition, allowing users to play in a virtual environment.

[0169] This invention relates to a system for realizing dynamic sports competition simulations that take user emotions into consideration. This system mainly consists of three elements: a server, a terminal, and a user.

[0170] The server first collects historical professional sports match data from various sources. Web scraping tools and APIs are often used for data collection. The server then analyzes the collected data using machine learning algorithms. Machine learning libraries such as TensorFlow and PyTorch can be used for this analysis. Based on this process, avatar data that mimics the abilities of the players is generated and stored in an information storage device.

[0171] The device communicates with the server when the user launches the game and retrieves player avatar data. The device also uses a microphone and camera to capture the user's voice and facial expressions in real time and infer their emotional state. A voice API is used for voice recognition, and an image processing library is used for facial expression recognition. The device sends this emotional data to the server for use in the game simulation.

[0172] During gameplay, users control player avatars and progress through the competition within the simulation. The user's emotions are detected by an emotion engine, and this data is analyzed on the server in real time. Based on the analyzed emotion data, the in-game audio, graphics, and even the difficulty of the competition are dynamically adjusted. For example, if a user is winning an MLB game and is excited after hitting many home runs, the system detects their emotion and processes it by increasing the volume of the crowd's cheers or adding effects to the graphics.

[0173] Here's an example of a prompt message to input into a generative AI model: "Analyze the data of a player who hit three home runs in yesterday's game and train an AI model to use in today's game. Generate data to provide a game experience that also integrates real-time emotional response based on this player's abilities."

[0174] Thus, the present invention provides a dynamic game environment that incorporates the user's emotions, thereby realizing a sports simulation that gives the user a deep sense of immersion.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server collects historical sports match data using web scraping tools and APIs. This data includes player statistics, match results, and team statistics. The collected data undergoes preprocessing, such as removing unnecessary data and cleaning the data, and is then converted into an analyzable format.

[0178] Step 2:

[0179] The server inputs the collected data into a machine learning algorithm to evaluate the players' performance. In this step, libraries such as TensorFlow and PyTorch are used to train the model and calculate the players' ability scores. The resulting ability scores are generated as player avatar data and stored in an information storage device.

[0180] Step 3:

[0181] The device communicates with the server when the user launches the game and retrieves player avatar data. When the user selects a player or team, that player's avatar data is displayed in the game. The input is the selection of player options, and the output is the visual display of the player's avatar.

[0182] Step 4:

[0183] The device acquires the user's voice and facial expressions in real time and analyzes their emotional state using an emotion engine. The inputs used are the user's voice and image data, which are collected through the microphone and camera. The data is analyzed using voice APIs and image processing libraries, and emotional data is obtained as output. This data is based on the user's heart rate, changes in facial expressions, and other factors.

[0184] Step 5:

[0185] The device sends analyzed emotion data to the server, which then dynamically adjusts elements within the game based on that data. Emotion data is received by the server as input, and the game's audio and graphics are adjusted as output. For example, if the user is excited, sound effects and visual effects are enhanced.

[0186] Step 6:

[0187] Users experience the finely tuned in-game elements and control their avatars to progress through the competition. Through this process, users can enjoy a individually customized game environment. The output is an interactive and immersive gaming experience.

[0188] (Application Example 2)

[0189] 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".

[0190] Modern sports viewing experiences often lack immersion because they don't dynamically adapt to viewer emotions or real-time game situations. Furthermore, there's a lack of accurate and interactive presentations based on the athletes' performances, and there's a need to provide viewers with a deeper sense of immersion.

[0191] 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.

[0192] In this invention, the server includes means for analyzing past competition data and calculating an evaluation index for athletes, means for generating avatar data that mimics the characteristics of athletes based on the analysis results, and means for sensing the user's emotional state and dynamically adjusting audio and video content based on that data. This makes it possible to provide a personalized and dynamic viewing experience that responds to the user's emotions and the situation of the match.

[0193] "Competition data" refers to data that includes all information related to past competitions, such as player performance, match results, and tactical trends.

[0194] "Athlete" refers to a person or group that participates in a particular competition and influences its outcome.

[0195] An "evaluation index" is a standard or numerical value used to quantitatively evaluate an athlete's performance, measuring their skills and achievements.

[0196] "Avatar data" refers to digitized models that mimic the characteristics and movements of athletes, and are used in competition simulations.

[0197] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a particular moment, and includes feelings such as excitement, joy, and tension.

[0198] "Audio and video content" refers to the collective audio tracks and visual images provided to the viewer, which may change in real time.

[0199] "Dynamic adjustment" refers to the process of changing content and expression in real time in response to changes in circumstances and conditions.

[0200] The system that realizes this invention consists of a server, a terminal, and user interaction. The server first collects past competition data and uses machine learning algorithms to analyze the characteristics of the competitors in detail. Based on the analyzed data, the competitor's avatar data is generated and stored in a storage device.

[0201] When a user selects a sport to watch, the device communicates with a server to obtain the necessary athlete avatar data. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice and facial expression data in real time. This engine uses the camera and microphone to sense the user's emotional state and sends that data to the server.

[0202] Users can enjoy a personalized viewing experience during competitions, with dynamically adjusted video feeds and audio content. Specifically, video editing and audio enhancement are performed in real time based on the user's emotional information, providing a more immersive experience. For example, if a user is moved by a particular athlete's outstanding play, that emotion can be emphasized through visual and auditory effects.

[0203] Hardware such as smartphones and VR devices are used, while software such as audio and video processing libraries (e.g., FFmpeg) and machine learning frameworks (e.g., TensorFlow, PyTorch) are utilized.

[0204] For example, when a user is watching a soccer match and their favorite team scores a goal, the system can detect their excitement level and, accordingly, enhance the commentator's remarks and the cheers of the crowd, providing a more thrilling viewing experience.

[0205] An example of a prompt might be, "When a user is watching a soccer match on their smartphone and their team scores a goal, how do you sense the user's emotions and change the effect accordingly?"

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server collects historical competition data. This data, including competitor performance and match results, is retrieved from online databases and archived videos. The collected data then serves as input for analysis by machine learning algorithms.

[0209] Step 2:

[0210] The server analyzes the collected competition data and calculates performance metrics for the athletes. Here, machine learning models are applied to process the data, quantifying the athletes' characteristics and performance tendencies. The output is the analyzed performance metrics.

[0211] Step 3:

[0212] Based on the analysis results, the server generates avatar data that mimics the characteristics of the competitor. This avatar data reflects the competitor's features in a digital model and is stored in memory. This makes it possible to use the competitor avatar.

[0213] Step 4:

[0214] The device communicates with the server based on the sport selected by the user and retrieves the necessary avatar data. This data is used as input information to run a dynamic match simulation on the device.

[0215] Step 5:

[0216] The device uses an emotion engine to acquire and analyze the user's voice and facial expression data in real time. It reads the voice and facial expressions through the camera and microphone, and estimates the user's emotional state using a generative AI model. The output is the estimated emotion data.

[0217] Step 6:

[0218] The server receives sentiment data transmitted from the terminal and dynamically adjusts the audio and video content based on that data. Specifically, it adds video effects and emphasizes audio comments to provide a viewing experience optimized for the user. The output of this step is the adjusted audio and video feed.

[0219] Step 7:

[0220] Users gain a more immersive viewing experience through optimized audio and video feeds. Changes in visual and auditory effects enhance the sense of realism in the match. The user's experience is then fed back into the emotion engine as feedback.

[0221] 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.

[0222] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] In the system implementing this invention, the server, terminal, and user each play a specific role and work together in cooperation.

[0238] Server Functions

[0239] The server first accesses the official databases of professional sports leagues such as NPB and MLB, and periodically collects historical game data. This is extensive data, including individual player statistics and detailed game results. Next, a data processing algorithm using machine learning is applied to analyze this collected data. In this analysis process, the server quantifies each player's abilities and characteristics and calculates performance indicators. Subsequently, it generates avatar data based on the analysis results and stores this data in a highly efficient database. The server keeps the specified avatar data ready to be sent in response to access requests from users.

[0240] Device functions

[0241] When a user launches the game, the device communicates with the server to download the necessary game data and displays the interface. Here, the user can select players and teams, and the device retrieves corresponding avatar data from the server based on their selections. Based on the avatar data, the device generates player characters in the game and accurately recreates the match environment. The movements of the players controlled by the user and the flow of the match are processed in real time, providing a realistic simulation based on the characteristics of actual players.

[0242] User actions

[0243] At the start of the game, users can choose their favorite players and teams. After selecting players, the game begins, and users can perform various actions using their chosen players. For example, if a pitcher is selected, the user can give detailed instructions on pitching control and pitch type; if a batter is selected, the user can adjust batting timing and swing direction. The flow of the game is realistically reproduced based on the characteristics of the players, and users will develop strategies accordingly. After the game ends, users can review player statistics and game highlights, and use this information to improve their strategies for the next game.

[0244] Thus, the system based on the present invention provides users with a realistic sports experience, bringing a level of immersion and enjoyment not found in conventional sports games.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The server connects to the official databases of professional sports leagues and collects match data for players from the past 10 years. This data includes match dates and times, player statistics, opponents, and match results.

[0248] Step 2:

[0249] The server cleanses the collected data, removing missing and outlier values ​​to prepare it for analysis. This process also verifies data integrity and performs optimization.

[0250] Step 3:

[0251] The server uses machine learning models to calculate player performance metrics based on the organized data. The models quantify the players' abilities such as speed, power, and technique, and create profiles.

[0252] Step 4:

[0253] The server generates player avatar data based on calculated performance metrics. This data virtually reproduces the player's characteristics and abilities.

[0254] Step 5:

[0255] The server saves the generated avatar data to a database so that users can access it later. The saved data is indexed by player ID.

[0256] Step 6:

[0257] When the game is launched, the device connects to the server, retrieves information about currently available players and teams, and displays it to the user.

[0258] Step 7:

[0259] The user selects the players or teams they want to challenge in the game. The selected information is sent to the server as a request via the device.

[0260] Step 8:

[0261] The server sends player avatar data corresponding to the user's request to the terminal. The terminal generates a player character based on the received data and displays it on the game screen.

[0262] Step 9:

[0263] Users control in-game matches that take place in real time. Player actions are realistically reproduced on the device based on their characteristics, and the system reacts instantly to every action the user takes.

[0264] Step 10:

[0265] The terminal displays player data and match statistics to the user after the match ends. This information can be used to develop strategies for the next match.

[0266] (Example 1)

[0267] 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."

[0268] Conventional competition simulation systems have struggled to efficiently collect past competition data and conduct detailed analysis. Furthermore, providing users with a realistic, real-time competition experience that reflects participant characteristics has been difficult. Additionally, there has been a lack of technical means to perform more advanced analysis and simulations by incorporating generative AI models into the information gathering process.

[0269] 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.

[0270] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating performance indicators for participants, and means for generating virtual data that mimics the characteristics of the participants based on the analysis results. This enables users to have an interactive competition simulation experience and allows for advanced, real-time simulations based on real-world data.

[0271] "Past competition data" refers to information about results, records, and participant performance from previously held competition events.

[0272] "Means of collection" refers to the methods and technologies used to accumulate and preserve specific information.

[0273] "Means of analysis" refers to methods and techniques for analyzing data to derive regularities and characteristics.

[0274] "Participant performance indicators" refer to numerical values ​​or indicators that represent the technical skills and abilities of individual participants in a competition.

[0275] "Virtual data" refers to data created through calculations and simulations based on actual data.

[0276] An "information base" refers to a system or structured data storage area used to aggregate and manage various types of data and information.

[0277] An "interactive competition simulation experience" refers to a virtual environment where users can interactively experience a competition from the perspective of a participant.

[0278] "Generative AI models" refer to artificial intelligence technologies that learn from large amounts of data to automate or optimize specific tasks.

[0279] A "prompt" refers to a text instruction entered into a generative AI model to cause it to perform a specific action or task.

[0280] The system for implementing this invention involves a server, terminal, and user each playing a specific role and working in cooperation. The server first accesses a database related to past competition events via a network connection and collects competition data. For data collection, it uses HTTP requests, for example, with the "requests" library.

[0281] The server analyzes the collected data using machine learning algorithms. Libraries such as "scikit-learn" and "TensorFlow" are used for this analysis. This analysis allows for the calculation of participant performance metrics, making it possible to accurately quantify the characteristics of each participant.

[0282] Next, the server generates virtual data, or avatar data, that mimics the characteristics of the participants based on the analysis results. This avatar data is stored in a database such as PostgreSQL so that it can be accessed quickly later.

[0283] The terminal downloads necessary data from the server when the user launches the game. A GUI toolkit such as "PyQt" is used for the data interface. The terminal generates a virtual environment with the user's selected participants and teams, providing an interactive simulated game experience. Player characters and match situations are reproduced in real time, allowing users to enjoy an interactive experience.

[0284] As a specific example, by the server inputting the prompt sentence "Use data of 200 or more matches to evaluate the hitting performance with a machine learning model and predict the success rate of each participant" into the generation AI model, more accurate analysis is realized. Thereby, a competitive simulation experience closer to reality can be provided to the user.

[0285] Through this system, the user can construct a competitive strategy based on statistical information and performance analysis and reflect it in the next game play.

[0286] The flow of the specific process in Example 1 will be described with reference to FIG. 11.

[0287] Step 1:

[0288] The server accesses the past competition database via the network. Using an API, it acquires data related to past competition events. In this step, the query to the database is the input, and the competition data is acquired as the output. Specific operations are performed to collect the game data in JSON format using the "requests" library in Python.

[0289] Step 2:

[0290] The server applies a machine learning algorithm to analyze the collected competition data. The input of this analysis is the collected competition data, and the output is the digitized data of the performance indicators and characteristics of the players. Specific operations are performed to train a model that evaluates the hitting and defensive performances of the participants based on the data using "TensorFlow" and "scikit - learn".

[0291] Step 3:

[0292] The server generates virtual data that mimics the characteristics of the participants based on the analysis results. The input is the analysis results from the previous step, and the output is avatar data used in the game. The server then performs the specific action of saving the generated avatar data to a database system such as PostgreSQL.

[0293] Step 4:

[0294] The device downloads the necessary game data from the server when the user launches the game. It takes the user's selection (participants or team) as input and outputs the corresponding avatar data. The specific operation of displaying the user interface is performed using "PyQt".

[0295] Step 5:

[0296] The device generates a player character within the game based on acquired avatar data and performs a simulation. The input is avatar data, and the output is an interactive game simulation experience. It uses a 3D modeling engine to perform specific actions that reproduce the game situation in real time.

[0297] Step 6:

[0298] Users control players and teams within the game to advance the match. The match progression is output based on user input (e.g., selection of actions and strategies). Users use a controller to perform actions in real time, executing specific actions to strategically advance the match.

[0299] Step 7:

[0300] After the match ends, the server displays match statistics and performance analysis to the user. Match data is the input, and statistics and analysis results are provided to the user as output. The server retrieves match results from the database and performs specific actions such as displaying highlights and analysis results on the user interface.

[0301] (Application Example 1)

[0302] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0303] In recent years, the demand for making sports experiences more immersive and interactive has been increasing. However, conventional simulation games and virtual experiences lack reality and do not meet users' expectations. Also, since the visualization of product trials in virtual spaces is limited, it is difficult for users to deeply understand the characteristics and applicability of products. The present invention eliminates the drawbacks of such conventional systems and provides an environment that supports purchase decision-making with a high degree of reality.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0305] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating performance indicators of participants, means for generating avatar data that mimics the capabilities of participants based on the analysis results, means for storing the generated avatar data in an information storage device, means for providing an interactive competition simulation experience to users, and means for visualizing a simulation experience of product use in a virtual store. As a result, users can obtain a detailed and realistic experience in real time, and a new type of virtual experience that integrates sports experience and product trial is realized.

[0306] "Competition data" is a collection of information on past matches and performances in professional sports leagues.

[0307] "Participant avatar data" is data of a virtual character generated based on the analyzed capabilities and characteristics of participants.

[0308] An "information storage device" is a physical or virtual device used to store and manage digital information.

[0309] An "interactive competition simulation experience" is a two-way system that allows users to control and experience a competition in a virtual space in real time.

[0310] A "virtual store" is a platform for electronic buying and selling that exists on the internet.

[0311] "Simulated product usage experience" is a method of visualizing a user's experience using a product in a virtual environment as if they were actually using it.

[0312] The system of this invention achieves its functionality through the coordinated efforts of the server, terminal, and user components.

[0313] The server first automatically collects competition data from official APIs of professional sports leagues and other sources, and periodically imports it into a database. Next, the collected data is processed by data analysis algorithms using Python libraries (e.g., NumPy, Pandas, Scikit-Learn) to quantify participants' performance indicators and abilities. Based on these analysis results, participant avatar data is generated and stored efficiently in the information storage device. The server also provides this avatar data to users upon request.

[0314] The device uses a game engine such as Unity to visualize avatar data and provide users with an interactive, simulated game experience. When a user launches the game using the device, it communicates with a server to retrieve necessary data and generates a 3D environment in real time. Within this environment, users can visually see their avatar trying out products in a virtual store. This process is also performed and visualized through smartphones and smart glasses.

[0315] Users can select specific participants or groups to simulate product trials in a virtual store. This selection is made intuitively using a terminal interface. For example, a user who wants to try out new basketball shoes can apply player avatar data and see how they jump and move in the shoes in a virtual environment.

[0316] For example, if the server receives the prompt "Visualize in 3D a player avatar wearing the latest model of soccer cleats and scoring a goal in the virtual sports store app," it will generate a simulation based on the specified data. This experience helps users understand product characteristics better and serves as a reference for purchasing decisions.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The server accesses the official APIs of professional sports leagues to retrieve competition data. This input data mainly consists of past match results and player performance information. The retrieved data is temporarily stored and used for later data analysis. This process is performed periodically, and new data is continuously added.

[0320] Step 2:

[0321] The server analyzes the acquired competition data using Python data analysis libraries (NumPy, Pandas, Scikit-Learn). In this step, player abilities and performance indicators are extracted as numerical data. For example, players' batting averages and pitching speeds are calculated. These analysis results form the basis for avatar generation later on.

[0322] Step 3:

[0323] The server generates participant avatar data based on the analysis results. This avatar is a virtual character that mimics the characteristics of the player and is converted into a data format based on the analysis results. The generated avatar data is stored in a highly efficient information storage device, ready for subsequent access requests.

[0324] Step 4:

[0325] The device retrieves avatar data for the player or organization selected by the user from the server. In this step, the selection is made through the application interface, and the corresponding data is downloaded. Based on this input, the device prepares to visualize the player avatar.

[0326] Step 5:

[0327] The device uses the Unity game engine to generate a 3D environment in real time, providing users with an interactive simulated competitive experience. In this process, the virtual environment is designed based on acquired avatar data, and the user interacts with it through the device. Dynamic visual effects are rendered in response to user input.

[0328] Step 6:

[0329] Users access a virtual store through their device and experience a simulated product usage. In this processing step, a combination of product information and player avatar data is visually presented. For example, a player avatar wearing new sneakers and dribbling is displayed on the screen, serving to support the user's purchase decision.

[0330] 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.

[0331] This invention provides a system that combines player avatar data and user emotional states in real time in professional sports games. This system functions through the coordinated operation of a server, terminal, and user.

[0332] Server Functions

[0333] The server first collects historical professional sports match data and applies machine learning algorithms to analyze player abilities. The analyzed data is generated as player avatar data and stored in a database. Furthermore, the server also manages an emotion engine database and integrates sensors and AI models that can receive and analyze the user's emotional state in real time.

[0334] Device functions

[0335] The device communicates with the server when the user launches the game, retrieves player avatar data, and presents it to the user. When the user selects a player or team, the corresponding avatar data is reflected in the game. Furthermore, the device is equipped with an emotion engine that can infer emotions in real time from the user's voice and facial expression data. The device sends this emotion data to the server, which is used for simulations within the game.

[0336] User actions

[0337] During gameplay, users control player avatars to progress through the competition. An emotion engine detects the user's emotions during gameplay (e.g., joy, tension, frustration), and this data is analyzed in real time. Based on the analysis results, the in-game audio, graphics, and even the difficulty of the match are dynamically adjusted. This process provides a personalized gaming experience tailored to the user's emotions.

[0338] For example, if a user is in a state of excitement after winning an MLB game and hitting many home runs, the system will sense this excitement and react by increasing the volume of the crowd's cheers or adding effects to the graphics. In this way, by dynamically changing the game experience in response to the user's emotional changes, it becomes possible to provide a more immersive and realistic sports simulation.

[0339] The following describes the processing flow.

[0340] Step 1:

[0341] The server collects historical data from professional sports matches and performs data cleansing to prepare the data for analysis.

[0342] Step 2:

[0343] The server uses machine learning to analyze collected match data and calculate player performance metrics. This generates detailed avatar data tailored to each player.

[0344] Step 3:

[0345] The server stores the generated player avatar data in a database, preparing it for future access and searching.

[0346] Step 4:

[0347] The device connects to the server when the game starts and retrieves avatar data based on the user's player and team selections.

[0348] Step 5:

[0349] The device uses acquired avatar data to realistically recreate player characters within the game. The players and teams selected by the user are reflected on the game screen.

[0350] Step 6:

[0351] The emotion engine installed in the device monitors the user's voice and facial expression data in real time, inferring and analyzing their emotional state.

[0352] Step 7:

[0353] During a match, users control player avatars to perform actions such as throwing and hitting the ball.

[0354] Step 8:

[0355] The device uses an emotion engine to analyze the emotions the user expresses during a match and sends that information to the server.

[0356] Step 9:

[0357] The server dynamically adjusts the difficulty and presentation of the match (for example, audience reactions and sound effects) based on the transmitted emotion data.

[0358] Step 10:

[0359] The device changes the visual and auditory elements within the game based on instructions from the server, providing the user with an emotionally responsive gaming experience.

[0360] Step 11:

[0361] After a match ends, users can check statistics and match highlights on their devices to plan their next gameplay.

[0362] (Example 2)

[0363] 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".

[0364] Existing professional sports game systems do not adequately implement real-time interaction that takes into account the user's emotional state. As a result, users often lack immersion during gameplay, and the game experience tends to become monotonous. To solve this problem, there is a need for technology that can detect user emotions in real time and dynamically adjust the game environment accordingly.

[0365] 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.

[0366] In this invention, the server includes means for collecting and analyzing past match data, means for acquiring and analyzing the user's emotional state in real time, and means for dynamically adjusting the game simulation based on the analyzed emotional data. This enables a dynamic and interactive game experience that responds to the user's emotions.

[0367] "Past match data" refers to information related to the results of various matches in sports competitions, as well as the performance of individuals and teams.

[0368] "Avatar data" refers to a collection of digital information used to imitate and represent athletes in the digital space.

[0369] "User emotional state" refers to information that indicates the user's psychological or emotional state during gameplay.

[0370] "Means of acquiring and analyzing data in real time" refers to a system or method for instantly acquiring user emotional data and performing analysis based on that information.

[0371] "Means of dynamic adjustment" refers to methods and technologies for adjusting the operation and display content of a system on the fly based on the user's emotional state and other data.

[0372] "Competitive simulation" refers to a digital experience that simulates the conditions of a sports competition, allowing users to play in a virtual environment.

[0373] This invention relates to a system for realizing dynamic sports competition simulations that take user emotions into consideration. This system mainly consists of three elements: a server, a terminal, and a user.

[0374] The server first collects historical professional sports match data from various sources. Web scraping tools and APIs are often used for data collection. The server then analyzes the collected data using machine learning algorithms. Machine learning libraries such as TensorFlow and PyTorch can be used for this analysis. Based on this process, avatar data that mimics the abilities of the players is generated and stored in an information storage device.

[0375] The device communicates with the server when the user launches the game and retrieves player avatar data. The device also uses a microphone and camera to capture the user's voice and facial expressions in real time and infer their emotional state. A voice API is used for voice recognition, and an image processing library is used for facial expression recognition. The device sends this emotional data to the server for use in the game simulation.

[0376] During gameplay, users control player avatars and progress through the competition within the simulation. The user's emotions are detected by an emotion engine, and this data is analyzed on the server in real time. Based on the analyzed emotion data, the in-game audio, graphics, and even the difficulty of the competition are dynamically adjusted. For example, if a user is winning an MLB game and is excited after hitting many home runs, the system detects their emotion and processes it by increasing the volume of the crowd's cheers or adding effects to the graphics.

[0377] Here's an example of a prompt message to input into a generative AI model: "Analyze the data of a player who hit three home runs in yesterday's game and train an AI model to use in today's game. Generate data to provide a game experience that also integrates real-time emotional response based on this player's abilities."

[0378] Thus, the present invention provides a dynamic game environment that incorporates the user's emotions, thereby realizing a sports simulation that gives the user a deep sense of immersion.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The server collects historical sports match data using web scraping tools and APIs. This data includes player statistics, match results, and team statistics. The collected data undergoes preprocessing, such as removing unnecessary data and cleaning the data, and is then converted into an analyzable format.

[0382] Step 2:

[0383] The server inputs the collected data into a machine learning algorithm to evaluate the players' performance. In this step, libraries such as TensorFlow and PyTorch are used to train the model and calculate the players' ability scores. The resulting ability scores are generated as player avatar data and stored in an information storage device.

[0384] Step 3:

[0385] The device communicates with the server when the user launches the game and retrieves player avatar data. When the user selects a player or team, that player's avatar data is displayed in the game. The input is the selection of player options, and the output is the visual display of the player's avatar.

[0386] Step 4:

[0387] The device acquires the user's voice and facial expressions in real time and analyzes their emotional state using an emotion engine. The inputs used are the user's voice and image data, which are collected through the microphone and camera. The data is analyzed using voice APIs and image processing libraries, and emotional data is obtained as output. This data is based on the user's heart rate, changes in facial expressions, and other factors.

[0388] Step 5:

[0389] The device sends analyzed emotion data to the server, which then dynamically adjusts elements within the game based on that data. Emotion data is received by the server as input, and the game's audio and graphics are adjusted as output. For example, if the user is excited, sound effects and visual effects are enhanced.

[0390] Step 6:

[0391] Users experience the finely tuned in-game elements and control their avatars to progress through the competition. Through this process, users can enjoy a individually customized game environment. The output is an interactive and immersive gaming experience.

[0392] (Application Example 2)

[0393] 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."

[0394] Modern sports viewing experiences often lack immersion because they don't dynamically adapt to viewer emotions or real-time game situations. Furthermore, there's a lack of accurate and interactive presentations based on the athletes' performances, and there's a need to provide viewers with a deeper sense of immersion.

[0395] 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.

[0396] In this invention, the server includes means for analyzing past competition data and calculating an evaluation index for athletes, means for generating avatar data that mimics the characteristics of athletes based on the analysis results, and means for sensing the user's emotional state and dynamically adjusting audio and video content based on that data. This makes it possible to provide a personalized and dynamic viewing experience that responds to the user's emotions and the situation of the match.

[0397] "Competition data" refers to data that includes all information related to past competitions, such as player performance, match results, and tactical trends.

[0398] "Athlete" refers to a person or group that participates in a particular competition and influences its outcome.

[0399] An "evaluation index" is a standard or numerical value used to quantitatively evaluate an athlete's performance, measuring their skills and achievements.

[0400] "Avatar data" refers to digitized models that mimic the characteristics and movements of athletes, and are used in competition simulations.

[0401] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a particular moment, and includes feelings such as excitement, joy, and tension.

[0402] "Audio and video content" refers to the collective audio tracks and visual images provided to the viewer, which may change in real time.

[0403] "Dynamic adjustment" refers to the process of changing content and expression in real time in response to changes in circumstances and conditions.

[0404] The system that realizes this invention consists of a server, a terminal, and user interaction. The server first collects past competition data and uses machine learning algorithms to analyze the characteristics of the competitors in detail. Based on the analyzed data, the competitor's avatar data is generated and stored in a storage device.

[0405] When a user selects a sport to watch, the device communicates with a server to obtain the necessary athlete avatar data. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice and facial expression data in real time. This engine uses the camera and microphone to sense the user's emotional state and sends that data to the server.

[0406] Users can enjoy a personalized viewing experience during competitions, with dynamically adjusted video feeds and audio content. Specifically, video editing and audio enhancement are performed in real time based on the user's emotional information, providing a more immersive experience. For example, if a user is moved by a particular athlete's outstanding play, that emotion can be emphasized through visual and auditory effects.

[0407] Hardware such as smartphones and VR devices are used, while software such as audio and video processing libraries (e.g., FFmpeg) and machine learning frameworks (e.g., TensorFlow, PyTorch) are utilized.

[0408] For example, when a user is watching a soccer match and their favorite team scores a goal, the system can detect their excitement level and, accordingly, enhance the commentator's remarks and the cheers of the crowd, providing a more thrilling viewing experience.

[0409] An example of a prompt might be, "When a user is watching a soccer match on their smartphone and their team scores a goal, how do you sense the user's emotions and change the effect accordingly?"

[0410] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0411] Step 1:

[0412] The server collects historical competition data. This data, including competitor performance and match results, is retrieved from online databases and archived videos. The collected data then serves as input for analysis by machine learning algorithms.

[0413] Step 2:

[0414] The server analyzes the collected competition data and calculates performance metrics for the athletes. Here, machine learning models are applied to process the data, quantifying the athletes' characteristics and performance tendencies. The output is the analyzed performance metrics.

[0415] Step 3:

[0416] Based on the analysis results, the server generates avatar data that mimics the characteristics of the competitor. This avatar data reflects the competitor's features in a digital model and is stored in memory. This makes it possible to use the competitor avatar.

[0417] Step 4:

[0418] The device communicates with the server based on the sport selected by the user and retrieves the necessary avatar data. This data is used as input information to run a dynamic match simulation on the device.

[0419] Step 5:

[0420] The device uses an emotion engine to acquire and analyze the user's voice and facial expression data in real time. It reads the voice and facial expressions through the camera and microphone, and estimates the user's emotional state using a generative AI model. The output is the estimated emotion data.

[0421] Step 6:

[0422] The server receives sentiment data transmitted from the terminal and dynamically adjusts the audio and video content based on that data. Specifically, it adds video effects and emphasizes audio comments to provide a viewing experience optimized for the user. The output of this step is the adjusted audio and video feed.

[0423] Step 7:

[0424] Users gain a more immersive viewing experience through optimized audio and video feeds. Changes in visual and auditory effects enhance the sense of realism in the match. The user's experience is then fed back into the emotion engine as feedback.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] [Third Embodiment]

[0429] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0430] 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.

[0431] 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).

[0432] 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.

[0433] 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.

[0434] 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).

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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".

[0441] In the system implementing this invention, the server, terminal, and user each play a specific role and work together in cooperation.

[0442] Server Functions

[0443] The server first accesses the official databases of professional sports leagues such as NPB and MLB, and periodically collects historical game data. This is extensive data, including individual player statistics and detailed game results. Next, a data processing algorithm using machine learning is applied to analyze this collected data. In this analysis process, the server quantifies each player's abilities and characteristics and calculates performance indicators. Subsequently, it generates avatar data based on the analysis results and stores this data in a highly efficient database. The server keeps the specified avatar data ready to be sent in response to access requests from users.

[0444] Device functions

[0445] When a user launches the game, the device communicates with the server to download the necessary game data and displays the interface. Here, the user can select players and teams, and the device retrieves corresponding avatar data from the server based on their selections. Based on the avatar data, the device generates player characters in the game and accurately recreates the match environment. The movements of the players controlled by the user and the flow of the match are processed in real time, providing a realistic simulation based on the characteristics of actual players.

[0446] User actions

[0447] At the start of the game, users can choose their favorite players and teams. After selecting players, the game begins, and users can perform various actions using their chosen players. For example, if a pitcher is selected, the user can give detailed instructions on pitching control and pitch type; if a batter is selected, the user can adjust batting timing and swing direction. The flow of the game is realistically reproduced based on the characteristics of the players, and users will develop strategies accordingly. After the game ends, users can review player statistics and game highlights, and use this information to improve their strategies for the next game.

[0448] Thus, the system based on the present invention provides users with a realistic sports experience, bringing a level of immersion and enjoyment not found in conventional sports games.

[0449] The following describes the processing flow.

[0450] Step 1:

[0451] The server connects to the official databases of professional sports leagues and collects match data for players from the past 10 years. This data includes match dates and times, player statistics, opponents, and match results.

[0452] Step 2:

[0453] The server cleanses the collected data, removing missing and outlier values ​​to prepare it for analysis. This process also verifies data integrity and performs optimization.

[0454] Step 3:

[0455] The server uses machine learning models to calculate player performance metrics based on the organized data. The models quantify the players' abilities such as speed, power, and technique, and create profiles.

[0456] Step 4:

[0457] The server generates player avatar data based on calculated performance metrics. This data virtually reproduces the player's characteristics and abilities.

[0458] Step 5:

[0459] The server saves the generated avatar data to a database so that users can access it later. The saved data is indexed by player ID.

[0460] Step 6:

[0461] When the game is launched, the device connects to the server, retrieves information about currently available players and teams, and displays it to the user.

[0462] Step 7:

[0463] The user selects the players or teams they want to challenge in the game. The selected information is sent to the server as a request via the device.

[0464] Step 8:

[0465] The server sends player avatar data corresponding to the user's request to the terminal. The terminal generates a player character based on the received data and displays it on the game screen.

[0466] Step 9:

[0467] Users control in-game matches that take place in real time. Player actions are realistically reproduced on the device based on their characteristics, and the system reacts instantly to every action the user takes.

[0468] Step 10:

[0469] The terminal displays player data and match statistics to the user after the match ends. This information can be used to develop strategies for the next match.

[0470] (Example 1)

[0471] 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."

[0472] Conventional competition simulation systems have struggled to efficiently collect past competition data and conduct detailed analysis. Furthermore, providing users with a realistic, real-time competition experience that reflects participant characteristics has been difficult. Additionally, there has been a lack of technical means to perform more advanced analysis and simulations by incorporating generative AI models into the information gathering process.

[0473] 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.

[0474] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating performance indicators for participants, and means for generating virtual data that mimics the characteristics of the participants based on the analysis results. This enables users to have an interactive competition simulation experience and allows for advanced, real-time simulations based on real-world data.

[0475] "Past competition data" refers to information about results, records, and participant performance from previously held competition events.

[0476] "Means of collection" refers to the methods and technologies used to accumulate and preserve specific information.

[0477] "Means of analysis" refers to methods and techniques for analyzing data to derive regularities and characteristics.

[0478] "Participant performance indicators" refer to numerical values ​​or indicators that represent the technical skills and abilities of individual participants in a competition.

[0479] "Virtual data" refers to data created through calculations and simulations based on actual data.

[0480] An "information base" refers to a system or structured data storage area used to aggregate and manage various types of data and information.

[0481] An "interactive competition simulation experience" refers to a virtual environment where users can interactively experience a competition from the perspective of a participant.

[0482] "Generative AI models" refer to artificial intelligence technologies that learn from large amounts of data to automate or optimize specific tasks.

[0483] A "prompt" refers to a text instruction entered into a generative AI model to cause it to perform a specific action or task.

[0484] The system for implementing this invention involves a server, terminal, and user each playing a specific role and working in cooperation. The server first accesses a database related to past competition events via a network connection and collects competition data. For data collection, it uses HTTP requests, for example, with the "requests" library.

[0485] The server analyzes the collected data using machine learning algorithms. Libraries such as "scikit-learn" and "TensorFlow" are used for this analysis. This analysis allows for the calculation of participant performance metrics, making it possible to accurately quantify the characteristics of each participant.

[0486] Next, the server generates virtual data, or avatar data, that mimics the characteristics of the participants based on the analysis results. This avatar data is stored in a database such as PostgreSQL so that it can be accessed quickly later.

[0487] The terminal downloads necessary data from the server when the user launches the game. A GUI toolkit such as "PyQt" is used for the data interface. The terminal generates a virtual environment with the user's selected participants and teams, providing an interactive simulated game experience. Player characters and match situations are reproduced in real time, allowing users to enjoy an interactive experience.

[0488] As a concrete example, the server inputs the prompt message "Use data from over 200 games to evaluate batting performance using a machine learning model and predict the success rate of each participant" into the generating AI model, enabling more accurate analysis. This allows the user to experience a realistic simulation of the game.

[0489] Through this system, users can develop competitive strategies based on statistical information and performance analysis, and then apply them to their next gameplay.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The server accesses a database of past competitions via the network. It uses an API to retrieve data related to past competition events. In this step, queries to the database are input, and competition data is retrieved as output. The Python library "requests" is used to perform the specific operation of collecting match data in JSON format.

[0493] Step 2:

[0494] The server applies machine learning algorithms to analyze the collected competition data. The input to this analysis is the collected competition data, and the output is quantified data of the players' performance metrics and characteristics. Using "TensorFlow" and "scikit-learn," the server performs the specific operation of training a model that evaluates the participants' hitting and defensive performance based on the data.

[0495] Step 3:

[0496] The server generates virtual data that mimics the characteristics of the participants based on the analysis results. The input is the analysis results from the previous step, and the output is avatar data used in the game. The server then performs the specific action of saving the generated avatar data to a database system such as PostgreSQL.

[0497] Step 4:

[0498] The device downloads the necessary game data from the server when the user launches the game. It takes the user's selection (participants or team) as input and outputs the corresponding avatar data. The specific operation of displaying the user interface is performed using "PyQt".

[0499] Step 5:

[0500] The device generates a player character within the game based on acquired avatar data and performs a simulation. The input is avatar data, and the output is an interactive game simulation experience. It uses a 3D modeling engine to perform specific actions that reproduce the game situation in real time.

[0501] Step 6:

[0502] Users control players and teams within the game to advance the match. The match progression is output based on user input (e.g., selection of actions and strategies). Users use a controller to perform actions in real time, executing specific actions to strategically advance the match.

[0503] Step 7:

[0504] After the match ends, the server displays match statistics and performance analysis to the user. Match data is the input, and statistics and analysis results are provided to the user as output. The server retrieves match results from the database and performs specific actions such as displaying highlights and analysis results on the user interface.

[0505] (Application Example 1)

[0506] 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."

[0507] In recent years, there has been a growing demand for more immersive and interactive sports experiences. However, traditional simulation games and virtual experiences lack realism and fail to meet user expectations. Furthermore, the limited visualization of product trials in virtual space makes it difficult for users to deeply understand product characteristics and applicability. This invention overcomes these shortcomings of conventional systems and provides an environment that supports purchasing decisions with a high degree of realism.

[0508] 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.

[0509] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating participant performance indicators, means for generating avatar data that mimics the participant's abilities based on the analysis results, means for storing the generated avatar data in an information storage device, means for providing users with an interactive competition simulation experience, and means for visualizing a simulated product usage experience within a virtual store. This enables users to obtain a detailed and realistic experience in real time, realizing a new type of virtual experience that integrates sports experience and product trial.

[0510] "Competition data" refers to a collection of information about past matches and performances in professional sports leagues.

[0511] "Participant avatar data" refers to the data of a virtual character generated based on the analyzed abilities and characteristics of the participants.

[0512] An "information storage device" is a physical or virtual device used to store and manage digital information.

[0513] An "interactive competition simulation experience" is a two-way system that allows users to control and experience a competition in a virtual space in real time.

[0514] A "virtual store" is a platform for electronic buying and selling that exists on the internet.

[0515] "Simulated product usage experience" is a method of visualizing a user's experience using a product in a virtual environment as if they were actually using it.

[0516] The system of this invention achieves its functionality through the coordinated efforts of the server, terminal, and user components.

[0517] The server first automatically collects competition data from official APIs of professional sports leagues and other sources, and periodically imports it into a database. Next, the collected data is processed by data analysis algorithms using Python libraries (e.g., NumPy, Pandas, Scikit-Learn) to quantify participants' performance indicators and abilities. Based on these analysis results, participant avatar data is generated and stored efficiently in the information storage device. The server also provides this avatar data to users upon request.

[0518] The device uses a game engine such as Unity to visualize avatar data and provide users with an interactive, simulated game experience. When a user launches the game using the device, it communicates with a server to retrieve necessary data and generates a 3D environment in real time. Within this environment, users can visually see their avatar trying out products in a virtual store. This process is also performed and visualized through smartphones and smart glasses.

[0519] Users can select specific participants or groups to simulate product trials in a virtual store. This selection is made intuitively using a terminal interface. For example, a user who wants to try out new basketball shoes can apply player avatar data and see how they jump and move in the shoes in a virtual environment.

[0520] For example, if the server receives the prompt "Visualize in 3D a player avatar wearing the latest model of soccer cleats and scoring a goal in the virtual sports store app," it will generate a simulation based on the specified data. This experience helps users understand product characteristics better and serves as a reference for purchasing decisions.

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The server accesses the official APIs of professional sports leagues to retrieve competition data. This input data mainly consists of past match results and player performance information. The retrieved data is temporarily stored and used for later data analysis. This process is performed periodically, and new data is continuously added.

[0524] Step 2:

[0525] The server analyzes the acquired competition data using Python data analysis libraries (NumPy, Pandas, Scikit-Learn). In this step, player abilities and performance indicators are extracted as numerical data. For example, players' batting averages and pitching speeds are calculated. These analysis results form the basis for avatar generation later on.

[0526] Step 3:

[0527] The server generates participant avatar data based on the analysis results. This avatar is a virtual character that mimics the characteristics of the player and is converted into a data format based on the analysis results. The generated avatar data is stored in a highly efficient information storage device, ready for subsequent access requests.

[0528] Step 4:

[0529] The device retrieves avatar data for the player or organization selected by the user from the server. In this step, the selection is made through the application interface, and the corresponding data is downloaded. Based on this input, the device prepares to visualize the player avatar.

[0530] Step 5:

[0531] The device uses the Unity game engine to generate a 3D environment in real time, providing users with an interactive simulated competitive experience. In this process, the virtual environment is designed based on acquired avatar data, and the user interacts with it through the device. Dynamic visual effects are rendered in response to user input.

[0532] Step 6:

[0533] Users access a virtual store through their device and experience a simulated product usage. In this processing step, a combination of product information and player avatar data is visually presented. For example, a player avatar wearing new sneakers and dribbling is displayed on the screen, serving to support the user's purchase decision.

[0534] 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.

[0535] This invention provides a system that combines player avatar data and user emotional states in real time in professional sports games. This system functions through the coordinated operation of a server, terminal, and user.

[0536] Server Functions

[0537] The server first collects historical professional sports match data and applies machine learning algorithms to analyze player abilities. The analyzed data is generated as player avatar data and stored in a database. Furthermore, the server also manages an emotion engine database and integrates sensors and AI models that can receive and analyze the user's emotional state in real time.

[0538] Device functions

[0539] The device communicates with the server when the user launches the game, retrieves player avatar data, and presents it to the user. When the user selects a player or team, the corresponding avatar data is reflected in the game. Furthermore, the device is equipped with an emotion engine that can infer emotions in real time from the user's voice and facial expression data. The device sends this emotion data to the server, which is used for simulations within the game.

[0540] User actions

[0541] During gameplay, users control player avatars to progress through the competition. An emotion engine detects the user's emotions during gameplay (e.g., joy, tension, frustration), and this data is analyzed in real time. Based on the analysis results, the in-game audio, graphics, and even the difficulty of the match are dynamically adjusted. This process provides a personalized gaming experience tailored to the user's emotions.

[0542] For example, if a user is in a state of excitement after winning an MLB game and hitting many home runs, the system will sense this excitement and react by increasing the volume of the crowd's cheers or adding effects to the graphics. In this way, by dynamically changing the game experience in response to the user's emotional changes, it becomes possible to provide a more immersive and realistic sports simulation.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The server collects historical data from professional sports matches and performs data cleansing to prepare the data for analysis.

[0546] Step 2:

[0547] The server uses machine learning to analyze collected match data and calculate player performance metrics. This generates detailed avatar data tailored to each player.

[0548] Step 3:

[0549] The server stores the generated player avatar data in a database, preparing it for future access and searching.

[0550] Step 4:

[0551] The device connects to the server when the game starts and retrieves avatar data based on the user's player and team selections.

[0552] Step 5:

[0553] The device uses acquired avatar data to realistically recreate player characters within the game. The players and teams selected by the user are reflected on the game screen.

[0554] Step 6:

[0555] The emotion engine installed in the device monitors the user's voice and facial expression data in real time, inferring and analyzing their emotional state.

[0556] Step 7:

[0557] During a match, users control player avatars to perform actions such as throwing and hitting the ball.

[0558] Step 8:

[0559] The device uses an emotion engine to analyze the emotions the user expresses during a match and sends that information to the server.

[0560] Step 9:

[0561] The server dynamically adjusts the difficulty and presentation of the match (for example, audience reactions and sound effects) based on the transmitted emotion data.

[0562] Step 10:

[0563] The device changes the visual and auditory elements within the game based on instructions from the server, providing the user with an emotionally responsive gaming experience.

[0564] Step 11:

[0565] After a match ends, users can check statistics and match highlights on their devices to plan their next gameplay.

[0566] (Example 2)

[0567] 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."

[0568] Existing professional sports game systems do not adequately implement real-time interaction that takes into account the user's emotional state. As a result, users often lack immersion during gameplay, and the game experience tends to become monotonous. To solve this problem, there is a need for technology that can detect user emotions in real time and dynamically adjust the game environment accordingly.

[0569] 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.

[0570] In this invention, the server includes means for collecting and analyzing past match data, means for acquiring and analyzing the user's emotional state in real time, and means for dynamically adjusting the game simulation based on the analyzed emotional data. This enables a dynamic and interactive game experience that responds to the user's emotions.

[0571] "Past match data" refers to information related to the results of various matches in sports competitions, as well as the performance of individuals and teams.

[0572] "Avatar data" refers to a collection of digital information used to imitate and represent athletes in the digital space.

[0573] "User emotional state" refers to information that indicates the user's psychological or emotional state during gameplay.

[0574] "Means of acquiring and analyzing data in real time" refers to a system or method for instantly acquiring user emotional data and performing analysis based on that information.

[0575] "Means of dynamic adjustment" refers to methods and technologies for adjusting the operation and display content of a system on the fly based on the user's emotional state and other data.

[0576] "Competitive simulation" refers to a digital experience that simulates the conditions of a sports competition, allowing users to play in a virtual environment.

[0577] This invention relates to a system for realizing dynamic sports competition simulations that take user emotions into consideration. This system mainly consists of three elements: a server, a terminal, and a user.

[0578] The server first collects historical professional sports match data from various sources. Web scraping tools and APIs are often used for data collection. The server then analyzes the collected data using machine learning algorithms. Machine learning libraries such as TensorFlow and PyTorch can be used for this analysis. Based on this process, avatar data that mimics the abilities of the players is generated and stored in an information storage device.

[0579] The device communicates with the server when the user launches the game and retrieves player avatar data. The device also uses a microphone and camera to capture the user's voice and facial expressions in real time and infer their emotional state. A voice API is used for voice recognition, and an image processing library is used for facial expression recognition. The device sends this emotional data to the server for use in the game simulation.

[0580] During gameplay, users control player avatars and progress through the competition within the simulation. The user's emotions are detected by an emotion engine, and this data is analyzed on the server in real time. Based on the analyzed emotion data, the in-game audio, graphics, and even the difficulty of the competition are dynamically adjusted. For example, if a user is winning an MLB game and is excited after hitting many home runs, the system detects their emotion and processes it by increasing the volume of the crowd's cheers or adding effects to the graphics.

[0581] Here's an example of a prompt message to input into a generative AI model: "Analyze the data of a player who hit three home runs in yesterday's game and train an AI model to use in today's game. Generate data to provide a game experience that also integrates real-time emotional response based on this player's abilities."

[0582] Thus, the present invention provides a dynamic game environment that incorporates the user's emotions, thereby realizing a sports simulation that gives the user a deep sense of immersion.

[0583] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0584] Step 1:

[0585] The server collects historical sports match data using web scraping tools and APIs. This data includes player statistics, match results, and team statistics. The collected data undergoes preprocessing, such as removing unnecessary data and cleaning the data, and is then converted into an analyzable format.

[0586] Step 2:

[0587] The server inputs the collected data into a machine learning algorithm to evaluate the players' performance. In this step, libraries such as TensorFlow and PyTorch are used to train the model and calculate the players' ability scores. The resulting ability scores are generated as player avatar data and stored in an information storage device.

[0588] Step 3:

[0589] The device communicates with the server when the user launches the game and retrieves player avatar data. When the user selects a player or team, that player's avatar data is displayed in the game. The input is the selection of player options, and the output is the visual display of the player's avatar.

[0590] Step 4:

[0591] The device acquires the user's voice and facial expressions in real time and analyzes their emotional state using an emotion engine. The inputs used are the user's voice and image data, which are collected through the microphone and camera. The data is analyzed using voice APIs and image processing libraries, and emotional data is obtained as output. This data is based on the user's heart rate, changes in facial expressions, and other factors.

[0592] Step 5:

[0593] The device sends analyzed emotion data to the server, which then dynamically adjusts elements within the game based on that data. Emotion data is received by the server as input, and the game's audio and graphics are adjusted as output. For example, if the user is excited, sound effects and visual effects are enhanced.

[0594] Step 6:

[0595] Users experience the finely tuned in-game elements and control their avatars to progress through the competition. Through this process, users can enjoy a individually customized game environment. The output is an interactive and immersive gaming experience.

[0596] (Application Example 2)

[0597] 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."

[0598] Modern sports viewing experiences often lack immersion because they don't dynamically adapt to viewer emotions or real-time game situations. Furthermore, there's a lack of accurate and interactive presentations based on the athletes' performances, and there's a need to provide viewers with a deeper sense of immersion.

[0599] 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.

[0600] In this invention, the server includes means for analyzing past competition data and calculating an evaluation index for athletes, means for generating avatar data that mimics the characteristics of athletes based on the analysis results, and means for sensing the user's emotional state and dynamically adjusting audio and video content based on that data. This makes it possible to provide a personalized and dynamic viewing experience that responds to the user's emotions and the situation of the match.

[0601] "Competition data" refers to data that includes all information related to past competitions, such as player performance, match results, and tactical trends.

[0602] "Athlete" refers to a person or group that participates in a particular competition and influences its outcome.

[0603] An "evaluation index" is a standard or numerical value used to quantitatively evaluate an athlete's performance, measuring their skills and achievements.

[0604] "Avatar data" refers to digitized models that mimic the characteristics and movements of athletes, and are used in competition simulations.

[0605] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a particular moment, and includes feelings such as excitement, joy, and tension.

[0606] "Audio and video content" refers to the collective audio tracks and visual images provided to the viewer, which may change in real time.

[0607] "Dynamic adjustment" refers to the process of changing content and expression in real time in response to changes in circumstances and conditions.

[0608] The system that realizes this invention consists of a server, a terminal, and user interaction. The server first collects past competition data and uses machine learning algorithms to analyze the characteristics of the competitors in detail. Based on the analyzed data, the competitor's avatar data is generated and stored in a storage device.

[0609] When a user selects a sport to watch, the device communicates with a server to obtain the necessary athlete avatar data. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice and facial expression data in real time. This engine uses the camera and microphone to sense the user's emotional state and sends that data to the server.

[0610] Users can enjoy a personalized viewing experience during competitions, with dynamically adjusted video feeds and audio content. Specifically, video editing and audio enhancement are performed in real time based on the user's emotional information, providing a more immersive experience. For example, if a user is moved by a particular athlete's outstanding play, that emotion can be emphasized through visual and auditory effects.

[0611] Hardware such as smartphones and VR devices are used, while software such as audio and video processing libraries (e.g., FFmpeg) and machine learning frameworks (e.g., TensorFlow, PyTorch) are utilized.

[0612] For example, when a user is watching a soccer match and their favorite team scores a goal, the system can detect their excitement level and, accordingly, enhance the commentator's remarks and the cheers of the crowd, providing a more thrilling viewing experience.

[0613] An example of a prompt might be, "When a user is watching a soccer match on their smartphone and their team scores a goal, how do you sense the user's emotions and change the effect accordingly?"

[0614] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0615] Step 1:

[0616] The server collects historical competition data. This data, including competitor performance and match results, is retrieved from online databases and archived videos. The collected data then serves as input for analysis by machine learning algorithms.

[0617] Step 2:

[0618] The server analyzes the collected competition data and calculates performance metrics for the athletes. Here, machine learning models are applied to process the data, quantifying the athletes' characteristics and performance tendencies. The output is the analyzed performance metrics.

[0619] Step 3:

[0620] Based on the analysis results, the server generates avatar data that mimics the characteristics of the competitor. This avatar data reflects the competitor's features in a digital model and is stored in memory. This makes it possible to use the competitor avatar.

[0621] Step 4:

[0622] The device communicates with the server based on the sport selected by the user and retrieves the necessary avatar data. This data is used as input information to run a dynamic match simulation on the device.

[0623] Step 5:

[0624] The device uses an emotion engine to acquire and analyze the user's voice and facial expression data in real time. It reads the voice and facial expressions through the camera and microphone, and estimates the user's emotional state using a generative AI model. The output is the estimated emotion data.

[0625] Step 6:

[0626] The server receives sentiment data transmitted from the terminal and dynamically adjusts the audio and video content based on that data. Specifically, it adds video effects and emphasizes audio comments to provide a viewing experience optimized for the user. The output of this step is the adjusted audio and video feed.

[0627] Step 7:

[0628] Users gain a more immersive viewing experience through optimized audio and video feeds. Changes in visual and auditory effects enhance the sense of realism in the match. The user's experience is then fed back into the emotion engine as feedback.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] [Fourth Embodiment]

[0633] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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).

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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".

[0646] In the system implementing this invention, the server, terminal, and user each play a specific role and work together in cooperation.

[0647] Server Functions

[0648] The server first accesses the official databases of professional sports leagues such as NPB and MLB, and periodically collects historical game data. This is extensive data, including individual player statistics and detailed game results. Next, a data processing algorithm using machine learning is applied to analyze this collected data. In this analysis process, the server quantifies each player's abilities and characteristics and calculates performance indicators. Subsequently, it generates avatar data based on the analysis results and stores this data in a highly efficient database. The server keeps the specified avatar data ready to be sent in response to access requests from users.

[0649] Device functions

[0650] When a user launches the game, the device communicates with the server to download the necessary game data and displays the interface. Here, the user can select players and teams, and the device retrieves corresponding avatar data from the server based on their selections. Based on the avatar data, the device generates player characters in the game and accurately recreates the match environment. The movements of the players controlled by the user and the flow of the match are processed in real time, providing a realistic simulation based on the characteristics of actual players.

[0651] User actions

[0652] At the start of the game, users can choose their favorite players and teams. After selecting players, the game begins, and users can perform various actions using their chosen players. For example, if a pitcher is selected, the user can give detailed instructions on pitching control and pitch type; if a batter is selected, the user can adjust batting timing and swing direction. The flow of the game is realistically reproduced based on the characteristics of the players, and users will develop strategies accordingly. After the game ends, users can review player statistics and game highlights, and use this information to improve their strategies for the next game.

[0653] Thus, the system based on the present invention provides users with a realistic sports experience, bringing a level of immersion and enjoyment not found in conventional sports games.

[0654] The following describes the processing flow.

[0655] Step 1:

[0656] The server connects to the official databases of professional sports leagues and collects match data for players from the past 10 years. This data includes match dates and times, player statistics, opponents, and match results.

[0657] Step 2:

[0658] The server cleanses the collected data, removing missing and outlier values ​​to prepare it for analysis. This process also verifies data integrity and performs optimization.

[0659] Step 3:

[0660] The server uses machine learning models to calculate player performance metrics based on the organized data. The models quantify the players' abilities such as speed, power, and technique, and create profiles.

[0661] Step 4:

[0662] The server generates player avatar data based on calculated performance metrics. This data virtually reproduces the player's characteristics and abilities.

[0663] Step 5:

[0664] The server saves the generated avatar data to a database so that users can access it later. The saved data is indexed by player ID.

[0665] Step 6:

[0666] When the game is launched, the device connects to the server, retrieves information about currently available players and teams, and displays it to the user.

[0667] Step 7:

[0668] The user selects the players or teams they want to challenge in the game. The selected information is sent to the server as a request via the device.

[0669] Step 8:

[0670] The server sends player avatar data corresponding to the user's request to the terminal. The terminal generates a player character based on the received data and displays it on the game screen.

[0671] Step 9:

[0672] Users control in-game matches that take place in real time. Player actions are realistically reproduced on the device based on their characteristics, and the system reacts instantly to every action the user takes.

[0673] Step 10:

[0674] The terminal displays player data and match statistics to the user after the match ends. This information can be used to develop strategies for the next match.

[0675] (Example 1)

[0676] 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".

[0677] Conventional competition simulation systems have struggled to efficiently collect past competition data and conduct detailed analysis. Furthermore, providing users with a realistic, real-time competition experience that reflects participant characteristics has been difficult. Additionally, there has been a lack of technical means to perform more advanced analysis and simulations by incorporating generative AI models into the information gathering process.

[0678] 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.

[0679] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating performance indicators for participants, and means for generating virtual data that mimics the characteristics of the participants based on the analysis results. This enables users to have an interactive competition simulation experience and allows for advanced, real-time simulations based on real-world data.

[0680] "Past competition data" refers to information about results, records, and participant performance from previously held competition events.

[0681] "Means of collection" refers to the methods and technologies used to accumulate and preserve specific information.

[0682] "Means of analysis" refers to methods and techniques for analyzing data to derive regularities and characteristics.

[0683] "Participant performance indicators" refer to numerical values ​​or indicators that represent the technical skills and abilities of individual participants in a competition.

[0684] "Virtual data" refers to data created through calculations and simulations based on actual data.

[0685] An "information base" refers to a system or structured data storage area used to aggregate and manage various types of data and information.

[0686] An "interactive competition simulation experience" refers to a virtual environment where users can interactively experience a competition from the perspective of a participant.

[0687] "Generative AI models" refer to artificial intelligence technologies that learn from large amounts of data to automate or optimize specific tasks.

[0688] A "prompt" refers to a text instruction entered into a generative AI model to cause it to perform a specific action or task.

[0689] The system for implementing this invention involves a server, terminal, and user each playing a specific role and working in cooperation. The server first accesses a database related to past competition events via a network connection and collects competition data. For data collection, it uses HTTP requests, for example, with the "requests" library.

[0690] The server analyzes the collected data using machine learning algorithms. Libraries such as "scikit-learn" and "TensorFlow" are used for this analysis. This analysis allows for the calculation of participant performance metrics, making it possible to accurately quantify the characteristics of each participant.

[0691] Next, the server generates virtual data, or avatar data, that mimics the characteristics of the participants based on the analysis results. This avatar data is stored in a database such as PostgreSQL so that it can be accessed quickly later.

[0692] The terminal downloads necessary data from the server when the user launches the game. A GUI toolkit such as "PyQt" is used for the data interface. The terminal generates a virtual environment with the user's selected participants and teams, providing an interactive simulated game experience. Player characters and match situations are reproduced in real time, allowing users to enjoy an interactive experience.

[0693] As a concrete example, the server inputs the prompt message "Use data from over 200 games to evaluate batting performance using a machine learning model and predict the success rate of each participant" into the generating AI model, enabling more accurate analysis. This allows the user to experience a realistic simulation of the game.

[0694] Through this system, users can develop competitive strategies based on statistical information and performance analysis, and then apply them to their next gameplay.

[0695] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0696] Step 1:

[0697] The server accesses a database of past competitions via the network. It uses an API to retrieve data related to past competition events. In this step, queries to the database are input, and competition data is retrieved as output. The Python library "requests" is used to perform the specific operation of collecting match data in JSON format.

[0698] Step 2:

[0699] The server applies machine learning algorithms to analyze the collected competition data. The input to this analysis is the collected competition data, and the output is quantified data of the players' performance metrics and characteristics. Using "TensorFlow" and "scikit-learn," the server performs the specific operation of training a model that evaluates the participants' hitting and defensive performance based on the data.

[0700] Step 3:

[0701] The server generates virtual data that mimics the characteristics of the participants based on the analysis results. The input is the analysis results from the previous step, and the output is avatar data used in the game. The server then performs the specific action of saving the generated avatar data to a database system such as PostgreSQL.

[0702] Step 4:

[0703] The device downloads the necessary game data from the server when the user launches the game. It takes the user's selection (participants or team) as input and outputs the corresponding avatar data. The specific operation of displaying the user interface is performed using "PyQt".

[0704] Step 5:

[0705] The device generates a player character within the game based on acquired avatar data and performs a simulation. The input is avatar data, and the output is an interactive game simulation experience. It uses a 3D modeling engine to perform specific actions that reproduce the game situation in real time.

[0706] Step 6:

[0707] Users control players and teams within the game to advance the match. The match progression is output based on user input (e.g., selection of actions and strategies). Users use a controller to perform actions in real time, executing specific actions to strategically advance the match.

[0708] Step 7:

[0709] After the match ends, the server displays match statistics and performance analysis to the user. Match data is the input, and statistics and analysis results are provided to the user as output. The server retrieves match results from the database and performs specific actions such as displaying highlights and analysis results on the user interface.

[0710] (Application Example 1)

[0711] 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".

[0712] In recent years, there has been a growing demand for more immersive and interactive sports experiences. However, traditional simulation games and virtual experiences lack realism and fail to meet user expectations. Furthermore, the limited visualization of product trials in virtual space makes it difficult for users to deeply understand product characteristics and applicability. This invention overcomes these shortcomings of conventional systems and provides an environment that supports purchasing decisions with a high degree of realism.

[0713] 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.

[0714] In this invention, the server includes means for collecting past competition data, means for analyzing the collected competition data and calculating participant performance indicators, means for generating avatar data that mimics the participant's abilities based on the analysis results, means for storing the generated avatar data in an information storage device, means for providing users with an interactive competition simulation experience, and means for visualizing a simulated product usage experience within a virtual store. This enables users to obtain a detailed and realistic experience in real time, realizing a new type of virtual experience that integrates sports experience and product trial.

[0715] "Competition data" refers to a collection of information about past matches and performances in professional sports leagues.

[0716] "Participant avatar data" refers to the data of a virtual character generated based on the analyzed abilities and characteristics of the participants.

[0717] An "information storage device" is a physical or virtual device used to store and manage digital information.

[0718] An "interactive competition simulation experience" is a two-way system that allows users to control and experience a competition in a virtual space in real time.

[0719] A "virtual store" is a platform for electronic buying and selling that exists on the internet.

[0720] "Simulated product usage experience" is a method of visualizing a user's experience using a product in a virtual environment as if they were actually using it.

[0721] The system of this invention achieves its functionality through the coordinated efforts of the server, terminal, and user components.

[0722] The server first automatically collects competition data from official APIs of professional sports leagues and other sources, and periodically imports it into a database. Next, the collected data is processed by data analysis algorithms using Python libraries (e.g., NumPy, Pandas, Scikit-Learn) to quantify participants' performance indicators and abilities. Based on these analysis results, participant avatar data is generated and stored efficiently in the information storage device. The server also provides this avatar data to users upon request.

[0723] The device uses a game engine such as Unity to visualize avatar data and provide users with an interactive, simulated game experience. When a user launches the game using the device, it communicates with a server to retrieve necessary data and generates a 3D environment in real time. Within this environment, users can visually see their avatar trying out products in a virtual store. This process is also performed and visualized through smartphones and smart glasses.

[0724] Users can select specific participants or groups to simulate product trials in a virtual store. This selection is made intuitively using a terminal interface. For example, a user who wants to try out new basketball shoes can apply player avatar data and see how they jump and move in the shoes in a virtual environment.

[0725] For example, if the server receives the prompt "Visualize in 3D a player avatar wearing the latest model of soccer cleats and scoring a goal in the virtual sports store app," it will generate a simulation based on the specified data. This experience helps users understand product characteristics better and serves as a reference for purchasing decisions.

[0726] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0727] Step 1:

[0728] The server accesses the official APIs of professional sports leagues to retrieve competition data. This input data mainly consists of past match results and player performance information. The retrieved data is temporarily stored and used for later data analysis. This process is performed periodically, and new data is continuously added.

[0729] Step 2:

[0730] The server analyzes the acquired competition data using Python data analysis libraries (NumPy, Pandas, Scikit-Learn). In this step, player abilities and performance indicators are extracted as numerical data. For example, players' batting averages and pitching speeds are calculated. These analysis results form the basis for avatar generation later on.

[0731] Step 3:

[0732] The server generates participant avatar data based on the analysis results. This avatar is a virtual character that mimics the characteristics of the player and is converted into a data format based on the analysis results. The generated avatar data is stored in a highly efficient information storage device, ready for subsequent access requests.

[0733] Step 4:

[0734] The device retrieves avatar data for the player or organization selected by the user from the server. In this step, the selection is made through the application interface, and the corresponding data is downloaded. Based on this input, the device prepares to visualize the player avatar.

[0735] Step 5:

[0736] The device uses the Unity game engine to generate a 3D environment in real time, providing users with an interactive simulated competitive experience. In this process, the virtual environment is designed based on acquired avatar data, and the user interacts with it through the device. Dynamic visual effects are rendered in response to user input.

[0737] Step 6:

[0738] Users access a virtual store through their device and experience a simulated product usage. In this processing step, a combination of product information and player avatar data is visually presented. For example, a player avatar wearing new sneakers and dribbling is displayed on the screen, serving to support the user's purchase decision.

[0739] 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.

[0740] This invention provides a system that combines player avatar data and user emotional states in real time in professional sports games. This system functions through the coordinated operation of a server, terminal, and user.

[0741] Server Functions

[0742] The server first collects historical professional sports match data and applies machine learning algorithms to analyze player abilities. The analyzed data is generated as player avatar data and stored in a database. Furthermore, the server also manages an emotion engine database and integrates sensors and AI models that can receive and analyze the user's emotional state in real time.

[0743] Device functions

[0744] The device communicates with the server when the user launches the game, retrieves player avatar data, and presents it to the user. When the user selects a player or team, the corresponding avatar data is reflected in the game. Furthermore, the device is equipped with an emotion engine that can infer emotions in real time from the user's voice and facial expression data. The device sends this emotion data to the server, which is used for simulations within the game.

[0745] User actions

[0746] During gameplay, users control player avatars to progress through the competition. An emotion engine detects the user's emotions during gameplay (e.g., joy, tension, frustration), and this data is analyzed in real time. Based on the analysis results, the in-game audio, graphics, and even the difficulty of the match are dynamically adjusted. This process provides a personalized gaming experience tailored to the user's emotions.

[0747] For example, if a user is in a state of excitement after winning an MLB game and hitting many home runs, the system will sense this excitement and react by increasing the volume of the crowd's cheers or adding effects to the graphics. In this way, by dynamically changing the game experience in response to the user's emotional changes, it becomes possible to provide a more immersive and realistic sports simulation.

[0748] The following describes the processing flow.

[0749] Step 1:

[0750] The server collects historical data from professional sports matches and performs data cleansing to prepare the data for analysis.

[0751] Step 2:

[0752] The server uses machine learning to analyze collected match data and calculate player performance metrics. This generates detailed avatar data tailored to each player.

[0753] Step 3:

[0754] The server stores the generated player avatar data in a database, preparing it for future access and searching.

[0755] Step 4:

[0756] The device connects to the server when the game starts and retrieves avatar data based on the user's player and team selections.

[0757] Step 5:

[0758] The device uses acquired avatar data to realistically recreate player characters within the game. The players and teams selected by the user are reflected on the game screen.

[0759] Step 6:

[0760] The emotion engine installed in the device monitors the user's voice and facial expression data in real time, inferring and analyzing their emotional state.

[0761] Step 7:

[0762] During a match, users control player avatars to perform actions such as throwing and hitting the ball.

[0763] Step 8:

[0764] The device uses an emotion engine to analyze the emotions the user expresses during a match and sends that information to the server.

[0765] Step 9:

[0766] The server dynamically adjusts the difficulty and presentation of the match (for example, audience reactions and sound effects) based on the transmitted emotion data.

[0767] Step 10:

[0768] The device changes the visual and auditory elements within the game based on instructions from the server, providing the user with an emotionally responsive gaming experience.

[0769] Step 11:

[0770] After a match ends, users can check statistics and match highlights on their devices to plan their next gameplay.

[0771] (Example 2)

[0772] 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".

[0773] Existing professional sports game systems do not adequately implement real-time interaction that takes into account the user's emotional state. As a result, users often lack immersion during gameplay, and the game experience tends to become monotonous. To solve this problem, there is a need for technology that can detect user emotions in real time and dynamically adjust the game environment accordingly.

[0774] 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.

[0775] In this invention, the server includes means for collecting and analyzing past match data, means for acquiring and analyzing the user's emotional state in real time, and means for dynamically adjusting the game simulation based on the analyzed emotional data. This enables a dynamic and interactive game experience that responds to the user's emotions.

[0776] "Past match data" refers to information related to the results of various matches in sports competitions, as well as the performance of individuals and teams.

[0777] "Avatar data" refers to a collection of digital information used to imitate and represent athletes in the digital space.

[0778] "User emotional state" refers to information that indicates the user's psychological or emotional state during gameplay.

[0779] "Means of acquiring and analyzing data in real time" refers to a system or method for instantly acquiring user emotional data and performing analysis based on that information.

[0780] "Means of dynamic adjustment" refers to methods and technologies for adjusting the operation and display content of a system on the fly based on the user's emotional state and other data.

[0781] "Competitive simulation" refers to a digital experience that simulates the conditions of a sports competition, allowing users to play in a virtual environment.

[0782] This invention relates to a system for realizing dynamic sports competition simulations that take user emotions into consideration. This system mainly consists of three elements: a server, a terminal, and a user.

[0783] The server first collects historical professional sports match data from various sources. Web scraping tools and APIs are often used for data collection. The server then analyzes the collected data using machine learning algorithms. Machine learning libraries such as TensorFlow and PyTorch can be used for this analysis. Based on this process, avatar data that mimics the abilities of the players is generated and stored in an information storage device.

[0784] The device communicates with the server when the user launches the game and retrieves player avatar data. The device also uses a microphone and camera to capture the user's voice and facial expressions in real time and infer their emotional state. A voice API is used for voice recognition, and an image processing library is used for facial expression recognition. The device sends this emotional data to the server for use in the game simulation.

[0785] During gameplay, users control player avatars and progress through the competition within the simulation. The user's emotions are detected by an emotion engine, and this data is analyzed on the server in real time. Based on the analyzed emotion data, the in-game audio, graphics, and even the difficulty of the competition are dynamically adjusted. For example, if a user is winning an MLB game and is excited after hitting many home runs, the system detects their emotion and processes it by increasing the volume of the crowd's cheers or adding effects to the graphics.

[0786] Here's an example of a prompt message to input into a generative AI model: "Analyze the data of a player who hit three home runs in yesterday's game and train an AI model to use in today's game. Generate data to provide a game experience that also integrates real-time emotional response based on this player's abilities."

[0787] Thus, the present invention provides a dynamic game environment that incorporates the user's emotions, thereby realizing a sports simulation that gives the user a deep sense of immersion.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] The server collects historical sports match data using web scraping tools and APIs. This data includes player statistics, match results, and team statistics. The collected data undergoes preprocessing, such as removing unnecessary data and cleaning the data, and is then converted into an analyzable format.

[0791] Step 2:

[0792] The server inputs the collected data into a machine learning algorithm to evaluate the players' performance. In this step, libraries such as TensorFlow and PyTorch are used to train the model and calculate the players' ability scores. The resulting ability scores are generated as player avatar data and stored in an information storage device.

[0793] Step 3:

[0794] The device communicates with the server when the user launches the game and retrieves player avatar data. When the user selects a player or team, that player's avatar data is displayed in the game. The input is the selection of player options, and the output is the visual display of the player's avatar.

[0795] Step 4:

[0796] The device acquires the user's voice and facial expressions in real time and analyzes their emotional state using an emotion engine. The inputs used are the user's voice and image data, which are collected through the microphone and camera. The data is analyzed using voice APIs and image processing libraries, and emotional data is obtained as output. This data is based on the user's heart rate, changes in facial expressions, and other factors.

[0797] Step 5:

[0798] The device sends analyzed emotion data to the server, which then dynamically adjusts elements within the game based on that data. Emotion data is received by the server as input, and the game's audio and graphics are adjusted as output. For example, if the user is excited, sound effects and visual effects are enhanced.

[0799] Step 6:

[0800] Users experience the finely tuned in-game elements and control their avatars to progress through the competition. Through this process, users can enjoy a individually customized game environment. The output is an interactive and immersive gaming experience.

[0801] (Application Example 2)

[0802] 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".

[0803] Modern sports viewing experiences often lack immersion because they don't dynamically adapt to viewer emotions or real-time game situations. Furthermore, there's a lack of accurate and interactive presentations based on the athletes' performances, and there's a need to provide viewers with a deeper sense of immersion.

[0804] 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.

[0805] In this invention, the server includes means for analyzing past competition data and calculating an evaluation index for athletes, means for generating avatar data that mimics the characteristics of athletes based on the analysis results, and means for sensing the user's emotional state and dynamically adjusting audio and video content based on that data. This makes it possible to provide a personalized and dynamic viewing experience that responds to the user's emotions and the situation of the match.

[0806] "Competition data" refers to data that includes all information related to past competitions, such as player performance, match results, and tactical trends.

[0807] "Athlete" refers to a person or group that participates in a particular competition and influences its outcome.

[0808] An "evaluation index" is a standard or numerical value used to quantitatively evaluate an athlete's performance, measuring their skills and achievements.

[0809] "Avatar data" refers to digitized models that mimic the characteristics and movements of athletes, and are used in competition simulations.

[0810] "Emotional state" refers to the type and intensity of emotions a user is experiencing at a particular moment, and includes feelings such as excitement, joy, and tension.

[0811] "Audio and video content" refers to the collective audio tracks and visual images provided to the viewer, which may change in real time.

[0812] "Dynamic adjustment" refers to the process of changing content and expression in real time in response to changes in circumstances and conditions.

[0813] The system that realizes this invention consists of a server, a terminal, and user interaction. The server first collects past competition data and uses machine learning algorithms to analyze the characteristics of the competitors in detail. Based on the analyzed data, the competitor's avatar data is generated and stored in a storage device.

[0814] When a user selects a sport to watch, the device communicates with a server to obtain the necessary athlete avatar data. Furthermore, the device is equipped with an emotion engine that analyzes the user's voice and facial expression data in real time. This engine uses the camera and microphone to sense the user's emotional state and sends that data to the server.

[0815] Users can enjoy a personalized viewing experience during competitions, with dynamically adjusted video feeds and audio content. Specifically, video editing and audio enhancement are performed in real time based on the user's emotional information, providing a more immersive experience. For example, if a user is moved by a particular athlete's outstanding play, that emotion can be emphasized through visual and auditory effects.

[0816] Hardware such as smartphones and VR devices are used, while software such as audio and video processing libraries (e.g., FFmpeg) and machine learning frameworks (e.g., TensorFlow, PyTorch) are utilized.

[0817] For example, when a user is watching a soccer match and their favorite team scores a goal, the system can detect their excitement level and, accordingly, enhance the commentator's remarks and the cheers of the crowd, providing a more thrilling viewing experience.

[0818] An example of a prompt might be, "When a user is watching a soccer match on their smartphone and their team scores a goal, how do you sense the user's emotions and change the effect accordingly?"

[0819] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0820] Step 1:

[0821] The server collects historical competition data. This data, including competitor performance and match results, is retrieved from online databases and archived videos. The collected data then serves as input for analysis by machine learning algorithms.

[0822] Step 2:

[0823] The server analyzes the collected competition data and calculates performance metrics for the athletes. Here, machine learning models are applied to process the data, quantifying the athletes' characteristics and performance tendencies. The output is the analyzed performance metrics.

[0824] Step 3:

[0825] Based on the analysis results, the server generates avatar data that mimics the characteristics of the competitor. This avatar data reflects the competitor's features in a digital model and is stored in memory. This makes it possible to use the competitor avatar.

[0826] Step 4:

[0827] The device communicates with the server based on the sport selected by the user and retrieves the necessary avatar data. This data is used as input information to run a dynamic match simulation on the device.

[0828] Step 5:

[0829] The device uses an emotion engine to acquire and analyze the user's voice and facial expression data in real time. It reads the voice and facial expressions through the camera and microphone, and estimates the user's emotional state using a generative AI model. The output is the estimated emotion data.

[0830] Step 6:

[0831] The server receives sentiment data transmitted from the terminal and dynamically adjusts the audio and video content based on that data. Specifically, it adds video effects and emphasizes audio comments to provide a viewing experience optimized for the user. The output of this step is the adjusted audio and video feed.

[0832] Step 7:

[0833] Users gain a more immersive viewing experience through optimized audio and video feeds. Changes in visual and auditory effects enhance the sense of realism in the match. The user's experience is then fed back into the emotion engine as feedback.

[0834] 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.

[0835] 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.

[0836] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] Means of collecting past match data,

[0858] A method for analyzing collected match data and calculating player performance indicators,

[0859] A means for generating avatar data that mimics the abilities of a player based on the analysis results,

[0860] A means of storing the generated avatar data in a database,

[0861] A means of providing users with an interactive competition simulation using player avatar data,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising means for obtaining avatar data when a user selects a player or team, and means for simulating a competition in real time.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising means for displaying statistical information and performance analysis to the user based on player avatar data and match result data.

[0867] "Example 1"

[0868] (Claim 1)

[0869] Means for collecting past competition data,

[0870] A means of analyzing collected competition data and calculating performance indicators for participants,

[0871] A means for generating virtual data that mimics the characteristics of participants based on the analysis results,

[0872] A means for storing the generated virtual data in an information base,

[0873] A means of providing users with an interactive competition simulation experience using participant virtual data,

[0874] Methods for utilizing generative AI models in the information gathering process,

[0875] A means for generating prompt sentences formed from competition data,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, comprising means for acquiring virtual data when a user selects participants or groups, and means for immediately simulating a competition.

[0879] (Claim 3)

[0880] The system according to claim 1, comprising means for displaying statistical information and performance analysis to the user based on virtual participant data and competition result data.

[0881] "Application Example 1"

[0882] (Claim 1)

[0883] Means for collecting past competition data,

[0884] A means of analyzing collected competition data and calculating participant performance indicators,

[0885] A means for generating avatar data that mimics the abilities of participants based on the analysis results,

[0886] A means for storing the generated avatar data in an information storage device,

[0887] A means of providing users with an interactive competition simulation experience using participant avatar data,

[0888] A means for users to visualize a simulated product usage experience within a virtual store using participant avatar data,

[0889] A system that includes this.

[0890] (Claim 2)

[0891] The system according to claim 1, comprising means for obtaining avatar data when a user selects a participant or group, and means for simulating a competition in real time.

[0892] (Claim 3)

[0893] The system according to claim 1, comprising means for displaying statistical information and performance analysis to users based on participant avatar data and competition result data.

[0894] "Example 2 of combining an emotion engine"

[0895] (Claim 1)

[0896] Means of collecting past match data,

[0897] A method for analyzing collected match data and calculating player performance indicators,

[0898] A means for generating avatar data that mimics the abilities of a player based on the analysis results,

[0899] A means for storing the generated avatar data in an information storage device,

[0900] A means of acquiring and analyzing the user's emotional state in real time,

[0901] A means for dynamically adjusting elements of an interactive competition simulation based on analyzed emotional data,

[0902] A means of providing users with a modified simulation,

[0903] A system that includes this.

[0904] (Claim 2)

[0905] The system according to claim 1, comprising means for acquiring avatar data when a user selects a player or team, simulating a competition in real time, and dynamically changing the simulation content based on the user's emotional state at that time.

[0906] (Claim 3)

[0907] The system according to claim 1, further comprising means for displaying statistical information and performance analysis to the user based on player avatar data and match result data, and further customizing the displayed content according to the user's emotional state.

[0908] "Application example 2 when combining with an emotional engine"

[0909] (Claim 1)

[0910] Means for collecting past competition data,

[0911] A means of analyzing collected competition data and calculating an evaluation index for athletes,

[0912] A means for generating avatar data that mimics the characteristics of the athlete based on the analysis results,

[0913] A means for saving the generated avatar data to a storage device,

[0914] A means of providing users with dynamic match simulations using player avatar data,

[0915] A means for sensing the user's emotional state and dynamically adjusting audio and video content based on that data,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, comprising means for obtaining avatar data when a user selects a competitor or organization, and means for recreating a match in real time.

[0919] (Claim 3)

[0920] The system according to claim 1, comprising means for displaying statistical information and performance evaluations to the user based on athlete avatar data and match performance data, and further comprising means for providing an interactive viewing experience according to the user's emotional state. [Explanation of Symbols]

[0921] 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 past match data, A method for analyzing collected match data and calculating player performance indicators, A means for generating avatar data that mimics the abilities of a player based on the analysis results, A means of storing the generated avatar data in a database, A means of providing users with an interactive competition simulation using player avatar data, A system that includes this.

2. The system according to claim 1, comprising means for obtaining avatar data when a user selects a player or team, and means for simulating a competition in real time.

3. The system according to claim 1, comprising means for displaying statistical information and performance analysis to the user based on player avatar data and match result data.

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