system

Abstract

We provide the system. [Solution] A processing means for collecting user behavior data, A processing means that inputs the aforementioned data into a neural network and generates dynamic game mechanics that are suitable for the user's play style, Processing means for applying the generated game mechanics in real time, 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】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In recent game development, it has been required to provide a game experience adjusted for each player in real time. However, with current technologies, it is difficult to generate dynamic game mechanics, make immediate changes to the environment according to the player's actions, and maintain an appropriate difficulty level each time. As a result, development costs have increased, and it has become difficult to provide games of sufficient quality with limited resources. To address this issue, a new system for efficiently and effectively improving the game experience is required. 【Means for Solving the Problems】 【0005】 This invention provides a means for continuously collecting user behavior data and generating dynamic game mechanics based on the player's play style using a neural network. It also includes means for evolving the game environment according to user choices and automatically adjusting the game balance based on real-time event data. This makes it possible to provide a game experience tailored to the player's actions and create an immersive game with optimal difficulty maintained in a constantly changing environment. 【0006】 "User behavior data" refers to recorded information such as the actions and choices a user makes during gameplay. 【0007】 A "processing means" refers to a function or module incorporated within a system to perform a specific task. 【0008】 A "neural network" is a type of algorithm used in machine learning that mimics the neural circuits of the human brain to learn from data and identify complex patterns. 【0009】 "Dynamic game mechanics" refers to game rules and systems that change in real time according to the player's actions and play style. 【0010】 "Application" refers to the act of reflecting the generated content or results in the actual game environment or system. 【0011】 "Real-time" refers to processing and responses occurring almost instantly, meaning that user input is handled without delay. 【0012】 "Game environment" refers to the entire world and setting that the user interacts with within the game, including the characters, scenes, and backgrounds that appear. 【0013】 "Game balance" refers to a state in which various elements within a game are in harmony, and the game is designed to present players with appropriate challenges. [Brief explanation of the drawing] 【0014】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined. 【Embodiments for Implementing the Invention】 【0015】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0016】 First, the terms used in the following description will be explained. 【0017】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include 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. 【0018】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0019】 In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0020】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0021】 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." 【0022】 [First Embodiment] 【0023】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0024】 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. 【0025】 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). 【0026】 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. 【0027】 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. 【0028】 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. 【0029】 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. 【0030】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0031】 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. 【0032】 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. 【0033】 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. 【0034】 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". 【0035】 The system of this invention collects user behavior data in real time, analyzes it using a neural network, and generates dynamic game mechanics. Furthermore, it has the function to evolve the game environment based on user choices and adjust the game balance in real time based on player data. 【0036】 The device continuously records data related to the user's actions and behavior patterns while they are playing the game. This data is important for understanding how the user is progressing through the game. Data collected includes the user's movement patterns, item usage frequency, and strategic choices. 【0037】 The server receives data sent from the terminal, preprocesses it, and then inputs it into a neural network. This allows the server to generate game mechanics tailored to the user's play style. For example, for a player who prioritizes defense, the server will customize their gameplay by enhancing their defensive skill options. 【0038】 Furthermore, the servers have the flexibility to adaptively change the game environment based on the choices made by the user within the game. Enemy placement and terrain characteristics change depending on the route the user chooses and the outcome of the mission. This provides a constantly fresh and unpredictable game experience. 【0039】 To maintain a precise balance, the terminal analyzes real-time data such as the player's success rate, failure rate, and the time taken to complete the game. Based on this data, the server utilizes machine learning algorithms to make optimal balancing adjustments for each player's abilities. For example, if a challenge is too difficult, the server automatically adjusts its difficulty. 【0040】 For example, if a user who frequently uses defensive skills repeatedly fails on a particular stage, the device collects data and sends it to the server, which then modifies the enemy's attack patterns. Additionally, new obstacles may be added to the user's chosen route, allowing the player to try new strategies. 【0041】 In this way, the system of the present invention can provide a customized and immersive gaming experience based on the user's individual actions and choices. 【0042】 The following describes the processing flow. 【0043】 Step 1: 【0044】 The device collects real-time data on the user's actions during gameplay. This data includes movement patterns, skills used, and the number of times items are used. 【0045】 Step 2: 【0046】 The device sends collected behavioral data to the server at regular intervals. This minimizes data latency and allows for immediate reflection of changes in real time. 【0047】 Step 3: 【0048】 The server preprocesses the received data. Specifically, it removes noise, extracts necessary features, and formats the data into a form that is easy for the neural network to process. 【0049】 Step 4: 【0050】 The server inputs pre-processed data into a neural network to generate dynamic game mechanics tailored to the user's play style. This includes developing new skills and adjusting existing mechanics. 【0051】 Step 5: 【0052】 The server sends the generated game mechanics to the terminal, which then applies them within the game. After application, the user can experience the new mechanics. 【0053】 Step 6: 【0054】 The device records the user's choices within the game and the resulting consequences. This information is used for environmental evolution and balance adjustments. 【0055】 Step 7: 【0056】 The server runs an environment evolution algorithm based on the user's selection data to create a new game environment that matches the user's choices. This will be reflected in the environment the user faces the next time they play. 【0057】 Step 8: 【0058】 The terminals collect real-time data and analyze it to evaluate the difficulty of completing the game and the success rate of events. The server adjusts the game balance according to the analysis results. 【0059】 Step 9: 【0060】 The server quickly transmits any necessary balance adjustments to the device, which then reflects these changes in the game. This ensures that users always experience the game at the optimally adjusted difficulty level. 【0061】 (Example 1) 【0062】 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." 【0063】 In modern game systems, providing a game experience tailored to each user's individual play style in real time is challenging. Furthermore, while dynamic adaptation and balancing of the game environment based on user choices are necessary, the technologies capable of effectively achieving this are limited. This highlights the growing need for systems that can enhance user immersion and provide a personalized experience. 【0064】 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. 【0065】 In this invention, the server includes means for recording user operation information, means for analyzing the recorded information via a network, and means for generating personalized game elements based on the analysis results. This enables the generation of dynamic game elements tailored to the user and real-time adaptation to the environment. 【0066】 "User operation information" refers to information that records the details of the actions a user performs within the game. 【0067】 "Analyzing data via a network" means processing data using multiple computer systems to analyze user behavior patterns. 【0068】 "Generating personalized game elements" means creating specific game elements and mechanics that are tailored to the user's play style. 【0069】 "Applying in real time" means instantly reflecting the generated game elements in the game environment, allowing users to immediately experience their effects. 【0070】 "Adaptively changing the environment" means dynamically altering the game's situation and conditions based on user choices and actions. 【0071】 "Dynamically adjusting the level of challenge" means adjusting the difficulty and challenges of the game on the fly based on the user's performance and success rate. 【0072】 "Identifying frequently used actions" means recording and analyzing actions that users repeatedly perform and using that information to improve game progression. 【0073】 This invention aims to build an advanced game system that enhances the user's gameplay experience by recording and analyzing user input information and generating personalized game elements. 【0074】 Hardware and software configuration 【0075】 The terminal is a device that records the user's actions while they are playing a game. This may be a conventional electronic gaming device equipped with a touchscreen or input buttons. The data is transmitted in real time to a server via a network. The server is a computer with advanced computing power that analyzes the data and generates game elements tailored to the user's play style. 【0076】 The server uses generative AI models and neural networks for data analysis. This allows it to analyze specific user behavior patterns and perform dynamic game balance adjustments and environmental adaptations. The analysis results are immediately reflected in the game environment and provided to the user in real time. 【0077】 Specific examples of operation 【0078】 For example, if a user frequently performs defensive actions, the server analyzes that information and offers options to enhance defensive skills within the game. Also, if a user's challenge completion rate is low, the server can adjust the difficulty and suggest challenges that are optimal for the user. These adaptations ensure that users always have a fresh and engaging gaming experience. 【0079】 Example of a prompt 【0080】 "Think of ways to optimize the game by proposing new skills that respond to the user's defensive strategies." 【0081】 A game system configured in this way can take into account each user's individual play style and provide a comfortable and personalized gaming experience. 【0082】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0083】 Step 1: 【0084】 The device records the user's in-game actions in real time. This includes user interactions such as touch operations, movement, attacking, and defending. All user action data on the device is received as input, and a recorded action log is generated as output. This log includes actions such as recording which buttons the user pressed and how often. 【0085】 Step 2: 【0086】 The terminal sends recorded operation logs to the server. The logs are sent in batches at regular time intervals. The input is the operation log generated by the terminal, and the output is the data packets sent to the server. This transmission takes place in real time over the network, continuously informing the server of the user's gameplay status. 【0087】 Step 3: 【0088】 The server analyzes the operation logs received from the terminal. This includes data normalization and feature extraction. The server takes operation logs sent from the terminal as input and outputs user behavior patterns as analysis results. Specifically, the server classifies the data into categories and calculates frequency distributions. 【0089】 Step 4: 【0090】 The server uses a generative AI model based on the analysis results to generate game elements adapted to the user. This generation process selects skill sets and items according to the user's play style. The input is the user's behavior patterns, and the output is the generated, personalized game elements. For example, a user who frequently performs defensive actions will have their defensive skills enhanced. 【0091】 Step 5: 【0092】 The server applies the generated game elements to the game environment in real time. This allows users to instantly experience a customized game. The input is the generated game elements, and the output is the change in the game state after application. This operation includes updating the in-game UI and changing enemy placement. 【0093】 Step 6: 【0094】 The server continuously receives, analyzes, and adjusts data during gameplay. Machine learning algorithms are used to adjust the game balance based on user performance. The input is the user's continuous actions during gameplay, and the output is the adjusted game balance. Specifically, automatic difficulty adjustment is part of this process. 【0095】 (Application Example 1) 【0096】 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." 【0097】 Traditional interactive systems have limitations in customization based on user behavior, and therefore cannot adequately provide dynamic experiences that meet individual user needs. For this reason, it is necessary to provide adaptive experiences tailored to user characteristics and create immersive experiences that are engaging and prevent boredom. 【0098】 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. 【0099】 In this invention, the server includes information processing means for collecting user behavior information, information processing means for inputting the information into a neural network and generating dynamic interactive mechanics that are suited to the user's usage characteristics, information processing means for applying the generated interactive mechanics in real time, and information processing means for dynamically optimizing the interactive environment based on user information and providing a customized experience. This makes it possible to provide a highly personalized experience based on the user's behavior. 【0100】 "User behavior information" refers to behavioral data such as patterns of operations and movements, and strategic choices, generated when a user uses a system. 【0101】 "Information processing means" refers to a processing device or program for analyzing collected data and generating, transforming, or manipulating information according to a specific purpose. 【0102】 A "neural network" is a type of machine learning algorithm that analyzes input data through multiple layers of computing nodes and generates specific output information. 【0103】 "Usage characteristics" refer to characteristic information that indicates a user's preferences and behavioral patterns, and are elements that provide a customized experience based on this information. 【0104】 "Dynamic interactive mechanics" refers to interaction mechanisms and systems that change and adapt in real time in response to user actions or choices. 【0105】 "Real-time application" refers to a process that responds immediately to user actions and enables on-the-spot data processing. 【0106】 An "interactive environment" is an experience or simulation environment in which users can directly and dynamically participate, manipulate, and provide feedback. 【0107】 "Dynamic optimization" is the process by which a system autonomously adjusts itself to the best possible state in response to changing user behavior and circumstances, thereby maintaining an optimal state. 【0108】 To implement this invention, a system is needed to collect and analyze user behavior in real time. The terminal senses user behavior information and transmits it to a database. This terminal can be a smartphone or a computer and is equipped with sensors and a communication module that record operation information in detail. Specifically, iOS devices and Android® devices can be used as hardware. 【0109】 The server receives behavioral information sent from the user, preprocesses the information, and then performs analysis using a neural network. This neural network framework utilizes TENSORFLOW® to achieve rapid and highly accurate predictions. The neural network generates dynamic interactive mechanics based on the user's usage characteristics and sends them back to the terminal in real time. 【0110】 The software of this system works in conjunction with game engines such as Unity, providing an adaptive environment that responds immediately to user actions. The environment the user experiences is constantly updated with new data input from the server, and customized content is presented. The interactive environment is not limited to game fields, but extends to a wide range of applications, including educational platforms and fitness experience services. 【0111】 A concrete example would be a puzzle-style interactive application that runs on smartphones. Based on the user's preferences and past actions, it would generate new stages, helping users to continue taking on new challenges without getting bored. 【0112】 The generation AI model can generate prompts like the following based on the user's behavior history: "We have analyzed data from User X's five play sessions and found that the user prefers defensive mechanics. Based on this information, please propose a new stage design." This allows for a customized experience for each user. 【0113】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0114】 Step 1: 【0115】 The device monitors user actions in real time and collects behavioral information. This data includes user taps, swipe directions, and input speed. This data is recorded as logs and packaged together in JSON format. 【0116】 Step 2: 【0117】 The terminal sends the collected user behavior information to the server. The server receives this JSON data and performs preprocessing. Specifically, this preprocessing involves removing outliers and normalizing the data to make it easier for neural networks to handle. The output of this processing is a clean, analyzable dataset. 【0118】 Step 3: 【0119】 The server inputs pre-processed user behavior information into a neural network. The neural network uses the TensorFlow framework to analyze user behavior and generate dynamic interactive mechanics based on that analysis. The output of this process is customized data optimized for each user. 【0120】 Step 4: 【0121】 The server sends the generated dynamic interactive mechanics to the terminal in real time. The terminal receives this data and applies it to the interactive environment that the user operates using the Unity game engine or similar. The user can instantly experience a personalized experience based on the latest data. This output is an updated interactive scenario provided to the user. 【0122】 Step 5: 【0123】 Users continue to interact in a personalized environment, generating new behavioral information. This new behavioral information then returns to step 1, forming a continuous cycle of collection, analysis, and application. This iterative process builds a system that directly reflects user feedback. 【0124】 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. 【0125】 The system of this invention integrates user behavioral data and emotional data to realize a personalized gaming experience. This allows for dynamic and real-time adjustment of the game environment and mechanics according to the user's emotional state, providing a highly immersive experience. 【0126】 The device collects real-time data on the user's gameplay behavior and emotional data collected by the emotion engine. The emotion engine analyzes and obtains emotional data from the user's facial expressions, voice tone, and other factors. Specifically, it can recognize emotions such as joy, surprise, stress, and excitement. 【0127】 The server receives behavioral and emotional data transmitted from the terminal and integrates them during the preprocessing stage. This integrated data is then input into a neural network to generate dynamic game mechanics tailored to the user's play style and emotions. For example, if the user is experiencing stress, a mechanic that lowers the game's difficulty might be suggested. 【0128】 Furthermore, the server uses emotional data recognized by the emotion engine to adaptively change the game environment. This change provides an experience that allows users to maintain positive emotions at all times. If the user is surprised, new scenes or unexpected events are incorporated into the game to further promote engagement. 【0129】 The real-time game balance adjustment feature analyzes emotional data and adjusts the game balance according to the user's emotional state. For example, if the user is excited, the difficulty level will be set slightly higher to provide a challenge. 【0130】 For example, if a user expresses satisfaction during gameplay, the device sends that emotional data to the server. The server receives this data and either maintains the game mechanics as they are or adds more positive elements. It may also change the frequency of item appearances to provide a more meaningful experience for the user. 【0131】 By incorporating emotional data in this way, it becomes possible to provide a truly customized gaming experience for each user. This system is effective in improving the enjoyment and immersion of games and maintaining user engagement over the long term. 【0132】 The following describes the processing flow. 【0133】 Step 1: 【0134】 The device collects behavioral and emotional data in real time while the user is playing the game. Emotional data is extracted from the user's facial expressions and voice and analyzed by an emotion engine to identify emotions such as joy, surprise, and stress. 【0135】 Step 2: 【0136】 The device transmits the collected behavioral and emotional data to the server. Efficient data compression and communication methods are used to ensure that the data is transmitted without delay. 【0137】 Step 3: 【0138】 The server preprocesses the received user data. This preprocessing includes noise reduction and integration of sentiment and behavioral data, which creates a dataset ready for subsequent processing. 【0139】 Step 4: 【0140】 The server inputs pre-processed data into a neural network using deep learning. This network generates dynamic game mechanics that take into account the user's emotional state. For example, it suggests mechanics to reduce tension for a user who is stressed. 【0141】 Step 5: 【0142】 The server sends the generated mechanics to the terminal, which immediately applies them in the game. Users feel that the gameplay experience is adjusted in real time by the changes in mechanics. 【0143】 Step 6: 【0144】 The device records the user's choices during gameplay and the resulting outcomes, and sends this data to the server. The server uses this data to dynamically evolve the game environment in response to the user's choices. 【0145】 Step 7: 【0146】 The server analyzes emotional data and generates a new game environment that matches the user's emotional state. For example, if it detects surprise, it will introduce a new challenge or an unexpected event. 【0147】 Step 8: 【0148】 The server performs real-time game balance adjustments based on the user's emotional state. For example, if a user is excited, the difficulty level is fine-tuned to provide a challenge that offers a greater sense of accomplishment. 【0149】 Step 9: 【0150】 Once all adjustments are complete, the device will present the user with the final game environment and balance applied. Users can then enjoy a customized experience optimized for their emotional state. 【0151】 (Example 2) 【0152】 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". 【0153】 Traditional game systems, which provide a uniform experience without considering the user's emotional state, have struggled to deliver a customized and immersive experience for each individual user. This can lead to decreased user satisfaction and engagement. Therefore, there is a need to develop a system that dynamically reflects the user's emotional state and provides a personalized experience in real time. 【0154】 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. 【0155】 In this invention, the server includes means for collecting user behavior data and emotional data; means for analyzing the emotional data to identify the user's emotional state; means for inputting the behavior data and identified emotional state into a neural network to generate dynamic game mechanics that are suited to the user's play style and emotions; and means for applying the generated game mechanics in real time to adaptively adjust the game environment. This makes it possible to provide a customized game experience for each user in real time and to create an immersive game environment. 【0156】 "User behavior data" refers to data that represents a user's actions, choices, and operation patterns during gameplay. 【0157】 "Emotional data" refers to data about a user's emotional state obtained from their facial expressions and voice tone. 【0158】 A "neural network" is an algorithm or digital model that uses large amounts of data to mimic the neural circuits of the human brain and perform learning and reasoning. 【0159】 "Playstyle" is a concept that comprehensively represents the strategies, behavioral characteristics, and preferences a user adopts within a game. 【0160】 "Dynamic game mechanics" refers to the rules and mechanisms of a game that change in real time in response to the user's actions and emotional state. 【0161】 "Real-time application" means that the system and data are reflected instantly with virtually no time delay. 【0162】 "Game environment" refers to the virtual space that includes all elements that support gameplay, namely graphics, sound, story, characters, rules, etc. 【0163】 "Adaptively adjusting" refers to flexibly changing system settings and behavior in response to the user's identified emotional state. 【0164】 The system of this invention is a technology for collecting user behavioral and emotional data to provide a personalized gaming experience. During gameplay, the user's behavioral data, such as game operations and choices, is collected in real time by the device. In addition, the device uses sensors and cameras to acquire emotional data, such as facial expressions and voice tone. This emotional data is analyzed through an emotion engine designed to analyze emotional states. 【0165】 Behavioral and emotional data collected by the terminals are transmitted to the server using a high-speed and secure communication protocol. The server receives the data, performs preprocessing, imputes missing values, and removes noise. This preprocessing integrates the behavioral and emotional data and optimizes it as input for analysis. 【0166】 The server uses a neural network to analyze this integrated data and generate game mechanics that are tailored to the user's emotional state and play style. This network learns from a large amount of training data, enabling real-time prediction and suggestions. Based on the generated game mechanics, the server sends instructions to the terminal to adjust the game environment, adaptively changing in-game parameters. This allows the game to provide a dynamic experience that responds to the user's emotions. 【0167】 Specifically, if the server determines that a user is experiencing stress during gameplay, the game's difficulty will be automatically lowered. Conversely, if a user shows signs of excitement, the in-game challenges will increase, slightly raising the difficulty. 【0168】 An example of a prompt message is as follows: 【0169】 "Please explain how to collect emotional data from user facial expressions and voice tone, and customize game difficulty in real time." 【0170】 By relying on this system, we can provide a game experience that incorporates emotional data, thereby increasing user immersion and satisfaction. The system is extremely effective as a means of generating deeper engagement. 【0171】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0172】 Step 1: 【0173】 The device monitors the user's actions in real time using sensors and a camera. Specifically, the camera captures the user's facial expressions, and the microphone records their voice tone. This data is collected as behavioral and emotional data and stored on the device as an initial dataset. 【0174】 Step 2: 【0175】 The device transmits collected behavioral and emotional data to the server via a communication module. Encryption technology is used for this data transmission to ensure the security of the received data and prevent interference by third parties. The input data includes the user's real-time behavior and emotions, and is stored in the server's database. 【0176】 Step 3: 【0177】 The server preprocesses the received data. It performs a cleanup process to impute missing values ​​in the behavioral and sentiment data received as input and to reduce noise. Specifically, outliers are removed and normalization is performed to ensure data integrity. The output is an integrated dataset suitable for analysis. 【0178】 Step 4: 【0179】 The server inputs a pre-processed integrated dataset into a neural network. This network model analyzes user behavior patterns and emotional states based on the large amount of training data. This generates dynamic game mechanics based on the user's play style and emotions. The output provides guidance for specific game mechanics. 【0180】 Step 5: 【0181】 The server creates instructions to adjust the game environment based on the generated game mechanics and sends them to the terminal. The terminal receives these instructions and changes the in-game parameters in real time. Specifically, operations such as adjusting enemy behavior patterns and difficulty levels, and triggering new events are performed. As an output, a dynamic game experience adapted to the user's emotions is realized. 【0182】 (Application Example 2) 【0183】 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 device 14 will be referred to as the "terminal." 【0184】 The challenge lies in providing adaptive responses tailored to each user's individual emotional state, thereby achieving a highly customized user experience that goes beyond conventional static responses. In particular, there is a need to dynamically change user interactions in consumer electronic devices in response to the user's emotions. 【0185】 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. 【0186】 In this invention, the server includes means for acquiring user behavior data and emotional data; means for inputting the behavior data and emotional data into a neural network and generating dynamic action mechanics that are adapted to the user's play style and emotional state; and means for applying the generated action mechanics in real time and adjusting the response of electronic devices. This makes it possible to provide a highly personalized experience that is tailored to the user's emotional state. 【0187】 "User behavior data" refers to information about user operations and actions, and is data provided to the target electronic device or system. 【0188】 "Emotional data" refers to data that provides information about a user's emotions, such as their facial expressions and tone of voice. 【0189】 A "neural network" is a computational model inspired by biological neural circuits, used for data pattern recognition and machine learning. 【0190】 "Action mechanics" refers to the rules and methods of operation and response in a system or game, and are the elements that determine how a particular action is performed. 【0191】 "Real-time" refers to a dynamic environment where data and processes are processed instantly, meaning they respond without delay. 【0192】 "Electronic device response" refers to the actions or reactions that electronic devices take in response to user input or changes in the environment. 【0193】 In this invention, input / output devices such as cameras and microphones are equipped in home electronic devices to acquire user behavioral and emotional data. Specifically, a video capture device commonly used as a camera and an audio acquisition device used as a microphone are required. This makes it possible to collect emotional data in real time from the user's facial expressions and tone of voice. The emotional and behavioral data are transmitted to a server running a neural network. 【0194】 The server inputs this data into a neural network to generate behavioral mechanics appropriate to the user's emotional state and actions. These mechanics are then transmitted directly to electronic devices and dynamically applied during user interaction. This enables customized responses tailored to the user's emotions, optimizing the in-home interaction experience. 【0195】 For example, if a user is feeling stressed, the server can instruct the system to play relaxing music or suggest light exercise to alleviate stress. This allows for a better life experience tailored to the user's emotional state. 【0196】 An example of a prompt to input into the generating AI model is the following text: "Please provide code that provides a robot function that can determine emotions from the user's facial expressions and tone of voice, and then determine whether they are stressed or relaxed." 【0197】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0198】 Step 1: 【0199】 The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is represented in the form of a facial image and audio waveforms. 【0200】 Step 2: 【0201】 The device immediately transmits the acquired video and audio data to the server. The server receives this data and applies an emotion recognition algorithm to analyze the user's emotional state. This analysis uses facial features and voice tone as indicators to extract emotions such as joy and stress. 【0202】 Step 3: 【0203】 The server inputs the analyzed emotional state and user behavior data into a neural network. Here, data processing is performed that considers the emotional state and behavioral patterns to generate dynamic action mechanics that are best suited to the user's situation. The output is the action mechanics to be applied. 【0204】 Step 4: 【0205】 The server sends the generated motion mechanics to the terminal. Based on these instructions, the terminal causes the electronic device to perform a response appropriate to the user's emotional state. For example, it might play relaxing music or offer verbal exercise suggestions. 【0206】 Step 5: 【0207】 The user experiences a response from the device. Based on the response, the device collects feedback on how the user's emotional state changed as emotional data and sends a prompt to the server. This prompt contributes to improving the generative AI model, enabling an even better customized experience. 【0208】 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. 【0209】 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. 【0210】 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. 【0211】 [Second Embodiment] 【0212】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0213】 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. 【0214】 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). 【0215】 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. 【0216】 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. 【0217】 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). 【0218】 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. 【0219】 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. 【0220】 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. 【0221】 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. 【0222】 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. 【0223】 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". 【0224】 The system of this invention collects user behavior data in real time, analyzes it using a neural network, and generates dynamic game mechanics. Furthermore, it has the function to evolve the game environment based on user choices and adjust the game balance in real time based on player data. 【0225】 The device continuously records data related to the user's actions and behavior patterns while they are playing the game. This data is important for understanding how the user is progressing through the game. Data collected includes the user's movement patterns, item usage frequency, and strategic choices. 【0226】 The server receives data sent from the terminal, preprocesses it, and then inputs it into a neural network. This allows the server to generate game mechanics tailored to the user's play style. For example, for a player who prioritizes defense, the server will customize their gameplay by enhancing their defensive skill options. 【0227】 Furthermore, the servers have the flexibility to adaptively change the game environment based on the choices made by the user within the game. Enemy placement and terrain characteristics change depending on the route the user chooses and the outcome of the mission. This provides a constantly fresh and unpredictable game experience. 【0228】 To maintain a precise balance, the terminal analyzes real-time data such as the player's success rate, failure rate, and the time taken to complete the game. Based on this data, the server utilizes machine learning algorithms to make optimal balancing adjustments for each player's abilities. For example, if a challenge is too difficult, the server automatically adjusts its difficulty. 【0229】 For example, if a user who frequently uses defensive skills repeatedly fails on a particular stage, the device collects data and sends it to the server, which then modifies the enemy's attack patterns. Additionally, new obstacles may be added to the user's chosen route, allowing the player to try new strategies. 【0230】 In this way, the system of the present invention can provide a customized and immersive gaming experience based on the user's individual actions and choices. 【0231】 The following describes the processing flow. 【0232】 Step 1: 【0233】 The device collects real-time data on the user's actions during gameplay. This data includes movement patterns, skills used, and the number of times items are used. 【0234】 Step 2: 【0235】 The device sends collected behavioral data to the server at regular intervals. This minimizes data latency and allows for immediate reflection of changes in real time. 【0236】 Step 3: 【0237】 The server preprocesses the received data. Specifically, it removes noise, extracts necessary features, and formats the data into a form that is easy for the neural network to process. 【0238】 Step 4: 【0239】 The server inputs pre-processed data into a neural network to generate dynamic game mechanics tailored to the user's play style. This includes developing new skills and adjusting existing mechanics. 【0240】 Step 5: 【0241】 The server sends the generated game mechanics to the terminal, which then applies them within the game. After application, the user can experience the new mechanics. 【0242】 Step 6: 【0243】 The device records the user's choices within the game and the resulting consequences. This information is used for environmental evolution and balance adjustments. 【0244】 Step 7: 【0245】 The server runs an environment evolution algorithm based on the user's selection data to create a new game environment that matches the user's choices. This will be reflected in the environment the user faces the next time they play. 【0246】 Step 8: 【0247】 The terminals collect real-time data and analyze it to evaluate the difficulty of completing the game and the success rate of events. The server adjusts the game balance according to the analysis results. 【0248】 Step 9: 【0249】 The server quickly transmits any necessary balance adjustments to the device, which then reflects these changes in the game. This ensures that users always experience the game at the optimally adjusted difficulty level. 【0250】 (Example 1) 【0251】 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." 【0252】 In modern game systems, providing a game experience tailored to each user's individual play style in real time is challenging. Furthermore, while dynamic adaptation and balancing of the game environment based on user choices are necessary, the technologies capable of effectively achieving this are limited. This highlights the growing need for systems that can enhance user immersion and provide a personalized experience. 【0253】 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. 【0254】 In this invention, the server includes means for recording user operation information, means for analyzing the recorded information via a network, and means for generating personalized game elements based on the analysis results. This enables the generation of dynamic game elements tailored to the user and real-time adaptation to the environment. 【0255】 "User operation information" refers to information that records the details of the actions a user performs within the game. 【0256】 "Analyzing data via a network" means processing data using multiple computer systems to analyze user behavior patterns. 【0257】 "Generating personalized game elements" means creating specific game elements and mechanics that are tailored to the user's play style. 【0258】 "Applying in real time" means instantly reflecting the generated game elements in the game environment, allowing users to immediately experience their effects. 【0259】 "Adaptively changing the environment" means dynamically altering the game's situation and conditions based on user choices and actions. 【0260】 "Dynamically adjusting the level of challenge" means adjusting the difficulty and challenges of the game on the fly based on the user's performance and success rate. 【0261】 "Identifying frequently used actions" means recording and analyzing actions that users repeatedly perform and using that information to improve game progression. 【0262】 This invention aims to build an advanced game system that enhances the user's gameplay experience by recording and analyzing user input information and generating personalized game elements. 【0263】 Hardware and software configuration 【0264】 The terminal is a device that records the user's actions while they are playing a game. This may be a conventional electronic gaming device equipped with a touchscreen or input buttons. The data is transmitted in real time to a server via a network. The server is a computer with advanced computing power that analyzes the data and generates game elements tailored to the user's play style. 【0265】 The server uses generative AI models and neural networks for data analysis. This allows it to analyze specific user behavior patterns and perform dynamic game balance adjustments and environmental adaptations. The analysis results are immediately reflected in the game environment and provided to the user in real time. 【0266】 Specific examples of operation 【0267】 For example, if a user frequently performs defensive actions, the server analyzes that information and offers options to enhance defensive skills within the game. Also, if a user's challenge completion rate is low, the server can adjust the difficulty and suggest challenges that are optimal for the user. These adaptations ensure that users always have a fresh and engaging gaming experience. 【0268】 Example of a prompt 【0269】 "Think of ways to optimize the game by proposing new skills that respond to the user's defensive strategies." 【0270】 A game system configured in this way can take into account each user's individual play style and provide a comfortable and personalized gaming experience. 【0271】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0272】 Step 1: 【0273】 The device records the user's in-game actions in real time. This includes user interactions such as touch operations, movement, attacking, and defending. All user action data on the device is received as input, and a recorded action log is generated as output. This log includes actions such as recording which buttons the user pressed and how often. 【0274】 Step 2: 【0275】 The terminal sends recorded operation logs to the server. The logs are sent in batches at regular time intervals. The input is the operation log generated by the terminal, and the output is the data packets sent to the server. This transmission takes place in real time over the network, continuously informing the server of the user's gameplay status. 【0276】 Step 3: 【0277】 The server analyzes the operation logs received from the terminal. This includes data normalization and feature extraction. The server takes operation logs sent from the terminal as input and outputs user behavior patterns as analysis results. Specifically, the server classifies the data into categories and calculates frequency distributions. 【0278】 Step 4: 【0279】 The server uses a generative AI model based on the analysis results to generate game elements adapted to the user. This generation process selects skill sets and items according to the user's play style. The input is the user's behavior patterns, and the output is the generated, personalized game elements. For example, a user who frequently performs defensive actions will have their defensive skills enhanced. 【0280】 Step 5: 【0281】 The server applies the generated game elements to the game environment in real time. As a result, the user can immediately obtain a customized game experience. The input is the generated game elements, and the output is the change in the game state after application. This operation includes UI updates within the game and changes in enemy placement. 【0282】 Step 6: 【0283】 The server continuously receives data during gameplay and performs analysis and adjustment. Here, a machine learning algorithm is used to adjust the game balance according to the user's performance. The input is the continuous operation data of the user during gameplay, and the output is the adjusted game balance. Specifically, automatic adjustment of difficulty is part of this operation. 【0284】 (Application Example 1) 【0285】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0286】 In a conventional interactive system, customization based on the user's behavior is limited, and there is a problem that a dynamic experience according to the individual needs of the user cannot be sufficiently provided. Therefore, it is necessary to provide an adaptive experience according to the characteristics of the user and realize an immersive experience that never gets boring. 【0287】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0288】 In this invention, the server includes information processing means for collecting user behavior information, information processing means for inputting the information into a neural network and generating dynamic interactive mechanics that are suited to the user's usage characteristics, information processing means for applying the generated interactive mechanics in real time, and information processing means for dynamically optimizing the interactive environment based on user information and providing a customized experience. This makes it possible to provide a highly personalized experience based on the user's behavior. 【0289】 "User behavior information" refers to behavioral data such as patterns of operations and movements, and strategic choices, generated when a user uses a system. 【0290】 "Information processing means" refers to a processing device or program for analyzing collected data and generating, transforming, or manipulating information according to a specific purpose. 【0291】 A "neural network" is a type of machine learning algorithm that analyzes input data through multiple layers of computing nodes and generates specific output information. 【0292】 "Usage characteristics" refer to characteristic information that indicates a user's preferences and behavioral patterns, and are elements that provide a customized experience based on this information. 【0293】 "Dynamic interactive mechanics" refers to interaction mechanisms and systems that change and adapt in real time in response to user actions or choices. 【0294】 "Real-time application" refers to a process that responds immediately to user actions and enables on-the-spot data processing. 【0295】 An "interactive environment" is an experience or simulation environment in which users can directly and dynamically participate, manipulate, and provide feedback. 【0296】 "Dynamic optimization" is the process by which a system autonomously adjusts itself to the best possible state in response to changing user behavior and circumstances, thereby maintaining an optimal state. 【0297】 To implement this invention, a system is needed to collect and analyze user behavior in real time. The terminal senses user behavior information and transmits it to a database. This terminal can be a smartphone or a computer, and is equipped with sensors and a communication module that record operation information in detail. Specifically, iOS devices or Android devices can be used as hardware. 【0298】 The server receives behavioral information sent by the user, preprocesses the information, and then performs analysis using a neural network. This neural network framework utilizes TensorFlow to achieve rapid and highly accurate predictions. The neural network generates dynamic interactive mechanics based on the user's usage characteristics and sends them back to the terminal in real time. 【0299】 The software of this system works in conjunction with game engines such as Unity, providing an adaptive environment that responds immediately to user actions. The environment the user experiences is constantly updated with new data input from the server, and customized content is presented. The interactive environment is not limited to game fields, but extends to a wide range of applications, including educational platforms and fitness experience services. 【0300】 A concrete example would be a puzzle-style interactive application that runs on smartphones. Based on the user's preferences and past actions, it would generate new stages, helping users to continue taking on new challenges without getting bored. 【0301】 The generated AI model can generate the following prompt sentences based on the user's action history. "Analyze the data obtained from five plays of User X and show that the user prefers defensive mechanics. Based on this information, please propose a new stage design." This enables the provision of a customized experience for each user. 【0302】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0303】 Step 1: 【0304】 The terminal monitors the user's operations in real time and collects action information. The data collected includes the user's tap actions, swipe directions, input speeds, etc. These data are recorded as logs and are packaged together in JSON format. 【0305】 Step 2: 【0306】 The terminal transmits the collected user action information to the server. The server receives this JSON data and performs preprocessing. Specific preprocessing includes removing outliers and normalizing the data, and processing it into a form that is easy for the neural network to handle. The output of this process is a clean and analyzable dataset. 【0307】 Step 3: 【0308】 The server inputs the preprocessed user action information into the neural network. The neural network analyzes the user's usage characteristics using the TensorFlow framework and generates dynamic interactive mechanics based on them. The output of this process is customized data optimized for each user. 【0309】 Step 4: 【0310】 The server sends the generated dynamic interactive mechanics to the terminal in real time. The terminal receives this data and applies it to the interactive environment that the user operates using the Unity game engine or similar. The user can instantly experience a personalized experience based on the latest data. This output is an updated interactive scenario provided to the user. 【0311】 Step 5: 【0312】 Users continue to interact in a personalized environment, generating new behavioral information. This new behavioral information then returns to step 1, forming a continuous cycle of collection, analysis, and application. This iterative process builds a system that directly reflects user feedback. 【0313】 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. 【0314】 The system of this invention integrates user behavioral data and emotional data to realize a personalized gaming experience. This allows for dynamic and real-time adjustment of the game environment and mechanics according to the user's emotional state, providing a highly immersive experience. 【0315】 The device collects real-time data on the user's gameplay behavior and emotional data collected by the emotion engine. The emotion engine analyzes and obtains emotional data from the user's facial expressions, voice tone, and other factors. Specifically, it can recognize emotions such as joy, surprise, stress, and excitement. 【0316】 The server receives behavioral and emotional data transmitted from the terminal and integrates them during the preprocessing stage. This integrated data is then input into a neural network to generate dynamic game mechanics tailored to the user's play style and emotions. For example, if the user is experiencing stress, a mechanic that lowers the game's difficulty might be suggested. 【0317】 Furthermore, the server uses emotional data recognized by the emotion engine to adaptively change the game environment. This change provides an experience that allows users to maintain positive emotions at all times. If the user is surprised, new scenes or unexpected events are incorporated into the game to further promote engagement. 【0318】 The real-time game balance adjustment feature analyzes emotional data and adjusts the game balance according to the user's emotional state. For example, if the user is excited, the difficulty level will be set slightly higher to provide a challenge. 【0319】 For example, if a user expresses satisfaction during gameplay, the device sends that emotional data to the server. The server receives this data and either maintains the game mechanics as they are or adds more positive elements. It may also change the frequency of item appearances to provide a more meaningful experience for the user. 【0320】 By incorporating emotional data in this way, it becomes possible to provide a truly customized gaming experience for each user. This system is effective in improving the enjoyment and immersion of games and maintaining user engagement over the long term. 【0321】 The following describes the processing flow. 【0322】 Step 1: 【0323】 The device collects behavioral and emotional data in real time while the user is playing the game. Emotional data is extracted from the user's facial expressions and voice and analyzed by an emotion engine to identify emotions such as joy, surprise, and stress. 【0324】 Step 2: 【0325】 The device transmits the collected behavioral and emotional data to the server. Efficient data compression and communication methods are used to ensure that the data is transmitted without delay. 【0326】 Step 3: 【0327】 The server preprocesses the received user data. This preprocessing includes noise reduction and integration of sentiment and behavioral data, which creates a dataset ready for subsequent processing. 【0328】 Step 4: 【0329】 The server inputs pre-processed data into a neural network using deep learning. This network generates dynamic game mechanics that take into account the user's emotional state. For example, it suggests mechanics to reduce tension for a user who is stressed. 【0330】 Step 5: 【0331】 The server sends the generated mechanics to the terminal, which immediately applies them in the game. Users feel that the gameplay experience is adjusted in real time by the changes in mechanics. 【0332】 Step 6: 【0333】 The device records the user's choices during gameplay and the resulting outcomes, and sends this data to the server. The server uses this data to dynamically evolve the game environment in response to the user's choices. 【0334】 Step 7: 【0335】 The server analyzes emotional data and generates a new game environment that matches the user's emotional state. For example, if it detects surprise, it will introduce a new challenge or an unexpected event. 【0336】 Step 8: 【0337】 The server performs real-time game balance adjustments based on the user's emotional state. For example, if a user is excited, the difficulty level is fine-tuned to provide a challenge that offers a greater sense of accomplishment. 【0338】 Step 9: 【0339】 Once all adjustments are complete, the device will present the user with the final game environment and balance applied. Users can then enjoy a customized experience optimized for their emotional state. 【0340】 (Example 2) 【0341】 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". 【0342】 Traditional game systems, which provide a uniform experience without considering the user's emotional state, have struggled to deliver a customized and immersive experience for each individual user. This can lead to decreased user satisfaction and engagement. Therefore, there is a need to develop a system that dynamically reflects the user's emotional state and provides a personalized experience in real time. 【0343】 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. 【0344】 In this invention, the server includes means for collecting user behavior data and emotional data; means for analyzing the emotional data to identify the user's emotional state; means for inputting the behavior data and identified emotional state into a neural network to generate dynamic game mechanics that are suited to the user's play style and emotions; and means for applying the generated game mechanics in real time to adaptively adjust the game environment. This makes it possible to provide a customized game experience for each user in real time and to create an immersive game environment. 【0345】 "User behavior data" refers to data that represents a user's actions, choices, and operation patterns during gameplay. 【0346】 "Emotional data" refers to data about a user's emotional state obtained from their facial expressions and voice tone. 【0347】 A "neural network" is an algorithm or digital model that uses large amounts of data to mimic the neural circuits of the human brain and perform learning and reasoning. 【0348】 "Playstyle" is a concept that comprehensively represents the strategies, behavioral characteristics, and preferences a user adopts within a game. 【0349】 "Dynamic game mechanics" refers to the rules and mechanisms of a game that change in real time in response to the user's actions and emotional state. 【0350】 "Real-time application" means that the system and data are reflected instantly with virtually no time delay. 【0351】 "Game environment" refers to the virtual space that includes all elements that support gameplay, namely graphics, sound, story, characters, rules, etc. 【0352】 "Adaptively adjusting" refers to flexibly changing system settings and behavior in response to the user's identified emotional state. 【0353】 The system of this invention is a technology for collecting user behavioral and emotional data to provide a personalized gaming experience. During gameplay, the user's behavioral data, such as game operations and choices, is collected in real time by the device. In addition, the device uses sensors and cameras to acquire emotional data, such as facial expressions and voice tone. This emotional data is analyzed through an emotion engine designed to analyze emotional states. 【0354】 Behavioral and emotional data collected by the terminals are transmitted to the server using a high-speed and secure communication protocol. The server receives the data, performs preprocessing, imputes missing values, and removes noise. This preprocessing integrates the behavioral and emotional data and optimizes it as input for analysis. 【0355】 The server uses a neural network to analyze this integrated data and generate game mechanics that are tailored to the user's emotional state and play style. This network learns from a large amount of training data, enabling real-time prediction and suggestions. Based on the generated game mechanics, the server sends instructions to the terminal to adjust the game environment, adaptively changing in-game parameters. This allows the game to provide a dynamic experience that responds to the user's emotions. 【0356】 Specifically, if the server determines that a user is experiencing stress during gameplay, the game's difficulty will be automatically lowered. Conversely, if a user shows signs of excitement, the in-game challenges will increase, slightly raising the difficulty. 【0357】 An example of a prompt message is as follows: 【0358】 "Please explain how to collect emotional data from user facial expressions and voice tone, and customize game difficulty in real time." 【0359】 By relying on this system, we can provide a game experience that incorporates emotional data, thereby increasing user immersion and satisfaction. The system is extremely effective as a means of generating deeper engagement. 【0360】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0361】 Step 1: 【0362】 The device monitors the user's actions in real time using sensors and a camera. Specifically, the camera captures the user's facial expressions, and the microphone records their voice tone. This data is collected as behavioral and emotional data and stored on the device as an initial dataset. 【0363】 Step 2: 【0364】 The device transmits collected behavioral and emotional data to the server via a communication module. Encryption technology is used for this data transmission to ensure the security of the received data and prevent interference by third parties. The input data includes the user's real-time behavior and emotions, and is stored in the server's database. 【0365】 Step 3: 【0366】 The server preprocesses the received data. It performs a cleanup process to impute missing values ​​in the behavioral and sentiment data received as input and to reduce noise. Specifically, outliers are removed and normalization is performed to ensure data integrity. The output is an integrated dataset suitable for analysis. 【0367】 Step 4: 【0368】 The server inputs a pre-processed integrated dataset into a neural network. This network model analyzes user behavior patterns and emotional states based on the large amount of training data. This generates dynamic game mechanics based on the user's play style and emotions. The output provides guidance for specific game mechanics. 【0369】 Step 5: 【0370】 The server creates instructions to adjust the game environment based on the generated game mechanics and sends them to the terminal. The terminal receives these instructions and changes the in-game parameters in real time. Specifically, operations such as adjusting enemy behavior patterns and difficulty levels, and triggering new events are performed. As an output, a dynamic game experience adapted to the user's emotions is realized. 【0371】 (Application Example 2) 【0372】 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." 【0373】 The challenge lies in providing adaptive responses tailored to each user's individual emotional state, thereby achieving a highly customized user experience that goes beyond conventional static responses. In particular, there is a need to dynamically change user interactions in consumer electronic devices in response to the user's emotions. 【0374】 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. 【0375】 In this invention, the server includes means for acquiring user behavior data and emotional data; means for inputting the behavior data and emotional data into a neural network and generating dynamic action mechanics that are adapted to the user's play style and emotional state; and means for applying the generated action mechanics in real time and adjusting the response of electronic devices. This makes it possible to provide a highly personalized experience that is tailored to the user's emotional state. 【0376】 "User behavior data" refers to information about user operations and actions, and is data provided to the target electronic device or system. 【0377】 "Emotional data" refers to data that provides information about a user's emotions, such as their facial expressions and tone of voice. 【0378】 A "neural network" is a computational model inspired by biological neural circuits, used for data pattern recognition and machine learning. 【0379】 "Action mechanics" refers to the rules and methods of operation and response in a system or game, and are the elements that determine how a particular action is performed. 【0380】 "Real-time" refers to a dynamic environment where data and processes are processed instantly, meaning they respond without delay. 【0381】 "Electronic device response" refers to the actions or reactions that electronic devices take in response to user input or changes in the environment. 【0382】 In this invention, input / output devices such as cameras and microphones are equipped in home electronic devices to acquire user behavioral and emotional data. Specifically, a video capture device commonly used as a camera and an audio acquisition device used as a microphone are required. This makes it possible to collect emotional data in real time from the user's facial expressions and tone of voice. The emotional and behavioral data are transmitted to a server running a neural network. 【0383】 The server inputs this data into a neural network to generate behavioral mechanics appropriate to the user's emotional state and actions. These mechanics are then transmitted directly to electronic devices and dynamically applied during user interaction. This enables customized responses tailored to the user's emotions, optimizing the in-home interaction experience. 【0384】 For example, if a user is feeling stressed, the server can instruct the system to play relaxing music or suggest light exercise to alleviate stress. This allows for a better life experience tailored to the user's emotional state. 【0385】 An example of a prompt to input into the generating AI model is the following text: "Please provide code that provides a robot function that can determine emotions from the user's facial expressions and tone of voice, and then determine whether they are stressed or relaxed." 【0386】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0387】 Step 1: 【0388】 The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is represented in the form of a facial image and audio waveforms. 【0389】 Step 2: 【0390】 The device immediately transmits the acquired video and audio data to the server. The server receives this data and applies an emotion recognition algorithm to analyze the user's emotional state. This analysis uses facial features and voice tone as indicators to extract emotions such as joy and stress. 【0391】 Step 3: 【0392】 The server inputs the analyzed emotional state and user behavior data into a neural network. Here, data processing is performed that considers the emotional state and behavioral patterns to generate dynamic action mechanics that are best suited to the user's situation. The output is the action mechanics to be applied. 【0393】 Step 4: 【0394】 The server sends the generated motion mechanics to the terminal. Based on these instructions, the terminal causes the electronic device to perform a response appropriate to the user's emotional state. For example, it might play relaxing music or offer verbal exercise suggestions. 【0395】 Step 5: 【0396】 The user experiences a response from the device. Based on the response, the device collects feedback on how the user's emotional state changed as emotional data and sends a prompt to the server. This prompt contributes to improving the generative AI model, enabling an even better customized experience. 【0397】 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. 【0398】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0399】 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. 【0400】 [Third Embodiment] 【0401】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0402】 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. 【0403】 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). 【0404】 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. 【0405】 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. 【0406】 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). 【0407】 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. 【0408】 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. 【0409】 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. 【0410】 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. 【0411】 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. 【0412】 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". 【0413】 The system of this invention collects user behavior data in real time, analyzes it using a neural network, and generates dynamic game mechanics. Furthermore, it has the function to evolve the game environment based on user choices and adjust the game balance in real time based on player data. 【0414】 The device continuously records data related to the user's actions and behavior patterns while they are playing the game. This data is important for understanding how the user is progressing through the game. Data collected includes the user's movement patterns, item usage frequency, and strategic choices. 【0415】 The server receives data sent from the terminal, preprocesses it, and then inputs it into a neural network. This allows the server to generate game mechanics tailored to the user's play style. For example, for a player who prioritizes defense, the server will customize their gameplay by enhancing their defensive skill options. 【0416】 Furthermore, the servers have the flexibility to adaptively change the game environment based on the choices made by the user within the game. Enemy placement and terrain characteristics change depending on the route the user chooses and the outcome of the mission. This provides a constantly fresh and unpredictable game experience. 【0417】 To maintain a precise balance, the terminal analyzes real-time data such as the player's success rate, failure rate, and the time taken to complete the game. Based on this data, the server utilizes machine learning algorithms to make optimal balancing adjustments for each player's abilities. For example, if a challenge is too difficult, the server automatically adjusts its difficulty. 【0418】 For example, if a user who frequently uses defensive skills repeatedly fails on a particular stage, the device collects data and sends it to the server, which then modifies the enemy's attack patterns. Additionally, new obstacles may be added to the user's chosen route, allowing the player to try new strategies. 【0419】 In this way, the system of the present invention can provide a customized and immersive gaming experience based on the user's individual actions and choices. 【0420】 The following describes the processing flow. 【0421】 Step 1: 【0422】 The device collects real-time data on the user's actions during gameplay. This data includes movement patterns, skills used, and the number of times items are used. 【0423】 Step 2: 【0424】 The device sends collected behavioral data to the server at regular intervals. This minimizes data latency and allows for immediate reflection of changes in real time. 【0425】 Step 3: 【0426】 The server preprocesses the received data. Specifically, it removes noise, extracts necessary features, and formats the data into a form that is easy for the neural network to process. 【0427】 Step 4: 【0428】 The server inputs pre-processed data into a neural network to generate dynamic game mechanics tailored to the user's play style. This includes developing new skills and adjusting existing mechanics. 【0429】 Step 5: 【0430】 The server sends the generated game mechanics to the terminal, which then applies them within the game. After application, the user can experience the new mechanics. 【0431】 Step 6: 【0432】 The device records the user's choices within the game and the resulting consequences. This information is used for environmental evolution and balance adjustments. 【0433】 Step 7: 【0434】 The server runs an environment evolution algorithm based on the user's selection data to create a new game environment that matches the user's choices. This will be reflected in the environment the user faces the next time they play. 【0435】 Step 8: 【0436】 The terminals collect real-time data and analyze it to evaluate the difficulty of completing the game and the success rate of events. The server adjusts the game balance according to the analysis results. 【0437】 Step 9: 【0438】 The server quickly transmits any necessary balance adjustments to the device, which then reflects these changes in the game. This ensures that users always experience the game at the optimally adjusted difficulty level. 【0439】 (Example 1) 【0440】 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." 【0441】 In modern game systems, providing a game experience tailored to each user's individual play style in real time is challenging. Furthermore, while dynamic adaptation and balancing of the game environment based on user choices are necessary, the technologies capable of effectively achieving this are limited. This highlights the growing need for systems that can enhance user immersion and provide a personalized experience. 【0442】 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. 【0443】 In this invention, the server includes means for recording user operation information, means for analyzing the recorded information via a network, and means for generating personalized game elements based on the analysis results. This enables the generation of dynamic game elements tailored to the user and real-time adaptation to the environment. 【0444】 "User operation information" refers to information that records the details of the actions a user performs within the game. 【0445】 "Analyzing data via a network" means processing data using multiple computer systems to analyze user behavior patterns. 【0446】 "Generating personalized game elements" means creating specific game elements and mechanics that are tailored to the user's play style. 【0447】 "Applying in real time" means instantly reflecting the generated game elements in the game environment, allowing users to immediately experience their effects. 【0448】 "Adaptively changing the environment" means dynamically altering the game's situation and conditions based on user choices and actions. 【0449】 "Dynamically adjusting the level of challenge" means adjusting the difficulty and challenges of the game on the fly based on the user's performance and success rate. 【0450】 "Identifying frequently used actions" means recording and analyzing actions that users repeatedly perform and using that information to improve game progression. 【0451】 This invention aims to build an advanced game system that enhances the user's gameplay experience by recording and analyzing user input information and generating personalized game elements. 【0452】 Hardware and software configuration 【0453】 The terminal is a device that records the user's actions while they are playing a game. This may be a conventional electronic gaming device equipped with a touchscreen or input buttons. The data is transmitted in real time to a server via a network. The server is a computer with advanced computing power that analyzes the data and generates game elements tailored to the user's play style. 【0454】 The server uses generative AI models and neural networks for data analysis. This allows it to analyze specific user behavior patterns and perform dynamic game balance adjustments and environmental adaptations. The analysis results are immediately reflected in the game environment and provided to the user in real time. 【0455】 Specific examples of operation 【0456】 For example, if a user frequently performs defensive actions, the server analyzes that information and offers options to enhance defensive skills within the game. Also, if a user's challenge completion rate is low, the server can adjust the difficulty and suggest challenges that are optimal for the user. These adaptations ensure that users always have a fresh and engaging gaming experience. 【0457】 Example of a prompt 【0458】 "Think of ways to optimize the game by proposing new skills that respond to the user's defensive strategies." 【0459】 A game system configured in this way can take into account each user's individual play style and provide a comfortable and personalized gaming experience. 【0460】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0461】 Step 1: 【0462】 The device records the user's in-game actions in real time. This includes user interactions such as touch operations, movement, attacking, and defending. All user action data on the device is received as input, and a recorded action log is generated as output. This log includes actions such as recording which buttons the user pressed and how often. 【0463】 Step 2: 【0464】 The terminal sends recorded operation logs to the server. The logs are sent in batches at regular time intervals. The input is the operation log generated by the terminal, and the output is the data packets sent to the server. This transmission takes place in real time over the network, continuously informing the server of the user's gameplay status. 【0465】 Step 3: 【0466】 The server analyzes the operation logs received from the terminal. This includes data normalization and feature extraction. The server takes operation logs sent from the terminal as input and outputs user behavior patterns as analysis results. Specifically, the server classifies the data into categories and calculates frequency distributions. 【0467】 Step 4: 【0468】 The server uses a generative AI model based on the analysis results to generate game elements adapted to the user. This generation process selects skill sets and items according to the user's play style. The input is the user's behavior patterns, and the output is the generated, personalized game elements. For example, a user who frequently performs defensive actions will have their defensive skills enhanced. 【0469】 Step 5: 【0470】 The server applies the generated game elements to the game environment in real time. This allows users to instantly experience a customized game. The input is the generated game elements, and the output is the change in the game state after application. This operation includes updating the in-game UI and changing enemy placement. 【0471】 Step 6: 【0472】 The server continuously receives, analyzes, and adjusts data during gameplay. Machine learning algorithms are used to adjust the game balance based on user performance. The input is the user's continuous actions during gameplay, and the output is the adjusted game balance. Specifically, automatic difficulty adjustment is part of this process. 【0473】 (Application Example 1) 【0474】 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." 【0475】 Traditional interactive systems have limitations in customization based on user behavior, and therefore cannot adequately provide dynamic experiences that meet individual user needs. For this reason, it is necessary to provide adaptive experiences tailored to user characteristics and create immersive experiences that are engaging and prevent boredom. 【0476】 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. 【0477】 In this invention, the server includes information processing means for collecting user behavior information, information processing means for inputting the information into a neural network and generating dynamic interactive mechanics that are suited to the user's usage characteristics, information processing means for applying the generated interactive mechanics in real time, and information processing means for dynamically optimizing the interactive environment based on user information and providing a customized experience. This makes it possible to provide a highly personalized experience based on the user's behavior. 【0478】 "User behavior information" refers to behavioral data such as patterns of operations and movements, and strategic choices, generated when a user uses a system. 【0479】 "Information processing means" refers to a processing device or program for analyzing collected data and generating, transforming, or manipulating information according to a specific purpose. 【0480】 A "neural network" is a type of machine learning algorithm that analyzes input data through multiple layers of computing nodes and generates specific output information. 【0481】 "Usage characteristics" refer to characteristic information that indicates a user's preferences and behavioral patterns, and are elements that provide a customized experience based on this information. 【0482】 "Dynamic interactive mechanics" refers to interaction mechanisms and systems that change and adapt in real time in response to user actions or choices. 【0483】 "Real-time application" refers to a process that responds immediately to user actions and enables on-the-spot data processing. 【0484】 An "interactive environment" is an experience or simulation environment in which users can directly and dynamically participate, manipulate, and provide feedback. 【0485】 "Dynamic optimization" is the process by which a system autonomously adjusts itself to the best possible state in response to changing user behavior and circumstances, thereby maintaining an optimal state. 【0486】 To implement this invention, a system is needed to collect and analyze user behavior in real time. The terminal senses user behavior information and transmits it to a database. This terminal can be a smartphone or a computer, and is equipped with sensors and a communication module that record operation information in detail. Specifically, iOS devices or Android devices can be used as hardware. 【0487】 The server receives behavioral information sent by the user, preprocesses the information, and then performs analysis using a neural network. This neural network framework utilizes TensorFlow to achieve rapid and highly accurate predictions. The neural network generates dynamic interactive mechanics based on the user's usage characteristics and sends them back to the terminal in real time. 【0488】 The software of this system works in conjunction with game engines such as Unity, providing an adaptive environment that responds immediately to user actions. The environment the user experiences is constantly updated with new data input from the server, and customized content is presented. The interactive environment is not limited to game fields, but extends to a wide range of applications, including educational platforms and fitness experience services. 【0489】 A concrete example would be a puzzle-style interactive application that runs on smartphones. Based on the user's preferences and past actions, it would generate new stages, helping users to continue taking on new challenges without getting bored. 【0490】 The generation AI model can generate prompts like the following based on the user's behavior history: "We have analyzed data from User X's five play sessions and found that the user prefers defensive mechanics. Based on this information, please propose a new stage design." This allows for a customized experience for each user. 【0491】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0492】 Step 1: 【0493】 The device monitors user actions in real time and collects behavioral information. This data includes user taps, swipe directions, and input speed. This data is recorded as logs and packaged together in JSON format. 【0494】 Step 2: 【0495】 The terminal sends the collected user behavior information to the server. The server receives this JSON data and performs preprocessing. Specifically, this preprocessing involves removing outliers and normalizing the data to make it easier for neural networks to handle. The output of this processing is a clean, analyzable dataset. 【0496】 Step 3: 【0497】 The server inputs pre-processed user behavior information into a neural network. The neural network uses the TensorFlow framework to analyze user behavior and generate dynamic interactive mechanics based on that analysis. The output of this process is customized data optimized for each user. 【0498】 Step 4: 【0499】 The server sends the generated dynamic interactive mechanics to the terminal in real time. The terminal receives this data and applies it to the interactive environment that the user operates using the Unity game engine or similar. The user can instantly experience a personalized experience based on the latest data. This output is an updated interactive scenario provided to the user. 【0500】 Step 5: 【0501】 Users continue to interact in a personalized environment, generating new behavioral information. This new behavioral information then returns to step 1, forming a continuous cycle of collection, analysis, and application. This iterative process builds a system that directly reflects user feedback. 【0502】 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. 【0503】 The system of this invention integrates user behavioral data and emotional data to realize a personalized gaming experience. This allows for dynamic and real-time adjustment of the game environment and mechanics according to the user's emotional state, providing a highly immersive experience. 【0504】 The device collects real-time data on the user's gameplay behavior and emotional data collected by the emotion engine. The emotion engine analyzes and obtains emotional data from the user's facial expressions, voice tone, and other factors. Specifically, it can recognize emotions such as joy, surprise, stress, and excitement. 【0505】 The server receives behavioral and emotional data transmitted from the terminal and integrates them during the preprocessing stage. This integrated data is then input into a neural network to generate dynamic game mechanics tailored to the user's play style and emotions. For example, if the user is experiencing stress, a mechanic that lowers the game's difficulty might be suggested. 【0506】 Furthermore, the server uses emotional data recognized by the emotion engine to adaptively change the game environment. This change provides an experience that allows users to maintain positive emotions at all times. If the user is surprised, new scenes or unexpected events are incorporated into the game to further promote engagement. 【0507】 The real-time game balance adjustment feature analyzes emotional data and adjusts the game balance according to the user's emotional state. For example, if the user is excited, the difficulty level will be set slightly higher to provide a challenge. 【0508】 For example, if a user expresses satisfaction during gameplay, the device sends that emotional data to the server. The server receives this data and either maintains the game mechanics as they are or adds more positive elements. It may also change the frequency of item appearances to provide a more meaningful experience for the user. 【0509】 By incorporating emotional data in this way, it becomes possible to provide a truly customized gaming experience for each user. This system is effective in improving the enjoyment and immersion of games and maintaining user engagement over the long term. 【0510】 The following describes the processing flow. 【0511】 Step 1: 【0512】 The device collects behavioral and emotional data in real time while the user is playing the game. Emotional data is extracted from the user's facial expressions and voice and analyzed by an emotion engine to identify emotions such as joy, surprise, and stress. 【0513】 Step 2: 【0514】 The device transmits the collected behavioral and emotional data to the server. Efficient data compression and communication methods are used to ensure that the data is transmitted without delay. 【0515】 Step 3: 【0516】 The server preprocesses the received user data. This preprocessing includes noise reduction and integration of sentiment and behavioral data, which creates a dataset ready for subsequent processing. 【0517】 Step 4: 【0518】 The server inputs pre-processed data into a neural network using deep learning. This network generates dynamic game mechanics that take into account the user's emotional state. For example, it suggests mechanics to reduce tension for a user who is stressed. 【0519】 Step 5: 【0520】 The server sends the generated mechanics to the terminal, which immediately applies them in the game. Users feel that the gameplay experience is adjusted in real time by the changes in mechanics. 【0521】 Step 6: 【0522】 The device records the user's choices during gameplay and the resulting outcomes, and sends this data to the server. The server uses this data to dynamically evolve the game environment in response to the user's choices. 【0523】 Step 7: 【0524】 The server analyzes emotional data and generates a new game environment that matches the user's emotional state. For example, if it detects surprise, it will introduce a new challenge or an unexpected event. 【0525】 Step 8: 【0526】 The server performs real-time game balance adjustments based on the user's emotional state. For example, if a user is excited, the difficulty level is fine-tuned to provide a challenge that offers a greater sense of accomplishment. 【0527】 Step 9: 【0528】 Once all adjustments are complete, the device will present the user with the final game environment and balance applied. Users can then enjoy a customized experience optimized for their emotional state. 【0529】 (Example 2) 【0530】 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." 【0531】 Traditional game systems, which provide a uniform experience without considering the user's emotional state, have struggled to deliver a customized and immersive experience for each individual user. This can lead to decreased user satisfaction and engagement. Therefore, there is a need to develop a system that dynamically reflects the user's emotional state and provides a personalized experience in real time. 【0532】 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. 【0533】 In this invention, the server includes means for collecting user behavior data and emotional data; means for analyzing the emotional data to identify the user's emotional state; means for inputting the behavior data and identified emotional state into a neural network to generate dynamic game mechanics that are suited to the user's play style and emotions; and means for applying the generated game mechanics in real time to adaptively adjust the game environment. This makes it possible to provide a customized game experience for each user in real time and to create an immersive game environment. 【0534】 "User behavior data" refers to data that represents a user's actions, choices, and operation patterns during gameplay. 【0535】 "Emotional data" refers to data about a user's emotional state obtained from their facial expressions and voice tone. 【0536】 A "neural network" is an algorithm or digital model that uses large amounts of data to mimic the neural circuits of the human brain and perform learning and reasoning. 【0537】 "Playstyle" is a concept that comprehensively represents the strategies, behavioral characteristics, and preferences a user adopts within a game. 【0538】 "Dynamic game mechanics" refers to the rules and mechanisms of a game that change in real time in response to the user's actions and emotional state. 【0539】 "Real-time application" means that the system and data are reflected instantly with virtually no time delay. 【0540】 "Game environment" refers to the virtual space that includes all elements that support gameplay, namely graphics, sound, story, characters, rules, etc. 【0541】 "Adaptively adjusting" refers to flexibly changing system settings and behavior in response to the user's identified emotional state. 【0542】 The system of this invention is a technology for collecting user behavioral and emotional data to provide a personalized gaming experience. During gameplay, the user's behavioral data, such as game operations and choices, is collected in real time by the device. In addition, the device uses sensors and cameras to acquire emotional data, such as facial expressions and voice tone. This emotional data is analyzed through an emotion engine designed to analyze emotional states. 【0543】 Behavioral and emotional data collected by the terminals are transmitted to the server using a high-speed and secure communication protocol. The server receives the data, performs preprocessing, imputes missing values, and removes noise. This preprocessing integrates the behavioral and emotional data and optimizes it as input for analysis. 【0544】 The server uses a neural network to analyze this integrated data and generate game mechanics that are tailored to the user's emotional state and play style. This network learns from a large amount of training data, enabling real-time prediction and suggestions. Based on the generated game mechanics, the server sends instructions to the terminal to adjust the game environment, adaptively changing in-game parameters. This allows the game to provide a dynamic experience that responds to the user's emotions. 【0545】 Specifically, if the server determines that a user is experiencing stress during gameplay, the game's difficulty will be automatically lowered. Conversely, if a user shows signs of excitement, the in-game challenges will increase, slightly raising the difficulty. 【0546】 An example of a prompt message is as follows: 【0547】 "Please explain how to collect emotional data from user facial expressions and voice tone, and customize game difficulty in real time." 【0548】 By relying on this system, we can provide a game experience that incorporates emotional data, thereby increasing user immersion and satisfaction. The system is extremely effective as a means of generating deeper engagement. 【0549】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0550】 Step 1: 【0551】 The device monitors the user's actions in real time using sensors and a camera. Specifically, the camera captures the user's facial expressions, and the microphone records their voice tone. This data is collected as behavioral and emotional data and stored on the device as an initial dataset. 【0552】 Step 2: 【0553】 The device transmits collected behavioral and emotional data to the server via a communication module. Encryption technology is used for this data transmission to ensure the security of the received data and prevent interference by third parties. The input data includes the user's real-time behavior and emotions, and is stored in the server's database. 【0554】 Step 3: 【0555】 The server preprocesses the received data. It performs a cleanup process to impute missing values ​​in the behavioral and sentiment data received as input and to reduce noise. Specifically, outliers are removed and normalization is performed to ensure data integrity. The output is an integrated dataset suitable for analysis. 【0556】 Step 4: 【0557】 The server inputs a pre-processed integrated dataset into a neural network. This network model analyzes user behavior patterns and emotional states based on the large amount of training data. This generates dynamic game mechanics based on the user's play style and emotions. The output provides guidance for specific game mechanics. 【0558】 Step 5: 【0559】 The server creates instructions to adjust the game environment based on the generated game mechanics and sends them to the terminal. The terminal receives these instructions and changes the in-game parameters in real time. Specifically, operations such as adjusting enemy behavior patterns and difficulty levels, and triggering new events are performed. As an output, a dynamic game experience adapted to the user's emotions is realized. 【0560】 (Application Example 2) 【0561】 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." 【0562】 The challenge lies in providing adaptive responses tailored to each user's individual emotional state, thereby achieving a highly customized user experience that goes beyond conventional static responses. In particular, there is a need to dynamically change user interactions in consumer electronic devices in response to the user's emotions. 【0563】 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. 【0564】 In this invention, the server includes means for acquiring user behavior data and emotional data; means for inputting the behavior data and emotional data into a neural network and generating dynamic action mechanics that are adapted to the user's play style and emotional state; and means for applying the generated action mechanics in real time and adjusting the response of electronic devices. This makes it possible to provide a highly personalized experience that is tailored to the user's emotional state. 【0565】 "User behavior data" refers to information about user operations and actions, and is data provided to the target electronic device or system. 【0566】 "Emotional data" refers to data that provides information about a user's emotions, such as their facial expressions and tone of voice. 【0567】 A "neural network" is a computational model inspired by biological neural circuits, used for data pattern recognition and machine learning. 【0568】 "Action mechanics" refers to the rules and methods of operation and response in a system or game, and are the elements that determine how a particular action is performed. 【0569】 "Real-time" refers to a dynamic environment where data and processes are processed instantly, meaning they respond without delay. 【0570】 "Electronic device response" refers to the actions or reactions that electronic devices take in response to user input or changes in the environment. 【0571】 In this invention, input / output devices such as cameras and microphones are equipped in home electronic devices to acquire user behavioral and emotional data. Specifically, a video capture device commonly used as a camera and an audio acquisition device used as a microphone are required. This makes it possible to collect emotional data in real time from the user's facial expressions and tone of voice. The emotional and behavioral data are transmitted to a server running a neural network. 【0572】 The server inputs this data into a neural network to generate behavioral mechanics appropriate to the user's emotional state and actions. These mechanics are then transmitted directly to electronic devices and dynamically applied during user interaction. This enables customized responses tailored to the user's emotions, optimizing the in-home interaction experience. 【0573】 For example, if a user is feeling stressed, the server can instruct the system to play relaxing music or suggest light exercise to alleviate stress. This allows for a better life experience tailored to the user's emotional state. 【0574】 An example of a prompt to input into the generating AI model is the following text: "Please provide code that provides a robot function that can determine emotions from the user's facial expressions and tone of voice, and then determine whether they are stressed or relaxed." 【0575】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0576】 Step 1: 【0577】 The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is represented in the form of a facial image and audio waveforms. 【0578】 Step 2: 【0579】 The device immediately transmits the acquired video and audio data to the server. The server receives this data and applies an emotion recognition algorithm to analyze the user's emotional state. This analysis uses facial features and voice tone as indicators to extract emotions such as joy and stress. 【0580】 Step 3: 【0581】 The server inputs the analyzed emotional state and user behavior data into a neural network. Here, data processing is performed that considers the emotional state and behavioral patterns to generate dynamic action mechanics that are best suited to the user's situation. The output is the action mechanics to be applied. 【0582】 Step 4: 【0583】 The server sends the generated motion mechanics to the terminal. Based on these instructions, the terminal causes the electronic device to perform a response appropriate to the user's emotional state. For example, it might play relaxing music or offer verbal exercise suggestions. 【0584】 Step 5: 【0585】 The user experiences a response from the device. Based on the response, the device collects feedback on how the user's emotional state changed as emotional data and sends a prompt to the server. This prompt contributes to improving the generative AI model, enabling an even better customized experience. 【0586】 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. 【0587】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0588】 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. 【0589】 [Fourth Embodiment] 【0590】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0591】 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. 【0592】 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). 【0593】 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. 【0594】 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. 【0595】 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). 【0596】 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. 【0597】 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. 【0598】 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. 【0599】 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. 【0600】 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. 【0601】 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. 【0602】 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". 【0603】 The system of this invention collects user behavior data in real time, analyzes it using a neural network, and generates dynamic game mechanics. Furthermore, it has the function to evolve the game environment based on user choices and adjust the game balance in real time based on player data. 【0604】 The device continuously records data related to the user's actions and behavior patterns while they are playing the game. This data is important for understanding how the user is progressing through the game. Data collected includes the user's movement patterns, item usage frequency, and strategic choices. 【0605】 The server receives data sent from the terminal, preprocesses it, and then inputs it into a neural network. This allows the server to generate game mechanics tailored to the user's play style. For example, for a player who prioritizes defense, the server will customize their gameplay by enhancing their defensive skill options. 【0606】 Furthermore, the servers have the flexibility to adaptively change the game environment based on the choices made by the user within the game. Enemy placement and terrain characteristics change depending on the route the user chooses and the outcome of the mission. This provides a constantly fresh and unpredictable game experience. 【0607】 To maintain a precise balance, the terminal analyzes real-time data such as the player's success rate, failure rate, and the time taken to complete the game. Based on this data, the server utilizes machine learning algorithms to make optimal balancing adjustments for each player's abilities. For example, if a challenge is too difficult, the server automatically adjusts its difficulty. 【0608】 For example, if a user who frequently uses defensive skills repeatedly fails on a particular stage, the device collects data and sends it to the server, which then modifies the enemy's attack patterns. Additionally, new obstacles may be added to the user's chosen route, allowing the player to try new strategies. 【0609】 In this way, the system of the present invention can provide a customized and immersive gaming experience based on the user's individual actions and choices. 【0610】 The following describes the processing flow. 【0611】 Step 1: 【0612】 The device collects real-time data on the user's actions during gameplay. This data includes movement patterns, skills used, and the number of times items are used. 【0613】 Step 2: 【0614】 The device sends collected behavioral data to the server at regular intervals. This minimizes data latency and allows for immediate reflection of changes in real time. 【0615】 Step 3: 【0616】 The server preprocesses the received data. Specifically, it removes noise, extracts necessary features, and formats the data into a form that is easy for the neural network to process. 【0617】 Step 4: 【0618】 The server inputs pre-processed data into a neural network to generate dynamic game mechanics tailored to the user's play style. This includes developing new skills and adjusting existing mechanics. 【0619】 Step 5: 【0620】 The server sends the generated game mechanics to the terminal, which then applies them within the game. After application, the user can experience the new mechanics. 【0621】 Step 6: 【0622】 The device records the user's choices within the game and the resulting consequences. This information is used for environmental evolution and balance adjustments. 【0623】 Step 7: 【0624】 The server runs an environment evolution algorithm based on the user's selection data to create a new game environment that matches the user's choices. This will be reflected in the environment the user faces the next time they play. 【0625】 Step 8: 【0626】 The terminals collect real-time data and analyze it to evaluate the difficulty of completing the game and the success rate of events. The server adjusts the game balance according to the analysis results. 【0627】 Step 9: 【0628】 The server quickly transmits any necessary balance adjustments to the device, which then reflects these changes in the game. This ensures that users always experience the game at the optimally adjusted difficulty level. 【0629】 (Example 1) 【0630】 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". 【0631】 In modern game systems, providing a game experience tailored to each user's individual play style in real time is challenging. Furthermore, while dynamic adaptation and balancing of the game environment based on user choices are necessary, the technologies capable of effectively achieving this are limited. This highlights the growing need for systems that can enhance user immersion and provide a personalized experience. 【0632】 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. 【0633】 In this invention, the server includes means for recording user operation information, means for analyzing the recorded information via a network, and means for generating personalized game elements based on the analysis results. This enables the generation of dynamic game elements tailored to the user and real-time adaptation to the environment. 【0634】 "User operation information" refers to information that records the details of the actions a user performs within the game. 【0635】 "Analyzing data via a network" means processing data using multiple computer systems to analyze user behavior patterns. 【0636】 "Generating personalized game elements" means creating specific game elements and mechanics that are tailored to the user's play style. 【0637】 "Applying in real time" means instantly reflecting the generated game elements in the game environment, allowing users to immediately experience their effects. 【0638】 "Adaptively changing the environment" means dynamically altering the game's situation and conditions based on user choices and actions. 【0639】 "Dynamically adjusting the level of challenge" means adjusting the difficulty and challenges of the game on the fly based on the user's performance and success rate. 【0640】 "Identifying frequently used actions" means recording and analyzing actions that users repeatedly perform and using that information to improve game progression. 【0641】 This invention aims to build an advanced game system that enhances the user's gameplay experience by recording and analyzing user input information and generating personalized game elements. 【0642】 Hardware and software configuration 【0643】 The terminal is a device that records the user's actions while they are playing a game. This may be a conventional electronic gaming device equipped with a touchscreen or input buttons. The data is transmitted in real time to a server via a network. The server is a computer with advanced computing power that analyzes the data and generates game elements tailored to the user's play style. 【0644】 The server uses generative AI models and neural networks for data analysis. This allows it to analyze specific user behavior patterns and perform dynamic game balance adjustments and environmental adaptations. The analysis results are immediately reflected in the game environment and provided to the user in real time. 【0645】 Specific examples of operation 【0646】 For example, if a user frequently performs defensive actions, the server analyzes that information and offers options to enhance defensive skills within the game. Also, if a user's challenge completion rate is low, the server can adjust the difficulty and suggest challenges that are optimal for the user. These adaptations ensure that users always have a fresh and engaging gaming experience. 【0647】 Example of a prompt 【0648】 "Think of ways to optimize the game by proposing new skills that respond to the user's defensive strategies." 【0649】 A game system configured in this way can take into account each user's individual play style and provide a comfortable and personalized gaming experience. 【0650】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0651】 Step 1: 【0652】 The device records the user's in-game actions in real time. This includes user interactions such as touch operations, movement, attacking, and defending. All user action data on the device is received as input, and a recorded action log is generated as output. This log includes actions such as recording which buttons the user pressed and how often. 【0653】 Step 2: 【0654】 The terminal sends recorded operation logs to the server. The logs are sent in batches at regular time intervals. The input is the operation log generated by the terminal, and the output is the data packets sent to the server. This transmission takes place in real time over the network, continuously informing the server of the user's gameplay status. 【0655】 Step 3: 【0656】 The server analyzes the operation logs received from the terminal. This includes data normalization and feature extraction. The server takes operation logs sent from the terminal as input and outputs user behavior patterns as analysis results. Specifically, the server classifies the data into categories and calculates frequency distributions. 【0657】 Step 4: 【0658】 The server uses a generative AI model based on the analysis results to generate game elements adapted to the user. This generation process selects skill sets and items according to the user's play style. The input is the user's behavior patterns, and the output is the generated, personalized game elements. For example, a user who frequently performs defensive actions will have their defensive skills enhanced. 【0659】 Step 5: 【0660】 The server applies the generated game elements to the game environment in real time. This allows users to instantly experience a customized game. The input is the generated game elements, and the output is the change in the game state after application. This operation includes updating the in-game UI and changing enemy placement. 【0661】 Step 6: 【0662】 The server continuously receives, analyzes, and adjusts data during gameplay. Machine learning algorithms are used to adjust the game balance based on user performance. The input is the user's continuous actions during gameplay, and the output is the adjusted game balance. Specifically, automatic difficulty adjustment is part of this process. 【0663】 (Application Example 1) 【0664】 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". 【0665】 Traditional interactive systems have limitations in customization based on user behavior, and therefore cannot adequately provide dynamic experiences that meet individual user needs. For this reason, it is necessary to provide adaptive experiences tailored to user characteristics and create immersive experiences that are engaging and prevent boredom. 【0666】 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. 【0667】 In this invention, the server includes information processing means for collecting user behavior information, information processing means for inputting the information into a neural network and generating dynamic interactive mechanics that are suited to the user's usage characteristics, information processing means for applying the generated interactive mechanics in real time, and information processing means for dynamically optimizing the interactive environment based on user information and providing a customized experience. This makes it possible to provide a highly personalized experience based on the user's behavior. 【0668】 "User behavior information" refers to behavioral data such as patterns of operations and movements, and strategic choices, generated when a user uses a system. 【0669】 "Information processing means" refers to a processing device or program for analyzing collected data and generating, transforming, or manipulating information according to a specific purpose. 【0670】 A "neural network" is a type of machine learning algorithm that analyzes input data through multiple layers of computing nodes and generates specific output information. 【0671】 "Usage characteristics" refer to characteristic information that indicates a user's preferences and behavioral patterns, and are elements that provide a customized experience based on this information. 【0672】 "Dynamic interactive mechanics" refers to interaction mechanisms and systems that change and adapt in real time in response to user actions or choices. 【0673】 "Real-time application" refers to a process that responds immediately to user actions and enables on-the-spot data processing. 【0674】 An "interactive environment" is an experience or simulation environment in which users can directly and dynamically participate, manipulate, and provide feedback. 【0675】 "Dynamic optimization" is the process by which a system autonomously adjusts itself to the best possible state in response to changing user behavior and circumstances, thereby maintaining an optimal state. 【0676】 To implement this invention, a system is needed to collect and analyze user behavior in real time. The terminal senses user behavior information and transmits it to a database. This terminal can be a smartphone or a computer, and is equipped with sensors and a communication module that record operation information in detail. Specifically, iOS devices or Android devices can be used as hardware. 【0677】 The server receives behavioral information sent by the user, preprocesses the information, and then performs analysis using a neural network. This neural network framework utilizes TensorFlow to achieve rapid and highly accurate predictions. The neural network generates dynamic interactive mechanics based on the user's usage characteristics and sends them back to the terminal in real time. 【0678】 The software of this system works in conjunction with game engines such as Unity, providing an adaptive environment that responds immediately to user actions. The environment the user experiences is constantly updated with new data input from the server, and customized content is presented. The interactive environment is not limited to game fields, but extends to a wide range of applications, including educational platforms and fitness experience services. 【0679】 A concrete example would be a puzzle-style interactive application that runs on smartphones. Based on the user's preferences and past actions, it would generate new stages, helping users to continue taking on new challenges without getting bored. 【0680】 The generation AI model can generate prompts like the following based on the user's behavior history: "We have analyzed data from User X's five play sessions and found that the user prefers defensive mechanics. Based on this information, please propose a new stage design." This allows for a customized experience for each user. 【0681】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0682】 Step 1: 【0683】 The device monitors user actions in real time and collects behavioral information. This data includes user taps, swipe directions, and input speed. This data is recorded as logs and packaged together in JSON format. 【0684】 Step 2: 【0685】 The terminal sends the collected user behavior information to the server. The server receives this JSON data and performs preprocessing. Specifically, this preprocessing involves removing outliers and normalizing the data to make it easier for neural networks to handle. The output of this processing is a clean, analyzable dataset. 【0686】 Step 3: 【0687】 The server inputs pre-processed user behavior information into a neural network. The neural network uses the TensorFlow framework to analyze user behavior and generate dynamic interactive mechanics based on that analysis. The output of this process is customized data optimized for each user. 【0688】 Step 4: 【0689】 The server sends the generated dynamic interactive mechanics to the terminal in real time. The terminal receives this data and applies it to the interactive environment that the user operates using the Unity game engine or similar. The user can instantly experience a personalized experience based on the latest data. This output is an updated interactive scenario provided to the user. 【0690】 Step 5: 【0691】 Users continue to interact in a personalized environment, generating new behavioral information. This new behavioral information then returns to step 1, forming a continuous cycle of collection, analysis, and application. This iterative process builds a system that directly reflects user feedback. 【0692】 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. 【0693】 The system of this invention integrates user behavioral data and emotional data to realize a personalized gaming experience. This allows for dynamic and real-time adjustment of the game environment and mechanics according to the user's emotional state, providing a highly immersive experience. 【0694】 The device collects real-time data on the user's gameplay behavior and emotional data collected by the emotion engine. The emotion engine analyzes and obtains emotional data from the user's facial expressions, voice tone, and other factors. Specifically, it can recognize emotions such as joy, surprise, stress, and excitement. 【0695】 The server receives behavioral and emotional data transmitted from the terminal and integrates them during the preprocessing stage. This integrated data is then input into a neural network to generate dynamic game mechanics tailored to the user's play style and emotions. For example, if the user is experiencing stress, a mechanic that lowers the game's difficulty might be suggested. 【0696】 Furthermore, the server uses emotional data recognized by the emotion engine to adaptively change the game environment. This change provides an experience that allows users to maintain positive emotions at all times. If the user is surprised, new scenes or unexpected events are incorporated into the game to further promote engagement. 【0697】 The real-time game balance adjustment feature analyzes emotional data and adjusts the game balance according to the user's emotional state. For example, if the user is excited, the difficulty level will be set slightly higher to provide a challenge. 【0698】 For example, if a user expresses satisfaction during gameplay, the device sends that emotional data to the server. The server receives this data and either maintains the game mechanics as they are or adds more positive elements. It may also change the frequency of item appearances to provide a more meaningful experience for the user. 【0699】 By incorporating emotional data in this way, it becomes possible to provide a truly customized gaming experience for each user. This system is effective in improving the enjoyment and immersion of games and maintaining user engagement over the long term. 【0700】 The following describes the processing flow. 【0701】 Step 1: 【0702】 The device collects behavioral and emotional data in real time while the user is playing the game. Emotional data is extracted from the user's facial expressions and voice and analyzed by an emotion engine to identify emotions such as joy, surprise, and stress. 【0703】 Step 2: 【0704】 The device transmits the collected behavioral and emotional data to the server. Efficient data compression and communication methods are used to ensure that the data is transmitted without delay. 【0705】 Step 3: 【0706】 The server preprocesses the received user data. This preprocessing includes noise reduction and integration of sentiment and behavioral data, which creates a dataset ready for subsequent processing. 【0707】 Step 4: 【0708】 The server inputs pre-processed data into a neural network using deep learning. This network generates dynamic game mechanics that take into account the user's emotional state. For example, it suggests mechanics to reduce tension for a user who is stressed. 【0709】 Step 5: 【0710】 The server sends the generated mechanics to the terminal, which immediately applies them in the game. Users feel that the gameplay experience is adjusted in real time by the changes in mechanics. 【0711】 Step 6: 【0712】 The device records the user's choices during gameplay and the resulting outcomes, and sends this data to the server. The server uses this data to dynamically evolve the game environment in response to the user's choices. 【0713】 Step 7: 【0714】 The server analyzes emotional data and generates a new game environment that matches the user's emotional state. For example, if it detects surprise, it will introduce a new challenge or an unexpected event. 【0715】 Step 8: 【0716】 The server performs real-time game balance adjustments based on the user's emotional state. For example, if a user is excited, the difficulty level is fine-tuned to provide a challenge that offers a greater sense of accomplishment. 【0717】 Step 9: 【0718】 Once all adjustments are complete, the device will present the user with the final game environment and balance applied. Users can then enjoy a customized experience optimized for their emotional state. 【0719】 (Example 2) 【0720】 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". 【0721】 Traditional game systems, which provide a uniform experience without considering the user's emotional state, have struggled to deliver a customized and immersive experience for each individual user. This can lead to decreased user satisfaction and engagement. Therefore, there is a need to develop a system that dynamically reflects the user's emotional state and provides a personalized experience in real time. 【0722】 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. 【0723】 In this invention, the server includes means for collecting user behavior data and emotional data; means for analyzing the emotional data to identify the user's emotional state; means for inputting the behavior data and identified emotional state into a neural network to generate dynamic game mechanics that are suited to the user's play style and emotions; and means for applying the generated game mechanics in real time to adaptively adjust the game environment. This makes it possible to provide a customized game experience for each user in real time and to create an immersive game environment. 【0724】 "User behavior data" refers to data that represents a user's actions, choices, and operation patterns during gameplay. 【0725】 "Emotional data" refers to data about a user's emotional state obtained from their facial expressions and voice tone. 【0726】 A "neural network" is an algorithm or digital model that uses large amounts of data to mimic the neural circuits of the human brain and perform learning and reasoning. 【0727】 "Playstyle" is a concept that comprehensively represents the strategies, behavioral characteristics, and preferences a user adopts within a game. 【0728】 "Dynamic game mechanics" refers to the rules and mechanisms of a game that change in real time in response to the user's actions and emotional state. 【0729】 "Real-time application" means that the system and data are reflected instantly with virtually no time delay. 【0730】 "Game environment" refers to the virtual space that includes all elements that support gameplay, namely graphics, sound, story, characters, rules, etc. 【0731】 "Adaptively adjusting" refers to flexibly changing system settings and behavior in response to the user's identified emotional state. 【0732】 The system of this invention is a technology for collecting user behavioral and emotional data to provide a personalized gaming experience. During gameplay, the user's behavioral data, such as game operations and choices, is collected in real time by the device. In addition, the device uses sensors and cameras to acquire emotional data, such as facial expressions and voice tone. This emotional data is analyzed through an emotion engine designed to analyze emotional states. 【0733】 Behavioral and emotional data collected by the terminals are transmitted to the server using a high-speed and secure communication protocol. The server receives the data, performs preprocessing, imputes missing values, and removes noise. This preprocessing integrates the behavioral and emotional data and optimizes it as input for analysis. 【0734】 The server uses a neural network to analyze this integrated data and generate game mechanics that are tailored to the user's emotional state and play style. This network learns from a large amount of training data, enabling real-time prediction and suggestions. Based on the generated game mechanics, the server sends instructions to the terminal to adjust the game environment, adaptively changing in-game parameters. This allows the game to provide a dynamic experience that responds to the user's emotions. 【0735】 Specifically, if the server determines that a user is experiencing stress during gameplay, the game's difficulty will be automatically lowered. Conversely, if a user shows signs of excitement, the in-game challenges will increase, slightly raising the difficulty. 【0736】 An example of a prompt message is as follows: 【0737】 "Please explain how to collect emotional data from user facial expressions and voice tone, and customize game difficulty in real time." 【0738】 By relying on this system, we can provide a game experience that incorporates emotional data, thereby increasing user immersion and satisfaction. The system is extremely effective as a means of generating deeper engagement. 【0739】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0740】 Step 1: 【0741】 The device monitors the user's actions in real time using sensors and a camera. Specifically, the camera captures the user's facial expressions, and the microphone records their voice tone. This data is collected as behavioral and emotional data and stored on the device as an initial dataset. 【0742】 Step 2: 【0743】 The device transmits collected behavioral and emotional data to the server via a communication module. Encryption technology is used for this data transmission to ensure the security of the received data and prevent interference by third parties. The input data includes the user's real-time behavior and emotions, and is stored in the server's database. 【0744】 Step 3: 【0745】 The server preprocesses the received data. It performs a cleanup process to impute missing values ​​in the behavioral and sentiment data received as input and to reduce noise. Specifically, outliers are removed and normalization is performed to ensure data integrity. The output is an integrated dataset suitable for analysis. 【0746】 Step 4: 【0747】 The server inputs a pre-processed integrated dataset into a neural network. This network model analyzes user behavior patterns and emotional states based on the large amount of training data. This generates dynamic game mechanics based on the user's play style and emotions. The output provides guidance for specific game mechanics. 【0748】 Step 5: 【0749】 The server creates instructions to adjust the game environment based on the generated game mechanics and sends them to the terminal. The terminal receives these instructions and changes the in-game parameters in real time. Specifically, operations such as adjusting enemy behavior patterns and difficulty levels, and triggering new events are performed. As an output, a dynamic game experience adapted to the user's emotions is realized. 【0750】 (Application Example 2) 【0751】 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". 【0752】 The challenge lies in providing adaptive responses tailored to each user's individual emotional state, thereby achieving a highly customized user experience that goes beyond conventional static responses. In particular, there is a need to dynamically change user interactions in consumer electronic devices in response to the user's emotions. 【0753】 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. 【0754】 In this invention, the server includes means for acquiring user behavior data and emotional data; means for inputting the behavior data and emotional data into a neural network and generating dynamic action mechanics that are adapted to the user's play style and emotional state; and means for applying the generated action mechanics in real time and adjusting the response of electronic devices. This makes it possible to provide a highly personalized experience that is tailored to the user's emotional state. 【0755】 "User behavior data" refers to information about user operations and actions, and is data provided to the target electronic device or system. 【0756】 "Emotional data" refers to data that provides information about a user's emotions, such as their facial expressions and tone of voice. 【0757】 A "neural network" is a computational model inspired by biological neural circuits, used for data pattern recognition and machine learning. 【0758】 "Action mechanics" refers to the rules and methods of operation and response in a system or game, and are the elements that determine how a particular action is performed. 【0759】 "Real-time" refers to a dynamic environment where data and processes are processed instantly, meaning they respond without delay. 【0760】 "Electronic device response" refers to the actions or reactions that electronic devices take in response to user input or changes in the environment. 【0761】 In this invention, input / output devices such as cameras and microphones are equipped in home electronic devices to acquire user behavioral and emotional data. Specifically, a video capture device commonly used as a camera and an audio acquisition device used as a microphone are required. This makes it possible to collect emotional data in real time from the user's facial expressions and tone of voice. The emotional and behavioral data are transmitted to a server running a neural network. 【0762】 The server inputs this data into a neural network to generate behavioral mechanics appropriate to the user's emotional state and actions. These mechanics are then transmitted directly to electronic devices and dynamically applied during user interaction. This enables customized responses tailored to the user's emotions, optimizing the in-home interaction experience. 【0763】 For example, if a user is feeling stressed, the server can instruct the system to play relaxing music or suggest light exercise to alleviate stress. This allows for a better life experience tailored to the user's emotional state. 【0764】 An example of a prompt to input into the generating AI model is the following text: "Please provide code that provides a robot function that can determine emotions from the user's facial expressions and tone of voice, and then determine whether they are stressed or relaxed." 【0765】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0766】 Step 1: 【0767】 The device uses a camera and microphone to capture the user's facial expressions and voice in real time. This input data is represented in the form of a facial image and audio waveforms. 【0768】 Step 2: 【0769】 The device immediately transmits the acquired video and audio data to the server. The server receives this data and applies an emotion recognition algorithm to analyze the user's emotional state. This analysis uses facial features and voice tone as indicators to extract emotions such as joy and stress. 【0770】 Step 3: 【0771】 The server inputs the analyzed emotional state and user behavior data into a neural network. Here, data processing is performed that considers the emotional state and behavioral patterns to generate dynamic action mechanics that are best suited to the user's situation. The output is the action mechanics to be applied. 【0772】 Step 4: 【0773】 The server sends the generated motion mechanics to the terminal. Based on these instructions, the terminal causes the electronic device to perform a response appropriate to the user's emotional state. For example, it might play relaxing music or offer verbal exercise suggestions. 【0774】 Step 5: 【0775】 The user experiences a response from the device. Based on the response, the device collects feedback on how the user's emotional state changed as emotional data and sends a prompt to the server. This prompt contributes to improving the generative AI model, enabling an even better customized experience. 【0776】 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. 【0777】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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. 【0778】 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. 【0779】 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. 【0780】 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. 【0781】 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. 【0782】 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. 【0783】 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. 【0784】 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." 【0785】 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. 【0786】 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. 【0787】 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. 【0788】 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. 【0789】 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. 【0790】 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. 【0791】 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. 【0792】 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. 【0793】 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. 【0794】 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. 【0795】 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. 【0796】 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. 【0797】 The following is further disclosed regarding the embodiments described above. 【0798】 (Claim 1) 【0799】 A processing means for collecting user behavior data, 【0800】 A processing means that inputs the aforementioned data into a neural network and generates dynamic game mechanics that are suitable for the user's play style, 【0801】 Processing means for applying the generated game mechanics in real time, 【0802】 A system that includes this. 【0803】 (Claim 2) 【0804】 The system according to claim 1, further comprising processing means for evolving the game environment based on user choices and actions. 【0805】 (Claim 3) 【0806】 The system according to claim 1, further comprising processing means for analyzing event data during gameplay and adjusting the game balance in real time. 【0807】 "Example 1" 【0808】 (Claim 1) 【0809】 A means of recording user operation information, 【0810】 A means of analyzing recorded information via a network, 【0811】 A means for generating individualized game elements based on analysis results, 【0812】 A means of applying the generated elements in real time, 【0813】 A means of adaptively changing the environment based on user selection, 【0814】 A system that includes this. 【0815】 (Claim 2) 【0816】 The system according to claim 1, further comprising processing means for dynamically adjusting the level of challenge according to the user's behavior pattern. 【0817】 (Claim 3) 【0818】 The system according to claim 1, further comprising processing means for identifying operations frequently used by the user and optimizing game progression based on these operations. 【0819】 "Application Example 1" 【0820】 (Claim 1) 【0821】 Information processing means for collecting user behavior information, 【0822】 Information processing means that inputs the aforementioned information into a neural network and generates dynamic interactive mechanics that are suited to the user's usage characteristics, 【0823】 Information processing means for applying the generated interactive mechanics in real time, 【0824】 An information processing means that dynamically optimizes the interactive environment based on user information and provides a customized experience, 【0825】 A system that includes this. 【0826】 (Claim 2) 【0827】 The system according to claim 1, further comprising information processing means for evolving the interactive environment based on user selections and actions. 【0828】 (Claim 3) 【0829】 The system according to claim 1, further comprising information processing means for analyzing event information in use and adaptively adjusting the environmental balance in real time. 【0830】 "Example 2 of combining an emotion engine" 【0831】 (Claim 1) 【0832】 A processing means for collecting user behavior data and emotional data, 【0833】 A means for analyzing the aforementioned emotional data to identify the user's emotional state, 【0834】 A processing means that inputs the aforementioned behavioral data and identified emotional states into a neural network and generates dynamic game mechanics that are suited to the user's play style and emotions, 【0835】 Processing means for applying the generated game mechanics in real time and adaptively adjusting the game environment, 【0836】 A system that includes this. 【0837】 (Claim 2) 【0838】 The system according to claim 1, further comprising processing means for evolving the game environment based on the analyzed emotional state using the user's choices and actions. 【0839】 (Claim 3) 【0840】 The system according to claim 1, further comprising processing means for analyzing event data and emotion data during gameplay and adjusting the game balance in real time. 【0841】 "Application example 2 when combining with an emotional engine" 【0842】 (Claim 1) 【0843】 A processing method for acquiring user behavior data and emotional data, 【0844】 A processing means that inputs the behavioral data and emotional data into a neural network and generates dynamic action mechanics that are adapted to the user's play style and emotional state, 【0845】 Processing means for applying the generated operating mechanics in real time and adjusting the response of the electronic device, 【0846】 A system that includes this. 【0847】 (Claim 2) 【0848】 The system according to claim 1, further comprising processing means for performing appropriate environmental adjustments based on the user's emotional state. 【0849】 (Claim 3) 【0850】 The system according to claim 1, further comprising processing means for analyzing voice data and providing an adaptive response according to the user's emotional state. [Explanation of Symbols] 【0851】 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

[Claim 1] A processing means for collecting user behavior data, A processing means that inputs the aforementioned data into a neural network and generates dynamic game mechanics that are suitable for the user's play style, Processing means for applying the generated game mechanics in real time, A system that includes this. [Claim 2] The system according to claim 1, further comprising processing means for evolving the game environment based on user choices and actions. [Claim 3] The system according to claim 1, further comprising processing means for analyzing event data during gameplay and adjusting the game balance in real time.

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