system

Abstract

We provide the system. [Solution] A means for collecting user behavior information and detecting it using a deep learning model, A means for automatically generating user-optimized electronic content settings based on the aforementioned detection results, A means of applying automatically generated electronic content settings to a linked content environment and updating them in real time, A system that includes means for analyzing viewing data using machine learning algorithms and dynamically adjusting content delivery.

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional game development, it has been difficult to provide a game experience optimized for each user, and the development costs and time have been enormous. Furthermore, since dynamic content generation and balance adjustment according to the user's play style were performed manually, there were also problems with efficiency. Also, it has been challenging to maintain the user's interest in long-term play.

Means for Solving the Problems

[0005] This system collects user behavior data, analyzes it using neural networks, and provides a means to automatically generate game mechanics based on the results. Furthermore, it includes a means to update the game environment in real time and dynamically adjust the game balance by analyzing gameplay data using machine learning algorithms, thereby realizing an optimized game experience for each individual user. This makes it possible to attract long-term user interest and streamline debugging and testing.

[0006] "Action data" refers to information about all actions and choices a user makes during gameplay, including details such as movement, attacks, and skill usage.

[0007] A "neural network" is a form of artificial intelligence that has a structure that mimics the neural circuits of the human brain and is a technology that learns and identifies complex patterns from input data.

[0008] "Game mechanics" refers to the collection of various elements that determine the basic operation and rules of a game, and are involved in user interaction and game progression.

[0009] "Automated generation" refers to the process by which a system creates content or data based on specific rules or algorithms without manual intervention.

[0010] "Real-time updates" refer to the process by which a system instantly changes and applies data and content in response to the user's current actions and circumstances.

[0011] A "machine learning algorithm" is a set of rules or methods that allow computers to analyze empirical data, learn patterns, and automate future predictions and decision-making.

[0012] "Game balance" refers to a state where various elements within a game (e.g., difficulty, challenges, rewards) are evenly adjusted to provide a consistent user experience overall. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] 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.

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

[0016] In the following embodiments, the 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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention relates to the construction of a game system that personalizes and adapts to the user's game experience in real time. This system consists of three main entities: a server, a terminal, and a user.

[0035] The server continuously collects user behavior data. This data includes actions, choices, and strategies that users perform within the game. The server retains this behavior data and analyzes the user's play style by inputting the collected data into a neural network. This analysis identifies user preferences and tendencies, and generates new game mechanics based on them.

[0036] The generated game mechanics are automatically applied to match the user's play style. For example, if it is determined that the user is a strategy-oriented player, the server will generate a scenario with more complex strategic elements, send it to the device, and apply it to the game. The server also uses machine learning algorithms to analyze data accumulated during gameplay and make real-time balance adjustments. This ensures that the game's difficulty and reward distribution are always appropriately adjusted, allowing users to enjoy an optimized gaming experience.

[0037] The user's device receives new mechanics and environmental information sent from the server and reflects it in the game. This ensures that the user can always play with the latest game settings. The device also captures the user's reactions and sends them back to the server, complementing the collection of new data.

[0038] As a concrete example, let's consider an RPG (role-playing game). If a user has a play style that strongly favors exploration, the server automatically generates complex mazes and additional quests, sending them to the device and incorporating them into the game experience. Furthermore, the frequency and difficulty of enemy appearances are adjusted using machine learning, allowing users to feel a moderate challenge.

[0039] This system allows the game to grow and change in response to user actions, rather than simply following a predetermined flow, thus providing a fresh and engaging experience over a longer period.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The server collects user activity data in real time during gameplay. It records actions such as attacks, movement, and skill usage, and stores them in an action log.

[0043] Step 2:

[0044] The server inputs the collected behavioral logs into a neural network model for analysis. This identifies the user's play style and preferences, particularly analyzing frequently used actions and selected tactics.

[0045] Step 3:

[0046] The server generates game mechanics tailored to the user based on the analysis results. This process involves strengthening specific enemy characters and designing new levels based on the user's preferences. These generated results are then sent from the server to the user's device.

[0047] Step 4:

[0048] The device integrates new game mechanics and scenarios received from the server into the game in real time, allowing the user to immediately continue playing with the new settings. The device also records the user's reactions and subsequent actions.

[0049] Step 5:

[0050] The server uses machine learning algorithms to analyze new gameplay data accumulated during user gameplay and attempts to adjust the game balance as needed. For example, if a user frequently fails a certain quest, the server will adjust the difficulty of that quest to an appropriate level.

[0051] Step 6:

[0052] The server resends the adjusted game balance information to the user's device, updating the game environment in real time. This ensures that users can always enjoy the game in an optimized state.

[0053] (Example 1)

[0054] 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."

[0055] Providing a game experience optimized for individual users is challenging in game systems. Maintaining fresh and interesting game content while accommodating diverse play styles and preferences is a significant burden and technical challenge for developers. Furthermore, appropriately adjusting game balance and difficulty in real time to enhance user satisfaction is also a crucial challenge.

[0056] 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.

[0057] In this invention, the server includes means for collecting digital information about user behavior and analyzing it using an artificial neural network, means for automatically constructing a game structure that is optimally suited to the user based on the analysis results, and means for applying the constructed game structure to the associated game environment and modifying it immediately. This makes it possible to evolve the game environment in accordance with the user's behavior patterns and to provide a game experience that is appropriately tailored to each individual user.

[0058] "Digital information about user behavior" refers to data recorded in digital format, including actions, choices, and strategies used by users within a game.

[0059] An "artificial neural network" is a computer model that mimics the nervous system of a living organism, and is a machine learning technique used for data analysis and pattern recognition.

[0060] "Automatically constructing a game structure that is optimally suited to the user based on analysis results" refers to the process of generating new game elements and scenarios that correspond to the user's preferences and play style, based on the analyzed user data.

[0061] "Constructed game structure" refers to a collection of in-game rules and scenarios that are customized specifically for the user.

[0062] "Applying to the game environment and making immediate corrections" means instantly updating the current game settings based on new game information obtained from the server, thereby providing users with a new gaming experience.

[0063] "Machine learning technology" is a branch of artificial intelligence that learns patterns from data to perform predictions and classifications.

[0064] "Game operation data" refers to various types of data used to manage the progress of the game, such as the difficulty level of the game, the success rate of players, and the distribution of rewards.

[0065] "User behavior patterns" refer to consistent behavioral patterns and play styles exhibited by users within the game.

[0066] This invention is a system that personalizes and dynamically adapts to the user's gaming experience. The system mainly consists of a server, a terminal, and the user.

[0067] The server collects behavioral data such as actions, choices, and strategies performed by the user within the game. This data is analyzed by an artificial neural network and used to detect the user's play style. Based on this analysis, the server uses a generative AI model to build new game mechanics optimized for the user. This process requires high-performance computing power for data analysis, such as a server-type computer.

[0068] The generated game mechanics are sent from the server to the terminal. The terminal immediately reflects the new game information received in the game environment and provides it to the user. For example, if it is determined that the user's play style is one of exploration, the terminal implements a new scenario into the game, including automatically generated complex mazes and additional quests. The terminal also records the user's reactions and actions during the game and sends them back to the server. This allows for more effective data analysis in the future.

[0069] An example of a specific prompt for a generative AI model is: "Generate an evolved version of the maze and adjust the enemy spawn rate appropriately for users who prefer exploration." By using prompts like these, the server can generate new game elements, ensuring that users always enjoy a fresh and engaging gaming experience.

[0070] This system allows the game to grow and change in response to the actions of individual users, rather than simply following a predetermined flow, thus contributing to increased user satisfaction.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The server collects digital information about user behavior in real time. Inputs include data such as actions, selections, items used, and locations reached within the game. Specifically, the server dynamically retrieves this information through the game's API. The output is a user behavior dataset.

[0074] Step 2:

[0075] The server inputs the collected user behavior data into an artificial neural network for analysis. The input data is the behavior data collected in Step 1. Here, feature quantities are calculated from the data to extract characteristics such as the user's selection tendencies and strategic thinking, and the play style is estimated using a neural network model. The output is information about the user's play style.

[0076] Step 3:

[0077] The server uses a generative AI model based on the analysis results to automatically build game mechanics optimized for the user. The input for this step is the analysis results from step 2. Specifically, prompt statements are input to the generative AI model to create new game elements and scenarios. For example, a prompt statement such as "Generate a complex maze for users who enjoy exploration" is used. The output is the constructed game mechanics.

[0078] Step 4:

[0079] The server sends the built game mechanics to the terminal. The input is the new game elements generated in step 3. Specifically, the server sends this information to the terminal via the endpoint, allowing the terminal to reflect that information in the game. The output is the updated game environment information.

[0080] Step 5:

[0081] The terminal immediately applies the newly received game mechanics to the game environment. The input is the game mechanics received from the server in step 4. In its specific operation, the terminal updates the game's configuration files and database, enabling the user to play in the new environment. The output is the latest game experience provided to the user.

[0082] Step 6:

[0083] The device observes the user's gameplay and records newly generated data. The input is the user's real-time in-game actions. Specifically, the device continuously records user operation logs and clickstream data. The output is a new behavioral dataset to be used for the next server analysis.

[0084] (Application Example 1)

[0085] 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."

[0086] In modern content delivery platforms, providing personalized viewing experiences based on users' interests and preferences is becoming increasingly important. However, traditional systems have struggled to analyze users' viewing patterns in real time and dynamically optimize content based on that data. As a result, users are often shown content irrelevant to their interests, leading to decreased satisfaction.

[0087] 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.

[0088] In this invention, the server includes means for collecting user behavior information and detecting it using a deep learning model, means for automatically generating optimized electronic content settings for the user based on the detection results, and means for applying the automatically generated electronic content settings to a linked content environment and updating them in real time. This makes it possible to provide a personalized viewing experience for each user in real time.

[0089] "Users" refer to individual consumers who use content distribution services to view content.

[0090] "Behavioral information" refers to data that shows a user's viewing history, selection patterns, and preferences when browsing content.

[0091] A "deep learning model" is an algorithm that uses multi-layered neural networks to analyze complex data patterns and make predictions.

[0092] "Detection" is the process of analyzing collected data to infer user preferences and behavioral patterns.

[0093] "Electronic content settings" are parameters for suggesting and delivering content optimized based on the user's viewing patterns.

[0094] A "linked content environment" refers to the platform or application where content provided to users is held and can be viewed.

[0095] "Real-time updates" refers to a state where data collection, analysis, and feedback to users occur almost instantly.

[0096] In the system that realizes this invention, the program is constructed as follows: The server continuously collects user viewing behavior data and inputs it into a deep learning model. Specifically, it uses Amazon DynamoDB to store data such as viewing history and selection patterns, and analyzes this data using TENSORFLOW®. This analysis reveals the user's viewing patterns, and based on these, settings for electronic content are automatically generated.

[0097] The device receives the generated electronic content settings and reflects them in the user's viewing in real time. This ensures that the user always has the latest viewing experience. The system can also improve content recommendations based on continuous user feedback.

[0098] For example, if a user frequently watches action movies, the system can quickly suggest trailers for new action movies that are suitable for that user.

[0099] Examples of prompts to input into a generative AI model:

[0100] I want to build a model that recommends personalized content based on a user's viewing history. What kind of neural network architecture would be suitable? Please explain in detail.

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

[0102] Step 1:

[0103] The server collects user viewing behavior data. Specifically, it obtains data such as the genre of content viewed, playback time, and rating from the user's device in real time and stores it in Amazon DynamoDB. The input data is the user's viewing history, and the output is obtained by recording this in the database.

[0104] Step 2:

[0105] The server inputs the collected viewing data into a deep learning model. A neural network using TensorFlow analyzes the user's viewing patterns from the data. The input is viewing history stored in a database, which is used to identify viewing trends, and the results are output. Based on these analysis results, preferences for specific genres are extracted.

[0106] Step 3:

[0107] The server automatically generates electronic content settings based on the analysis results. It creates a new recommendation list that reflects viewing trends and sends that list to the device. The input is the analysis results, and it outputs an automatically generated recommendation list. Specifically, a list is generated that includes the latest works and related content in the user's preferred genre.

[0108] Step 4:

[0109] The terminal receives electronic content settings from the server and presents them to the user in real time. A recommendation list is displayed on the terminal screen, allowing the user to easily select newly recommended content. The input is the recommendation list from the server, and the output is the content displayed to the user.

[0110] Step 5:

[0111] The user selects and views the presented content. This behavioral data is collected again and sent to the server as feedback. This returns to step 1, and the entire process is continuously improved. The input is the user's viewing selection, and new behavioral data is output and collected.

[0112] 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.

[0113] This invention relates to a system that uses user behavioral data and emotional data to construct and adapt a personalized game environment in real time. The system consists of a server, a terminal, a user, and an emotional engine.

[0114] The server captures the user's actions during gameplay and collects information necessary to recognize behavioral patterns. This includes data such as the user's movement, attacks, skill usage, and selected options, which are stored in a database in real time.

[0115] The emotion engine detects and evaluates the user's emotions in real time. This engine uses the user's facial expressions, voice, heart rate, etc., to recognize their emotional state during gameplay. The data obtained by the emotion engine is sent to the server and integrated with behavioral data.

[0116] The server uses a neural network to analyze integrated data and identify the user's play style as well as their current emotional state. Based on this analysis, it automatically generates new game mechanics and scenarios that the user will enjoy most. This generation process includes adjustments such as automatically lowering the game difficulty if the user is feeling fatigued.

[0117] The terminal immediately receives new mechanics and environment settings sent from the server, providing the user with the latest game environment. The terminal also records the user's reactions during gameplay and feeds this information back to the server and emotion engine, enabling the entire system to repeat a series of adaptive cycles.

[0118] For example, in an action game, if emotional data indicating user stress is detected, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. In this way, the system can utilize data from both emotions and behavior to provide the user with the optimal gaming experience in real time.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The user starts the game. The device begins collecting both action data during gameplay and emotion data from emotion sensors in real time. This includes behavioral data such as the user's movement speed and skill usage frequency, as well as emotion data including facial expressions and voice tone.

[0122] Step 2:

[0123] The device sends the collected data to the server. The server records the received behavioral and emotional data in a database. This data shows how the user is behaving within the game and their current emotional state.

[0124] Step 3:

[0125] The server uses a neural network to analyze behavioral and emotional data. This analysis identifies play styles and emotional responses, deriving crucial information to personalize the user experience. For example, if a user shows tension in a challenging situation, the server determines that elements that promote relaxation are needed.

[0126] Step 4:

[0127] Based on the analysis results, the server generates game mechanics and scenarios optimized for the player's emotions and behavior. For example, if the player is under high stress, it creates an environment with fewer obstacles and enhanced exploration elements.

[0128] Step 5:

[0129] The server sends the newly generated game settings to the terminal and applies them to the game immediately. This includes elements such as changing enemy placement and adjusting item spawn rates.

[0130] Step 6:

[0131] The device reflects updates from the server in real time, presenting the game situation to the user. The user continues to experience these changes, and their actions and emotions are collected again, naturally transitioning to the next cycle.

[0132] This enables gameplay that takes the user's emotional state into account, resulting in a significantly more personalized experience.

[0133] (Example 2)

[0134] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0135] Traditional game systems often provided a uniform gaming experience without considering the user's play style or emotional state. This sometimes made it difficult for users to continue enjoying the game. Furthermore, large-scale manual verification testing is labor-intensive and time-consuming, highlighting the need for more efficient verification methods. There is a need to address these challenges and provide personalized gaming experiences optimized for each user.

[0136] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0137] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing them using a neural network; means for automatically generating personalized game mechanics and scenarios for the user based on the analysis results; and means for analyzing game participation data using machine learning algorithms and dynamically adjusting the difficulty of the game. This enables the provision of a real-time, adaptive game experience and efficient verification based on the user's behavior and emotions.

[0138] "User behavior data" refers to information about in-game activities that users exhibit during gameplay, such as movement, attacks, skill usage, and selected options.

[0139] "Emotional data" refers to information about a user's emotional state, obtained from the user's facial expressions, voice, heart rate, etc.

[0140] A "neural network" refers to a computational model that learns from large amounts of data and performs pattern recognition and prediction.

[0141] "Game mechanics" refers to elements that influence the rules, controls, and play style within a game.

[0142] "Scenario" refers to the settings and flow of a game's story and progression.

[0143] "Environment settings" refer to the physical or visual conditions within a game that are presented to the user.

[0144] A "machine learning algorithm" refers to a method for analyzing data and automatically finding patterns and rules from it.

[0145] "Game participation data" refers to data that shows how users interacted with and reacted within a game.

[0146] An "adaptive gaming experience" refers to a game that dynamically changes in response to the user's actions and emotions, providing the most suitable playing environment for the user.

[0147] "Verification testing" refers to tests conducted to confirm that a product or system functions correctly.

[0148] This invention provides a system that enables the creation of a personalized game environment that adapts in real time using user behavioral data and emotional data. The system consists of a server, a terminal, a user, and an emotional engine.

[0149] The server first collects user behavior data in real time during gameplay. This behavior data includes user movement, attacks, skill usage, and selected options. This data is obtained through in-game sensors and logs and immediately stored in a database.

[0150] The emotion engine detects the user's emotional data in real time. Specifically, it captures the user's facial expressions with a camera, analyzes emotions from their voice, and obtains their heart rate with a sensor. By combining this information, the emotion engine evaluates the user's emotional state and sends that data to the server.

[0151] The server integrates this behavioral and emotional data and analyzes it using a neural network. This is to identify the user's play style and emotional state, and to generate optimal game mechanics and scenarios for that user. The generated game mechanics and environment settings are sent to the terminal and immediately applied within the game.

[0152] The terminal receives new game mechanics and environment settings sent from the server and immediately applies them to the game environment. It also records user reactions and provides feedback to the server and emotion engine. This enables the system to achieve an adaptive cycle and provide the user with the best possible gaming experience.

[0153] As a concrete example, in an action game, if emotional data indicating user stress is recognized, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. An example of a prompt to the generating AI model is, "Show how to adjust the game difficulty in real time based on the user's emotions." In this way, the system can provide an optimized game experience based on both the user's behavior and emotions.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The server collects user behavior data in real time during gameplay. Inputs include user actions such as controls, movement, attacks, and skill usage. This data is obtained from sensors and log files and stored in a database. Output is the organized behavior data stored in the database.

[0157] Step 2:

[0158] The emotion engine acquires user emotional data in real time. Inputs include physiological signals such as the user's facial expressions, voice, and heart rate. These signals are collected from cameras, microphones, and sensors, and the emotion engine analyzes the emotional state. The output is the transmission of the detected emotional data to the server.

[0159] Step 3:

[0160] The server integrates collected behavioral and emotional data. Input data consists of behavioral data stored in a database and emotional data transmitted from the emotion engine. A neural network is used to normalize this data and perform data processing and analysis to identify the user's play style and emotional state. The output is user profile information derived from the analysis.

[0161] Step 4:

[0162] The server generates optimal game mechanics and scenarios for the user based on the analysis results. The input is the analysis results from step 3. The generation AI model is used to dynamically adjust game elements according to the user's emotions and actions. The output is the generated game mechanics and scenarios, which are sent to the terminal.

[0163] Step 5:

[0164] The terminal receives new game mechanics and settings sent from the server. Input is server-generated content. The game engine immediately applies this data and presents it to the user within the game. Output is the provision of the latest game state.

[0165] Step 6:

[0166] The device records the user's reactions during gameplay and feeds them back to the server and the emotion engine. The input is the user's reaction data. This data is used to analyze and adjust the next adaptive cycle. The output is the transmission of newly acquired user reaction data to the server and the emotion engine.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] There is a need for systems that can enhance the in-home entertainment experience and provide appropriate activities that respond to the user's behavior and emotions. However, current systems have difficulty responding to individual user needs in real time, making it difficult to significantly improve the quality and satisfaction of entertainment. This invention aims to provide a means to improve the quality of interaction in the user's living environment.

[0170] 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.

[0171] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing it using a neural network; means for automatically generating entertainment activities suitable for the user based on the analysis results; and means for applying the automatically generated entertainment activities to the user's living environment and updating them in real time. This makes it possible to provide interactive activities adapted to the user's current emotional state and behavior, thereby improving the overall entertainment experience.

[0172] "User behavior data" refers to a record of a user's actions and behaviors when interacting with the system.

[0173] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice, and physiological indicators.

[0174] A "neural network" is a type of artificial intelligence technology that mimics the neural structure of the human brain to perform data analysis and learning.

[0175] "Analysis" is a procedure for processing collected data and deriving specific results.

[0176] "Entertainment activities" refer to activities that include entertainment and interaction aimed at providing users with enjoyment and satisfaction.

[0177] "Automatic generation" refers to the process of generating data and information mechanically, rather than manually, using algorithms.

[0178] "Living environment" refers to the physical and social conditions and spaces in which a user spends their daily life.

[0179] "Updating" refers to the act of changing information or data to the latest version.

[0180] "Machine learning technology" is a technology that gives computers the ability to learn from data and automatically improve at specific tasks.

[0181] "Interaction" refers to the interaction or dialogue between the user and the system.

[0182] This invention is a system for providing users with personalized entertainment experiences. The following describes the system's embodiments and operation.

[0183] The server is responsible for collecting user behavior and emotional data. This data is acquired by facial recognition cameras, microphones, and heart rate sensors installed in the in-home entertainment robot. The collected data is analyzed for emotional states using the Microsoft® Azure® Emotion API. Subsequently, a neural network using TensorFlow in Python integrates and analyzes the user's behavior patterns and emotional data to generate entertainment activities tailored to the user.

[0184] The device applies server-generated activities to the home environment. Based on the user's behavior and emotions, it provides the user with automatically generated activities—such as games, dancing, and conversations—in real time. The activities then analyze the user's responses and adjust their content and difficulty as needed.

[0185] As a concrete example, when spending time with family in the evening, the robot checks the user's emotional state and analyzes whether they are relaxed. Based on this result, the robot suggests relaxation music and quizzes to create a warm atmosphere for the family. The system continuously monitors the user's emotions and reactions and adjusts the next appropriate activity in real time.

[0186] An example of a generated AI prompt might be, "Check the emotional state of the family and suggest relaxing activities." Based on this prompt, the system can provide appropriate entertainment content.

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

[0188] Step 1:

[0189] The server receives user behavior and emotional data collected by a home entertainment robot. Inputs include facial expression data from the camera, voice tone data from the microphone, and physiological data from the heart rate sensor. This data is analyzed using the Microsoft Azure Emotion API to identify the user's emotional state. The output is the user's emotional state data as a result of the analysis.

[0190] Step 2:

[0191] The server integrates emotional state data and user behavior data, and analyzes user behavior patterns by applying a neural network model using TensorFlow. The input is the emotional state data and behavior data obtained in step 1. Data analysis using a neural network identifies the entertainment activities preferred by the user. The output is a recommendation of entertainment activities suitable for the user.

[0192] Step 3:

[0193] The terminal receives entertainment activities transmitted from the server and presents them to the user in real time. The input is the instruction for the entertainment activity identified in step 2. The robot provides the user with activities such as games, dancing, and conversation. The output is the provided entertainment activity and the user's response.

[0194] Step 4:

[0195] The terminal monitors the user's responses during the activity and feeds that data back to the server. The input is the user's actual response data. Based on the user's emotional state and reactions, the server determines whether the activity content or difficulty needs to be adjusted. The output is instructions for the next appropriate, adjusted activity.

[0196] Step 5:

[0197] The server updates the entertainment activities based on the feedback data collected in step 4 and regenerates new activity plans as needed. The input is the user's latest response data. The generative AI model is used to readjust activity suggestions based on prompt sentences. The output is the updated instructions for the entertainment activities.

[0198] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

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

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0214] This invention relates to the construction of a game system that personalizes and adapts to the user's game experience in real time. This system consists of three main entities: a server, a terminal, and a user.

[0215] The server continuously collects user behavior data. This data includes actions, choices, and strategies that users perform within the game. The server retains this behavior data and analyzes the user's play style by inputting the collected data into a neural network. This analysis identifies user preferences and tendencies, and generates new game mechanics based on them.

[0216] The generated game mechanics are automatically applied to match the user's play style. For example, if it is determined that the user is a strategy-oriented player, the server will generate a scenario with more complex strategic elements, send it to the device, and apply it to the game. The server also uses machine learning algorithms to analyze data accumulated during gameplay and make real-time balance adjustments. This ensures that the game's difficulty and reward distribution are always appropriately adjusted, allowing users to enjoy an optimized gaming experience.

[0217] The user's device receives new mechanics and environmental information sent from the server and reflects it in the game. This ensures that the user can always play with the latest game settings. The device also captures the user's reactions and sends them back to the server, complementing the collection of new data.

[0218] As a concrete example, let's consider an RPG (role-playing game). If a user has a play style that strongly favors exploration, the server automatically generates complex mazes and additional quests, sending them to the device and incorporating them into the game experience. Furthermore, the frequency and difficulty of enemy appearances are adjusted using machine learning, allowing users to feel a moderate challenge.

[0219] This system allows the game to grow and change in response to user actions, rather than simply following a predetermined flow, thus providing a fresh and engaging experience over a longer period.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The server collects user activity data in real time during gameplay. It records user actions such as attacks, movement, and skill usage, and stores them in an action log.

[0223] Step 2:

[0224] The server inputs the collected behavioral logs into a neural network model for analysis. This identifies the user's play style and preferences, particularly by analyzing frequently used actions and selected tactics.

[0225] Step 3:

[0226] The server generates game mechanics tailored to the user based on the analysis results. This process involves strengthening specific enemy characters and designing new levels based on the user's preferences. These generated results are then sent from the server to the user's device.

[0227] Step 4:

[0228] The device integrates new game mechanics and scenarios received from the server into the game in real time, allowing the user to immediately continue playing with the new settings. The device also records the user's reactions and subsequent actions.

[0229] Step 5:

[0230] The server uses machine learning algorithms to analyze new gameplay data accumulated during user gameplay and attempts to adjust the game balance as needed. For example, if a user frequently fails a certain quest, the server will adjust the difficulty of that quest to an appropriate level.

[0231] Step 6:

[0232] The server resends the adjusted game balance information to the user's device, updating the game environment in real time. This ensures that users can always enjoy the game in an optimized state.

[0233] (Example 1)

[0234] 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."

[0235] Providing a game experience optimized for individual users is challenging in game systems. Maintaining fresh and interesting game content while accommodating diverse play styles and preferences is a significant burden and technical challenge for developers. Furthermore, appropriately adjusting game balance and difficulty in real time to enhance user satisfaction is also a crucial challenge.

[0236] 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.

[0237] In this invention, the server includes means for collecting digital information about user behavior and analyzing it using an artificial neural network, means for automatically constructing a game structure that is optimally suited to the user based on the analysis results, and means for applying the constructed game structure to the associated game environment and modifying it immediately. This makes it possible to evolve the game environment in accordance with the user's behavior patterns and to provide a game experience that is appropriately tailored to each individual user.

[0238] "Digital information about user behavior" refers to data recorded in digital format, including actions, choices, and strategies used by users within a game.

[0239] An "artificial neural network" is a computer model that mimics the nervous system of a living organism, and is a machine learning technique used for data analysis and pattern recognition.

[0240] "Automatically constructing a game structure that is optimally suited to the user based on analysis results" refers to the process of generating new game elements and scenarios that correspond to the user's preferences and play style, based on the analyzed user data.

[0241] "Constructed game structure" refers to a collection of in-game rules and scenarios that are customized specifically for the user.

[0242] "Applying to the game environment and making immediate corrections" means instantly updating the current game settings based on new game information obtained from the server, thereby providing users with a new gaming experience.

[0243] "Machine learning technology" is a branch of artificial intelligence that learns patterns from data to perform predictions and classifications.

[0244] "Game operation data" refers to various types of data used to manage the progress of the game, such as the game's difficulty level, player success rates, and reward distribution.

[0245] "User behavior patterns" refer to consistent behavioral patterns and play styles exhibited by users within a game.

[0246] This invention is a system that personalizes and dynamically adapts to the user's gaming experience. The system mainly consists of a server, a terminal, and the user.

[0247] The server collects behavioral data such as actions, choices, and strategies performed by the user within the game. This data is analyzed by an artificial neural network and used to detect the user's play style. Based on this analysis, the server uses a generative AI model to build new game mechanics optimized for the user. This process requires high-performance computing power for data analysis, such as a server-type computer.

[0248] The generated game mechanics are sent from the server to the terminal. The terminal immediately reflects the new game information received in the game environment and provides it to the user. For example, if it is determined that the user's play style is one of exploration, the terminal implements a new scenario into the game, including automatically generated complex mazes and additional quests. The terminal also records the user's reactions and actions during the game and sends them back to the server. This allows for more effective data analysis in the future.

[0249] An example of a specific prompt for a generative AI model is: "Generate an evolved version of the maze and adjust the enemy spawn rate appropriately for users who prefer exploration." By using prompts like these, the server can generate new game elements, ensuring that users always enjoy a fresh and engaging gaming experience.

[0250] This system allows the game to grow and change in response to the actions of individual users, rather than simply following a predetermined flow, thus contributing to increased user satisfaction.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The server collects digital information about user behavior in real time. Inputs include data such as actions, selections, items used, and locations reached within the game. Specifically, the server dynamically retrieves this information through the game's API. The output is a user behavior dataset.

[0254] Step 2:

[0255] The server inputs the collected user behavior data into an artificial neural network for analysis. The input data is the behavior data collected in Step 1. Here, feature quantities are calculated from the data to extract characteristics such as the user's selection tendencies and strategic thinking, and the play style is estimated using a neural network model. The output is information about the user's play style.

[0256] Step 3:

[0257] The server uses a generative AI model based on the analysis results to automatically build game mechanics optimized for the user. The input for this step is the analysis results from step 2. Specifically, prompt statements are input to the generative AI model to create new game elements and scenarios. For example, a prompt statement such as "Generate a complex maze for users who enjoy exploration" is used. The output is the constructed game mechanics.

[0258] Step 4:

[0259] The server sends the built game mechanics to the terminal. The input is the new game elements generated in step 3. Specifically, the server sends this information to the terminal via the endpoint, allowing the terminal to reflect that information in the game. The output is the updated game environment information.

[0260] Step 5:

[0261] The terminal immediately applies the newly received game mechanics to the game environment. The input is the game mechanics received from the server in step 4. In its specific operation, the terminal updates the game's configuration files and database, enabling the user to play in the new environment. The output is the latest game experience provided to the user.

[0262] Step 6:

[0263] The device observes the user's gameplay and records newly generated data. The input is the user's real-time in-game actions. Specifically, the device continuously records user operation logs and clickstream data. The output is a new behavioral dataset to be used for the next server analysis.

[0264] (Application Example 1)

[0265] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0266] In modern content delivery platforms, providing personalized viewing experiences based on users' interests and preferences is becoming increasingly important. However, traditional systems have struggled to analyze users' viewing patterns in real time and dynamically optimize content based on that data. As a result, users are often shown content irrelevant to their interests, leading to decreased satisfaction.

[0267] 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.

[0268] In this invention, the server includes means for collecting user behavior information and detecting it using a deep learning model, means for automatically generating optimized electronic content settings for the user based on the detection results, and means for applying the automatically generated electronic content settings to a linked content environment and updating them in real time. This makes it possible to provide a personalized viewing experience for each user in real time.

[0269] "Users" refer to individual consumers who use content distribution services to view content.

[0270] "Behavioral information" refers to data that shows a user's viewing history, selection patterns, and preferences when browsing content.

[0271] A "deep learning model" is an algorithm that uses multi-layered neural networks to analyze complex data patterns and make predictions.

[0272] "Detection" is the process of analyzing collected data to infer user preferences and behavioral patterns.

[0273] "Electronic content settings" are parameters for suggesting and delivering content optimized based on the user's viewing patterns.

[0274] A "linked content environment" refers to the platform or application where content provided to users is held and can be viewed.

[0275] "Real-time updates" refers to a state where data collection, analysis, and feedback to users occur almost instantly.

[0276] In the system that realizes this invention, the program is constructed as follows: The server continuously collects user viewing behavior data and inputs it into a deep learning model. Specifically, it uses Amazon DynamoDB to store data such as viewing history and selection patterns, and uses TensorFlow to analyze that data. Through this analysis, the user's viewing patterns are revealed, and based on that, the settings for electronic content are automatically generated.

[0277] The device receives the generated electronic content settings and reflects them in the user's viewing in real time. This ensures that the user always has the latest viewing experience. The system can also improve content recommendations based on continuous user feedback.

[0278] For example, if a user frequently watches action movies, the system can quickly suggest trailers for new action movies that are suitable for that user.

[0279] Examples of prompt sentences to be input into the AI model:

[0280] I want to build a model that recommends personalized content based on the user's viewing history. What kind of neural network architecture is suitable? Please tell me specifically.

[0281] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0282] Step 1:

[0283] The server collects the user's viewing behavior data. Specifically, data such as the genre, playback time, and evaluation of the viewed content is obtained in real time from the terminal used by the user and stored in Amazon DynamoDB. The input data is the user's viewing history, and the output is obtained by recording this in the database.

[0284] Step 2:

[0285] The server inputs the collected viewing data into the deep learning model. Using a neural network with TensorFlow, the user's viewing pattern is analyzed from the data. The input is the viewing history stored in the database, and this is used to identify the viewing tendency and output the result. Based on this analysis result, preferences for specific genres and the like are extracted.

[0286] Step 3:

[0287] The server automatically generates electronic content settings based on the analysis result. A new recommendation list reflecting the viewing tendency is created and sent to the terminal. The input is the analysis result, and the automatically generated recommendation list is output. Specifically, a list containing the latest works and related content of the preferred genre is generated.

[0288] Step 4:

[0289] The terminal receives electronic content settings from the server and presents them to the user in real time. A recommendation list is displayed on the terminal screen, allowing the user to easily select newly recommended content. The input is the recommendation list from the server, and the output is the content displayed to the user.

[0290] Step 5:

[0291] The user selects and views the presented content. This behavioral data is collected again and sent to the server as feedback. This returns to step 1, and the entire process is continuously improved. The input is the user's viewing selection, and new behavioral data is output and collected.

[0292] 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.

[0293] This invention relates to a system that uses user behavioral data and emotional data to construct and adapt a personalized game environment in real time. The system consists of a server, a terminal, a user, and an emotional engine.

[0294] The server captures the user's actions during gameplay and collects information necessary to recognize behavioral patterns. This includes data such as the user's movement, attacks, skill usage, and selected options, which are stored in a database in real time.

[0295] The emotion engine detects and evaluates the user's emotions in real time. This engine uses the user's facial expressions, voice, heart rate, etc., to recognize their emotional state during gameplay. The data obtained by the emotion engine is sent to the server and integrated with behavioral data.

[0296] The server uses a neural network to analyze integrated data and identify the user's play style as well as their current emotional state. Based on this analysis, it automatically generates new game mechanics and scenarios that the user will enjoy most. This generation process includes adjustments such as automatically lowering the game difficulty if the user is feeling fatigued.

[0297] The terminal immediately receives new mechanics and environment settings sent from the server, providing the user with the latest game environment. The terminal also records the user's reactions during gameplay and feeds this information back to the server and emotion engine, enabling the entire system to repeat a series of adaptive cycles.

[0298] For example, in an action game, if emotional data indicating user stress is detected, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. In this way, the system can utilize data from both emotions and behavior to provide the user with the optimal gaming experience in real time.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The user starts the game. The device begins collecting both action data during gameplay and emotion data from emotion sensors in real time. This includes behavioral data such as the user's movement speed and skill usage frequency, as well as emotion data including facial expressions and voice tone.

[0302] Step 2:

[0303] The device sends the collected data to the server. The server records the received behavioral and emotional data in a database. This data shows how the user is behaving within the game and their current emotional state.

[0304] Step 3:

[0305] The server uses a neural network to analyze the action data and emotion data. The analysis identifies play styles and emotional reactions, deriving important information for customizing the user's experience. For example, if the user shows tension in a challenging situation, it is determined that elements that create a more relaxing environment are needed.

[0306] Step 4:

[0307] Based on the analysis results, the server generates game mechanics and scenarios that are optimal for emotions and actions. For example, when stress is high, an environment with fewer obstacles and enhanced exploration elements is created.

[0308] Step 5:

[0309] The server sends the newly generated game settings to the terminal and immediately applies them to the game. This includes elements such as changing the placement of enemies and adjusting the item appearance rate.

[0310] Step 6:

[0311] The terminal reflects the update from the server in real time and presents the game situation to the user. The user continues to experience these changes, and their actions and emotions are collected again, naturally transitioning to the next cycle.

[0312] This enables game play that takes into account the user's emotional state, realizing the provision of a highly personalized experience.

[0313] (Example 2)

[0314] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". -

[0315] Traditional game systems often provided a uniform gaming experience without considering the user's play style or emotional state. This sometimes made it difficult for users to continue enjoying the game. Furthermore, large-scale manual verification testing is labor-intensive and time-consuming, highlighting the need for more efficient verification methods. There is a need to address these challenges and provide personalized gaming experiences optimized for each user.

[0316] 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.

[0317] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing them using a neural network; means for automatically generating personalized game mechanics and scenarios for the user based on the analysis results; and means for analyzing game participation data using machine learning algorithms and dynamically adjusting the difficulty of the game. This enables the provision of a real-time, adaptive game experience and efficient verification based on the user's behavior and emotions.

[0318] "User behavior data" refers to information about in-game activities that users exhibit during gameplay, such as movement, attacks, skill usage, and selected options.

[0319] "Emotional data" refers to information about a user's emotional state, obtained from the user's facial expressions, voice, heart rate, etc.

[0320] A "neural network" refers to a computational model that learns from large amounts of data and performs pattern recognition and prediction.

[0321] "Game mechanics" refers to elements that influence the rules, controls, and play style within a game.

[0322] "Scenario" refers to the settings and flow of a game's story and progression.

[0323] "Environment settings" refer to the physical or visual conditions within a game that are presented to the user.

[0324] A "machine learning algorithm" refers to a method for analyzing data and automatically finding patterns and rules from it.

[0325] "Game participation data" refers to data that shows how users interacted with and reacted within a game.

[0326] An "adaptive gaming experience" refers to a game that dynamically changes in response to the user's actions and emotions, providing the most suitable playing environment for the user.

[0327] "Verification testing" refers to tests conducted to confirm that a product or system functions correctly.

[0328] This invention provides a system that enables the creation of a personalized game environment that adapts in real time using user behavioral data and emotional data. The system consists of a server, a terminal, a user, and an emotional engine.

[0329] The server first collects user behavior data in real time during gameplay. This behavior data includes user movement, attacks, skill usage, and selected options. This data is obtained through in-game sensors and logs and immediately stored in a database.

[0330] The emotion engine detects the user's emotional data in real time. Specifically, it captures the user's facial expressions with a camera, analyzes emotions from their voice, and obtains their heart rate with a sensor. By combining this information, the emotion engine evaluates the user's emotional state and sends that data to the server.

[0331] The server integrates this behavioral and emotional data and analyzes it using a neural network. This is to identify the user's play style and emotional state, and to generate optimal game mechanics and scenarios for that user. The generated game mechanics and environment settings are sent to the terminal and immediately applied within the game.

[0332] The terminal receives new game mechanics and environment settings sent from the server and immediately applies them to the game environment. It also records user reactions and provides feedback to the server and emotion engine. This enables the system to achieve an adaptive cycle and provide the user with the best possible gaming experience.

[0333] As a concrete example, in an action game, if emotional data indicating user stress is recognized, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. An example of a prompt to the generating AI model is, "Show how to adjust the game difficulty in real time based on the user's emotions." In this way, the system can provide an optimized game experience based on both the user's behavior and emotions.

[0334] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0335] Step 1:

[0336] The server collects user behavior data in real time during gameplay. Inputs include user actions such as controls, movement, attacks, and skill usage. This data is obtained from sensors and log files and stored in a database. Output is the organized behavior data stored in the database.

[0337] Step 2:

[0338] The emotion engine acquires user emotional data in real time. Inputs include physiological signals such as the user's facial expressions, voice, and heart rate. These signals are collected from cameras, microphones, and sensors, and the emotion engine analyzes the emotional state. The output is the transmission of the detected emotional data to the server.

[0339] Step 3:

[0340] The server integrates collected behavioral and emotional data. Input data consists of behavioral data stored in a database and emotional data transmitted from the emotion engine. A neural network is used to normalize this data and perform data processing and analysis to identify the user's play style and emotional state. The output is user profile information derived from the analysis.

[0341] Step 4:

[0342] The server generates optimal game mechanics and scenarios for the user based on the analysis results. The input is the analysis results from step 3. The generation AI model is used to dynamically adjust game elements according to the user's emotions and actions. The output is the generated game mechanics and scenarios, which are sent to the terminal.

[0343] Step 5:

[0344] The terminal receives new game mechanics and settings sent from the server. Input is server-generated content. The game engine immediately applies this data and presents it to the user within the game. Output is the provision of the latest game state.

[0345] Step 6:

[0346] The device records the user's reactions during gameplay and feeds them back to the server and the emotion engine. The input is the user's reaction data. This data is used to analyze and adjust the next adaptive cycle. The output is the transmission of newly acquired user reaction data to the server and the emotion engine.

[0347] (Application Example 2)

[0348] 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."

[0349] There is a need for systems that can enhance the in-home entertainment experience and provide appropriate activities that respond to the user's behavior and emotions. However, current systems have difficulty responding to individual user needs in real time, making it difficult to significantly improve the quality and satisfaction of entertainment. This invention aims to provide a means to improve the quality of interaction in the user's living environment.

[0350] 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.

[0351] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing it using a neural network; means for automatically generating entertainment activities suitable for the user based on the analysis results; and means for applying the automatically generated entertainment activities to the user's living environment and updating them in real time. This makes it possible to provide interactive activities adapted to the user's current emotional state and behavior, thereby improving the overall entertainment experience.

[0352] "User behavior data" refers to a record of the actions and behaviors of users when they interact with the system.

[0353] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice, and physiological indicators.

[0354] A "neural network" is a type of artificial intelligence technology that mimics the neural structure of the human brain to perform data analysis and learning.

[0355] "Analysis" is the process of processing collected data to derive specific results.

[0356] "Entertainment activities" refer to activities that include entertainment and interaction aimed at providing users with enjoyment and satisfaction.

[0357] "Automatic generation" refers to the process of generating data and information mechanically, rather than manually, using algorithms.

[0358] "Living environment" refers to the physical and social conditions and spaces in which a user spends their daily life.

[0359] "Updating" refers to the act of changing information or data to the latest version.

[0360] "Machine learning technology" is a technology that gives computers the ability to learn from data and automatically improve at specific tasks.

[0361] "Interaction" refers to the interaction or dialogue between the user and the system.

[0362] This invention is a system for providing users with personalized entertainment experiences. The following describes the system's embodiments and operation.

[0363] The server is responsible for collecting user behavior and emotional data. This data is acquired by facial recognition cameras, microphones, and heart rate sensors installed in the home entertainment robot. The collected data is analyzed for emotional states using the Microsoft Azure Emotion API. Subsequently, a neural network using TensorFlow in Python integrates and analyzes the user's behavior patterns and emotional data to generate entertainment activities tailored to the user.

[0364] The device applies server-generated activities to the home environment. Based on the user's behavior and emotions, it provides the user with automatically generated activities—such as games, dancing, and conversations—in real time. The activities then analyze the user's responses and adjust their content and difficulty as needed.

[0365] As a concrete example, when spending time with family in the evening, the robot checks the user's emotional state and analyzes whether they are relaxed. Based on this result, the robot suggests relaxation music and quizzes to create a warm atmosphere for the family. The system continuously monitors the user's emotions and reactions and adjusts the next appropriate activity in real time.

[0366] An example of a generated AI prompt might be, "Check the emotional state of the family and suggest relaxing activities." Based on this prompt, the system can provide appropriate entertainment content.

[0367] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0368] Step 1:

[0369] The server receives user behavior and emotional data collected by a home entertainment robot. Inputs include facial expression data from the camera, voice tone data from the microphone, and physiological data from the heart rate sensor. This data is analyzed using the Microsoft Azure Emotion API to identify the user's emotional state. The output is the user's emotional state data as a result of the analysis.

[0370] Step 2:

[0371] The server integrates emotional state data and user behavior data, and analyzes user behavior patterns by applying a neural network model using TensorFlow. The input is the emotional state data and behavior data obtained in step 1. Data analysis using a neural network identifies the entertainment activities preferred by the user. The output is a recommendation of entertainment activities suitable for the user.

[0372] Step 3:

[0373] The terminal receives entertainment activities transmitted from the server and presents them to the user in real time. The input is the instruction for the entertainment activity identified in step 2. The robot provides the user with activities such as games, dancing, and conversation. The output is the provided entertainment activity and the user's response.

[0374] Step 4:

[0375] The terminal monitors the user's responses during the activity and feeds that data back to the server. The input is the user's actual response data. Based on the user's emotional state and reactions, the server determines whether the activity content or difficulty needs to be adjusted. The output is instructions for the next appropriate, adjusted activity.

[0376] Step 5:

[0377] The server updates the entertainment activities based on the feedback data collected in step 4 and regenerates new activity plans as needed. The input is the user's latest response data. The generative AI model is used to readjust activity suggestions based on prompt sentences. The output is the updated instructions for the entertainment activities.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] [Third Embodiment]

[0382] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0383] 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.

[0384] 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).

[0385] 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.

[0386] 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.

[0387] 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).

[0388] 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.

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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".

[0394] This invention relates to the construction of a game system that personalizes and adapts to the user's game experience in real time. This system consists of three main entities: a server, a terminal, and a user.

[0395] The server continuously collects user behavior data. This data includes actions, choices, and strategies that users perform within the game. The server retains this behavior data and analyzes the user's play style by inputting the collected data into a neural network. This analysis identifies user preferences and tendencies, and generates new game mechanics based on them.

[0396] The generated game mechanics are automatically applied to match the user's play style. For example, if it is determined that the user is a strategy-oriented player, the server will generate a scenario with more complex strategic elements, send it to the device, and apply it to the game. The server also uses machine learning algorithms to analyze data accumulated during gameplay and make real-time balance adjustments. This ensures that the game's difficulty and reward distribution are always appropriately adjusted, allowing users to enjoy an optimized gaming experience.

[0397] The user's device receives new mechanics and environmental information sent from the server and reflects it in the game. This ensures that the user can always play with the latest game settings. The device also captures the user's reactions and sends them back to the server, complementing the collection of new data.

[0398] As a concrete example, let's consider an RPG (role-playing game). If a user has a play style that strongly favors exploration, the server automatically generates complex mazes and additional quests, sending them to the device and incorporating them into the game experience. Furthermore, the frequency and difficulty of enemy appearances are adjusted using machine learning, allowing users to feel a moderate challenge.

[0399] This system allows the game to grow and change in response to user actions, rather than simply following a predetermined flow, thus providing a fresh and engaging experience over a longer period.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The server collects user activity data in real time during gameplay. It records user actions such as attacks, movement, and skill usage, and stores them in an action log.

[0403] Step 2:

[0404] The server inputs the collected behavioral logs into a neural network model for analysis. This identifies the user's play style and preferences, particularly by analyzing frequently used actions and selected tactics.

[0405] Step 3:

[0406] The server generates game mechanics tailored to the user based on the analysis results. This process involves strengthening specific enemy characters and designing new levels based on the user's preferences. These generated results are then sent from the server to the user's device.

[0407] Step 4:

[0408] The device integrates new game mechanics and scenarios received from the server into the game in real time, allowing the user to immediately continue playing with the new settings. The device also records the user's reactions and subsequent actions.

[0409] Step 5:

[0410] The server uses machine learning algorithms to analyze new gameplay data accumulated during user gameplay and attempts to adjust the game balance as needed. For example, if a user frequently fails a certain quest, the server will adjust the difficulty of that quest to an appropriate level.

[0411] Step 6:

[0412] The server resends the adjusted game balance information to the user's device, updating the game environment in real time. This ensures that users can always enjoy the game in an optimized state.

[0413] (Example 1)

[0414] 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."

[0415] Providing a game experience optimized for individual users is challenging in game systems. Maintaining fresh and interesting game content while accommodating diverse play styles and preferences is a significant burden and technical challenge for developers. Furthermore, appropriately adjusting game balance and difficulty in real time to enhance user satisfaction is also a crucial challenge.

[0416] 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.

[0417] In this invention, the server includes means for collecting digital information about user behavior and analyzing it using an artificial neural network, means for automatically constructing a game structure that is optimally suited to the user based on the analysis results, and means for applying the constructed game structure to the associated game environment and modifying it immediately. This makes it possible to evolve the game environment in accordance with the user's behavior patterns and to provide a game experience that is appropriately tailored to each individual user.

[0418] "Digital information about user behavior" refers to data recorded in digital format, including actions, choices, and strategies used by users within a game.

[0419] An "artificial neural network" is a computer model that mimics the nervous system of a living organism, and is a machine learning technique used for data analysis and pattern recognition.

[0420] "Automatically constructing a game structure that is optimally suited to the user based on analysis results" refers to the process of generating new game elements and scenarios that correspond to the user's preferences and play style, based on the analyzed user data.

[0421] "Constructed game structure" refers to a collection of in-game rules and scenarios that are customized specifically for the user.

[0422] "Applying to the game environment and making immediate corrections" means instantly updating the current game settings based on new game information obtained from the server, thereby providing users with a new gaming experience.

[0423] "Machine learning technology" is a branch of artificial intelligence that learns patterns from data to perform predictions and classifications.

[0424] "Game operation data" refers to various types of data used to manage the progress of the game, such as the game's difficulty level, player success rates, and reward distribution.

[0425] "User behavior patterns" refer to consistent behavioral patterns and play styles exhibited by users within a game.

[0426] This invention is a system that personalizes and dynamically adapts to the user's gaming experience. The system mainly consists of a server, a terminal, and the user.

[0427] The server collects behavioral data such as actions, choices, and strategies performed by the user within the game. This data is analyzed by an artificial neural network and used to detect the user's play style. Based on this analysis, the server uses a generative AI model to build new game mechanics optimized for the user. This process requires high-performance computing power for data analysis, such as a server-type computer.

[0428] The generated game mechanics are sent from the server to the terminal. The terminal immediately reflects the new game information received in the game environment and provides it to the user. For example, if it is determined that the user's play style is one of exploration, the terminal implements a new scenario into the game, including automatically generated complex mazes and additional quests. The terminal also records the user's reactions and actions during the game and sends them back to the server. This allows for more effective data analysis in the future.

[0429] An example of a specific prompt for a generative AI model is: "Generate an evolved version of the maze and adjust the enemy spawn rate appropriately for users who prefer exploration." By using prompts like these, the server can generate new game elements, ensuring that users always enjoy a fresh and engaging gaming experience.

[0430] This system allows the game to grow and change in response to the actions of individual users, rather than simply following a predetermined flow, thus contributing to increased user satisfaction.

[0431] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0432] Step 1:

[0433] The server collects digital information about user behavior in real time. Inputs include data such as actions, selections, items used, and locations reached within the game. Specifically, the server dynamically retrieves this information through the game's API. The output is a user behavior dataset.

[0434] Step 2:

[0435] The server inputs the collected user behavior data into an artificial neural network for analysis. The input data is the behavior data collected in Step 1. Here, feature quantities are calculated from the data to extract characteristics such as the user's selection tendencies and strategic thinking, and the play style is estimated using a neural network model. The output is information about the user's play style.

[0436] Step 3:

[0437] The server uses a generative AI model based on the analysis results to automatically build game mechanics optimized for the user. The input for this step is the analysis results from step 2. Specifically, prompt statements are input to the generative AI model to create new game elements and scenarios. For example, a prompt statement such as "Generate a complex maze for users who enjoy exploration" is used. The output is the constructed game mechanics.

[0438] Step 4:

[0439] The server sends the built game mechanics to the terminal. The input is the new game elements generated in step 3. Specifically, the server sends this information to the terminal via the endpoint, allowing the terminal to reflect that information in the game. The output is the updated game environment information.

[0440] Step 5:

[0441] The terminal immediately applies the newly received game mechanics to the game environment. The input is the game mechanics received from the server in step 4. In its specific operation, the terminal updates the game's configuration files and database, enabling the user to play in the new environment. The output is the latest game experience provided to the user.

[0442] Step 6:

[0443] The device observes the user's gameplay and records newly generated data. The input is the user's real-time in-game actions. Specifically, the device continuously records user operation logs and clickstream data. The output is a new behavioral dataset to be used for the next server analysis.

[0444] (Application Example 1)

[0445] 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."

[0446] In modern content delivery platforms, providing personalized viewing experiences based on users' interests and preferences is becoming increasingly important. However, traditional systems have struggled to analyze users' viewing patterns in real time and dynamically optimize content based on that data. As a result, users are often shown content irrelevant to their interests, leading to decreased satisfaction.

[0447] 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.

[0448] In this invention, the server includes means for collecting user behavior information and detecting it using a deep learning model, means for automatically generating optimized electronic content settings for the user based on the detection results, and means for applying the automatically generated electronic content settings to a linked content environment and updating them in real time. This makes it possible to provide a personalized viewing experience for each user in real time.

[0449] "Users" refer to individual consumers who use content distribution services to view content.

[0450] "Behavioral information" refers to data that shows a user's viewing history, selection patterns, and preferences when browsing content.

[0451] A "deep learning model" is an algorithm that uses multi-layered neural networks to analyze complex data patterns and make predictions.

[0452] "Detection" is the process of analyzing collected data to infer user preferences and behavioral patterns.

[0453] "Electronic content settings" are parameters for suggesting and delivering content optimized based on the user's viewing patterns.

[0454] A "linked content environment" refers to the platform or application where content provided to users is held and can be viewed.

[0455] "Real-time updates" refers to a state where data collection, analysis, and feedback to users occur almost instantly.

[0456] In the system that realizes this invention, the program is constructed as follows: The server continuously collects user viewing behavior data and inputs it into a deep learning model. Specifically, it uses Amazon DynamoDB to store data such as viewing history and selection patterns, and uses TensorFlow to analyze that data. Through this analysis, the user's viewing patterns are revealed, and based on that, the settings for electronic content are automatically generated.

[0457] The device receives the generated electronic content settings and reflects them in the user's viewing in real time. This ensures that the user always has the latest viewing experience. The system can also improve content recommendations based on continuous user feedback.

[0458] For example, if a user frequently watches action movies, the system can quickly suggest trailers for new action movies that are suitable for that user.

[0459] Examples of prompts to input into a generative AI model:

[0460] I want to build a model that recommends personalized content based on a user's viewing history. What kind of neural network architecture would be suitable? Please explain in detail.

[0461] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0462] Step 1:

[0463] The server collects user viewing behavior data. Specifically, it obtains data such as the genre of content viewed, playback time, and rating from the user's device in real time and stores it in Amazon DynamoDB. The input data is the user's viewing history, and the output is obtained by recording this in the database.

[0464] Step 2:

[0465] The server inputs the collected viewing data into a deep learning model. A neural network using TensorFlow analyzes the user's viewing patterns from the data. The input is viewing history stored in a database, which is used to identify viewing trends, and the results are output. Based on these analysis results, preferences for specific genres are extracted.

[0466] Step 3:

[0467] The server automatically generates electronic content settings based on the analysis results. It creates a new recommendation list that reflects viewing trends and sends that list to the device. The input is the analysis results, and it outputs an automatically generated recommendation list. Specifically, a list is generated that includes the latest works and related content in the user's preferred genre.

[0468] Step 4:

[0469] The terminal receives electronic content settings from the server and presents them to the user in real time. A recommendation list is displayed on the terminal screen, allowing the user to easily select newly recommended content. The input is the recommendation list from the server, and the output is the content displayed to the user.

[0470] Step 5:

[0471] The user selects and views the presented content. This behavioral data is collected again and sent to the server as feedback. This returns to step 1, and the entire process is continuously improved. The input is the user's viewing selection, and new behavioral data is output and collected.

[0472] 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.

[0473] This invention relates to a system that uses user behavioral data and emotional data to construct and adapt a personalized game environment in real time. The system consists of a server, a terminal, a user, and an emotional engine.

[0474] The server captures the user's actions during gameplay and collects the information necessary to recognize their behavioral patterns. This includes data such as the user's movement, attacks, skill usage, and selected options, which are stored in a database in real time.

[0475] The emotion engine detects and evaluates the user's emotions in real time. This engine uses the user's facial expressions, voice, heart rate, and other data to recognize their emotional state during gameplay. The data obtained by the emotion engine is sent to the server and integrated with behavioral data.

[0476] The server uses a neural network to analyze integrated data and identify the user's play style as well as their current emotional state. Based on this analysis, it automatically generates new game mechanics and scenarios that the user will enjoy most. This generation process includes adjustments such as automatically lowering the game difficulty if the user is feeling fatigued.

[0477] The terminal immediately receives new mechanics and environment settings sent from the server, providing the user with the latest game environment. The terminal also records the user's reactions during gameplay and feeds this information back to the server and emotion engine, enabling the entire system to repeat a series of adaptive cycles.

[0478] For example, in an action game, if emotional data indicating user stress is detected, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. In this way, the system can utilize data from both emotions and behavior to provide the user with the optimal gaming experience in real time.

[0479] The following describes the processing flow.

[0480] Step 1:

[0481] The user starts the game. The device begins collecting both action data during gameplay and emotion data from emotion sensors in real time. This includes behavioral data such as the user's movement speed and skill usage frequency, as well as emotion data including facial expressions and voice tone.

[0482] Step 2:

[0483] The device sends the collected data to the server. The server records the received behavioral and emotional data in a database. This data shows how the user is behaving within the game and their current emotional state.

[0484] Step 3:

[0485] The server uses a neural network to analyze behavioral and emotional data. This analysis identifies play styles and emotional responses, deriving crucial information to personalize the user experience. For example, if a user shows tension in a challenging situation, the server determines that elements that promote relaxation are needed.

[0486] Step 4:

[0487] Based on the analysis results, the server generates game mechanics and scenarios optimized for the player's emotions and behavior. For example, if the player is under high stress, it creates an environment with fewer obstacles and enhanced exploration elements.

[0488] Step 5:

[0489] The server sends the newly generated game settings to the terminal and applies them to the game immediately. This includes elements such as changing enemy placement and adjusting item spawn rates.

[0490] Step 6:

[0491] The device reflects updates from the server in real time, presenting the game situation to the user. The user continues to experience these changes, and their actions and emotions are collected again, naturally transitioning to the next cycle.

[0492] This enables gameplay that takes the user's emotional state into account, resulting in a significantly more personalized experience.

[0493] (Example 2)

[0494] 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."

[0495] Traditional game systems often provided a uniform gaming experience without considering the user's play style or emotional state. This sometimes made it difficult for users to continue enjoying the game. Furthermore, large-scale manual verification testing is labor-intensive and time-consuming, highlighting the need for more efficient verification methods. There is a need to address these challenges and provide personalized gaming experiences optimized for each user.

[0496] 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.

[0497] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing them using a neural network; means for automatically generating personalized game mechanics and scenarios for the user based on the analysis results; and means for analyzing game participation data using machine learning algorithms and dynamically adjusting the difficulty of the game. This enables the provision of a real-time, adaptive game experience and efficient verification based on the user's behavior and emotions.

[0498] "User behavior data" refers to information about in-game activities that users exhibit during gameplay, such as movement, attacks, skill usage, and selected options.

[0499] "Emotional data" refers to information about a user's emotional state, obtained from the user's facial expressions, voice, heart rate, etc.

[0500] A "neural network" refers to a computational model that learns from large amounts of data and performs pattern recognition and prediction.

[0501] "Game mechanics" refers to elements that influence the rules, controls, and play style within a game.

[0502] "Scenario" refers to the settings and flow of a game's story and progression.

[0503] "Environment settings" refer to the physical or visual conditions within a game that are presented to the user.

[0504] A "machine learning algorithm" refers to a method for analyzing data and automatically finding patterns and rules from it.

[0505] "Game participation data" refers to data that shows how users interacted with and reacted within a game.

[0506] An "adaptive gaming experience" refers to a game that dynamically changes in response to the user's actions and emotions, providing the most suitable playing environment for the user.

[0507] "Verification testing" refers to tests conducted to confirm that a product or system functions correctly.

[0508] This invention provides a system that enables the creation of a personalized game environment that adapts in real time using user behavioral data and emotional data. The system consists of a server, a terminal, a user, and an emotional engine.

[0509] The server first collects user behavior data in real time during gameplay. This behavior data includes user movement, attacks, skill usage, and selected options. This data is obtained through in-game sensors and logs and immediately stored in a database.

[0510] The emotion engine detects the user's emotional data in real time. Specifically, it captures the user's facial expressions with a camera, analyzes emotions from their voice, and obtains their heart rate with a sensor. By combining this information, the emotion engine evaluates the user's emotional state and sends that data to the server.

[0511] The server integrates this behavioral and emotional data and analyzes it using a neural network. This is to identify the user's play style and emotional state, and to generate optimal game mechanics and scenarios for that user. The generated game mechanics and environment settings are sent to the terminal and immediately applied within the game.

[0512] The terminal receives new game mechanics and environment settings sent from the server and immediately applies them to the game environment. It also records user reactions and provides feedback to the server and emotion engine. This enables the system to achieve an adaptive cycle and provide the user with the best possible gaming experience.

[0513] As a concrete example, in an action game, if emotional data indicating user stress is recognized, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. An example of a prompt to the generating AI model is, "Show how to adjust the game difficulty in real time based on the user's emotions." In this way, the system can provide an optimized game experience based on both the user's behavior and emotions.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The server collects user behavior data in real time during gameplay. Inputs include user actions such as controls, movement, attacks, and skill usage. This data is obtained from sensors and log files and stored in a database. Output is the organized behavior data stored in the database.

[0517] Step 2:

[0518] The emotion engine acquires user emotional data in real time. Inputs include physiological signals such as the user's facial expressions, voice, and heart rate. These signals are collected from cameras, microphones, and sensors, and the emotion engine analyzes the emotional state. The output is the transmission of the detected emotional data to the server.

[0519] Step 3:

[0520] The server integrates collected behavioral and emotional data. Input data consists of behavioral data stored in a database and emotional data transmitted from the emotion engine. A neural network is used to normalize this data and perform data processing and analysis to identify the user's play style and emotional state. The output is user profile information derived from the analysis.

[0521] Step 4:

[0522] The server generates optimal game mechanics and scenarios for the user based on the analysis results. The input is the analysis results from step 3. The generation AI model is used to dynamically adjust game elements according to the user's emotions and actions. The output is the generated game mechanics and scenarios, which are sent to the terminal.

[0523] Step 5:

[0524] The terminal receives new game mechanics and settings sent from the server. Input is server-generated content. The game engine immediately applies this data and presents it to the user within the game. Output is the provision of the latest game state.

[0525] Step 6:

[0526] The device records the user's reactions during gameplay and feeds them back to the server and the emotion engine. The input is the user's reaction data. This data is used to analyze and adjust the next adaptive cycle. The output is the transmission of newly acquired user reaction data to the server and the emotion engine.

[0527] (Application Example 2)

[0528] 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."

[0529] There is a need for systems that can enhance the in-home entertainment experience and provide appropriate activities that respond to the user's behavior and emotions. However, current systems have difficulty responding to individual user needs in real time, making it difficult to significantly improve the quality and satisfaction of entertainment. This invention aims to provide a means to improve the quality of interaction in the user's living environment.

[0530] 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.

[0531] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing it using a neural network; means for automatically generating entertainment activities suitable for the user based on the analysis results; and means for applying the automatically generated entertainment activities to the user's living environment and updating them in real time. This makes it possible to provide interactive activities adapted to the user's current emotional state and behavior, thereby improving the overall entertainment experience.

[0532] "User behavior data" refers to a record of the actions and behaviors of users when they interact with the system.

[0533] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice, and physiological indicators.

[0534] A "neural network" is a type of artificial intelligence technology that mimics the neural structure of the human brain to perform data analysis and learning.

[0535] "Analysis" is the process of processing collected data to derive specific results.

[0536] "Entertainment activities" refer to activities that include entertainment and interaction aimed at providing users with enjoyment and satisfaction.

[0537] "Automatic generation" refers to the process of generating data and information mechanically, rather than manually, using algorithms.

[0538] "Living environment" refers to the physical and social conditions and spaces in which a user spends their daily life.

[0539] "Updating" refers to the act of changing information or data to the latest version.

[0540] "Machine learning technology" is a technology that gives computers the ability to learn from data and automatically improve at specific tasks.

[0541] "Interaction" refers to the interaction or dialogue between the user and the system.

[0542] This invention is a system for providing users with personalized entertainment experiences. The following describes the system's embodiments and operation.

[0543] The server is responsible for collecting user behavior and emotional data. This data is acquired by facial recognition cameras, microphones, and heart rate sensors installed in the home entertainment robot. The collected data is analyzed for emotional states using the Microsoft Azure Emotion API. Subsequently, a neural network using TensorFlow in Python integrates and analyzes the user's behavior patterns and emotional data to generate entertainment activities tailored to the user.

[0544] The device applies server-generated activities to the home environment. Based on the user's behavior and emotions, it provides the user with automatically generated activities—such as games, dancing, and conversations—in real time. The activities then analyze the user's responses and adjust their content and difficulty as needed.

[0545] As a concrete example, when spending time with family in the evening, the robot checks the user's emotional state and analyzes whether they are relaxed. Based on this result, the robot suggests relaxation music and quizzes to create a warm atmosphere for the family. The system continuously monitors the user's emotions and reactions and adjusts the next appropriate activity in real time.

[0546] An example of a generated AI prompt might be, "Check the emotional state of the family and suggest relaxing activities." Based on this prompt, the system can provide appropriate entertainment content.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The server receives user behavior and emotional data collected by a home entertainment robot. Inputs include facial expression data from the camera, voice tone data from the microphone, and physiological data from the heart rate sensor. This data is analyzed using the Microsoft Azure Emotion API to identify the user's emotional state. The output is the user's emotional state data as a result of the analysis.

[0550] Step 2:

[0551] The server integrates emotional state data and user behavior data, and analyzes user behavior patterns by applying a neural network model using TensorFlow. The input is the emotional state data and behavior data obtained in step 1. Data analysis using a neural network identifies the entertainment activities preferred by the user. The output is a recommendation of entertainment activities suitable for the user.

[0552] Step 3:

[0553] The terminal receives entertainment activities transmitted from the server and presents them to the user in real time. The input is the instruction for the entertainment activity identified in step 2. The robot provides the user with activities such as games, dancing, and conversation. The output is the provided entertainment activity and the user's response.

[0554] Step 4:

[0555] The terminal monitors the user's responses during the activity and feeds that data back to the server. The input is the user's actual response data. Based on the user's emotional state and reactions, the server determines whether the activity content or difficulty needs to be adjusted. The output is instructions for the next appropriate, adjusted activity.

[0556] Step 5:

[0557] The server updates the entertainment activities based on the feedback data collected in step 4 and regenerates new activity plans as needed. The input is the user's latest response data. The generative AI model is used to readjust activity suggestions based on prompt sentences. The output is the updated instructions for the entertainment activities.

[0558] 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.

[0559] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0560] 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.

[0561] [Fourth Embodiment]

[0562] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0563] 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.

[0564] 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).

[0565] 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.

[0566] 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.

[0567] 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).

[0568] 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.

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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".

[0575] This invention relates to the construction of a game system that personalizes and adapts to the user's game experience in real time. This system consists of three main entities: a server, a terminal, and a user.

[0576] The server continuously collects user behavior data. This data includes actions, choices, and strategies that users perform within the game. The server retains this behavior data and analyzes the user's play style by inputting the collected data into a neural network. This analysis identifies user preferences and tendencies, and generates new game mechanics based on them.

[0577] The generated game mechanics are automatically applied to match the user's play style. For example, if it is determined that the user is a strategy-oriented player, the server will generate a scenario with more complex strategic elements, send it to the device, and apply it to the game. The server also uses machine learning algorithms to analyze data accumulated during gameplay and make real-time balance adjustments. This ensures that the game's difficulty and reward distribution are always appropriately adjusted, allowing users to enjoy an optimized gaming experience.

[0578] The user's device receives new mechanics and environmental information sent from the server and reflects it in the game. This ensures that the user can always play with the latest game settings. The device also captures the user's reactions and sends them back to the server, complementing the collection of new data.

[0579] As a concrete example, let's consider an RPG (role-playing game). If a user has a play style that strongly favors exploration, the server automatically generates complex mazes and additional quests, sending them to the device and incorporating them into the game experience. Furthermore, the frequency and difficulty of enemy appearances are adjusted using machine learning, allowing users to feel a moderate challenge.

[0580] This system allows the game to grow and change in response to user actions, rather than simply following a predetermined flow, thus providing a fresh and engaging experience over a longer period.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] The server collects user activity data in real time during gameplay. It records user actions such as attacks, movement, and skill usage, and stores them in an action log.

[0584] Step 2:

[0585] The server inputs the collected behavioral logs into a neural network model for analysis. This identifies the user's play style and preferences, particularly by analyzing frequently used actions and selected tactics.

[0586] Step 3:

[0587] The server generates game mechanics tailored to the user based on the analysis results. This process involves strengthening specific enemy characters and designing new levels based on the user's preferences. These generated results are then sent from the server to the user's device.

[0588] Step 4:

[0589] The device integrates new game mechanics and scenarios received from the server into the game in real time, allowing the user to immediately continue playing with the new settings. The device also records the user's reactions and subsequent actions.

[0590] Step 5:

[0591] The server uses machine learning algorithms to analyze new gameplay data accumulated during user gameplay and attempts to adjust the game balance as needed. For example, if a user frequently fails a certain quest, the server will adjust the difficulty of that quest to an appropriate level.

[0592] Step 6:

[0593] The server resends the adjusted game balance information to the user's device, updating the game environment in real time. This ensures that users can always enjoy the game in an optimized state.

[0594] (Example 1)

[0595] 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".

[0596] Providing a game experience optimized for individual users is challenging in game systems. Maintaining fresh and interesting game content while accommodating diverse play styles and preferences is a significant burden and technical challenge for developers. Furthermore, appropriately adjusting game balance and difficulty in real time to enhance user satisfaction is also a crucial challenge.

[0597] 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.

[0598] In this invention, the server includes means for collecting digital information about user behavior and analyzing it using an artificial neural network, means for automatically constructing a game structure that is optimally suited to the user based on the analysis results, and means for applying the constructed game structure to the associated game environment and modifying it immediately. This makes it possible to evolve the game environment in accordance with the user's behavior patterns and to provide a game experience that is appropriately tailored to each individual user.

[0599] "Digital information about user behavior" refers to data recorded in digital format, including actions, choices, and strategies used by users within a game.

[0600] An "artificial neural network" is a computer model that mimics the nervous system of a living organism, and is a machine learning technique used for data analysis and pattern recognition.

[0601] "Automatically constructing a game structure that is optimally suited to the user based on analysis results" refers to the process of generating new game elements and scenarios that correspond to the user's preferences and play style, based on the analyzed user data.

[0602] "Constructed game structure" refers to a collection of in-game rules and scenarios that are customized specifically for the user.

[0603] "Applying to the game environment and making immediate corrections" means instantly updating the current game settings based on new game information obtained from the server, thereby providing users with a new gaming experience.

[0604] "Machine learning technology" is a branch of artificial intelligence that learns patterns from data to perform predictions and classifications.

[0605] "Game operation data" refers to various types of data used to manage the progress of the game, such as the game's difficulty level, player success rates, and reward distribution.

[0606] "User behavior patterns" refer to consistent behavioral patterns and play styles exhibited by users within a game.

[0607] This invention is a system that personalizes and dynamically adapts to the user's gaming experience. The system mainly consists of a server, a terminal, and the user.

[0608] The server collects behavioral data such as actions, choices, and strategies performed by the user within the game. This data is analyzed by an artificial neural network and used to detect the user's play style. Based on this analysis, the server uses a generative AI model to build new game mechanics optimized for the user. This process requires high-performance computing power for data analysis, such as a server-type computer.

[0609] The generated game mechanics are sent from the server to the terminal. The terminal immediately reflects the new game information received in the game environment and provides it to the user. For example, if it is determined that the user's play style is one of exploration, the terminal implements a new scenario into the game, including automatically generated complex mazes and additional quests. The terminal also records the user's reactions and actions during the game and sends them back to the server. This allows for more effective data analysis in the future.

[0610] An example of a specific prompt for a generative AI model is: "Generate an evolved version of the maze and adjust the enemy spawn rate appropriately for users who prefer exploration." By using prompts like these, the server can generate new game elements, ensuring that users always enjoy a fresh and engaging gaming experience.

[0611] This system allows the game to grow and change in response to the actions of individual users, rather than simply following a predetermined flow, thus contributing to increased user satisfaction.

[0612] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0613] Step 1:

[0614] The server collects digital information about user behavior in real time. Inputs include data such as actions, selections, items used, and locations reached within the game. Specifically, the server dynamically retrieves this information through the game's API. The output is a user behavior dataset.

[0615] Step 2:

[0616] The server inputs the collected user behavior data into an artificial neural network for analysis. The input data is the behavior data collected in Step 1. Here, feature quantities are calculated from the data to extract characteristics such as the user's selection tendencies and strategic thinking, and the play style is estimated using a neural network model. The output is information about the user's play style.

[0617] Step 3:

[0618] The server uses a generative AI model based on the analysis results to automatically build game mechanics optimized for the user. The input for this step is the analysis results from step 2. Specifically, prompt statements are input to the generative AI model to create new game elements and scenarios. For example, a prompt statement such as "Generate a complex maze for users who enjoy exploration" is used. The output is the constructed game mechanics.

[0619] Step 4:

[0620] The server sends the built game mechanics to the terminal. The input is the new game elements generated in step 3. Specifically, the server sends this information to the terminal via the endpoint, allowing the terminal to reflect that information in the game. The output is the updated game environment information.

[0621] Step 5:

[0622] The terminal immediately applies the newly received game mechanics to the game environment. The input is the game mechanics received from the server in step 4. In its specific operation, the terminal updates the game's configuration files and database, enabling the user to play in the new environment. The output is the latest game experience provided to the user.

[0623] Step 6:

[0624] The device observes the user's gameplay and records newly generated data. The input is the user's real-time in-game actions. Specifically, the device continuously records user operation logs and clickstream data. The output is a new behavioral dataset to be used for the next server analysis.

[0625] (Application Example 1)

[0626] 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".

[0627] In modern content delivery platforms, providing personalized viewing experiences based on users' interests and preferences is becoming increasingly important. However, traditional systems have struggled to analyze users' viewing patterns in real time and dynamically optimize content based on that data. As a result, users are often shown content irrelevant to their interests, leading to decreased satisfaction.

[0628] 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.

[0629] In this invention, the server includes means for collecting user behavior information and detecting it using a deep learning model, means for automatically generating optimized electronic content settings for the user based on the detection results, and means for applying the automatically generated electronic content settings to a linked content environment and updating them in real time. This makes it possible to provide a personalized viewing experience for each user in real time.

[0630] "Users" refer to individual consumers who use content distribution services to view content.

[0631] "Behavioral information" refers to data that shows a user's viewing history, selection patterns, and preferences when browsing content.

[0632] A "deep learning model" is an algorithm that uses multi-layered neural networks to analyze complex data patterns and make predictions.

[0633] "Detection" is the process of analyzing collected data to infer user preferences and behavioral patterns.

[0634] "Electronic content settings" are parameters for suggesting and delivering content optimized based on the user's viewing patterns.

[0635] A "linked content environment" refers to the platform or application where content provided to users is held and can be viewed.

[0636] "Real-time updates" refers to a state where data collection, analysis, and feedback to users occur almost instantly.

[0637] In the system that realizes this invention, the program is constructed as follows: The server continuously collects user viewing behavior data and inputs it into a deep learning model. Specifically, it uses Amazon DynamoDB to store data such as viewing history and selection patterns, and uses TensorFlow to analyze that data. Through this analysis, the user's viewing patterns are revealed, and based on that, the settings for electronic content are automatically generated.

[0638] The device receives the generated electronic content settings and reflects them in the user's viewing in real time. This ensures that the user always has the latest viewing experience. The system can also improve content recommendations based on continuous user feedback.

[0639] For example, if a user frequently watches action movies, the system can quickly suggest trailers for new action movies that are suitable for that user.

[0640] Examples of prompts to input into a generative AI model:

[0641] I want to build a model that recommends personalized content based on a user's viewing history. What kind of neural network architecture would be suitable? Please explain in detail.

[0642] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0643] Step 1:

[0644] The server collects user viewing behavior data. Specifically, it obtains data such as the genre of content viewed, playback time, and rating from the user's device in real time and stores it in Amazon DynamoDB. The input data is the user's viewing history, and the output is obtained by recording this in the database.

[0645] Step 2:

[0646] The server inputs the collected viewing data into a deep learning model. A neural network using TensorFlow analyzes the user's viewing patterns from the data. The input is viewing history stored in a database, which is used to identify viewing trends, and the results are output. Based on these analysis results, preferences for specific genres are extracted.

[0647] Step 3:

[0648] The server automatically generates electronic content settings based on the analysis results. It creates a new recommendation list that reflects viewing trends and sends that list to the device. The input is the analysis results, and it outputs an automatically generated recommendation list. Specifically, a list is generated that includes the latest works and related content in the user's preferred genre.

[0649] Step 4:

[0650] The terminal receives electronic content settings from the server and presents them to the user in real time. A recommendation list is displayed on the terminal screen, allowing the user to easily select newly recommended content. The input is the recommendation list from the server, and the output is the content displayed to the user.

[0651] Step 5:

[0652] The user selects and views the presented content. This behavioral data is collected again and sent to the server as feedback. This returns to step 1, and the entire process is continuously improved. The input is the user's viewing selection, and new behavioral data is output and collected.

[0653] 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.

[0654] This invention relates to a system that uses user behavioral data and emotional data to construct and adapt a personalized game environment in real time. The system consists of a server, a terminal, a user, and an emotional engine.

[0655] The server captures the user's actions during gameplay and collects the information necessary to recognize their behavioral patterns. This includes data such as the user's movement, attacks, skill usage, and selected options, which are stored in a database in real time.

[0656] The emotion engine detects and evaluates the user's emotions in real time. This engine uses the user's facial expressions, voice, heart rate, and other data to recognize their emotional state during gameplay. The data obtained by the emotion engine is sent to the server and integrated with behavioral data.

[0657] The server uses a neural network to analyze integrated data and identify the user's play style as well as their current emotional state. Based on this analysis, it automatically generates new game mechanics and scenarios that the user will enjoy most. This generation process includes adjustments such as automatically lowering the game difficulty if the user is feeling fatigued.

[0658] The terminal immediately receives new mechanics and environment settings sent from the server, providing the user with the latest game environment. The terminal also records the user's reactions during gameplay and feeds this information back to the server and emotion engine, enabling the entire system to repeat a series of adaptive cycles.

[0659] For example, in an action game, if emotional data indicating user stress is detected, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. In this way, the system can utilize data from both emotions and behavior to provide the user with the optimal gaming experience in real time.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The user starts the game. The device begins collecting both action data during gameplay and emotion data from emotion sensors in real time. This includes behavioral data such as the user's movement speed and skill usage frequency, as well as emotion data including facial expressions and voice tone.

[0663] Step 2:

[0664] The device sends the collected data to the server. The server records the received behavioral and emotional data in a database. This data shows how the user is behaving within the game and their current emotional state.

[0665] Step 3:

[0666] The server uses a neural network to analyze behavioral and emotional data. This analysis identifies play styles and emotional responses, deriving crucial information to personalize the user experience. For example, if a user shows tension in a challenging situation, the server determines that elements that promote relaxation are needed.

[0667] Step 4:

[0668] Based on the analysis results, the server generates game mechanics and scenarios optimized for the player's emotions and behavior. For example, if the player is under high stress, it creates an environment with fewer obstacles and enhanced exploration elements.

[0669] Step 5:

[0670] The server sends the newly generated game settings to the terminal and applies them to the game immediately. This includes elements such as changing enemy placement and adjusting item spawn rates.

[0671] Step 6:

[0672] The device reflects updates from the server in real time, presenting the game situation to the user. The user continues to experience these changes, and their actions and emotions are collected again, naturally transitioning to the next cycle.

[0673] This enables gameplay that takes the user's emotional state into account, resulting in a significantly more personalized experience.

[0674] (Example 2)

[0675] 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".

[0676] Traditional game systems often provided a uniform gaming experience without considering the user's play style or emotional state. This sometimes made it difficult for users to continue enjoying the game. Furthermore, large-scale manual verification testing is labor-intensive and time-consuming, highlighting the need for more efficient verification methods. There is a need to address these challenges and provide personalized gaming experiences optimized for each user.

[0677] 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.

[0678] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing them using a neural network; means for automatically generating personalized game mechanics and scenarios for the user based on the analysis results; and means for analyzing game participation data using machine learning algorithms and dynamically adjusting the difficulty of the game. This enables the provision of a real-time, adaptive game experience and efficient verification based on the user's behavior and emotions.

[0679] "User behavior data" refers to information about in-game activities that users exhibit during gameplay, such as movement, attacks, skill usage, and selected options.

[0680] "Emotional data" refers to information about a user's emotional state, obtained from the user's facial expressions, voice, heart rate, etc.

[0681] A "neural network" refers to a computational model that learns from large amounts of data and performs pattern recognition and prediction.

[0682] "Game mechanics" refers to elements that influence the rules, controls, and play style within a game.

[0683] "Scenario" refers to the settings and flow of a game's story and progression.

[0684] "Environment settings" refer to the physical or visual conditions within a game that are presented to the user.

[0685] A "machine learning algorithm" refers to a method for analyzing data and automatically finding patterns and rules from it.

[0686] "Game participation data" refers to data that shows how users interacted with and reacted within a game.

[0687] An "adaptive gaming experience" refers to a game that dynamically changes in response to the user's actions and emotions, providing the most suitable playing environment for the user.

[0688] "Verification testing" refers to tests conducted to confirm that a product or system functions correctly.

[0689] This invention provides a system that enables the creation of a personalized game environment that adapts in real time using user behavioral data and emotional data. The system consists of a server, a terminal, a user, and an emotional engine.

[0690] The server first collects user behavior data in real time during gameplay. This behavior data includes user movement, attacks, skill usage, and selected options. This data is obtained through in-game sensors and logs and immediately stored in a database.

[0691] The emotion engine detects the user's emotional data in real time. Specifically, it captures the user's facial expressions with a camera, analyzes emotions from their voice, and obtains their heart rate with a sensor. By combining this information, the emotion engine evaluates the user's emotional state and sends that data to the server.

[0692] The server integrates this behavioral and emotional data and analyzes it using a neural network. This is to identify the user's play style and emotional state, and to generate optimal game mechanics and scenarios for that user. The generated game mechanics and environment settings are sent to the terminal and immediately applied within the game.

[0693] The terminal receives new game mechanics and environment settings sent from the server and immediately applies them to the game environment. It also records user reactions and provides feedback to the server and emotion engine. This enables the system to achieve an adaptive cycle and provide the user with the best possible gaming experience.

[0694] As a concrete example, in an action game, if emotional data indicating user stress is recognized, the server generates a new stage with reduced enemy numbers or altered timing to provide a more comfortable experience. An example of a prompt to the generating AI model is, "Show how to adjust the game difficulty in real time based on the user's emotions." In this way, the system can provide an optimized game experience based on both the user's behavior and emotions.

[0695] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0696] Step 1:

[0697] The server collects user behavior data in real time during gameplay. Inputs include user actions such as controls, movement, attacks, and skill usage. This data is obtained from sensors and log files and stored in a database. Output is the organized behavior data stored in the database.

[0698] Step 2:

[0699] The emotion engine acquires user emotional data in real time. Inputs include physiological signals such as the user's facial expressions, voice, and heart rate. These signals are collected from cameras, microphones, and sensors, and the emotion engine analyzes the emotional state. The output is the transmission of the detected emotional data to the server.

[0700] Step 3:

[0701] The server integrates collected behavioral and emotional data. Input data consists of behavioral data stored in a database and emotional data transmitted from the emotion engine. A neural network is used to normalize this data and perform data processing and analysis to identify the user's play style and emotional state. The output is user profile information derived from the analysis.

[0702] Step 4:

[0703] The server generates optimal game mechanics and scenarios for the user based on the analysis results. The input is the analysis results from step 3. The generation AI model is used to dynamically adjust game elements according to the user's emotions and actions. The output is the generated game mechanics and scenarios, which are sent to the terminal.

[0704] Step 5:

[0705] The terminal receives new game mechanics and settings sent from the server. Input is server-generated content. The game engine immediately applies this data and presents it to the user within the game. Output is the provision of the latest game state.

[0706] Step 6:

[0707] The device records the user's reactions during gameplay and feeds them back to the server and the emotion engine. The input is the user's reaction data. This data is used to analyze and adjust the next adaptive cycle. The output is the transmission of newly acquired user reaction data to the server and the emotion engine.

[0708] (Application Example 2)

[0709] 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".

[0710] There is a need for systems that can enhance the in-home entertainment experience and provide appropriate activities that respond to the user's behavior and emotions. However, current systems have difficulty responding to individual user needs in real time, making it difficult to significantly improve the quality and satisfaction of entertainment. This invention aims to provide a means to improve the quality of interaction in the user's living environment.

[0711] 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.

[0712] In this invention, the server includes means for collecting user behavioral and emotional data and analyzing it using a neural network; means for automatically generating entertainment activities suitable for the user based on the analysis results; and means for applying the automatically generated entertainment activities to the user's living environment and updating them in real time. This makes it possible to provide interactive activities adapted to the user's current emotional state and behavior, thereby improving the overall entertainment experience.

[0713] "User behavior data" refers to a record of the actions and behaviors of users when they interact with the system.

[0714] "Emotional data" refers to data that indicates a user's emotional state, and includes information such as facial expressions, voice, and physiological indicators.

[0715] A "neural network" is a type of artificial intelligence technology that mimics the neural structure of the human brain to perform data analysis and learning.

[0716] "Analysis" is the process of processing collected data to derive specific results.

[0717] "Entertainment activities" refer to activities that include entertainment and interaction aimed at providing users with enjoyment and satisfaction.

[0718] "Automatic generation" refers to the process of generating data and information mechanically, rather than manually, using algorithms.

[0719] "Living environment" refers to the physical and social conditions and spaces in which a user spends their daily life.

[0720] "Updating" refers to the act of changing information or data to the latest version.

[0721] "Machine learning technology" is a technology that gives computers the ability to learn from data and automatically improve at specific tasks.

[0722] "Interaction" refers to the interaction or dialogue between the user and the system.

[0723] This invention is a system for providing users with personalized entertainment experiences. The following describes the system's embodiments and operation.

[0724] The server is responsible for collecting user behavior and emotional data. This data is acquired by facial recognition cameras, microphones, and heart rate sensors installed in the home entertainment robot. The collected data is analyzed for emotional states using the Microsoft Azure Emotion API. Subsequently, a neural network using TensorFlow in Python integrates and analyzes the user's behavior patterns and emotional data to generate entertainment activities tailored to the user.

[0725] The device applies server-generated activities to the home environment. Based on the user's behavior and emotions, it provides the user with automatically generated activities—such as games, dancing, and conversations—in real time. The activities then analyze the user's responses and adjust their content and difficulty as needed.

[0726] As a concrete example, when spending time with family in the evening, the robot checks the user's emotional state and analyzes whether they are relaxed. Based on this result, the robot suggests relaxation music and quizzes to create a warm atmosphere for the family. The system continuously monitors the user's emotions and reactions and adjusts the next appropriate activity in real time.

[0727] An example of a generated AI prompt might be, "Check the emotional state of the family and suggest relaxing activities." Based on this prompt, the system can provide appropriate entertainment content.

[0728] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0729] Step 1:

[0730] The server receives user behavior and emotional data collected by a home entertainment robot. Inputs include facial expression data from the camera, voice tone data from the microphone, and physiological data from the heart rate sensor. This data is analyzed using the Microsoft Azure Emotion API to identify the user's emotional state. The output is the user's emotional state data as a result of the analysis.

[0731] Step 2:

[0732] The server integrates emotional state data and user behavior data, and analyzes user behavior patterns by applying a neural network model using TensorFlow. The input is the emotional state data and behavior data obtained in step 1. Data analysis using a neural network identifies the entertainment activities preferred by the user. The output is a recommendation of entertainment activities suitable for the user.

[0733] Step 3:

[0734] The terminal receives entertainment activities transmitted from the server and presents them to the user in real time. The input is the instruction for the entertainment activity identified in step 2. The robot provides the user with activities such as games, dancing, and conversation. The output is the provided entertainment activity and the user's response.

[0735] Step 4:

[0736] The terminal monitors the user's responses during the activity and feeds that data back to the server. The input is the user's actual response data. Based on the user's emotional state and reactions, the server determines whether the activity content or difficulty needs to be adjusted. The output is instructions for the next appropriate, adjusted activity.

[0737] Step 5:

[0738] The server updates the entertainment activities based on the feedback data collected in step 4 and regenerates new activity plans as needed. The input is the user's latest response data. The generative AI model is used to readjust activity suggestions based on prompt sentences. The output is the updated instructions for the entertainment activities.

[0739] 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.

[0740] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0741] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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."

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] The following is further disclosed regarding the embodiments described above.

[0761] (Claim 1)

[0762] A means of collecting user behavior data and analyzing it using a neural network,

[0763] Based on the aforementioned analysis results, a means for automatically generating game mechanics optimized for the user,

[0764] A means of applying automatically generated game mechanics to a linked game environment and updating them in real time,

[0765] A system that includes a means of dynamically adjusting game balance by analyzing gameplay data using machine learning algorithms.

[0766] (Claim 2)

[0767] The system according to claim 1, which evolves the game environment based on user behavior patterns and constructs a realistic and highly responsive game world.

[0768] (Claim 3)

[0769] The system according to claim 1, which uses an AI bot to automate large-scale playtesting and improve the efficiency of debugging and testing.

[0770] "Example 1"

[0771] (Claim 1)

[0772] A means of collecting digital information about user behavior and analyzing it using an artificial neural network,

[0773] A means to automatically construct a game structure that is optimally suited to the user based on the analysis results,

[0774] A means of applying the structure of a constructed game to the associated game environment and immediately modifying it,

[0775] A system that uses machine learning technology to analyze game operation data and dynamically adjust the game's balance.

[0776] (Claim 2)

[0777] The system according to claim 1, which evolves the game environment in accordance with the user's behavior patterns, creating a realistic and highly responsive game world.

[0778] (Claim 3)

[0779] The system according to claim 1, which uses AI to automate large-scale play verification and improve the efficiency of fault elimination and verification.

[0780] "Application Example 1"

[0781] (Claim 1)

[0782] A means for collecting user behavior information and detecting it using a deep learning model,

[0783] A means for automatically generating user-optimized electronic content settings based on the aforementioned detection results,

[0784] A means of applying automatically generated electronic content settings to a linked content environment and updating them in real time,

[0785] A system that includes means for analyzing viewing data using machine learning algorithms and dynamically adjusting content delivery.

[0786] (Claim 2)

[0787] The system according to claim 1, which evolves the content environment based on the user's behavior patterns and constructs a realistic and highly responsive content world.

[0788] (Claim 3)

[0789] The system according to claim 1, which uses an artificial intelligence agent to automate large-scale viewing tests and improve the efficiency of data processing and analysis.

[0790] "Example 2 of combining an emotion engine"

[0791] (Claim 1)

[0792] A means for collecting user behavioral and emotional data and analyzing it using a neural network,

[0793] Based on the aforementioned analysis results, a means for automatically generating personalized game mechanics and scenarios for the user,

[0794] A means for applying automatically generated game mechanics and environment settings to a linked game device and updating them in real time,

[0795] A system that includes a means of dynamically adjusting the difficulty of a game by analyzing game participant data using machine learning algorithms.

[0796] (Claim 2)

[0797] The system according to claim 1, which adapts the game environment based on user behavior and emotions to construct a highly responsive game world in real time.

[0798] (Claim 3)

[0799] The system according to claim 1, which uses an AI system to automate large-scale verification tests and improve the efficiency of verification and testing.

[0800] "Application example 2 when combining with an emotional engine"

[0801] (Claim 1)

[0802] A means of collecting user behavioral and emotional data and analyzing it using a neural network,

[0803] A means for automatically generating entertainment activities suitable for the user based on the analysis results,

[0804] A means of applying automatically generated entertainment activities to the user's living environment and updating them in real time,

[0805] A system that uses machine learning techniques to analyze user response data and dynamically adjust the difficulty and content of activities.

[0806] (Claim 2)

[0807] The system according to claim 1, which evolves the living environment based on the user's behavior patterns and emotional state, and constructs an interactive environment that responds in real time.

[0808] (Claim 3)

[0809] The system according to claim 1, which uses an AI agent to automate large-scale user interaction testing and improve the efficiency of component verification and evaluation. [Explanation of Symbols]

[0810] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting user behavior information and detecting it using a deep learning model, A means for automatically generating user-optimized electronic content settings based on the aforementioned detection results, A means of applying automatically generated electronic content settings to a linked content environment and updating them in real time, A system that includes means for analyzing viewing data using machine learning algorithms and dynamically adjusting content delivery.

2. The system according to claim 1, which evolves the content environment based on the user's behavior patterns and constructs a realistic and highly responsive content world.

3. The system according to claim 1, which uses an artificial intelligence agent to automate large-scale viewing tests and improve the efficiency of data processing and analysis.

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