System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting historical game play data and game preferences from a user; means for analyzing the collected data to identify a playing style and preferences of the user; means for generating customized game data based on the identified playing style and preferences of the user; means for providing the generated game data to the user; and means for collecting feedback from the user and adjusting the game data in real-time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] It indicates the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] Conventional games have a fixed design and progression pattern, which means they cannot flexibly accommodate diverse user play styles and individual preferences. Furthermore, existing games have difficulty fully reflecting the user's preferences and style for enjoyment. Therefore, it is necessary to solve the problem of not being able to provide an original gaming experience that users can truly enjoy. [Means for solving the problem]

[0006] The present invention solves the above-mentioned problems by providing a system that includes a means for collecting game play history data and game preferences from users, a means for analyzing the collected data to identify the user's play style and preferences, a means for generating customized game data based on the identified user's play style and preferences, a means for providing the generated game data to the user, and a means for collecting feedback from users and adjusting the game data in real time.

[0007] "User" means an individual who wishes to play a game using the System.

[0008] "Game play history data" refers to data including the types of games a user has played, the amount of time they have played, their progress in playing, and their behavioral logs.

[0009] "Gaming preferences" refers to personal preferences such as a user's preferred game genre or style, specific game mechanics, character settings, etc.

[0010] "Collection Methods" refers to all hardware and software used to obtain game play history data and game preferences from users.

[0011] "Means for analyzing data" refers to the algorithms and computing resources used to process collected data and identify users' playing styles and preferences.

[0012] "Play style" refers to the behavioral patterns and strategies that a user adopts as they progress through a game.

[0013] "Customized Game Data" refers to data including game story, mechanics, level design, etc., designed based on a user's play style and preferences.

[0014] "Means for generating" refers to all hardware and software for constructing customized game data based on the results of data analysis.

[0015] "Means for providing" refers to all hardware and software for sharing and distributing the generated customized game data to users.

[0016] "Feedback" refers to the ratings, behavioral logs, and reactions to the system that users generate while playing the game.

[0017] "Means for real-time adjustment" refers to all hardware and software used to instantly analyze user feedback and dynamically modify game data. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] ---

[0040] Overall system overview

[0041] The present invention is a system for providing users with a personalized gaming experience. The system collects game play history data and game preferences from users, analyzes them, and generates game data tailored to the user. Furthermore, the system continuously collects user feedback and adjusts the game in real time to continuously provide an optimal gaming experience.

[0042] Program processing overview (details)

[0043] 1. Login and User Information Collection

[0044] A user logs into the system from a device. After successful login, the device collects the user's gameplay history data and preferences, including the types of games played in the past, the amount of time spent playing, and in-game behavior patterns. Furthermore, a survey about the user's preferred genres and specific game mechanics is displayed and answered by the user.

[0045] 2. Data submission and analysis

[0046] The device sends the collected data to a server. Once the server receives the data, it analyzes it through a machine learning module. The goal of the analysis is to identify the user's playing style and gaming preferences. Specifically, clustering algorithms and pattern recognition techniques are used.

[0047] 3. Creating custom game data

[0048] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, etc. For example, if the server determines that the user has a strategy-focused play style, it will design highly strategic scenarios and challenges.

[0049] 4. Custom Game Offerings

[0050] The server sends the generated game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[0051] 5. Gather feedback and adjust in real time

[0052] As users play the game, their devices collect their behavioral logs and feedback, such as feedback that a particular boss battle is difficult or strategic bias. The devices then send this data to the server.

[0053] The server analyzes the feedback in real time and modifies the game data as necessary. The modified game data is then sent back to the device, providing the game in an optimized format for the user.

[0054] Specific examples

[0055] User: A

[0056] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games.

[0057] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[0058] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[0059] 4. The device provides this customized game to Person A, who then begins playing.

[0060] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[0061] As described above, the system of the present invention aims to increase user satisfaction by providing a gaming experience that meets the individual needs of the user and continuously adjusting it.

[0062] The processing flow will be explained below.

[0063] ---

[0064] Step 1:

[0065] A user logs in to the system from a terminal. They enter their user ID and password on the login screen, and if authentication is successful, the user's session begins.

[0066] Step 2:

[0067] The device collects the user's gameplay history data and preference information. Specifically, it retrieves information such as the types of games played in the past, play time, and progress from an internal database. It also displays a questionnaire to the user about their preferred game genres and play styles, and receives their responses.

[0068] Step 3:

[0069] The device sends the collected data to a server, which packages the data in a standardized format and transfers it to the server using a secure protocol.

[0070] Step 4:

[0071] The server analyzes the data received from the devices. A machine learning module is then activated and begins processing the collected data as an input data set. This module uses a clustering algorithm to identify patterns in the user's playing style and preferences.

[0072] Step 5:

[0073] The server stores the analysis results in a database and launches a custom game generation module, which creates a basic design for a game optimized for the user, including the story, game mechanics, and level design.

[0074] Step 6:

[0075] The server instantiates story modules and generates story plots based on the user's preferred genre and setting, for example, a complex storyline set in a medieval fantasy world.

[0076] Step 7:

[0077] The server uses the game mechanics module to set game mechanics according to the user's play style. For example, for strategy-oriented users, the server can make the battle system more complex and add elements that require strategic progression.

[0078] Step 8:

[0079] The server launches the level design module and creates detailed designs for each stage, including enemy placement, item placement, and stage progression, all based on user data.

[0080] Step 9:

[0081] The server combines all the design data and generates the final game data, which is then compressed in binary format and ready to be sent to the device.

[0082] Step 10:

[0083] The server generates custom game data and sends it to the device. Data transfer occurs over an encrypted communication channel.

[0084] Step 11:

[0085] The device unpacks the received game data and provides the user with a new, customized game. Real-time feedback is enabled when the user starts the game.

[0086] Step 12:

[0087] As users play the game, the device collects user behavior logs and feedback data in real time, such as repeated failures at a particular level.

[0088] Step 13:

[0089] The terminal sends the collected feedback data to the server, where it is packaged in a standardized format and securely transferred to the server.

[0090] Step 14:

[0091] The server analyzes the feedback data and adjusts the game's difficulty and content in real time. Specifically, it adjusts the strength of enemies and the difficulty of stages based on user feedback.

[0092] Step 15:

[0093] The server generates new adjusted game data and resends it to the device, which receives and immediately applies it, providing the user with an updated game experience.

[0094] This is the specific processing flow of the system, which makes it possible to continuously provide users with a personalized and optimal gaming experience.

[0095] Example 1

[0096] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0097] Modern game systems face challenges in meeting diverse user needs and preferences. To provide an optimal experience in real time during gameplay, it is necessary to dynamically adjust game content based on the user's play style and feedback. Current systems struggle to understand user preferences in advance and analyze and adapt feedback during gameplay in real time, resulting in lower user satisfaction.

[0098] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0099] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data to identify the user's play style and preferences, means for generating customized game data based on the identified user's play style and preferences, and means for generating game scenarios and character settings based on the use of a generative model and prompt sentences, thereby providing a personalized game experience according to the individual needs of users and dynamically adjusting game content based on real-time feedback during game play.

[0100] "User" means an individual player who plays a game using the System.

[0101] "Game play history data" refers to recorded information such as the types of games a user has played in the past, play time, and behavioral patterns.

[0102] "Preferences" refer to a user's preferences for a particular genre or game mechanic.

[0103] "Collection methods" refer to the methods and technologies used to acquire and store data, including, for example, sensors and software modules.

[0104] "Analysis methods" refers to the algorithms and processes used to identify patterns and features in the collected data.

[0105] "Customized Game Data" means game content that is specific to a user's individual needs and preferences.

[0106] "Means of generation" refers to the technology or process used to create specific data or information.

[0107] "Means of providing" means the technology or method for transmitting the generated game data to users and making it available to them.

[0108] "Feedback" refers to various reactions and evaluations provided by users while playing a game.

[0109] "Means of real-time adjustment" refers to technology that instantly reflects collected feedback information and updates or modifies game data.

[0110] A "generative model" is an AI model that generates new content based on large datasets.

[0111] A "prompt sentence" is text that is entered to provide specific instructions to a generative model.

[0112] The present invention is a system for providing users with a personalized gaming experience. The system collects and analyzes gameplay history data and game preferences from users, and generates and provides customized game data based on the collected data. Furthermore, the system collects feedback from users in real time and dynamically adjusts the game data.

[0113] Hardware and Software Requirements

[0114] Hardware:

[0115] Devices: personal computers, smartphones, tablets, etc.

[0116] Server: High-performance server (with database functionality and machine learning model execution environment)

[0117] software:

[0118] Machine learning frameworks: TensorFlow, Scikit-Learn

[0119] Database: SQL or NoSQL database (e.g., MySQL, MongoDB)

[0120] Communication protocol: HTTPS

[0121] Generative AI models: large-scale language models (e.g., GPT)

[0122] Program processing

[0123] 1. Log in to the system

[0124] The user enters login information (user ID, password) into the terminal. The terminal sends the authentication information to the server, which then refers to the database to perform authentication.

[0125] 2. Collection of User Information

[0126] After successfully logging in, the device will collect the user's past gameplay history and gaming preferences, including play time, types of games played, behavioral patterns, and survey results regarding preferred genres and specific mechanics.

[0127] 3. Data transmission

[0128] The device sends the collected data to a server, which receives the data and stores it in a database.

[0129] 4. Data Analysis

[0130] The server analyzes the data using machine learning modules (TensorFlow, Scikit-Learn), clustering algorithms (K-means) and pattern recognition techniques to identify users' playing styles and gaming preferences.

[0131] 5. Creating custom game data

[0132] Based on the analysis results, the server generates customized game data (story setting, combat mechanics, character progression system, level design, etc.) using a generative model to input prompts that generate specific game scenarios and character settings.

[0133] For example, use the prompt "Generate a medieval fantasy story that fits the user's playstyle."

[0134] 6. Custom Game Offerings

[0135] The server transmits the generated customized game data to the terminal, and the terminal provides the new game to the user based on the data, and the user starts playing the customized game.

[0136] 7. Gather feedback and adjust in real time

[0137] As users play the game, their devices collect and send action logs and feedback to the server, including the time it takes to complete the game, the number of failures, and the frequency of certain boss battles.

[0138] The server analyzes this feedback and modifies game data as needed, for example adjusting the difficulty of certain enemies.

[0139] The corrected data is then sent back to the device, providing the user with the latest customized game.

[0140] Specific examples

[0141] User: A

[0142] 1. User A logs in from their device and data on past RPG games is collected. It is analyzed that User A particularly likes story-driven games.

[0143] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[0144] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[0145] 4. The device provides this customized game to Person A, who then begins playing.

[0146] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[0147] In this way, the system provides a gaming experience that is tailored to the user's individual needs and adjusts in real time based on feedback during gameplay.

[0148] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0149] ---

[0150] Processing Steps

[0151] Step 1:

[0152] The user enters login information (user ID, password) into the terminal.

[0153] Input: User ID, Password

[0154] How it works: The device receives user input and sends authentication information to the server.

[0155] Output: Authentication information (user ID, password)

[0156] Step 2:

[0157] The server performs authentication by referencing the database based on the authentication information received. If authentication is successful, the server returns a success message to the terminal.

[0158] Input: Credentials

[0159] Operation: The server checks the database, verifies that the user ID and password match, and generates a success message.

[0160] Output: Success message

[0161] Step 3:

[0162] After the device successfully logs in, it will collect the user's past gameplay history and game preferences.

[0163] Input: Login success message

[0164] How it works: The device collects user data locally or from the cloud, then displays a survey to ask for additional preferences.

[0165] Output: Gameplay history data, preference data

[0166] Step 4:

[0167] The terminal transmits the collected data to the server.

[0168] Input: Gameplay history data, preference data

[0169] How it works: The device sends data to the server using HTTPS.

[0170] Output: Transmitted data

[0171] Step 5:

[0172] The server stores the received data in a database and launches a machine learning module to analyze the data.

[0173] Input: Send data

[0174] What it does: Store data in a database. Analyze it using TensorFlow and Scikit-Learn. Run clustering algorithms (K-means) and pattern recognition.

[0175] Output: Analysis results (play style, preference identification)

[0176] Step 6:

[0177] The server generates customized game data based on the analysis results, and uses a generative AI model to input prompts and generate scenarios and character settings.

[0178] Input: Analysis results

[0179] How it works: Enter the prompt "Generate a medieval fantasy story that suits the user's play style" into the generation AI model, and reflect the generated scenario and character settings in the custom game data.

[0180] Output: Customized game data

[0181] Step 7:

[0182] The server transmits the generated customized game data to the terminal.

[0183] Input: Customized game data

[0184] What it does: Sends data to the device using HTTPS.

[0185] Output: Game data sent

[0186] Step 8:

[0187] The terminal provides the user with a new game based on the customized game data received.

[0188] Input: Incoming game data

[0189] What it does: The device applies the received data to the game, allowing the user to start playing.

[0190] Output: Game Start

[0191] Step 9:

[0192] While the user plays the game, the device collects behavior logs and feedback.

[0193] Input: User behavior data during gameplay

[0194] Function: Records the time it takes to clear a specific stage, the number of failures, the frequency of boss battles, etc.

[0195] Output: Behavior log, feedback data

[0196] Step 10:

[0197] The terminal transmits the collected feedback data to the server.

[0198] Input: Behavior log, feedback data

[0199] How it works: Sends data to the server using HTTPS.

[0200] Output: Feedback data sent

[0201] Step 11:

[0202] The server analyzes the feedback data and adjusts game data in real time as needed.

[0203] Input: Feedback data

[0204] Action: Data analysis, adjustments to the difficulty of certain enemies, and other fixes.

[0205] Output: Modified game data

[0206] Step 12:

[0207] The server retransmits the modified game data to the device, and the device provides the user with the latest customized game.

[0208] Input: Modified game data

[0209] How it works: Data is sent over HTTPS. The device updates and re-serves the game data.

[0210] Output: Serving the updated game

[0211] The above is the flow of processing in the program for this system and the specific operations of each step.

[0212] (Application example 1)

[0213] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0214] Conventional content distribution services lack personalized content recommendations based on users' viewing history and preferences, resulting in insufficient optimization of the viewing experience. Furthermore, there is no system for incorporating user feedback in real time, making it difficult to increase viewer satisfaction.

[0215] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0216] In this invention, the server includes means for collecting viewing history data and content preferences from users, means for analyzing the collected data to identify the user's viewing style and preferences, means for generating customized content data based on the identified user's viewing style and preferences, means for providing the generated content data to the user, and means for collecting feedback from the user and adjusting the content data in real time, thereby enabling the provision of a viewing experience that meets the individual needs of the user.

[0217] A "user" is a person who uses a content distribution service to watch movies, dramas, etc.

[0218] "Viewing history data" refers to data that includes information about movies and dramas that a user has viewed in the past.

[0219] "Content preferences" refers to information such as the user's preferred genres, stories, and character types.

[0220] "Means for collecting" refers to a method or system for collecting user viewing history data and content preferences.

[0221] "Analysis means" refers to the methods and algorithms used to analyze the collected data and identify viewing styles and preferences therefrom.

[0222] "Means for identifying" refers to the methods or processes used to identify a user's viewing style and preferences from the analyzed data.

[0223] "Customized content data" refers to movie and TV show information tailored to a user's viewing style and preferences.

[0224] A "generating means" is a method or system for creating customized content data.

[0225] "Means for providing" refers to a method or system for delivering the generated content data to users.

[0226] "Feedback" refers to opinions and impressions about the viewing experience obtained from users.

[0227] "Adjustment means" refers to a method or system for modifying and optimizing content data based on user feedback.

[0228] "Viewing style" refers to a general pattern that indicates how, how often, and at what time a user watches movies and TV dramas.

[0229] "Genre" indicates the type of movie or drama, and corresponds to a category such as action, comedy, drama, etc.

[0230] "Story" refers to the content and development of a movie or drama.

[0231] The present invention is a system for providing a user with a personalized viewing experience. The system collects and analyzes the user's viewing history data and content preferences to generate content data suited to the user. Furthermore, the system collects user feedback and adjusts the content data in real time to continuously provide an optimal viewing experience.

[0232] The server first stores data collected while the user is watching in cloud storage. This data includes viewing history, genre of content viewed, and behavioral patterns while watching. The server then uses this data to analyze the user's viewing style and preferences. Specific analysis methods include clustering and pattern recognition using machine learning algorithms.

[0233] Once a user's viewing style and preferences are identified, the server generates customized content data based on this information. For example, if it determines that a particular user likes action movies, it will select and provide the most suitable works from that genre. This also takes into account the user's past viewing data and rating information.

[0234] The server transmits the generated customized content data to the terminal and provides it to the user. The terminal displays a new customized viewing list to the user based on the received content data.

[0235] Furthermore, as the user continues watching, the server collects feedback from the user, including detailed information about the viewing experience, such as whether the user liked a particular scene or disliked the storyline. Once the feedback is collected, the server analyzes it in real time and modifies the content data as necessary.

[0236] For example, if a user gives feedback that there are too many action scenes, the server will adjust the proportion of action scenes in the next recommendation to reduce it. This process continuously optimizes the user's viewing experience.

[0237] As a concrete example, when user A logs in from their device, data on content they have previously viewed is collected. The server analyzes that A particularly likes fantasy movies and recommends specific content based on this. As A continues to watch content, their device collects behavioral logs and feedback and sends them to the server. The server receives this information and adjusts the content data to better match A's preferences.

[0238] An example of a specific prompt sentence is, "Please list movies that you would recommend to user A, who wants to relax. User A has watched movies in the past, and he prefers dramas and comedies."

[0239] The system of the present invention allows users to continuously enjoy an optimal viewing experience that meets their individual needs.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] The user logs in from their device. The device collects the user's viewing history data and content preferences. Specifically, it acquires data such as the genre and ratings of content viewed in the past, as well as viewing times. This data becomes the input needed for subsequent analysis.

[0243] Step 2:

[0244] The device sends the collected data to the server, which analyzes the received viewing history data and content preferences. Specifically, it uses machine learning algorithms (e.g., clustering and pattern recognition techniques) to identify the user's viewing style and preferences. The results of this analysis serve as input for the next step.

[0245] Step 3:

[0246] The server generates customized content data based on the identified user's viewing style and preferences. Specifically, it creates a content list that reflects the user's preferred genres, story settings, viewing time slots, etc. This generated content data becomes the input for the next step.

[0247] Step 4:

[0248] The server sends the generated customized content data to the terminal, and the terminal provides the user with a new customized viewing list based on the received content data, allowing the user to start viewing content optimized to their preferences.

[0249] Step 5:

[0250] While the user is watching the content, the device continuously collects viewing behavior data and feedback. The collected data includes detailed information about the viewing experience, such as "I liked a particular scene" or "I didn't like the story development." This data serves as input for the next step.

[0251] Step 6:

[0252] The device sends the collected feedback data to the server. The server analyzes the received feedback in real time and modifies the content data as necessary. For example, if the feedback is "too many action scenes," the content list will be adjusted to reduce the proportion of action scenes in the next recommendation. This adjusted data becomes the input for the next step.

[0253] Step 7:

[0254] The server retransmits the adjusted content data to the terminal, and the terminal provides the user with a newly adjusted viewing list, allowing the user to enjoy an optimized viewing experience again, thereby continuously improving the user's viewing experience.

[0255] This process flow allows users to view content that is always customized to their preferences, increasing their satisfaction.

[0256] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0257] ---

[0258] Overall system overview

[0259] This invention combines a system for providing a personalized gaming experience with an emotion engine that recognizes the user's emotions, providing an adaptive gaming experience that corresponds to the user's emotional state. The system collects game play history data and game preferences from the user, analyzes them, and generates game data tailored to the user. Furthermore, it continuously collects feedback and emotion data from the user and adjusts the game in real time to provide a continuously optimal gaming experience.

[0260] Program processing overview (details)

[0261] 1. Login and User Information Collection

[0262] A user logs in to the system from a terminal. If the login is successful, the terminal activates the user's gameplay history data and preferences, as well as devices (camera, microphone, etc.) for recognizing the user's emotions, and collects emotional data.

[0263] 2. Data submission and analysis

[0264] The device transmits the collected gameplay history data, preference information, and emotional data to a server, where the collected data is packaged in a standardized format and transferred to the server using a secure protocol.

[0265] The server analyzes the data received from the device. A machine learning module is then activated and begins processing the collected data as an input dataset. This module uses a clustering algorithm to identify the user's play style, preferences, and emotional state.

[0266] 3. Creating custom game data

[0267] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, and even adjustments based on emotional data. For example, if the server determines that the user has a strategic play style and feels stressed while playing, it will adjust the game's difficulty to reduce stress.

[0268] 4. Custom Game Offerings

[0269] The server sends the generated custom game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[0270] 5. Collecting and analyzing feedback and sentiment data

[0271] As users play the game, their device collects their behavioral logs, feedback data, and real-time emotional data. For example, if they frequently fail a particular boss battle and their facial expressions show frustration, this data is immediately sent to the server.

[0272] The server analyzes the received feedback and emotional data and adjusts the game's difficulty and content in real time, specifically based on the user's emotional state (e.g., excitement, stress, enjoyment).

[0273] 6. Real-time adjustments and game delivery

[0274] Based on the analysis results, the server generates new game data and sends it to the device, which receives this data and provides the user with a gaming experience that reflects the latest adjustments.

[0275] Specific examples

[0276] User: A

[0277] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the device's camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[0278] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[0279] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[0280] 4. The device provides this customized game to Person A, who then begins playing.

[0281] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[0282] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[0283] As described above, the system of the present invention aims to provide a personalized and optimal gaming experience while taking into account the user's emotional state.

[0284] The processing flow will be explained below.

[0285] ---

[0286] Step 1:

[0287] The user logs in to the terminal. They enter their user ID and password on the login screen, and if authentication is successful, the session begins.

[0288] Step 2:

[0289] After the device successfully logs in, it will obtain the user's gameplay history data and game preferences from the internal database. Additionally, it will use the device's camera and microphone to collect the user's real-time facial expressions and voice.

[0290] Step 3:

[0291] The device will send the gameplay history data, preferences, and emotional data collected from the camera and microphone to the server. The data will be encrypted and transmitted using a secure communication method.

[0292] Step 4:

[0293] The server analyzes the data received from the device, activates a machine learning module, and uses a clustering algorithm to identify the user's playing style and emotional state.

[0294] Step 5:

[0295] The server then uses the data analysis results to generate custom game data based on the user's play style, preferences, and emotional state, including adjustments to the story, game mechanics, level design, and even emotions.

[0296] Step 6:

[0297] The server generates custom game data and sends it to the device, where it is re-encrypted and securely sent to the device.

[0298] Step 7:

[0299] The device unpacks the received game data and presents the new, customized game to the user, at which point the user begins playing the game.

[0300] Step 8:

[0301] While the user is playing the game, the device collects the user's behavior log, play data, and real-time emotional data (facial expressions, voice), such as the stress level and number of failures during a particular boss battle.

[0302] Step 9:

[0303] The device sends the collected feedback data and emotion data to the server. The data is sent in batches at regular intervals (e.g., after clearing each stage).

[0304] Step 10:

[0305] The server analyzes the feedback data and emotional data. Based on the analysis results, it identifies changes that need to be made to the game's difficulty and story progression. Specifically, if stress is detected from the user's facial expressions, it will make adjustments such as lowering the game's difficulty.

[0306] Step 11:

[0307] The server generates improved game data based on the new tuning results, including adaptive changes based on the user's emotional data.

[0308] Step 12:

[0309] The server retransmits the adjusted game data to the device, which then applies the received data to provide the user with the latest optimized game experience.

[0310] By repeating this process, it is possible to provide a continuously customized gaming experience while adapting to the user's emotional state in real time.

[0311] Example 2

[0312] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0313] Conventional game systems have struggled to provide a personalized gaming experience based on a user's play style and preferences. Furthermore, they lacked flexibility in the user's gaming experience, unable to adjust game difficulty or content based on real-time emotional state or feedback. Furthermore, there was a lack of technology to grasp a user's emotions in real time and dynamically adjust the game based on that.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0315] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data and identifying the user's emotional state using facial expression recognition and voice analysis technology, means for generating customized game data based on the identified user's play style and emotional state, means for transmitting the generated game data to the user's device to provide a customized game experience, and means for collecting real-time feedback and emotional data from users and dynamically adjusting the difficulty and content of the game in real time, thereby enabling the provision of a more personalized game experience that is adapted to the user's play style and emotional state.

[0316] "User" refers to the end user who uses this system.

[0317] "Game play history data" refers to data about games that a user has played in the past, and includes information such as play time, quests completed, items acquired, and achievement level.

[0318] "Gaming preferences" refers to the genres and styles of games that users particularly like, and can be categorized into categories such as RPG, action, and puzzle.

[0319] "Facial expression recognition" refers to a technology that analyzes video data acquired from a camera and identifies emotions based on the user's facial expressions.

[0320] "Voice analysis" refers to a technology that analyzes audio data obtained from a microphone and identifies emotions based on the tone of the user's voice and the content of what is being said.

[0321] "Emotional state" refers to the type of emotion the user is currently feeling, and includes, for example, excitement, stress, joy, frustration, and the like.

[0322] "Customized Game Data" refers to in-game data that is specifically tailored to a user's playing style and emotional state, including story settings, game mechanics, and level design.

[0323] "Real-time feedback" refers to feedback data provided by users while playing a game, including behavior logs, in-game choices, and directly entered ratings.

[0324] "Dynamic adjustment of game difficulty and content" refers to the process of changing game settings and elements in real time based on the user's emotional state and feedback.

[0325] "Terminal" refers to a hardware device that is directly operated by a user, including smartphones, tablets, and personal computers.

[0326] "Server" refers to the computer system that performs the main processing such as data collection, analysis, generation and transmission of game data.

[0327] These are the definitions of the main words.

[0328] DETAILED DESCRIPTION OF THE INVENTION The present invention is an advanced gaming system that personalizes a user's gaming experience and adjusts in real time according to their emotional state. Specific embodiments for carrying out the present invention will be described below.

[0329] Overall overview

[0330] The system collects and analyzes users' gameplay history and game preferences to provide them with an optimal gaming experience. It also recognizes users' emotional state in real time and dynamically adjusts game content accordingly.

[0331] Hardware and software used

[0332] Server: A computer system that analyzes data, generates game data, and processes feedback.

[0333] Device: The device operated by the user (e.g., smartphone, tablet, computer).

[0334] Facial expression recognition software: e.g. OpenCV.

[0335] Speech analysis software: e.g., Google Cloud Speech-to-Text.

[0336] Game engine: e.g. Unity, Unreal Engine.

[0337] Program processing explanation

[0338] 1. Login and User Information Collection

[0339] A user logs into the system on their device. After successful login, the device collects the user's gameplay history data and preferences from local storage. The device also activates the camera and microphone and uses facial expression recognition software and voice analysis software to collect real-time emotional data.

[0340] 2. Data submission and analysis

[0341] The device sends the collected data to the server, which packages the data in JSON format and transmits it securely using the HTTPS protocol. The server analyzes the data and uses machine learning modules (e.g., Scikit-learn) to identify the user's playing style, preferences, and emotional state.

[0342] 3. Creating custom game data

[0343] The server generates customized game data based on the analysis results, including story settings, combat mechanics, character progression systems, level design, etc. It also adjusts the game difficulty according to the user's emotional state.

[0344] 4. Custom Game Offerings

[0345] The server sends the generated custom game data to the device, which receives the data and uses the game engine to launch the new customized game, which the user then plays.

[0346] 5. Collecting and analyzing feedback and sentiment data

[0347] As users play the game, their devices collect their behavioral logs, feedback data, and emotional data. This data is then sent to a server, which then adjusts the game content in real time.

[0348] 6. Real-time adjustments and game delivery

[0349] The server analyzes the feedback and emotion data, regenerates the game data, and sends it to the device, which then applies the data to provide the user with a new, adjusted game experience.

[0350] Specific examples

[0351] User: A

[0352] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[0353] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[0354] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[0355] 4. The device provides this customized game to Person A, who then begins playing.

[0356] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[0357] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[0358] Prompt Sentence Examples

[0359] "After users log in, collect their past play history and real-time emotional data to deliver a gaming experience tailored to their specific playstyle."

[0360] keyword

[0361] Generative AI model, prompt sentence

[0362] The above is a detailed description of the embodiment of the present invention.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] ---

[0365] Step 1:

[0366] Login and user information collection

[0367] A user logs in to the system on a device. The user enters their login ID and password for authentication. The input is the login ID and password, and the output is the start of a session indicating successful login. If login is successful, the device collects the user's past gameplay history data and preferences from local storage. Specifically, it reads a JSON file from the device's file system and extracts data related to their gameplay history and preferences (e.g., categories such as RPG, action, and puzzle). At the same time, the device activates its camera and microphone and uses facial expression recognition software (e.g., OpenCV) and voice analysis software (e.g., Google Cloud Speech-to-Text) to collect real-time emotional data. The input is camera footage and microphone audio, and the output is emotional data (e.g., excitement, stress).

[0368] Step 2:

[0369] Data transmission and analysis

[0370] The gameplay history data, preference information, and emotional data collected by the device are packaged in JSON format and sent to the server. The input is the gameplay history data, preference information, and emotional data, and the output is the JSON data sent to the server. Data is sent securely using the HTTPS protocol. The server analyzes the received data. First, the server preprocesses the raw data, and then performs cluster analysis using a machine learning module (e.g., Scikit-learn). The input is the received JSON data, and the output is the analysis results that identify the user's play style, preferences, and emotional state. For example, preprocessing can filter out unnecessary information and convert it into a format that can be used for cluster analysis.

[0371] Step 3:

[0372] Generating Custom Game Data

[0373] The server generates customized game data based on the analysis results. The input is the analysis results obtained in step 2, and the output is customized game data. This includes the user's preferred story setting, combat mechanics, character progression system, and level design. Specifically, it executes SQL queries based on the analysis results to extract appropriate game elements from a database (e.g., PostgreSQL), combines them, and generates new game data. Furthermore, it adjusts the game difficulty according to the user's emotional state.

[0374] Step 4:

[0375] Custom Game Offering

[0376] The server sends the generated custom game data to the device. The input is the customized game data, and the output is the game data sent to the device. The device receives this data and launches the new customized game using a game engine (e.g., Unity, Unreal Engine). Specifically, the device saves the received data to local storage, launches the game engine, and loads the customized game. The user then plays this game.

[0377] Step 5:

[0378] Collecting and analyzing feedback and sentiment data

[0379] As a user plays a game, the device collects the user's action log, feedback data, and real-time emotional data. The input is the action log, feedback data, and emotional data during gameplay, and the output is the collected data. This data is sent to the server, which then analyzes it again. Specifically, the device records an event log during gameplay and collects emotional data using facial expression recognition software and voice analysis software. The server analyzes this data and identifies points where adjustments should be made to the game content.

[0380] Step 6:

[0381] Real-time adjustments and game delivery

[0382] The server analyzes the feedback and emotional data and makes appropriate game adjustments. The input is feedback and emotional data, and the output is new, adjusted game data. Specifically, new game data is generated based on the analysis results and sent back to the device. The device then applies this data locally, providing the user with a gaming experience that reflects the latest adjustments. Specifically, the device applies the new game data and restarts the game engine to reflect the adjustments, allowing the user to continue playing.

[0383] ---

[0384] The above is the specific flow and detailed operation of the program processing.

[0385] (Application example 2)

[0386] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0387] While conventional content delivery systems offer some degree of personalization based on a user's viewing history and preferences, they lack the ability to instantly adjust content to reflect the user's real-time emotional state. Furthermore, they lack the ability to instantly adjust content based on feedback, making it difficult to provide an optimal viewing experience. The present invention aims to provide an optimal entertainment experience by delivering personalized content based on a user's emotional state and adjusting content in real time while the user is viewing.

[0388] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting content viewing history data and content preferences from a user; means for analyzing the collected data and emotional data to identify the user's viewing style, preferences, and emotional state; means for generating customized content data based on the identified user's viewing style, preferences, and emotional state; means for providing the generated content data to the user; and means for collecting feedback and emotional data from the user and adjusting the content data in real time. This makes it possible to provide content that is instantly adapted according to the user's real-time emotional state, thereby achieving a more personalized viewing experience.

[0389] A user refers to an end user who uses a content distribution service to view content.

[0390] The content viewing history data refers to history information about content that a user has viewed in the past, and includes data such as viewing time, type of content viewed, and rating.

[0391] Content preferences refer to information such as the characteristics, genres, and themes of content that a user prefers.

[0392] Emotional data refers to data about a user's emotional state obtained by analyzing their facial expressions, voice, and other body language.

[0393] Collection means refers to the methods and devices used to collect the necessary data from users, such as cameras, microphones, and software.

[0394] Analytics refers to the algorithms and software that process collected data to identify users' viewing styles, preferences, and emotional states.

[0395] Customized content data refers to content information that is generated based on analyzed user data and is optimized for the user.

[0396] The provision means refers to a method or device for delivering the generated customized content to users, such as a delivery system via a network.

[0397] Feedback refers to the user's conscious or unconscious evaluation or reaction to the content provided.

[0398] Real-time adjustments refer to algorithms and systems that instantly change the content or difficulty level based on collected feedback and sentiment data.

[0399] The system that realizes this application example is a content distribution system using smart glasses and a cloud server. This system provides personalized content based on the user's viewing history and preferences, as well as analyzing real-time emotional data.

[0400] 1. System Program

[0401] The system uses the smart glasses' camera and microphone to analyze facial expressions and voices to collect emotional data as users watch content. The collected data is then transmitted over a network to a cloud server. The cloud server then analyzes the data using machine learning algorithms to identify the user's viewing style, preferences, and emotional state. The cloud server then generates customized content based on the analysis results and delivers the content data to the user. The system also collects user feedback and emotional data in real time while watching content and dynamically adjusts the content accordingly.

[0402] 2. Details of the processing

[0403] The server uses Google Cloud Platform (GCP) and TensorFlow to analyze the collected data. This analysis uses a clustering algorithm to identify the user's viewing style, preferences, and emotional state. After analysis, the server generates customized content data and transmits it to the smart glasses over the network.

[0404] The smart glasses use Android Wear OS and OpenCV to collect data from the camera and microphone to recognize emotions in real time, which is then sent to a cloud server that tailors content accordingly.

[0405] 3. Specific Examples

[0406] For example, suppose user B logs in wearing smart glasses. Data shows that in the past, user B mainly watches documentaries and likes to relax. The camera and microphone in the smart glasses analyze user B's facial expressions and voice, and the emotion engine recognizes that user B is in a relaxed state. The cloud server then delivers relaxing music and videos of natural scenery for user B. While watching, user B's expression clouds over for a moment, indicating that he is feeling stressed. This data is immediately sent to the cloud server. The cloud server analyzes the feedback and provides user B with the latest relaxation content.

[0407] 4. Examples of prompts

[0408] "Please suggest the most suitable relaxation content based on the user's past viewing history and real-time emotional data. The user is currently in a state of acute stress, so please select content that delivers music and videos that will help alleviate that stress."

[0409] As a result, the system can instantly adapt content to the user's real-time emotional state, enabling a more personalized viewing experience.

[0410] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0411] Step 1:

[0412] The user puts on the smart glasses and logs in. The device collects past viewing history data and preference information. At this time, the camera and microphone are activated, and the user's facial expressions and voice are analyzed to obtain real-time emotional data. The input includes viewing history data and real-time emotional data, and the output compiles this data for transmission to the server.

[0413] Step 2:

[0414] The viewing history data, preference information, and emotional data collected from the device are sent to a server via a network. The server receives the data and begins analyzing it using a machine learning algorithm. The input includes the data sent from the device, and the output is the analysis result, which identifies the user's viewing style, preferences, and emotional state.

[0415] Step 3:

[0416] The server generates customized content data based on the analysis results, including the user's preferred themes, genres, video composition, music, etc., and adjusts it to take into account the user's real-time emotional state. The input includes the user's viewing style, preferences, and emotional data analysis results, and the output includes the generated customized content data.

[0417] Step 4:

[0418] The server transmits the generated customized content data to the terminal via the network. The terminal receives this data and provides the content to the user. The input includes the customized content data, and the output includes the content provided as a user viewing experience.

[0419] Step 5:

[0420] While watching content, the device continuously collects user feedback and real-time emotion data. The device then transmits this data back to the server for real-time analysis. The input includes the continuously collected feedback data and emotion data, and the output includes the data sent to the server.

[0421] Step 6:

[0422] The server analyzes the feedback and real-time emotion data to dynamically adjust the difficulty and content of the content. It then sends the adjusted new content data to the device. The input includes the feedback data and emotion data, and the output includes the adjusted content data.

[0423] Step 7:

[0424] The terminal then provides the user with new, customized content again, optimizing the viewing experience so that the user can always enjoy content that best suits their emotional state. The input includes tailored content data, and the output includes an optimized viewing experience.

[0425] The above are the specific processing steps for carrying out the present invention.

[0426] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0427] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0428] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0429] [Second embodiment]

[0430] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0431] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0432] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0433] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0434] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0435] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0436] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0437] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0438] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0439] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0440] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0441] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0442] ---

[0443] Overall system overview

[0444] The present invention is a system for providing users with a personalized gaming experience. The system collects game play history data and game preferences from users, analyzes them, and generates game data tailored to the user. Furthermore, the system continuously collects user feedback and adjusts the game in real time to continuously provide an optimal gaming experience.

[0445] Program processing overview (details)

[0446] 1. Login and User Information Collection

[0447] A user logs into the system from a device. After successful login, the device collects the user's gameplay history data and preferences, including the types of games played in the past, the amount of time spent playing, and in-game behavior patterns. Furthermore, a survey about the user's preferred genres and specific game mechanics is displayed and answered by the user.

[0448] 2. Data submission and analysis

[0449] The device sends the collected data to a server. Once the server receives the data, it analyzes it through a machine learning module. The goal of the analysis is to identify the user's playing style and gaming preferences. Specifically, clustering algorithms and pattern recognition techniques are used.

[0450] 3. Creating custom game data

[0451] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, etc. For example, if the server determines that the user has a strategy-focused play style, it will design highly strategic scenarios and challenges.

[0452] 4. Custom Game Offerings

[0453] The server sends the generated game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[0454] 5. Gather feedback and adjust in real time

[0455] As users play the game, their devices collect their behavioral logs and feedback, such as feedback that a particular boss battle is difficult or strategic bias. The devices then send this data to the server.

[0456] The server analyzes the feedback in real time and modifies the game data as necessary. The modified game data is then sent back to the device, providing the game in an optimized format for the user.

[0457] Specific examples

[0458] User: A

[0459] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games.

[0460] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[0461] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[0462] 4. The device provides this customized game to Person A, who then begins playing.

[0463] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[0464] As described above, the system of the present invention aims to increase user satisfaction by providing a gaming experience that meets the individual needs of the user and continuously adjusting it.

[0465] The processing flow will be explained below.

[0466] ---

[0467] Step 1:

[0468] A user logs in to the system from a terminal. They enter their user ID and password on the login screen, and if authentication is successful, the user's session begins.

[0469] Step 2:

[0470] The device collects the user's gameplay history data and preference information. Specifically, it retrieves information such as the types of games played in the past, play time, and progress from an internal database. It also displays a questionnaire to the user about their preferred game genres and play styles, and receives their responses.

[0471] Step 3:

[0472] The device sends the collected data to a server, which packages the data in a standardized format and transfers it to the server using a secure protocol.

[0473] Step 4:

[0474] The server analyzes the data received from the devices. A machine learning module is then activated and begins processing the collected data as an input data set. This module uses a clustering algorithm to identify patterns in the user's playing style and preferences.

[0475] Step 5:

[0476] The server stores the analysis results in a database and launches a custom game generation module, which creates a basic design for a game optimized for the user, including the story, game mechanics, and level design.

[0477] Step 6:

[0478] The server instantiates story modules and generates story plots based on the user's preferred genre and setting, for example, a complex storyline set in a medieval fantasy world.

[0479] Step 7:

[0480] The server uses the game mechanics module to set game mechanics according to the user's play style. For example, for strategy-oriented users, the server can make the battle system more complex and add elements that require strategic progression.

[0481] Step 8:

[0482] The server launches the level design module and creates detailed designs for each stage, including enemy placement, item placement, and stage progression, all based on user data.

[0483] Step 9:

[0484] The server combines all the design data and generates the final game data, which is then compressed in binary format and ready to be sent to the device.

[0485] Step 10:

[0486] The server generates custom game data and sends it to the device. Data transfer occurs over an encrypted communication channel.

[0487] Step 11:

[0488] The device unpacks the received game data and provides the user with a new, customized game. Real-time feedback is enabled when the user starts the game.

[0489] Step 12:

[0490] As users play the game, the device collects user behavior logs and feedback data in real time, such as repeated failures at a particular level.

[0491] Step 13:

[0492] The terminal sends the collected feedback data to the server, where it is packaged in a standardized format and securely transferred to the server.

[0493] Step 14:

[0494] The server analyzes the feedback data and adjusts the game's difficulty and content in real time. Specifically, it adjusts the strength of enemies and the difficulty of stages based on user feedback.

[0495] Step 15:

[0496] The server generates new adjusted game data and resends it to the device, which receives and immediately applies it, providing the user with an updated game experience.

[0497] This is the specific processing flow of the system, which makes it possible to continuously provide users with a personalized and optimal gaming experience.

[0498] Example 1

[0499] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0500] Modern game systems face challenges in meeting diverse user needs and preferences. To provide an optimal experience in real time during gameplay, it is necessary to dynamically adjust game content based on the user's play style and feedback. Current systems struggle to understand user preferences in advance and analyze and adapt feedback during gameplay in real time, resulting in lower user satisfaction.

[0501] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0502] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data to identify the user's play style and preferences, means for generating customized game data based on the identified user's play style and preferences, and means for generating game scenarios and character settings based on the use of a generative model and prompt sentences, thereby providing a personalized game experience according to the individual needs of users and dynamically adjusting game content based on real-time feedback during game play.

[0503] "User" means an individual player who plays a game using the System.

[0504] "Game play history data" refers to recorded information such as the types of games a user has played in the past, play time, and behavioral patterns.

[0505] "Preferences" refer to a user's preferences for a particular genre or game mechanic.

[0506] "Collection methods" refer to the methods and technologies used to acquire and store data, including, for example, sensors and software modules.

[0507] "Analysis methods" refers to the algorithms and processes used to identify patterns and features in the collected data.

[0508] "Customized Game Data" means game content that is specific to a user's individual needs and preferences.

[0509] "Means of generation" refers to the technology or process used to create specific data or information.

[0510] "Means of providing" means the technology or method for transmitting the generated game data to users and making it available to them.

[0511] "Feedback" refers to various reactions and evaluations provided by users while playing a game.

[0512] "Means of real-time adjustment" refers to technology that instantly reflects collected feedback information and updates or modifies game data.

[0513] A "generative model" is an AI model that generates new content based on large datasets.

[0514] A "prompt sentence" is text that is entered to provide specific instructions to a generative model.

[0515] The present invention is a system for providing users with a personalized gaming experience. The system collects and analyzes gameplay history data and game preferences from users, and generates and provides customized game data based on the collected data. Furthermore, the system collects feedback from users in real time and dynamically adjusts the game data.

[0516] Hardware and Software Requirements

[0517] Hardware:

[0518] Devices: personal computers, smartphones, tablets, etc.

[0519] Server: High-performance server (with database functionality and machine learning model execution environment)

[0520] software:

[0521] Machine learning frameworks: TensorFlow, Scikit-Learn

[0522] Database: SQL or NoSQL database (e.g., MySQL, MongoDB)

[0523] Communication protocol: HTTPS

[0524] Generative AI models: large-scale language models (e.g., GPT)

[0525] Program processing

[0526] 1. Log in to the system

[0527] The user enters login information (user ID, password) into the terminal. The terminal sends the authentication information to the server, which then refers to the database to perform authentication.

[0528] 2. Collection of User Information

[0529] After successfully logging in, the device will collect the user's past gameplay history and gaming preferences, including play time, types of games played, behavioral patterns, and survey results regarding preferred genres and specific mechanics.

[0530] 3. Data transmission

[0531] The device sends the collected data to a server, which receives the data and stores it in a database.

[0532] 4. Data Analysis

[0533] The server analyzes the data using machine learning modules (TensorFlow, Scikit-Learn), clustering algorithms (K-means) and pattern recognition techniques to identify users' playing styles and gaming preferences.

[0534] 5. Creating custom game data

[0535] Based on the analysis results, the server generates customized game data (story setting, combat mechanics, character progression system, level design, etc.) using a generative model to input prompts that generate specific game scenarios and character settings.

[0536] For example, use the prompt "Generate a medieval fantasy story that fits the user's playstyle."

[0537] 6. Custom Game Offerings

[0538] The server transmits the generated customized game data to the terminal, and the terminal provides the new game to the user based on the data, and the user starts playing the customized game.

[0539] 7. Gather feedback and adjust in real time

[0540] As users play the game, their devices collect and send action logs and feedback to the server, including the time it takes to complete the game, the number of failures, and the frequency of certain boss battles.

[0541] The server analyzes this feedback and modifies game data as needed, for example adjusting the difficulty of certain enemies.

[0542] The corrected data is then sent back to the device, providing the user with the latest customized game.

[0543] Specific examples

[0544] User: A

[0545] 1. User A logs in from their device and data on past RPG games is collected. It is analyzed that User A particularly likes story-driven games.

[0546] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[0547] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[0548] 4. The device provides this customized game to Person A, who then begins playing.

[0549] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[0550] In this way, the system provides a gaming experience that is tailored to the user's individual needs and adjusts in real time based on feedback during gameplay.

[0551] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0552] ---

[0553] Processing Steps

[0554] Step 1:

[0555] The user enters login information (user ID, password) into the terminal.

[0556] Input: User ID, Password

[0557] How it works: The device receives user input and sends authentication information to the server.

[0558] Output: Authentication information (user ID, password)

[0559] Step 2:

[0560] The server performs authentication by referencing the database based on the authentication information received. If authentication is successful, the server returns a success message to the terminal.

[0561] Input: Credentials

[0562] Operation: The server checks the database, verifies that the user ID and password match, and generates a success message.

[0563] Output: Success message

[0564] Step 3:

[0565] After the device successfully logs in, it will collect the user's past gameplay history and game preferences.

[0566] Input: Login success message

[0567] How it works: The device collects user data locally or from the cloud, then displays a survey to ask for additional preferences.

[0568] Output: Gameplay history data, preference data

[0569] Step 4:

[0570] The terminal transmits the collected data to the server.

[0571] Input: Gameplay history data, preference data

[0572] How it works: The device sends data to the server using HTTPS.

[0573] Output: Transmitted data

[0574] Step 5:

[0575] The server stores the received data in a database and launches a machine learning module to analyze the data.

[0576] Input: Send data

[0577] What it does: Store data in a database. Analyze it using TensorFlow and Scikit-Learn. Run clustering algorithms (K-means) and pattern recognition.

[0578] Output: Analysis results (play style, preference identification)

[0579] Step 6:

[0580] The server generates customized game data based on the analysis results, and uses a generative AI model to input prompts and generate scenarios and character settings.

[0581] Input: Analysis results

[0582] How it works: Enter the prompt "Generate a medieval fantasy story that suits the user's play style" into the generation AI model, and reflect the generated scenario and character settings in the custom game data.

[0583] Output: Customized game data

[0584] Step 7:

[0585] The server transmits the generated customized game data to the terminal.

[0586] Input: Customized game data

[0587] What it does: Sends data to the device using HTTPS.

[0588] Output: Game data sent

[0589] Step 8:

[0590] The terminal provides the user with a new game based on the customized game data received.

[0591] Input: Incoming game data

[0592] What it does: The device applies the received data to the game, allowing the user to start playing.

[0593] Output: Game Start

[0594] Step 9:

[0595] While the user plays the game, the device collects behavior logs and feedback.

[0596] Input: User behavior data during gameplay

[0597] Function: Records the time it takes to clear a specific stage, the number of failures, the frequency of boss battles, etc.

[0598] Output: Behavior log, feedback data

[0599] Step 10:

[0600] The terminal transmits the collected feedback data to the server.

[0601] Input: Behavior log, feedback data

[0602] How it works: Sends data to the server using HTTPS.

[0603] Output: Feedback data sent

[0604] Step 11:

[0605] The server analyzes the feedback data and adjusts game data in real time as needed.

[0606] Input: Feedback data

[0607] Action: Data analysis, adjustments to the difficulty of certain enemies, and other fixes.

[0608] Output: Modified game data

[0609] Step 12:

[0610] The server retransmits the modified game data to the device, and the device provides the user with the latest customized game.

[0611] Input: Modified game data

[0612] How it works: Data is sent over HTTPS. The device updates and re-serves the game data.

[0613] Output: Serving the updated game

[0614] The above is the flow of processing in the program for this system and the specific operations of each step.

[0615] (Application example 1)

[0616] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0617] Conventional content distribution services lack personalized content recommendations based on users' viewing history and preferences, resulting in insufficient optimization of the viewing experience. Furthermore, there is no system for incorporating user feedback in real time, making it difficult to increase viewer satisfaction.

[0618] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0619] In this invention, the server includes means for collecting viewing history data and content preferences from users, means for analyzing the collected data to identify the user's viewing style and preferences, means for generating customized content data based on the identified user's viewing style and preferences, means for providing the generated content data to the user, and means for collecting feedback from the user and adjusting the content data in real time, thereby enabling the provision of a viewing experience that meets the individual needs of the user.

[0620] A "user" is a person who uses a content distribution service to watch movies, dramas, etc.

[0621] "Viewing history data" refers to data that includes information about movies and dramas that a user has viewed in the past.

[0622] "Content preferences" refers to information such as the user's preferred genres, stories, and character types.

[0623] "Means for collecting" refers to a method or system for collecting user viewing history data and content preferences.

[0624] "Analysis means" refers to the methods and algorithms used to analyze the collected data and identify viewing styles and preferences therefrom.

[0625] "Means for identifying" refers to the methods or processes used to identify a user's viewing style and preferences from the analyzed data.

[0626] "Customized content data" refers to movie and TV show information tailored to a user's viewing style and preferences.

[0627] A "generating means" is a method or system for creating customized content data.

[0628] "Means for providing" refers to a method or system for delivering the generated content data to users.

[0629] "Feedback" refers to opinions and impressions about the viewing experience obtained from users.

[0630] "Adjustment means" refers to a method or system for modifying and optimizing content data based on user feedback.

[0631] "Viewing style" refers to a general pattern that indicates how, how often, and at what time a user watches movies and TV dramas.

[0632] "Genre" indicates the type of movie or drama, and corresponds to a category such as action, comedy, drama, etc.

[0633] "Story" refers to the content and development of a movie or drama.

[0634] The present invention is a system for providing a user with a personalized viewing experience. The system collects and analyzes the user's viewing history data and content preferences to generate content data suited to the user. Furthermore, the system collects user feedback and adjusts the content data in real time to continuously provide an optimal viewing experience.

[0635] The server first stores data collected while the user is watching in cloud storage. This data includes viewing history, genre of content viewed, and behavioral patterns while watching. The server then uses this data to analyze the user's viewing style and preferences. Specific analysis methods include clustering and pattern recognition using machine learning algorithms.

[0636] Once a user's viewing style and preferences are identified, the server generates customized content data based on this information. For example, if it determines that a particular user likes action movies, it will select and provide the most suitable works from that genre. This also takes into account the user's past viewing data and rating information.

[0637] The server transmits the generated customized content data to the terminal and provides it to the user. The terminal displays a new customized viewing list to the user based on the received content data.

[0638] Furthermore, as the user continues watching, the server collects feedback from the user, including detailed information about the viewing experience, such as whether the user liked a particular scene or disliked the storyline. Once the feedback is collected, the server analyzes it in real time and modifies the content data as necessary.

[0639] For example, if a user gives feedback that there are too many action scenes, the server will adjust the proportion of action scenes in the next recommendation to reduce it. This process continuously optimizes the user's viewing experience.

[0640] As a concrete example, when user A logs in from their device, data on content they have previously viewed is collected. The server analyzes that A particularly likes fantasy movies and recommends specific content based on this. As A continues to watch content, their device collects behavioral logs and feedback and sends them to the server. The server receives this information and adjusts the content data to better match A's preferences.

[0641] An example of a specific prompt sentence is, "Please list movies that you would recommend to user A, who wants to relax. User A has watched movies in the past, and he prefers dramas and comedies."

[0642] The system of the present invention allows users to continuously enjoy an optimal viewing experience that meets their individual needs.

[0643] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0644] Step 1:

[0645] The user logs in from their device. The device collects the user's viewing history data and content preferences. Specifically, it acquires data such as the genre and ratings of content viewed in the past, as well as viewing times. This data becomes the input needed for subsequent analysis.

[0646] Step 2:

[0647] The device sends the collected data to the server, which analyzes the received viewing history data and content preferences. Specifically, it uses machine learning algorithms (e.g., clustering and pattern recognition techniques) to identify the user's viewing style and preferences. The results of this analysis serve as input for the next step.

[0648] Step 3:

[0649] The server generates customized content data based on the identified user's viewing style and preferences. Specifically, it creates a content list that reflects the user's preferred genres, story settings, viewing time slots, etc. This generated content data becomes the input for the next step.

[0650] Step 4:

[0651] The server sends the generated customized content data to the terminal, and the terminal provides the user with a new customized viewing list based on the received content data, allowing the user to start viewing content optimized to their preferences.

[0652] Step 5:

[0653] While the user is watching the content, the device continuously collects viewing behavior data and feedback. The collected data includes detailed information about the viewing experience, such as "I liked a particular scene" or "I didn't like the story development." This data serves as input for the next step.

[0654] Step 6:

[0655] The device sends the collected feedback data to the server. The server analyzes the received feedback in real time and modifies the content data as necessary. For example, if the feedback is "too many action scenes," the content list will be adjusted to reduce the proportion of action scenes in the next recommendation. This adjusted data becomes the input for the next step.

[0656] Step 7:

[0657] The server retransmits the adjusted content data to the terminal, and the terminal provides the user with a newly adjusted viewing list, allowing the user to enjoy an optimized viewing experience again, thereby continuously improving the user's viewing experience.

[0658] This process flow allows users to view content that is always customized to their preferences, increasing their satisfaction.

[0659] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0660] ---

[0661] Overall system overview

[0662] This invention combines a system for providing a personalized gaming experience with an emotion engine that recognizes the user's emotions, providing an adaptive gaming experience that corresponds to the user's emotional state. The system collects game play history data and game preferences from the user, analyzes them, and generates game data tailored to the user. Furthermore, it continuously collects feedback and emotion data from the user and adjusts the game in real time to provide a continuously optimal gaming experience.

[0663] Program processing overview (details)

[0664] 1. Login and User Information Collection

[0665] A user logs in to the system from a terminal. If the login is successful, the terminal activates the user's gameplay history data and preferences, as well as devices (camera, microphone, etc.) for recognizing the user's emotions, and collects emotional data.

[0666] 2. Data submission and analysis

[0667] The device transmits the collected gameplay history data, preference information, and emotional data to a server, where the collected data is packaged in a standardized format and transferred to the server using a secure protocol.

[0668] The server analyzes the data received from the device. A machine learning module is then activated and begins processing the collected data as an input dataset. This module uses a clustering algorithm to identify the user's play style, preferences, and emotional state.

[0669] 3. Creating custom game data

[0670] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, and even adjustments based on emotional data. For example, if the server determines that the user has a strategic play style and feels stressed while playing, it will adjust the game's difficulty to reduce stress.

[0671] 4. Custom Game Offerings

[0672] The server sends the generated custom game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[0673] 5. Collecting and analyzing feedback and sentiment data

[0674] As users play the game, their device collects their behavioral logs, feedback data, and real-time emotional data. For example, if they frequently fail a particular boss battle and their facial expressions show frustration, this data is immediately sent to the server.

[0675] The server analyzes the received feedback and emotional data and adjusts the game's difficulty and content in real time, specifically based on the user's emotional state (e.g., excitement, stress, enjoyment).

[0676] 6. Real-time adjustments and game delivery

[0677] Based on the analysis results, the server generates new game data and sends it to the device, which receives this data and provides the user with a gaming experience that reflects the latest adjustments.

[0678] Specific examples

[0679] User: A

[0680] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the device's camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[0681] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[0682] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[0683] 4. The device provides this customized game to Person A, who then begins playing.

[0684] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[0685] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[0686] As described above, the system of the present invention aims to provide a personalized and optimal gaming experience while taking into account the user's emotional state.

[0687] The processing flow will be explained below.

[0688] ---

[0689] Step 1:

[0690] The user logs in to the terminal. They enter their user ID and password on the login screen, and if authentication is successful, the session begins.

[0691] Step 2:

[0692] After the device successfully logs in, it will obtain the user's gameplay history data and game preferences from the internal database. Additionally, it will use the device's camera and microphone to collect the user's real-time facial expressions and voice.

[0693] Step 3:

[0694] The device will send the gameplay history data, preferences, and emotional data collected from the camera and microphone to the server. The data will be encrypted and transmitted using a secure communication method.

[0695] Step 4:

[0696] The server analyzes the data received from the device, activates a machine learning module, and uses a clustering algorithm to identify the user's playing style and emotional state.

[0697] Step 5:

[0698] The server then uses the data analysis results to generate custom game data based on the user's play style, preferences, and emotional state, including adjustments to the story, game mechanics, level design, and even emotions.

[0699] Step 6:

[0700] The server generates custom game data and sends it to the device, where it is re-encrypted and securely sent to the device.

[0701] Step 7:

[0702] The device unpacks the received game data and presents the new, customized game to the user, at which point the user begins playing the game.

[0703] Step 8:

[0704] While the user is playing the game, the device collects the user's behavior log, play data, and real-time emotional data (facial expressions, voice), such as the stress level and number of failures during a particular boss battle.

[0705] Step 9:

[0706] The device sends the collected feedback data and emotion data to the server. The data is sent in batches at regular intervals (e.g., after clearing each stage).

[0707] Step 10:

[0708] The server analyzes the feedback data and emotional data. Based on the analysis results, it identifies changes that need to be made to the game's difficulty and story progression. Specifically, if stress is detected from the user's facial expressions, it will make adjustments such as lowering the game's difficulty.

[0709] Step 11:

[0710] The server generates improved game data based on the new tuning results, including adaptive changes based on the user's emotional data.

[0711] Step 12:

[0712] The server retransmits the adjusted game data to the device, which then applies the received data to provide the user with the latest optimized game experience.

[0713] By repeating this process, it is possible to provide a continuously customized gaming experience while adapting to the user's emotional state in real time.

[0714] Example 2

[0715] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0716] Conventional game systems have struggled to provide a personalized gaming experience based on a user's play style and preferences. Furthermore, they lacked flexibility in the user's gaming experience, unable to adjust game difficulty or content based on real-time emotional state or feedback. Furthermore, there was a lack of technology to grasp a user's emotions in real time and dynamically adjust the game based on that.

[0717] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0718] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data and identifying the user's emotional state using facial expression recognition and voice analysis technology, means for generating customized game data based on the identified user's play style and emotional state, means for transmitting the generated game data to the user's device to provide a customized game experience, and means for collecting real-time feedback and emotional data from users and dynamically adjusting the difficulty and content of the game in real time, thereby enabling the provision of a more personalized game experience that is adapted to the user's play style and emotional state.

[0719] "User" refers to the end user who uses this system.

[0720] "Game play history data" refers to data about games that a user has played in the past, and includes information such as play time, quests completed, items acquired, and achievement level.

[0721] "Gaming preferences" refers to the genres and styles of games that users particularly like, and can be categorized into categories such as RPG, action, and puzzle.

[0722] "Facial expression recognition" refers to a technology that analyzes video data acquired from a camera and identifies emotions based on the user's facial expressions.

[0723] "Voice analysis" refers to a technology that analyzes audio data obtained from a microphone and identifies emotions based on the tone of the user's voice and the content of what is being said.

[0724] "Emotional state" refers to the type of emotion the user is currently feeling, and includes, for example, excitement, stress, joy, frustration, and the like.

[0725] "Customized Game Data" refers to in-game data that is specifically tailored to a user's playing style and emotional state, including story settings, game mechanics, and level design.

[0726] "Real-time feedback" refers to feedback data provided by users while playing a game, including behavior logs, in-game choices, and directly entered ratings.

[0727] "Dynamic adjustment of game difficulty and content" refers to the process of changing game settings and elements in real time based on the user's emotional state and feedback.

[0728] "Terminal" refers to a hardware device that is directly operated by a user, including smartphones, tablets, and personal computers.

[0729] "Server" refers to the computer system that performs the main processing such as data collection, analysis, generation and transmission of game data.

[0730] These are the definitions of the main words.

[0731] DETAILED DESCRIPTION OF THE INVENTION The present invention is an advanced gaming system that personalizes a user's gaming experience and adjusts in real time according to their emotional state. Specific embodiments for carrying out the present invention will be described below.

[0732] Overall overview

[0733] The system collects and analyzes users' gameplay history and game preferences to provide them with an optimal gaming experience. It also recognizes users' emotional state in real time and dynamically adjusts game content accordingly.

[0734] Hardware and software used

[0735] Server: A computer system that analyzes data, generates game data, and processes feedback.

[0736] Device: The device operated by the user (e.g., smartphone, tablet, computer).

[0737] Facial expression recognition software: e.g. OpenCV.

[0738] Speech analysis software: e.g., Google Cloud Speech-to-Text.

[0739] Game engine: e.g. Unity, Unreal Engine.

[0740] Program processing explanation

[0741] 1. Login and User Information Collection

[0742] A user logs into the system on their device. After successful login, the device collects the user's gameplay history data and preferences from local storage. The device also activates the camera and microphone and uses facial expression recognition software and voice analysis software to collect real-time emotional data.

[0743] 2. Data submission and analysis

[0744] The device sends the collected data to the server, which packages the data in JSON format and transmits it securely using the HTTPS protocol. The server analyzes the data and uses machine learning modules (e.g., Scikit-learn) to identify the user's playing style, preferences, and emotional state.

[0745] 3. Creating custom game data

[0746] The server generates customized game data based on the analysis results, including story settings, combat mechanics, character progression systems, level design, etc. It also adjusts the game difficulty according to the user's emotional state.

[0747] 4. Custom Game Offerings

[0748] The server sends the generated custom game data to the device, which receives the data and uses the game engine to launch the new customized game, which the user then plays.

[0749] 5. Collecting and analyzing feedback and sentiment data

[0750] As users play the game, their devices collect their behavioral logs, feedback data, and emotional data. This data is then sent to a server, which then adjusts the game content in real time.

[0751] 6. Real-time adjustments and game delivery

[0752] The server analyzes the feedback and emotion data, regenerates the game data, and sends it to the device, which then applies the data to provide the user with a new, adjusted game experience.

[0753] Specific examples

[0754] User: A

[0755] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[0756] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[0757] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[0758] 4. The device provides this customized game to Person A, who then begins playing.

[0759] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[0760] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[0761] Prompt Sentence Examples

[0762] "After users log in, collect their past play history and real-time emotional data to deliver a gaming experience tailored to their specific playstyle."

[0763] keyword

[0764] Generative AI model, prompt sentence

[0765] The above is a detailed description of the embodiment of the present invention.

[0766] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0767] ---

[0768] Step 1:

[0769] Login and user information collection

[0770] A user logs in to the system on a device. The user enters their login ID and password for authentication. The input is the login ID and password, and the output is the start of a session indicating successful login. If login is successful, the device collects the user's past gameplay history data and preferences from local storage. Specifically, it reads a JSON file from the device's file system and extracts data related to their gameplay history and preferences (e.g., categories such as RPG, action, and puzzle). At the same time, the device activates its camera and microphone and uses facial expression recognition software (e.g., OpenCV) and voice analysis software (e.g., Google Cloud Speech-to-Text) to collect real-time emotional data. The input is camera footage and microphone audio, and the output is emotional data (e.g., excitement, stress).

[0771] Step 2:

[0772] Data transmission and analysis

[0773] The gameplay history data, preference information, and emotional data collected by the device are packaged in JSON format and sent to the server. The input is the gameplay history data, preference information, and emotional data, and the output is the JSON data sent to the server. Data is sent securely using the HTTPS protocol. The server analyzes the received data. First, the server preprocesses the raw data, and then performs cluster analysis using a machine learning module (e.g., Scikit-learn). The input is the received JSON data, and the output is the analysis results that identify the user's play style, preferences, and emotional state. For example, preprocessing can filter out unnecessary information and convert it into a format that can be used for cluster analysis.

[0774] Step 3:

[0775] Generating Custom Game Data

[0776] The server generates customized game data based on the analysis results. The input is the analysis results obtained in step 2, and the output is customized game data. This includes the user's preferred story setting, combat mechanics, character progression system, and level design. Specifically, it executes SQL queries based on the analysis results to extract appropriate game elements from a database (e.g., PostgreSQL), combines them, and generates new game data. Furthermore, it adjusts the game difficulty according to the user's emotional state.

[0777] Step 4:

[0778] Custom Game Offering

[0779] The server sends the generated custom game data to the device. The input is the customized game data, and the output is the game data sent to the device. The device receives this data and launches the new customized game using a game engine (e.g., Unity, Unreal Engine). Specifically, the device saves the received data to local storage, launches the game engine, and loads the customized game. The user then plays this game.

[0780] Step 5:

[0781] Collecting and analyzing feedback and sentiment data

[0782] As a user plays a game, the device collects the user's action log, feedback data, and real-time emotional data. The input is the action log, feedback data, and emotional data during gameplay, and the output is the collected data. This data is sent to the server, which then analyzes it again. Specifically, the device records an event log during gameplay and collects emotional data using facial expression recognition software and voice analysis software. The server analyzes this data and identifies points where adjustments should be made to the game content.

[0783] Step 6:

[0784] Real-time adjustments and game delivery

[0785] The server analyzes the feedback and emotional data and makes appropriate game adjustments. The input is feedback and emotional data, and the output is new, adjusted game data. Specifically, new game data is generated based on the analysis results and sent back to the device. The device then applies this data locally, providing the user with a gaming experience that reflects the latest adjustments. Specifically, the device applies the new game data and restarts the game engine to reflect the adjustments, allowing the user to continue playing.

[0786] ---

[0787] The above is the specific flow and detailed operation of the program processing.

[0788] (Application example 2)

[0789] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0790] While conventional content delivery systems offer some degree of personalization based on a user's viewing history and preferences, they lack the ability to instantly adjust content to reflect the user's real-time emotional state. Furthermore, they lack the ability to instantly adjust content based on feedback, making it difficult to provide an optimal viewing experience. The present invention aims to provide an optimal entertainment experience by delivering personalized content based on a user's emotional state and adjusting content in real time while the user is viewing.

[0791] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting content viewing history data and content preferences from a user; means for analyzing the collected data and emotional data to identify the user's viewing style, preferences, and emotional state; means for generating customized content data based on the identified user's viewing style, preferences, and emotional state; means for providing the generated content data to the user; and means for collecting feedback and emotional data from the user and adjusting the content data in real time. This makes it possible to provide content that is instantly adapted according to the user's real-time emotional state, thereby achieving a more personalized viewing experience.

[0792] A user refers to an end user who uses a content distribution service to view content.

[0793] The content viewing history data refers to history information about content that a user has viewed in the past, and includes data such as viewing time, type of content viewed, and rating.

[0794] Content preferences refer to information such as the characteristics, genres, and themes of content that a user prefers.

[0795] Emotional data refers to data about a user's emotional state obtained by analyzing their facial expressions, voice, and other body language.

[0796] Collection means refers to the methods and devices used to collect the necessary data from users, such as cameras, microphones, and software.

[0797] Analytics refers to the algorithms and software that process collected data to identify users' viewing styles, preferences, and emotional states.

[0798] Customized content data refers to content information that is generated based on analyzed user data and is optimized for the user.

[0799] The provision means refers to a method or device for delivering the generated customized content to users, such as a delivery system via a network.

[0800] Feedback refers to the user's conscious or unconscious evaluation or reaction to the content provided.

[0801] Real-time adjustments refer to algorithms and systems that instantly change the content or difficulty level based on collected feedback and sentiment data.

[0802] The system that realizes this application example is a content distribution system using smart glasses and a cloud server. This system provides personalized content based on the user's viewing history and preferences, as well as analyzing real-time emotional data.

[0803] 1. System Program

[0804] The system uses the smart glasses' camera and microphone to analyze facial expressions and voices to collect emotional data as users watch content. The collected data is then transmitted over a network to a cloud server. The cloud server then analyzes the data using machine learning algorithms to identify the user's viewing style, preferences, and emotional state. The cloud server then generates customized content based on the analysis results and delivers the content data to the user. The system also collects user feedback and emotional data in real time while watching content and dynamically adjusts the content accordingly.

[0805] 2. Details of the processing

[0806] The server uses Google Cloud Platform (GCP) and TensorFlow to analyze the collected data. This analysis uses a clustering algorithm to identify the user's viewing style, preferences, and emotional state. After analysis, the server generates customized content data and transmits it to the smart glasses over the network.

[0807] The smart glasses use Android Wear OS and OpenCV to collect data from the camera and microphone to recognize emotions in real time, which is then sent to a cloud server that tailors content accordingly.

[0808] 3. Specific Examples

[0809] For example, suppose user B logs in wearing smart glasses. Data shows that in the past, user B mainly watches documentaries and likes to relax. The camera and microphone in the smart glasses analyze user B's facial expressions and voice, and the emotion engine recognizes that user B is in a relaxed state. The cloud server then delivers relaxing music and videos of natural scenery for user B. While watching, user B's expression clouds over for a moment, indicating that he is feeling stressed. This data is immediately sent to the cloud server. The cloud server analyzes the feedback and provides user B with the latest relaxation content.

[0810] 4. Examples of prompts

[0811] "Please suggest the most suitable relaxation content based on the user's past viewing history and real-time emotional data. The user is currently in a state of acute stress, so please select content that delivers music and videos that will help alleviate that stress."

[0812] As a result, the system can instantly adapt content to the user's real-time emotional state, enabling a more personalized viewing experience.

[0813] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0814] Step 1:

[0815] The user puts on the smart glasses and logs in. The device collects past viewing history data and preference information. At this time, the camera and microphone are activated, and the user's facial expressions and voice are analyzed to obtain real-time emotional data. The input includes viewing history data and real-time emotional data, and the output compiles this data for transmission to the server.

[0816] Step 2:

[0817] The viewing history data, preference information, and emotional data collected from the device are sent to a server via a network. The server receives the data and begins analyzing it using a machine learning algorithm. The input includes the data sent from the device, and the output is the analysis result, which identifies the user's viewing style, preferences, and emotional state.

[0818] Step 3:

[0819] The server generates customized content data based on the analysis results, including the user's preferred themes, genres, video composition, music, etc., and adjusts it to take into account the user's real-time emotional state. The input includes the user's viewing style, preferences, and emotional data analysis results, and the output includes the generated customized content data.

[0820] Step 4:

[0821] The server transmits the generated customized content data to the terminal via the network. The terminal receives this data and provides the content to the user. The input includes the customized content data, and the output includes the content provided as a user viewing experience.

[0822] Step 5:

[0823] While watching content, the device continuously collects user feedback and real-time emotion data. The device then transmits this data back to the server for real-time analysis. The input includes the continuously collected feedback data and emotion data, and the output includes the data sent to the server.

[0824] Step 6:

[0825] The server analyzes the feedback and real-time emotion data to dynamically adjust the difficulty and content of the content. It then sends the adjusted new content data to the device. The input includes the feedback data and emotion data, and the output includes the adjusted content data.

[0826] Step 7:

[0827] The terminal then provides the user with new, customized content again, optimizing the viewing experience so that the user can always enjoy content that best suits their emotional state. The input includes tailored content data, and the output includes an optimized viewing experience.

[0828] The above are the specific processing steps for carrying out the present invention.

[0829] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0830] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0831] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0832] [Third embodiment]

[0833] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0834] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0835] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0836] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0837] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0838] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0839] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0840] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0841] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0842] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0843] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0844] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0845] ---

[0846] Overall system overview

[0847] The present invention is a system for providing users with a personalized gaming experience. The system collects game play history data and game preferences from users, analyzes them, and generates game data tailored to the user. Furthermore, the system continuously collects user feedback and adjusts the game in real time to continuously provide an optimal gaming experience.

[0848] Program processing overview (details)

[0849] 1. Login and User Information Collection

[0850] A user logs into the system from a device. After successful login, the device collects the user's gameplay history data and preferences, including the types of games played in the past, the amount of time spent playing, and in-game behavior patterns. Furthermore, a survey about the user's preferred genres and specific game mechanics is displayed and answered by the user.

[0851] 2. Data submission and analysis

[0852] The device sends the collected data to a server. Once the server receives the data, it analyzes it through a machine learning module. The goal of the analysis is to identify the user's playing style and gaming preferences. Specifically, clustering algorithms and pattern recognition techniques are used.

[0853] 3. Creating custom game data

[0854] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, etc. For example, if the server determines that the user has a strategy-focused play style, it will design highly strategic scenarios and challenges.

[0855] 4. Custom Game Offerings

[0856] The server sends the generated game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[0857] 5. Gather feedback and adjust in real time

[0858] As users play the game, their devices collect their behavioral logs and feedback, such as feedback that a particular boss battle is difficult or strategic bias. The devices then send this data to the server.

[0859] The server analyzes the feedback in real time and modifies the game data as necessary. The modified game data is then sent back to the device, providing the game in an optimized format for the user.

[0860] Specific examples

[0861] User: A

[0862] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games.

[0863] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[0864] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[0865] 4. The device provides this customized game to Person A, who then begins playing.

[0866] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[0867] As described above, the system of the present invention aims to increase user satisfaction by providing a gaming experience that meets the individual needs of the user and continuously adjusting it.

[0868] The processing flow will be explained below.

[0869] ---

[0870] Step 1:

[0871] A user logs in to the system from a terminal. They enter their user ID and password on the login screen, and if authentication is successful, the user's session begins.

[0872] Step 2:

[0873] The device collects the user's gameplay history data and preference information. Specifically, it retrieves information such as the types of games played in the past, play time, and progress from an internal database. It also displays a questionnaire to the user about their preferred game genres and play styles, and receives their responses.

[0874] Step 3:

[0875] The device sends the collected data to a server, which packages the data in a standardized format and transfers it to the server using a secure protocol.

[0876] Step 4:

[0877] The server analyzes the data received from the devices. A machine learning module is then activated and begins processing the collected data as an input data set. This module uses a clustering algorithm to identify patterns in the user's playing style and preferences.

[0878] Step 5:

[0879] The server stores the analysis results in a database and launches a custom game generation module, which creates a basic design for a game optimized for the user, including the story, game mechanics, and level design.

[0880] Step 6:

[0881] The server instantiates story modules and generates story plots based on the user's preferred genre and setting, for example, a complex storyline set in a medieval fantasy world.

[0882] Step 7:

[0883] The server uses the game mechanics module to set game mechanics according to the user's play style. For example, for strategy-oriented users, the server can make the battle system more complex and add elements that require strategic progression.

[0884] Step 8:

[0885] The server launches the level design module and creates detailed designs for each stage, including enemy placement, item placement, and stage progression, all based on user data.

[0886] Step 9:

[0887] The server combines all the design data and generates the final game data, which is then compressed in binary format and ready to be sent to the device.

[0888] Step 10:

[0889] The server generates custom game data and sends it to the device. Data transfer occurs over an encrypted communication channel.

[0890] Step 11:

[0891] The device unpacks the received game data and provides the user with a new, customized game. Real-time feedback is enabled when the user starts the game.

[0892] Step 12:

[0893] As users play the game, the device collects user behavior logs and feedback data in real time, such as repeated failures at a particular level.

[0894] Step 13:

[0895] The terminal sends the collected feedback data to the server, where it is packaged in a standardized format and securely transferred to the server.

[0896] Step 14:

[0897] The server analyzes the feedback data and adjusts the game's difficulty and content in real time. Specifically, it adjusts the strength of enemies and the difficulty of stages based on user feedback.

[0898] Step 15:

[0899] The server generates new adjusted game data and resends it to the device, which receives and immediately applies it, providing the user with an updated game experience.

[0900] This is the specific processing flow of the system, which makes it possible to continuously provide users with a personalized and optimal gaming experience.

[0901] Example 1

[0902] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0903] Modern game systems face challenges in meeting diverse user needs and preferences. To provide an optimal experience in real time during gameplay, it is necessary to dynamically adjust game content based on the user's play style and feedback. Current systems struggle to understand user preferences in advance and analyze and adapt feedback during gameplay in real time, resulting in lower user satisfaction.

[0904] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0905] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data to identify the user's play style and preferences, means for generating customized game data based on the identified user's play style and preferences, and means for generating game scenarios and character settings based on the use of a generative model and prompt sentences, thereby providing a personalized game experience according to the individual needs of users and dynamically adjusting game content based on real-time feedback during game play.

[0906] "User" means an individual player who plays a game using the System.

[0907] "Game play history data" refers to recorded information such as the types of games a user has played in the past, play time, and behavioral patterns.

[0908] "Preferences" refer to a user's preferences for a particular genre or game mechanic.

[0909] "Collection methods" refer to the methods and technologies used to acquire and store data, including, for example, sensors and software modules.

[0910] "Analysis methods" refers to the algorithms and processes used to identify patterns and features in the collected data.

[0911] "Customized Game Data" means game content that is specific to a user's individual needs and preferences.

[0912] "Means of generation" refers to the technology or process used to create specific data or information.

[0913] "Means of providing" means the technology or method for transmitting the generated game data to users and making it available to them.

[0914] "Feedback" refers to various reactions and evaluations provided by users while playing a game.

[0915] "Means of real-time adjustment" refers to technology that instantly reflects collected feedback information and updates or modifies game data.

[0916] A "generative model" is an AI model that generates new content based on large datasets.

[0917] A "prompt sentence" is text that is entered to provide specific instructions to a generative model.

[0918] The present invention is a system for providing users with a personalized gaming experience. The system collects and analyzes gameplay history data and game preferences from users, and generates and provides customized game data based on the collected data. Furthermore, the system collects feedback from users in real time and dynamically adjusts the game data.

[0919] Hardware and Software Requirements

[0920] Hardware:

[0921] Devices: personal computers, smartphones, tablets, etc.

[0922] Server: High-performance server (with database functionality and machine learning model execution environment)

[0923] software:

[0924] Machine learning frameworks: TensorFlow, Scikit-Learn

[0925] Database: SQL or NoSQL database (e.g., MySQL, MongoDB)

[0926] Communication protocol: HTTPS

[0927] Generative AI models: large-scale language models (e.g., GPT)

[0928] Program processing

[0929] 1. Log in to the system

[0930] The user enters login information (user ID, password) into the terminal. The terminal sends the authentication information to the server, which then refers to the database to perform authentication.

[0931] 2. Collection of User Information

[0932] After successfully logging in, the device will collect the user's past gameplay history and gaming preferences, including play time, types of games played, behavioral patterns, and survey results regarding preferred genres and specific mechanics.

[0933] 3. Data transmission

[0934] The device sends the collected data to a server, which receives the data and stores it in a database.

[0935] 4. Data Analysis

[0936] The server analyzes the data using machine learning modules (TensorFlow, Scikit-Learn), clustering algorithms (K-means) and pattern recognition techniques to identify users' playing styles and gaming preferences.

[0937] 5. Creating custom game data

[0938] Based on the analysis results, the server generates customized game data (story setting, combat mechanics, character progression system, level design, etc.) using a generative model to input prompts that generate specific game scenarios and character settings.

[0939] For example, use the prompt "Generate a medieval fantasy story that fits the user's playstyle."

[0940] 6. Custom Game Offerings

[0941] The server transmits the generated customized game data to the terminal, and the terminal provides the new game to the user based on the data, and the user starts playing the customized game.

[0942] 7. Gather feedback and adjust in real time

[0943] As users play the game, their devices collect and send action logs and feedback to the server, including the time it takes to complete the game, the number of failures, and the frequency of certain boss battles.

[0944] The server analyzes this feedback and modifies game data as needed, for example adjusting the difficulty of certain enemies.

[0945] The corrected data is then sent back to the device, providing the user with the latest customized game.

[0946] Specific examples

[0947] User: A

[0948] 1. User A logs in from their device and data on past RPG games is collected. It is analyzed that User A particularly likes story-driven games.

[0949] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[0950] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[0951] 4. The device provides this customized game to Person A, who then begins playing.

[0952] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[0953] In this way, the system provides a gaming experience that is tailored to the user's individual needs and adjusts in real time based on feedback during gameplay.

[0954] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0955] ---

[0956] Processing Steps

[0957] Step 1:

[0958] The user enters login information (user ID, password) into the terminal.

[0959] Input: User ID, Password

[0960] How it works: The device receives user input and sends authentication information to the server.

[0961] Output: Authentication information (user ID, password)

[0962] Step 2:

[0963] The server performs authentication by referencing the database based on the authentication information received. If authentication is successful, the server returns a success message to the terminal.

[0964] Input: Credentials

[0965] Operation: The server checks the database, verifies that the user ID and password match, and generates a success message.

[0966] Output: Success message

[0967] Step 3:

[0968] After the device successfully logs in, it will collect the user's past gameplay history and game preferences.

[0969] Input: Login success message

[0970] How it works: The device collects user data locally or from the cloud, then displays a survey to ask for additional preferences.

[0971] Output: Gameplay history data, preference data

[0972] Step 4:

[0973] The terminal transmits the collected data to the server.

[0974] Input: Gameplay history data, preference data

[0975] How it works: The device sends data to the server using HTTPS.

[0976] Output: Transmitted data

[0977] Step 5:

[0978] The server stores the received data in a database and launches a machine learning module to analyze the data.

[0979] Input: Send data

[0980] What it does: Store data in a database. Analyze it using TensorFlow and Scikit-Learn. Run clustering algorithms (K-means) and pattern recognition.

[0981] Output: Analysis results (play style, preference identification)

[0982] Step 6:

[0983] The server generates customized game data based on the analysis results, and uses a generative AI model to input prompts and generate scenarios and character settings.

[0984] Input: Analysis results

[0985] How it works: Enter the prompt "Generate a medieval fantasy story that suits the user's play style" into the generation AI model, and reflect the generated scenario and character settings in the custom game data.

[0986] Output: Customized game data

[0987] Step 7:

[0988] The server transmits the generated customized game data to the terminal.

[0989] Input: Customized game data

[0990] What it does: Sends data to the device using HTTPS.

[0991] Output: Game data sent

[0992] Step 8:

[0993] The terminal provides the user with a new game based on the customized game data received.

[0994] Input: Incoming game data

[0995] What it does: The device applies the received data to the game, allowing the user to start playing.

[0996] Output: Game Start

[0997] Step 9:

[0998] While the user plays the game, the device collects behavior logs and feedback.

[0999] Input: User behavior data during gameplay

[1000] Function: Records the time it takes to clear a specific stage, the number of failures, the frequency of boss battles, etc.

[1001] Output: Behavior log, feedback data

[1002] Step 10:

[1003] The terminal transmits the collected feedback data to the server.

[1004] Input: Behavior log, feedback data

[1005] How it works: Sends data to the server using HTTPS.

[1006] Output: Feedback data sent

[1007] Step 11:

[1008] The server analyzes the feedback data and adjusts game data in real time as needed.

[1009] Input: Feedback data

[1010] Action: Data analysis, adjustments to the difficulty of certain enemies, and other fixes.

[1011] Output: Modified game data

[1012] Step 12:

[1013] The server retransmits the modified game data to the device, and the device provides the user with the latest customized game.

[1014] Input: Modified game data

[1015] How it works: Data is sent over HTTPS. The device updates and re-serves the game data.

[1016] Output: Serving the updated game

[1017] The above is the flow of processing in the program for this system and the specific operations of each step.

[1018] (Application example 1)

[1019] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1020] Conventional content distribution services lack personalized content recommendations based on users' viewing history and preferences, resulting in insufficient optimization of the viewing experience. Furthermore, there is no system for incorporating user feedback in real time, making it difficult to increase viewer satisfaction.

[1021] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1022] In this invention, the server includes means for collecting viewing history data and content preferences from users, means for analyzing the collected data to identify the user's viewing style and preferences, means for generating customized content data based on the identified user's viewing style and preferences, means for providing the generated content data to the user, and means for collecting feedback from the user and adjusting the content data in real time, thereby enabling the provision of a viewing experience that meets the individual needs of the user.

[1023] A "user" is a person who uses a content distribution service to watch movies, dramas, etc.

[1024] "Viewing history data" refers to data that includes information about movies and dramas that a user has viewed in the past.

[1025] "Content preferences" refers to information such as the user's preferred genres, stories, and character types.

[1026] "Means for collecting" refers to a method or system for collecting user viewing history data and content preferences.

[1027] "Analysis means" refers to the methods and algorithms used to analyze the collected data and identify viewing styles and preferences therefrom.

[1028] "Means for identifying" refers to the methods or processes used to identify a user's viewing style and preferences from the analyzed data.

[1029] "Customized content data" refers to movie and TV show information tailored to a user's viewing style and preferences.

[1030] A "generating means" is a method or system for creating customized content data.

[1031] "Means for providing" refers to a method or system for delivering the generated content data to users.

[1032] "Feedback" refers to opinions and impressions about the viewing experience obtained from users.

[1033] "Adjustment means" refers to a method or system for modifying and optimizing content data based on user feedback.

[1034] "Viewing style" refers to a general pattern that indicates how, how often, and at what time a user watches movies and TV dramas.

[1035] "Genre" indicates the type of movie or drama, and corresponds to a category such as action, comedy, drama, etc.

[1036] "Story" refers to the content and development of a movie or drama.

[1037] The present invention is a system for providing a user with a personalized viewing experience. The system collects and analyzes the user's viewing history data and content preferences to generate content data suited to the user. Furthermore, the system collects user feedback and adjusts the content data in real time to continuously provide an optimal viewing experience.

[1038] The server first stores data collected while the user is watching in cloud storage. This data includes viewing history, genre of content viewed, and behavioral patterns while watching. The server then uses this data to analyze the user's viewing style and preferences. Specific analysis methods include clustering and pattern recognition using machine learning algorithms.

[1039] Once a user's viewing style and preferences are identified, the server generates customized content data based on this information. For example, if it determines that a particular user likes action movies, it will select and provide the most suitable works from that genre. This also takes into account the user's past viewing data and rating information.

[1040] The server transmits the generated customized content data to the terminal and provides it to the user. The terminal displays a new customized viewing list to the user based on the received content data.

[1041] Furthermore, as the user continues watching, the server collects feedback from the user, including detailed information about the viewing experience, such as whether the user liked a particular scene or disliked the storyline. Once the feedback is collected, the server analyzes it in real time and modifies the content data as necessary.

[1042] For example, if a user gives feedback that there are too many action scenes, the server will adjust the proportion of action scenes in the next recommendation to reduce it. This process continuously optimizes the user's viewing experience.

[1043] As a concrete example, when user A logs in from their device, data on content they have previously viewed is collected. The server analyzes that A particularly likes fantasy movies and recommends specific content based on this. As A continues to watch content, their device collects behavioral logs and feedback and sends them to the server. The server receives this information and adjusts the content data to better match A's preferences.

[1044] An example of a specific prompt sentence is, "Please list movies that you would recommend to user A, who wants to relax. User A has watched movies in the past, and he prefers dramas and comedies."

[1045] The system of the present invention allows users to continuously enjoy an optimal viewing experience that meets their individual needs.

[1046] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1047] Step 1:

[1048] The user logs in from their device. The device collects the user's viewing history data and content preferences. Specifically, it acquires data such as the genre and ratings of content viewed in the past, as well as viewing times. This data becomes the input needed for subsequent analysis.

[1049] Step 2:

[1050] The device sends the collected data to the server, which analyzes the received viewing history data and content preferences. Specifically, it uses machine learning algorithms (e.g., clustering and pattern recognition techniques) to identify the user's viewing style and preferences. The results of this analysis serve as input for the next step.

[1051] Step 3:

[1052] The server generates customized content data based on the identified user's viewing style and preferences. Specifically, it creates a content list that reflects the user's preferred genres, story settings, viewing time slots, etc. This generated content data becomes the input for the next step.

[1053] Step 4:

[1054] The server sends the generated customized content data to the terminal, and the terminal provides the user with a new customized viewing list based on the received content data, allowing the user to start viewing content optimized to their preferences.

[1055] Step 5:

[1056] While the user is watching the content, the device continuously collects viewing behavior data and feedback. The collected data includes detailed information about the viewing experience, such as "I liked a particular scene" or "I didn't like the story development." This data serves as input for the next step.

[1057] Step 6:

[1058] The device sends the collected feedback data to the server. The server analyzes the received feedback in real time and modifies the content data as necessary. For example, if the feedback is "too many action scenes," the content list will be adjusted to reduce the proportion of action scenes in the next recommendation. This adjusted data becomes the input for the next step.

[1059] Step 7:

[1060] The server retransmits the adjusted content data to the terminal, and the terminal provides the user with a newly adjusted viewing list, allowing the user to enjoy an optimized viewing experience again, thereby continuously improving the user's viewing experience.

[1061] This process flow allows users to view content that is always customized to their preferences, increasing their satisfaction.

[1062] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1063] ---

[1064] Overall system overview

[1065] This invention combines a system for providing a personalized gaming experience with an emotion engine that recognizes the user's emotions, providing an adaptive gaming experience that corresponds to the user's emotional state. The system collects game play history data and game preferences from the user, analyzes them, and generates game data tailored to the user. Furthermore, it continuously collects feedback and emotion data from the user and adjusts the game in real time to provide a continuously optimal gaming experience.

[1066] Program processing overview (details)

[1067] 1. Login and User Information Collection

[1068] A user logs in to the system from a terminal. If the login is successful, the terminal activates the user's gameplay history data and preferences, as well as devices (camera, microphone, etc.) for recognizing the user's emotions, and collects emotional data.

[1069] 2. Data submission and analysis

[1070] The device transmits the collected gameplay history data, preference information, and emotional data to a server, where the collected data is packaged in a standardized format and transferred to the server using a secure protocol.

[1071] The server analyzes the data received from the device. A machine learning module is then activated and begins processing the collected data as an input dataset. This module uses a clustering algorithm to identify the user's play style, preferences, and emotional state.

[1072] 3. Creating custom game data

[1073] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, and even adjustments based on emotional data. For example, if the server determines that the user has a strategic play style and feels stressed while playing, it will adjust the game's difficulty to reduce stress.

[1074] 4. Custom Game Offerings

[1075] The server sends the generated custom game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[1076] 5. Collecting and analyzing feedback and sentiment data

[1077] As users play the game, their device collects their behavioral logs, feedback data, and real-time emotional data. For example, if they frequently fail a particular boss battle and their facial expressions show frustration, this data is immediately sent to the server.

[1078] The server analyzes the received feedback and emotional data and adjusts the game's difficulty and content in real time, specifically based on the user's emotional state (e.g., excitement, stress, enjoyment).

[1079] 6. Real-time adjustments and game delivery

[1080] Based on the analysis results, the server generates new game data and sends it to the device, which receives this data and provides the user with a gaming experience that reflects the latest adjustments.

[1081] Specific examples

[1082] User: A

[1083] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the device's camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[1084] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[1085] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[1086] 4. The device provides this customized game to Person A, who then begins playing.

[1087] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[1088] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[1089] As described above, the system of the present invention aims to provide a personalized and optimal gaming experience while taking into account the user's emotional state.

[1090] The processing flow will be explained below.

[1091] ---

[1092] Step 1:

[1093] The user logs in to the terminal. They enter their user ID and password on the login screen, and if authentication is successful, the session begins.

[1094] Step 2:

[1095] After the device successfully logs in, it will obtain the user's gameplay history data and game preferences from the internal database. Additionally, it will use the device's camera and microphone to collect the user's real-time facial expressions and voice.

[1096] Step 3:

[1097] The device will send the gameplay history data, preferences, and emotional data collected from the camera and microphone to the server. The data will be encrypted and transmitted using a secure communication method.

[1098] Step 4:

[1099] The server analyzes the data received from the device, activates a machine learning module, and uses a clustering algorithm to identify the user's playing style and emotional state.

[1100] Step 5:

[1101] The server then uses the data analysis results to generate custom game data based on the user's play style, preferences, and emotional state, including adjustments to the story, game mechanics, level design, and even emotions.

[1102] Step 6:

[1103] The server generates custom game data and sends it to the device, where it is re-encrypted and securely sent to the device.

[1104] Step 7:

[1105] The device unpacks the received game data and presents the new, customized game to the user, at which point the user begins playing the game.

[1106] Step 8:

[1107] While the user is playing the game, the device collects the user's behavior log, play data, and real-time emotional data (facial expressions, voice), such as the stress level and number of failures during a particular boss battle.

[1108] Step 9:

[1109] The device sends the collected feedback data and emotion data to the server. The data is sent in batches at regular intervals (e.g., after clearing each stage).

[1110] Step 10:

[1111] The server analyzes the feedback data and emotional data. Based on the analysis results, it identifies changes that need to be made to the game's difficulty and story progression. Specifically, if stress is detected from the user's facial expressions, it will make adjustments such as lowering the game's difficulty.

[1112] Step 11:

[1113] The server generates improved game data based on the new tuning results, including adaptive changes based on the user's emotional data.

[1114] Step 12:

[1115] The server retransmits the adjusted game data to the device, which then applies the received data to provide the user with the latest optimized game experience.

[1116] By repeating this process, it is possible to provide a continuously customized gaming experience while adapting to the user's emotional state in real time.

[1117] Example 2

[1118] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1119] Conventional game systems have struggled to provide a personalized gaming experience based on a user's play style and preferences. Furthermore, they lacked flexibility in the user's gaming experience, unable to adjust game difficulty or content based on real-time emotional state or feedback. Furthermore, there was a lack of technology to grasp a user's emotions in real time and dynamically adjust the game based on that.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1121] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data and identifying the user's emotional state using facial expression recognition and voice analysis technology, means for generating customized game data based on the identified user's play style and emotional state, means for transmitting the generated game data to the user's device to provide a customized game experience, and means for collecting real-time feedback and emotional data from users and dynamically adjusting the difficulty and content of the game in real time, thereby enabling the provision of a more personalized game experience that is adapted to the user's play style and emotional state.

[1122] "User" refers to the end user who uses this system.

[1123] "Game play history data" refers to data about games that a user has played in the past, and includes information such as play time, quests completed, items acquired, and achievement level.

[1124] "Gaming preferences" refers to the genres and styles of games that users particularly like, and can be categorized into categories such as RPG, action, and puzzle.

[1125] "Facial expression recognition" refers to a technology that analyzes video data acquired from a camera and identifies emotions based on the user's facial expressions.

[1126] "Voice analysis" refers to a technology that analyzes audio data obtained from a microphone and identifies emotions based on the tone of the user's voice and the content of what is being said.

[1127] "Emotional state" refers to the type of emotion the user is currently feeling, and includes, for example, excitement, stress, joy, frustration, and the like.

[1128] "Customized Game Data" refers to in-game data that is specifically tailored to a user's playing style and emotional state, including story settings, game mechanics, and level design.

[1129] "Real-time feedback" refers to feedback data provided by users while playing a game, including behavior logs, in-game choices, and directly entered ratings.

[1130] "Dynamic adjustment of game difficulty and content" refers to the process of changing game settings and elements in real time based on the user's emotional state and feedback.

[1131] "Terminal" refers to a hardware device that is directly operated by a user, including smartphones, tablets, and personal computers.

[1132] "Server" refers to the computer system that performs the main processing such as data collection, analysis, generation and transmission of game data.

[1133] These are the definitions of the main words.

[1134] DETAILED DESCRIPTION OF THE INVENTION The present invention is an advanced gaming system that personalizes a user's gaming experience and adjusts in real time according to their emotional state. Specific embodiments for carrying out the present invention will be described below.

[1135] Overall overview

[1136] The system collects and analyzes users' gameplay history and game preferences to provide them with an optimal gaming experience. It also recognizes users' emotional state in real time and dynamically adjusts game content accordingly.

[1137] Hardware and software used

[1138] Server: A computer system that analyzes data, generates game data, and processes feedback.

[1139] Device: The device operated by the user (e.g., smartphone, tablet, computer).

[1140] Facial expression recognition software: e.g. OpenCV.

[1141] Speech analysis software: e.g., Google Cloud Speech-to-Text.

[1142] Game engine: e.g. Unity, Unreal Engine.

[1143] Program processing explanation

[1144] 1. Login and User Information Collection

[1145] A user logs into the system on their device. After successful login, the device collects the user's gameplay history data and preferences from local storage. The device also activates the camera and microphone and uses facial expression recognition software and voice analysis software to collect real-time emotional data.

[1146] 2. Data submission and analysis

[1147] The device sends the collected data to the server, which packages the data in JSON format and transmits it securely using the HTTPS protocol. The server analyzes the data and uses machine learning modules (e.g., Scikit-learn) to identify the user's playing style, preferences, and emotional state.

[1148] 3. Creating custom game data

[1149] The server generates customized game data based on the analysis results, including story settings, combat mechanics, character progression systems, level design, etc. It also adjusts the game difficulty according to the user's emotional state.

[1150] 4. Custom Game Offerings

[1151] The server sends the generated custom game data to the device, which receives the data and uses the game engine to launch the new customized game, which the user then plays.

[1152] 5. Collecting and analyzing feedback and sentiment data

[1153] As users play the game, their devices collect their behavioral logs, feedback data, and emotional data. This data is then sent to a server, which then adjusts the game content in real time.

[1154] 6. Real-time adjustments and game delivery

[1155] The server analyzes the feedback and emotion data, regenerates the game data, and sends it to the device, which then applies the data to provide the user with a new, adjusted game experience.

[1156] Specific examples

[1157] User: A

[1158] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[1159] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[1160] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[1161] 4. The device provides this customized game to Person A, who then begins playing.

[1162] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[1163] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[1164] Prompt Sentence Examples

[1165] "After users log in, collect their past play history and real-time emotional data to deliver a gaming experience tailored to their specific playstyle."

[1166] keyword

[1167] Generative AI model, prompt sentence

[1168] The above is a detailed description of the embodiment of the present invention.

[1169] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1170] ---

[1171] Step 1:

[1172] Login and user information collection

[1173] A user logs in to the system on a device. The user enters their login ID and password for authentication. The input is the login ID and password, and the output is the start of a session indicating successful login. If login is successful, the device collects the user's past gameplay history data and preferences from local storage. Specifically, it reads a JSON file from the device's file system and extracts data related to their gameplay history and preferences (e.g., categories such as RPG, action, and puzzle). At the same time, the device activates its camera and microphone and uses facial expression recognition software (e.g., OpenCV) and voice analysis software (e.g., Google Cloud Speech-to-Text) to collect real-time emotional data. The input is camera footage and microphone audio, and the output is emotional data (e.g., excitement, stress).

[1174] Step 2:

[1175] Data transmission and analysis

[1176] The gameplay history data, preference information, and emotional data collected by the device are packaged in JSON format and sent to the server. The input is the gameplay history data, preference information, and emotional data, and the output is the JSON data sent to the server. Data is sent securely using the HTTPS protocol. The server analyzes the received data. First, the server preprocesses the raw data, and then performs cluster analysis using a machine learning module (e.g., Scikit-learn). The input is the received JSON data, and the output is the analysis results that identify the user's play style, preferences, and emotional state. For example, preprocessing can filter out unnecessary information and convert it into a format that can be used for cluster analysis.

[1177] Step 3:

[1178] Generating Custom Game Data

[1179] The server generates customized game data based on the analysis results. The input is the analysis results obtained in step 2, and the output is customized game data. This includes the user's preferred story setting, combat mechanics, character progression system, and level design. Specifically, it executes SQL queries based on the analysis results to extract appropriate game elements from a database (e.g., PostgreSQL), combines them, and generates new game data. Furthermore, it adjusts the game difficulty according to the user's emotional state.

[1180] Step 4:

[1181] Custom Game Offering

[1182] The server sends the generated custom game data to the device. The input is the customized game data, and the output is the game data sent to the device. The device receives this data and launches the new customized game using a game engine (e.g., Unity, Unreal Engine). Specifically, the device saves the received data to local storage, launches the game engine, and loads the customized game. The user then plays this game.

[1183] Step 5:

[1184] Collecting and analyzing feedback and sentiment data

[1185] As a user plays a game, the device collects the user's action log, feedback data, and real-time emotional data. The input is the action log, feedback data, and emotional data during gameplay, and the output is the collected data. This data is sent to the server, which then analyzes it again. Specifically, the device records an event log during gameplay and collects emotional data using facial expression recognition software and voice analysis software. The server analyzes this data and identifies points where adjustments should be made to the game content.

[1186] Step 6:

[1187] Real-time adjustments and game delivery

[1188] The server analyzes the feedback and emotional data and makes appropriate game adjustments. The input is feedback and emotional data, and the output is new, adjusted game data. Specifically, new game data is generated based on the analysis results and sent back to the device. The device then applies this data locally, providing the user with a gaming experience that reflects the latest adjustments. Specifically, the device applies the new game data and restarts the game engine to reflect the adjustments, allowing the user to continue playing.

[1189] ---

[1190] The above is the specific flow and detailed operation of the program processing.

[1191] (Application example 2)

[1192] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1193] While conventional content delivery systems offer some degree of personalization based on a user's viewing history and preferences, they lack the ability to instantly adjust content to reflect the user's real-time emotional state. Furthermore, they lack the ability to instantly adjust content based on feedback, making it difficult to provide an optimal viewing experience. The present invention aims to provide an optimal entertainment experience by delivering personalized content based on a user's emotional state and adjusting content in real time while the user is viewing.

[1194] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting content viewing history data and content preferences from a user; means for analyzing the collected data and emotional data to identify the user's viewing style, preferences, and emotional state; means for generating customized content data based on the identified user's viewing style, preferences, and emotional state; means for providing the generated content data to the user; and means for collecting feedback and emotional data from the user and adjusting the content data in real time. This makes it possible to provide content that is instantly adapted according to the user's real-time emotional state, thereby achieving a more personalized viewing experience.

[1195] A user refers to an end user who uses a content distribution service to view content.

[1196] The content viewing history data refers to history information about content that a user has viewed in the past, and includes data such as viewing time, type of content viewed, and rating.

[1197] Content preferences refer to information such as the characteristics, genres, and themes of content that a user prefers.

[1198] Emotional data refers to data about a user's emotional state obtained by analyzing their facial expressions, voice, and other body language.

[1199] Collection means refers to the methods and devices used to collect the necessary data from users, such as cameras, microphones, and software.

[1200] Analytics refers to the algorithms and software that process collected data to identify users' viewing styles, preferences, and emotional states.

[1201] Customized content data refers to content information that is generated based on analyzed user data and is optimized for the user.

[1202] The provision means refers to a method or device for delivering the generated customized content to users, such as a delivery system via a network.

[1203] Feedback refers to the user's conscious or unconscious evaluation or reaction to the content provided.

[1204] Real-time adjustments refer to algorithms and systems that instantly change the content or difficulty level based on collected feedback and sentiment data.

[1205] The system that realizes this application example is a content distribution system using smart glasses and a cloud server. This system provides personalized content based on the user's viewing history and preferences, as well as analyzing real-time emotional data.

[1206] 1. System Program

[1207] The system uses the smart glasses' camera and microphone to analyze facial expressions and voices to collect emotional data as users watch content. The collected data is then transmitted over a network to a cloud server. The cloud server then analyzes the data using machine learning algorithms to identify the user's viewing style, preferences, and emotional state. The cloud server then generates customized content based on the analysis results and delivers the content data to the user. The system also collects user feedback and emotional data in real time while watching content and dynamically adjusts the content accordingly.

[1208] 2. Details of the processing

[1209] The server uses Google Cloud Platform (GCP) and TensorFlow to analyze the collected data. This analysis uses a clustering algorithm to identify the user's viewing style, preferences, and emotional state. After analysis, the server generates customized content data and transmits it to the smart glasses over the network.

[1210] The smart glasses use Android Wear OS and OpenCV to collect data from the camera and microphone to recognize emotions in real time, which is then sent to a cloud server that tailors content accordingly.

[1211] 3. Specific Examples

[1212] For example, suppose user B logs in wearing smart glasses. Data shows that in the past, user B mainly watches documentaries and likes to relax. The camera and microphone in the smart glasses analyze user B's facial expressions and voice, and the emotion engine recognizes that user B is in a relaxed state. The cloud server then delivers relaxing music and videos of natural scenery for user B. While watching, user B's expression clouds over for a moment, indicating that he is feeling stressed. This data is immediately sent to the cloud server. The cloud server analyzes the feedback and provides user B with the latest relaxation content.

[1213] 4. Examples of prompts

[1214] "Please suggest the most suitable relaxation content based on the user's past viewing history and real-time emotional data. The user is currently in a state of acute stress, so please select content that delivers music and videos that will help alleviate that stress."

[1215] As a result, the system can instantly adapt content to the user's real-time emotional state, enabling a more personalized viewing experience.

[1216] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1217] Step 1:

[1218] The user puts on the smart glasses and logs in. The device collects past viewing history data and preference information. At this time, the camera and microphone are activated, and the user's facial expressions and voice are analyzed to obtain real-time emotional data. The input includes viewing history data and real-time emotional data, and the output compiles this data for transmission to the server.

[1219] Step 2:

[1220] The viewing history data, preference information, and emotional data collected from the device are sent to a server via a network. The server receives the data and begins analyzing it using a machine learning algorithm. The input includes the data sent from the device, and the output is the analysis result, which identifies the user's viewing style, preferences, and emotional state.

[1221] Step 3:

[1222] The server generates customized content data based on the analysis results, including the user's preferred themes, genres, video composition, music, etc., and adjusts it to take into account the user's real-time emotional state. The input includes the user's viewing style, preferences, and emotional data analysis results, and the output includes the generated customized content data.

[1223] Step 4:

[1224] The server transmits the generated customized content data to the terminal via the network. The terminal receives this data and provides the content to the user. The input includes the customized content data, and the output includes the content provided as a user viewing experience.

[1225] Step 5:

[1226] While watching content, the device continuously collects user feedback and real-time emotion data. The device then transmits this data back to the server for real-time analysis. The input includes the continuously collected feedback data and emotion data, and the output includes the data sent to the server.

[1227] Step 6:

[1228] The server analyzes the feedback and real-time emotion data to dynamically adjust the difficulty and content of the content. It then sends the adjusted new content data to the device. The input includes the feedback data and emotion data, and the output includes the adjusted content data.

[1229] Step 7:

[1230] The terminal then provides the user with new, customized content again, optimizing the viewing experience so that the user can always enjoy content that best suits their emotional state. The input includes tailored content data, and the output includes an optimized viewing experience.

[1231] The above are the specific processing steps for carrying out the present invention.

[1232] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1233] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1234] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1235] [Fourth embodiment]

[1236] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1237] 7, a 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.

[1238] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1239] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1240] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1241] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1242] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1243] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1244] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1245] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1246] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1247] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1248] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1249] ---

[1250] Overall system overview

[1251] The present invention is a system for providing users with a personalized gaming experience. The system collects game play history data and game preferences from users, analyzes them, and generates game data tailored to the user. Furthermore, the system continuously collects user feedback and adjusts the game in real time to continuously provide an optimal gaming experience.

[1252] Program processing overview (details)

[1253] 1. Login and User Information Collection

[1254] A user logs into the system from a device. After successful login, the device collects the user's gameplay history data and preferences, including the types of games played in the past, the amount of time spent playing, and in-game behavior patterns. Furthermore, a survey about the user's preferred genres and specific game mechanics is displayed and answered by the user.

[1255] 2. Data submission and analysis

[1256] The device sends the collected data to a server. Once the server receives the data, it analyzes it through a machine learning module. The goal of the analysis is to identify the user's playing style and gaming preferences. Specifically, clustering algorithms and pattern recognition techniques are used.

[1257] 3. Creating custom game data

[1258] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, etc. For example, if the server determines that the user has a strategy-focused play style, it will design highly strategic scenarios and challenges.

[1259] 4. Custom Game Offerings

[1260] The server sends the generated game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[1261] 5. Gather feedback and adjust in real time

[1262] As users play the game, their devices collect their behavioral logs and feedback, such as feedback that a particular boss battle is difficult or strategic bias. The devices then send this data to the server.

[1263] The server analyzes the feedback in real time and modifies the game data as necessary. The modified game data is then sent back to the device, providing the game in an optimized format for the user.

[1264] Specific examples

[1265] User: A

[1266] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games.

[1267] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[1268] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[1269] 4. The device provides this customized game to Person A, who then begins playing.

[1270] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[1271] As described above, the system of the present invention aims to increase user satisfaction by providing a gaming experience that meets the individual needs of the user and continuously adjusting it.

[1272] The processing flow will be explained below.

[1273] ---

[1274] Step 1:

[1275] A user logs in to the system from a terminal. They enter their user ID and password on the login screen, and if authentication is successful, the user's session begins.

[1276] Step 2:

[1277] The device collects the user's gameplay history data and preference information. Specifically, it retrieves information such as the types of games played in the past, play time, and progress from an internal database. It also displays a questionnaire to the user about their preferred game genres and play styles, and receives their responses.

[1278] Step 3:

[1279] The device sends the collected data to a server, which packages the data in a standardized format and transfers it to the server using a secure protocol.

[1280] Step 4:

[1281] The server analyzes the data received from the devices. A machine learning module is then activated and begins processing the collected data as an input data set. This module uses a clustering algorithm to identify patterns in the user's playing style and preferences.

[1282] Step 5:

[1283] The server stores the analysis results in a database and launches a custom game generation module, which creates a basic design for a game optimized for the user, including the story, game mechanics, and level design.

[1284] Step 6:

[1285] The server instantiates story modules and generates story plots based on the user's preferred genre and setting, for example, a complex storyline set in a medieval fantasy world.

[1286] Step 7:

[1287] The server uses the game mechanics module to set game mechanics according to the user's play style. For example, for strategy-oriented users, the server can make the battle system more complex and add elements that require strategic progression.

[1288] Step 8:

[1289] The server launches the level design module and creates detailed designs for each stage, including enemy placement, item placement, and stage progression, all based on user data.

[1290] Step 9:

[1291] The server combines all the design data and generates the final game data, which is then compressed in binary format and ready to be sent to the device.

[1292] Step 10:

[1293] The server generates custom game data and sends it to the device. Data transfer occurs over an encrypted communication channel.

[1294] Step 11:

[1295] The device unpacks the received game data and provides the user with a new, customized game. Real-time feedback is enabled when the user starts the game.

[1296] Step 12:

[1297] As users play the game, the device collects user behavior logs and feedback data in real time, such as repeated failures at a particular level.

[1298] Step 13:

[1299] The terminal sends the collected feedback data to the server, where it is packaged in a standardized format and securely transferred to the server.

[1300] Step 14:

[1301] The server analyzes the feedback data and adjusts the game's difficulty and content in real time. Specifically, it adjusts the strength of enemies and the difficulty of stages based on user feedback.

[1302] Step 15:

[1303] The server generates new adjusted game data and resends it to the device, which receives and immediately applies it, providing the user with an updated game experience.

[1304] This is the specific processing flow of the system, which makes it possible to continuously provide users with a personalized and optimal gaming experience.

[1305] Example 1

[1306] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1307] Modern game systems face challenges in meeting diverse user needs and preferences. To provide an optimal experience in real time during gameplay, it is necessary to dynamically adjust game content based on the user's play style and feedback. Current systems struggle to understand user preferences in advance and analyze and adapt feedback during gameplay in real time, resulting in lower user satisfaction.

[1308] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1309] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data to identify the user's play style and preferences, means for generating customized game data based on the identified user's play style and preferences, and means for generating game scenarios and character settings based on the use of a generative model and prompt sentences, thereby providing a personalized game experience according to the individual needs of users and dynamically adjusting game content based on real-time feedback during game play.

[1310] "User" means an individual player who plays a game using the System.

[1311] "Game play history data" refers to recorded information such as the types of games a user has played in the past, play time, and behavioral patterns.

[1312] "Preferences" refer to a user's preferences for a particular genre or game mechanic.

[1313] "Collection methods" refer to the methods and technologies used to acquire and store data, including, for example, sensors and software modules.

[1314] "Analysis methods" refers to the algorithms and processes used to identify patterns and features in the collected data.

[1315] "Customized Game Data" means game content that is specific to a user's individual needs and preferences.

[1316] "Means of generation" refers to the technology or process used to create specific data or information.

[1317] "Means of providing" means the technology or method for transmitting the generated game data to users and making it available to them.

[1318] "Feedback" refers to various reactions and evaluations provided by users while playing a game.

[1319] "Means of real-time adjustment" refers to technology that instantly reflects collected feedback information and updates or modifies game data.

[1320] A "generative model" is an AI model that generates new content based on large datasets.

[1321] A "prompt sentence" is text that is entered to provide specific instructions to a generative model.

[1322] The present invention is a system for providing users with a personalized gaming experience. The system collects and analyzes gameplay history data and game preferences from users, and generates and provides customized game data based on the collected data. Furthermore, the system collects feedback from users in real time and dynamically adjusts the game data.

[1323] Hardware and Software Requirements

[1324] Hardware:

[1325] Devices: personal computers, smartphones, tablets, etc.

[1326] Server: High-performance server (with database functionality and machine learning model execution environment)

[1327] software:

[1328] Machine learning frameworks: TensorFlow, Scikit-Learn

[1329] Database: SQL or NoSQL database (e.g., MySQL, MongoDB)

[1330] Communication protocol: HTTPS

[1331] Generative AI models: large-scale language models (e.g., GPT)

[1332] Program processing

[1333] 1. Log in to the system

[1334] The user enters login information (user ID, password) into the terminal. The terminal sends the authentication information to the server, which then refers to the database to perform authentication.

[1335] 2. Collection of User Information

[1336] After successfully logging in, the device will collect the user's past gameplay history and gaming preferences, including play time, types of games played, behavioral patterns, and survey results regarding preferred genres and specific mechanics.

[1337] 3. Data transmission

[1338] The device sends the collected data to a server, which receives the data and stores it in a database.

[1339] 4. Data Analysis

[1340] The server analyzes the data using machine learning modules (TensorFlow, Scikit-Learn), clustering algorithms (K-means) and pattern recognition techniques to identify users' playing styles and gaming preferences.

[1341] 5. Creating custom game data

[1342] Based on the analysis results, the server generates customized game data (story setting, combat mechanics, character progression system, level design, etc.) using a generative model to input prompts that generate specific game scenarios and character settings.

[1343] For example, use the prompt "Generate a medieval fantasy story that fits the user's playstyle."

[1344] 6. Custom Game Offerings

[1345] The server transmits the generated customized game data to the terminal, and the terminal provides the new game to the user based on the data, and the user starts playing the customized game.

[1346] 7. Gather feedback and adjust in real time

[1347] As users play the game, their devices collect and send action logs and feedback to the server, including the time it takes to complete the game, the number of failures, and the frequency of certain boss battles.

[1348] The server analyzes this feedback and modifies game data as needed, for example adjusting the difficulty of certain enemies.

[1349] The corrected data is then sent back to the device, providing the user with the latest customized game.

[1350] Specific examples

[1351] User: A

[1352] 1. User A logs in from their device and data on past RPG games is collected. It is analyzed that User A particularly likes story-driven games.

[1353] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A's play style emphasizes character growth and complex storylines.

[1354] 3. The server generates game data for A, with a medieval fantasy setting that emphasizes a detailed story and strategy.

[1355] 4. The device provides this customized game to Person A, who then begins playing.

[1356] 5. During gameplay, a behavioral log is collected showing that Person A frequently fails in a particular boss battle. This feedback is sent to the server, which adjusts the difficulty and strategy of the boss. The adjusted data is sent to the device and provided to Person A again.

[1357] In this way, the system provides a gaming experience that is tailored to the user's individual needs and adjusts in real time based on feedback during gameplay.

[1358] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1359] ---

[1360] Processing Steps

[1361] Step 1:

[1362] The user enters login information (user ID, password) into the terminal.

[1363] Input: User ID, Password

[1364] How it works: The device receives user input and sends authentication information to the server.

[1365] Output: Authentication information (user ID, password)

[1366] Step 2:

[1367] The server performs authentication by referencing the database based on the authentication information received. If authentication is successful, the server returns a success message to the terminal.

[1368] Input: Credentials

[1369] Operation: The server checks the database, verifies that the user ID and password match, and generates a success message.

[1370] Output: Success message

[1371] Step 3:

[1372] After the device successfully logs in, it will collect the user's past gameplay history and game preferences.

[1373] Input: Login success message

[1374] How it works: The device collects user data locally or from the cloud, then displays a survey to ask for additional preferences.

[1375] Output: Gameplay history data, preference data

[1376] Step 4:

[1377] The terminal transmits the collected data to the server.

[1378] Input: Gameplay history data, preference data

[1379] How it works: The device sends data to the server using HTTPS.

[1380] Output: Transmitted data

[1381] Step 5:

[1382] The server stores the received data in a database and launches a machine learning module to analyze the data.

[1383] Input: Send data

[1384] What it does: Store data in a database. Analyze it using TensorFlow and Scikit-Learn. Run clustering algorithms (K-means) and pattern recognition.

[1385] Output: Analysis results (play style, preference identification)

[1386] Step 6:

[1387] The server generates customized game data based on the analysis results, and uses a generative AI model to input prompts and generate scenarios and character settings.

[1388] Input: Analysis results

[1389] How it works: Enter the prompt "Generate a medieval fantasy story that suits the user's play style" into the generation AI model, and reflect the generated scenario and character settings in the custom game data.

[1390] Output: Customized game data

[1391] Step 7:

[1392] The server transmits the generated customized game data to the terminal.

[1393] Input: Customized game data

[1394] What it does: Sends data to the device using HTTPS.

[1395] Output: Game data sent

[1396] Step 8:

[1397] The terminal provides the user with a new game based on the customized game data received.

[1398] Input: Incoming game data

[1399] What it does: The device applies the received data to the game, allowing the user to start playing.

[1400] Output: Game Start

[1401] Step 9:

[1402] While the user plays the game, the device collects behavior logs and feedback.

[1403] Input: User behavior data during gameplay

[1404] Function: Records the time it takes to clear a specific stage, the number of failures, the frequency of boss battles, etc.

[1405] Output: Behavior log, feedback data

[1406] Step 10:

[1407] The terminal transmits the collected feedback data to the server.

[1408] Input: Behavior log, feedback data

[1409] How it works: Sends data to the server using HTTPS.

[1410] Output: Feedback data sent

[1411] Step 11:

[1412] The server analyzes the feedback data and adjusts game data in real time as needed.

[1413] Input: Feedback data

[1414] Action: Data analysis, adjustments to the difficulty of certain enemies, and other fixes.

[1415] Output: Modified game data

[1416] Step 12:

[1417] The server retransmits the modified game data to the device, and the device provides the user with the latest customized game.

[1418] Input: Modified game data

[1419] How it works: Data is sent over HTTPS. The device updates and re-serves the game data.

[1420] Output: Serving the updated game

[1421] The above is the flow of processing in the program for this system and the specific operations of each step.

[1422] (Application example 1)

[1423] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1424] Conventional content distribution services lack personalized content recommendations based on users' viewing history and preferences, resulting in insufficient optimization of the viewing experience. Furthermore, there is no system for incorporating user feedback in real time, making it difficult to increase viewer satisfaction.

[1425] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1426] In this invention, the server includes means for collecting viewing history data and content preferences from users, means for analyzing the collected data to identify the user's viewing style and preferences, means for generating customized content data based on the identified user's viewing style and preferences, means for providing the generated content data to the user, and means for collecting feedback from the user and adjusting the content data in real time, thereby enabling the provision of a viewing experience that meets the individual needs of the user.

[1427] A "user" is a person who uses a content distribution service to watch movies, dramas, etc.

[1428] "Viewing history data" refers to data that includes information about movies and dramas that a user has viewed in the past.

[1429] "Content preferences" refers to information such as the user's preferred genres, stories, and character types.

[1430] "Means for collecting" refers to a method or system for collecting user viewing history data and content preferences.

[1431] "Analysis means" refers to the methods and algorithms used to analyze the collected data and identify viewing styles and preferences therefrom.

[1432] "Means for identifying" refers to the methods or processes used to identify a user's viewing style and preferences from the analyzed data.

[1433] "Customized content data" refers to movie and TV show information tailored to a user's viewing style and preferences.

[1434] A "generating means" is a method or system for creating customized content data.

[1435] "Means for providing" refers to a method or system for delivering the generated content data to users.

[1436] "Feedback" refers to opinions and impressions about the viewing experience obtained from users.

[1437] "Adjustment means" refers to a method or system for modifying and optimizing content data based on user feedback.

[1438] "Viewing style" refers to a general pattern that indicates how, how often, and at what time a user watches movies and TV dramas.

[1439] "Genre" indicates the type of movie or drama, and corresponds to a category such as action, comedy, drama, etc.

[1440] "Story" refers to the content and development of a movie or drama.

[1441] The present invention is a system for providing a user with a personalized viewing experience. The system collects and analyzes the user's viewing history data and content preferences to generate content data suited to the user. Furthermore, the system collects user feedback and adjusts the content data in real time to continuously provide an optimal viewing experience.

[1442] The server first stores data collected while the user is watching in cloud storage. This data includes viewing history, genre of content viewed, and behavioral patterns while watching. The server then uses this data to analyze the user's viewing style and preferences. Specific analysis methods include clustering and pattern recognition using machine learning algorithms.

[1443] Once a user's viewing style and preferences are identified, the server generates customized content data based on this information. For example, if it determines that a particular user likes action movies, it will select and provide the most suitable works from that genre. This also takes into account the user's past viewing data and rating information.

[1444] The server transmits the generated customized content data to the terminal and provides it to the user. The terminal displays a new customized viewing list to the user based on the received content data.

[1445] Furthermore, as the user continues watching, the server collects feedback from the user, including detailed information about the viewing experience, such as whether the user liked a particular scene or disliked the storyline. Once the feedback is collected, the server analyzes it in real time and modifies the content data as necessary.

[1446] For example, if a user gives feedback that there are too many action scenes, the server will adjust the proportion of action scenes in the next recommendation to reduce it. This process continuously optimizes the user's viewing experience.

[1447] As a concrete example, when user A logs in from their device, data on content they have previously viewed is collected. The server analyzes that A particularly likes fantasy movies and recommends specific content based on this. As A continues to watch content, their device collects behavioral logs and feedback and sends them to the server. The server receives this information and adjusts the content data to better match A's preferences.

[1448] An example of a specific prompt sentence is, "Please list movies that you would recommend to user A, who wants to relax. User A has watched movies in the past, and he prefers dramas and comedies."

[1449] The system of the present invention allows users to continuously enjoy an optimal viewing experience that meets their individual needs.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1:

[1452] The user logs in from their device. The device collects the user's viewing history data and content preferences. Specifically, it acquires data such as the genre and ratings of content viewed in the past, as well as viewing times. This data becomes the input needed for subsequent analysis.

[1453] Step 2:

[1454] The device sends the collected data to the server, which analyzes the received viewing history data and content preferences. Specifically, it uses machine learning algorithms (e.g., clustering and pattern recognition techniques) to identify the user's viewing style and preferences. The results of this analysis serve as input for the next step.

[1455] Step 3:

[1456] The server generates customized content data based on the identified user's viewing style and preferences. Specifically, it creates a content list that reflects the user's preferred genres, story settings, viewing time slots, etc. This generated content data becomes the input for the next step.

[1457] Step 4:

[1458] The server sends the generated customized content data to the terminal, and the terminal provides the user with a new customized viewing list based on the received content data, allowing the user to start viewing content optimized to their preferences.

[1459] Step 5:

[1460] While the user is watching the content, the device continuously collects viewing behavior data and feedback. The collected data includes detailed information about the viewing experience, such as "I liked a particular scene" or "I didn't like the story development." This data serves as input for the next step.

[1461] Step 6:

[1462] The device sends the collected feedback data to the server. The server analyzes the received feedback in real time and modifies the content data as necessary. For example, if the feedback is "too many action scenes," the content list will be adjusted to reduce the proportion of action scenes in the next recommendation. This adjusted data becomes the input for the next step.

[1463] Step 7:

[1464] The server retransmits the adjusted content data to the terminal, and the terminal provides the user with a newly adjusted viewing list, allowing the user to enjoy an optimized viewing experience again, thereby continuously improving the user's viewing experience.

[1465] This process flow allows users to view content that is always customized to their preferences, increasing their satisfaction.

[1466] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1467] ---

[1468] Overall system overview

[1469] This invention combines a system for providing a personalized gaming experience with an emotion engine that recognizes the user's emotions, providing an adaptive gaming experience that corresponds to the user's emotional state. The system collects game play history data and game preferences from the user, analyzes them, and generates game data tailored to the user. Furthermore, it continuously collects feedback and emotion data from the user and adjusts the game in real time to provide a continuously optimal gaming experience.

[1470] Program processing overview (details)

[1471] 1. Login and User Information Collection

[1472] A user logs in to the system from a terminal. If the login is successful, the terminal activates the user's gameplay history data and preferences, as well as devices (camera, microphone, etc.) for recognizing the user's emotions, and collects emotional data.

[1473] 2. Data submission and analysis

[1474] The device transmits the collected gameplay history data, preference information, and emotional data to a server, where the collected data is packaged in a standardized format and transferred to the server using a secure protocol.

[1475] The server analyzes the data received from the device. A machine learning module is then activated and begins processing the collected data as an input dataset. This module uses a clustering algorithm to identify the user's play style, preferences, and emotional state.

[1476] 3. Creating custom game data

[1477] The server generates customized game data based on the analysis results, including the user's preferred story setting, combat mechanics, character progression system, level design, and even adjustments based on emotional data. For example, if the server determines that the user has a strategic play style and feels stressed while playing, it will adjust the game's difficulty to reduce stress.

[1478] 4. Custom Game Offerings

[1479] The server sends the generated custom game data to the device. The device receives the game data and provides the user with a new, customized game based on that data. The user then begins playing the game.

[1480] 5. Collecting and analyzing feedback and sentiment data

[1481] As users play the game, their device collects their behavioral logs, feedback data, and real-time emotional data. For example, if they frequently fail a particular boss battle and their facial expressions show frustration, this data is immediately sent to the server.

[1482] The server analyzes the received feedback and emotional data and adjusts the game's difficulty and content in real time, specifically based on the user's emotional state (e.g., excitement, stress, enjoyment).

[1483] 6. Real-time adjustments and game delivery

[1484] Based on the analysis results, the server generates new game data and sends it to the device, which receives this data and provides the user with a gaming experience that reflects the latest adjustments.

[1485] Specific examples

[1486] User: A

[1487] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the device's camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[1488] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[1489] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[1490] 4. The device provides this customized game to Person A, who then begins playing.

[1491] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[1492] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[1493] As described above, the system of the present invention aims to provide a personalized and optimal gaming experience while taking into account the user's emotional state.

[1494] The processing flow will be explained below.

[1495] ---

[1496] Step 1:

[1497] The user logs in to the terminal. They enter their user ID and password on the login screen, and if authentication is successful, the session begins.

[1498] Step 2:

[1499] After the device successfully logs in, it will obtain the user's gameplay history data and game preferences from the internal database. Additionally, it will use the device's camera and microphone to collect the user's real-time facial expressions and voice.

[1500] Step 3:

[1501] The device will send the gameplay history data, preferences, and emotional data collected from the camera and microphone to the server. The data will be encrypted and transmitted using a secure communication method.

[1502] Step 4:

[1503] The server analyzes the data received from the device, activates a machine learning module, and uses a clustering algorithm to identify the user's playing style and emotional state.

[1504] Step 5:

[1505] The server then uses the data analysis results to generate custom game data based on the user's play style, preferences, and emotional state, including adjustments to the story, game mechanics, level design, and even emotions.

[1506] Step 6:

[1507] The server generates custom game data and sends it to the device, where it is re-encrypted and securely sent to the device.

[1508] Step 7:

[1509] The device unpacks the received game data and presents the new, customized game to the user, at which point the user begins playing the game.

[1510] Step 8:

[1511] While the user is playing the game, the device collects the user's behavior log, play data, and real-time emotional data (facial expressions, voice), such as the stress level and number of failures during a particular boss battle.

[1512] Step 9:

[1513] The device sends the collected feedback data and emotion data to the server. The data is sent in batches at regular intervals (e.g., after clearing each stage).

[1514] Step 10:

[1515] The server analyzes the feedback data and emotional data. Based on the analysis results, it identifies changes that need to be made to the game's difficulty and story progression. Specifically, if stress is detected from the user's facial expressions, it will make adjustments such as lowering the game's difficulty.

[1516] Step 11:

[1517] The server generates improved game data based on the new tuning results, including adaptive changes based on the user's emotional data.

[1518] Step 12:

[1519] The server retransmits the adjusted game data to the device, which then applies the received data to provide the user with the latest optimized game experience.

[1520] By repeating this process, it is possible to provide a continuously customized gaming experience while adapting to the user's emotional state in real time.

[1521] Example 2

[1522] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1523] Conventional game systems have struggled to provide a personalized gaming experience based on a user's play style and preferences. Furthermore, they lacked flexibility in the user's gaming experience, unable to adjust game difficulty or content based on real-time emotional state or feedback. Furthermore, there was a lack of technology to grasp a user's emotions in real time and dynamically adjust the game based on that.

[1524] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1525] In this invention, the server includes means for collecting game play history data and game preferences from users, means for analyzing the collected data and identifying the user's emotional state using facial expression recognition and voice analysis technology, means for generating customized game data based on the identified user's play style and emotional state, means for transmitting the generated game data to the user's device to provide a customized game experience, and means for collecting real-time feedback and emotional data from users and dynamically adjusting the difficulty and content of the game in real time, thereby enabling the provision of a more personalized game experience that is adapted to the user's play style and emotional state.

[1526] "User" refers to the end user who uses this system.

[1527] "Game play history data" refers to data about games that a user has played in the past, and includes information such as play time, quests completed, items acquired, and achievement level.

[1528] "Gaming preferences" refers to the genres and styles of games that users particularly like, and can be categorized into categories such as RPG, action, and puzzle.

[1529] "Facial expression recognition" refers to a technology that analyzes video data acquired from a camera and identifies emotions based on the user's facial expressions.

[1530] "Voice analysis" refers to a technology that analyzes audio data obtained from a microphone and identifies emotions based on the tone of the user's voice and the content of what is being said.

[1531] "Emotional state" refers to the type of emotion the user is currently feeling, and includes, for example, excitement, stress, joy, frustration, and the like.

[1532] "Customized Game Data" refers to in-game data that is specifically tailored to a user's playing style and emotional state, including story settings, game mechanics, and level design.

[1533] "Real-time feedback" refers to feedback data provided by users while playing a game, including behavior logs, in-game choices, and directly entered ratings.

[1534] "Dynamic adjustment of game difficulty and content" refers to the process of changing game settings and elements in real time based on the user's emotional state and feedback.

[1535] "Terminal" refers to a hardware device that is directly operated by a user, including smartphones, tablets, and personal computers.

[1536] "Server" refers to the computer system that performs the main processing such as data collection, analysis, generation and transmission of game data.

[1537] These are the definitions of the main words.

[1538] DETAILED DESCRIPTION OF THE INVENTION The present invention is an advanced gaming system that personalizes a user's gaming experience and adjusts in real time according to their emotional state. Specific embodiments for carrying out the present invention will be described below.

[1539] Overall overview

[1540] The system collects and analyzes users' gameplay history and game preferences to provide them with an optimal gaming experience. It also recognizes users' emotional state in real time and dynamically adjusts game content accordingly.

[1541] Hardware and software used

[1542] Server: A computer system that analyzes data, generates game data, and processes feedback.

[1543] Device: The device operated by the user (e.g., smartphone, tablet, computer).

[1544] Facial expression recognition software: e.g. OpenCV.

[1545] Speech analysis software: e.g., Google Cloud Speech-to-Text.

[1546] Game engine: e.g. Unity, Unreal Engine.

[1547] Program processing explanation

[1548] 1. Login and User Information Collection

[1549] A user logs into the system on their device. After successful login, the device collects the user's gameplay history data and preferences from local storage. The device also activates the camera and microphone and uses facial expression recognition software and voice analysis software to collect real-time emotional data.

[1550] 2. Data submission and analysis

[1551] The device sends the collected data to the server, which packages the data in JSON format and transmits it securely using the HTTPS protocol. The server analyzes the data and uses machine learning modules (e.g., Scikit-learn) to identify the user's playing style, preferences, and emotional state.

[1552] 3. Creating custom game data

[1553] The server generates customized game data based on the analysis results, including story settings, combat mechanics, character progression systems, level design, etc. It also adjusts the game difficulty according to the user's emotional state.

[1554] 4. Custom Game Offerings

[1555] The server sends the generated custom game data to the device, which receives the data and uses the game engine to launch the new customized game, which the user then plays.

[1556] 5. Collecting and analyzing feedback and sentiment data

[1557] As users play the game, their devices collect their behavioral logs, feedback data, and emotional data. This data is then sent to a server, which then adjusts the game content in real time.

[1558] 6. Real-time adjustments and game delivery

[1559] The server analyzes the feedback and emotion data, regenerates the game data, and sends it to the device, which then applies the data to provide the user with a new, adjusted game experience.

[1560] Specific examples

[1561] User: A

[1562] 1. User A logs in from their device. Data on RPG games played in the past is collected, and it is discovered that User A particularly likes story-driven games. At the same time, the camera and microphone analyze User A's facial expressions and voice to collect emotional data.

[1563] 2. The device sends the collected data to the server, which analyzes it. From the analysis results, it is determined that Person A has a play style that emphasizes character growth and complex storylines, and tends to get excited when playing.

[1564] 3. The server generates game data for A, with a detailed story and emphasis on strategy in a medieval fantasy setting, and also makes adjustments to maintain a moderate level of excitement.

[1565] 4. The device provides this customized game to Person A, who then begins playing.

[1566] 5. During the game, Player A repeatedly fails in a particular boss battle, and his facial expression shows his frustration. This behavior log and emotional data are sent from his device to the server.

[1567] 6. The server analyzes the feedback and emotional data and adjusts the difficulty of the boss. The adjusted data is sent to the device and provided to A again.

[1568] Prompt Sentence Examples

[1569] "After users log in, collect their past play history and real-time emotional data to deliver a gaming experience tailored to their specific playstyle."

[1570] keyword

[1571] Generative AI model, prompt sentence

[1572] The above is a detailed description of the embodiment of the present invention.

[1573] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1574] ---

[1575] Step 1:

[1576] Login and user information collection

[1577] A user logs in to the system on a device. The user enters their login ID and password for authentication. The input is the login ID and password, and the output is the start of a session indicating successful login. If login is successful, the device collects the user's past gameplay history data and preferences from local storage. Specifically, it reads a JSON file from the device's file system and extracts data related to their gameplay history and preferences (e.g., categories such as RPG, action, and puzzle). At the same time, the device activates its camera and microphone and uses facial expression recognition software (e.g., OpenCV) and voice analysis software (e.g., Google Cloud Speech-to-Text) to collect real-time emotional data. The input is camera footage and microphone audio, and the output is emotional data (e.g., excitement, stress).

[1578] Step 2:

[1579] Data transmission and analysis

[1580] The gameplay history data, preference information, and emotional data collected by the device are packaged in JSON format and sent to the server. The input is the gameplay history data, preference information, and emotional data, and the output is the JSON data sent to the server. Data is sent securely using the HTTPS protocol. The server analyzes the received data. First, the server preprocesses the raw data, and then performs cluster analysis using a machine learning module (e.g., Scikit-learn). The input is the received JSON data, and the output is the analysis results that identify the user's play style, preferences, and emotional state. For example, preprocessing can filter out unnecessary information and convert it into a format that can be used for cluster analysis.

[1581] Step 3:

[1582] Generating Custom Game Data

[1583] The server generates customized game data based on the analysis results. The input is the analysis results obtained in step 2, and the output is customized game data. This includes the user's preferred story setting, combat mechanics, character progression system, and level design. Specifically, it executes SQL queries based on the analysis results to extract appropriate game elements from a database (e.g., PostgreSQL), combines them, and generates new game data. Furthermore, it adjusts the game difficulty according to the user's emotional state.

[1584] Step 4:

[1585] Custom Game Offering

[1586] The server sends the generated custom game data to the device. The input is the customized game data, and the output is the game data sent to the device. The device receives this data and launches the new customized game using a game engine (e.g., Unity, Unreal Engine). Specifically, the device saves the received data to local storage, launches the game engine, and loads the customized game. The user then plays this game.

[1587] Step 5:

[1588] Collecting and analyzing feedback and sentiment data

[1589] As a user plays a game, the device collects the user's action log, feedback data, and real-time emotional data. The input is the action log, feedback data, and emotional data during gameplay, and the output is the collected data. This data is sent to the server, which then analyzes it again. Specifically, the device records an event log during gameplay and collects emotional data using facial expression recognition software and voice analysis software. The server analyzes this data and identifies points where adjustments should be made to the game content.

[1590] Step 6:

[1591] Real-time adjustments and game delivery

[1592] The server analyzes the feedback and emotional data and makes appropriate game adjustments. The input is feedback and emotional data, and the output is new, adjusted game data. Specifically, new game data is generated based on the analysis results and sent back to the device. The device then applies this data locally, providing the user with a gaming experience that reflects the latest adjustments. Specifically, the device applies the new game data and restarts the game engine to reflect the adjustments, allowing the user to continue playing.

[1593] ---

[1594] The above is the specific flow and detailed operation of the program processing.

[1595] (Application example 2)

[1596] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1597] While conventional content delivery systems offer some degree of personalization based on a user's viewing history and preferences, they lack the ability to instantly adjust content to reflect the user's real-time emotional state. Furthermore, they lack the ability to instantly adjust content based on feedback, making it difficult to provide an optimal viewing experience. The present invention aims to provide an optimal entertainment experience by delivering personalized content based on a user's emotional state and adjusting content in real time while the user is viewing.

[1598] The specification processing by the specification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting content viewing history data and content preferences from a user; means for analyzing the collected data and emotional data to identify the user's viewing style, preferences, and emotional state; means for generating customized content data based on the identified user's viewing style, preferences, and emotional state; means for providing the generated content data to the user; and means for collecting feedback and emotional data from the user and adjusting the content data in real time. This makes it possible to provide content that is instantly adapted according to the user's real-time emotional state, thereby achieving a more personalized viewing experience.

[1599] A user refers to an end user who uses a content distribution service to view content.

[1600] The content viewing history data refers to history information about content that a user has viewed in the past, and includes data such as viewing time, type of content viewed, and rating.

[1601] Content preferences refer to information such as the characteristics, genres, and themes of content that a user prefers.

[1602] Emotional data refers to data about a user's emotional state obtained by analyzing their facial expressions, voice, and other body language.

[1603] Collection means refers to the methods and devices used to collect the necessary data from users, such as cameras, microphones, and software.

[1604] Analytics refers to the algorithms and software that process collected data to identify users' viewing styles, preferences, and emotional states.

[1605] Customized content data refers to content information that is generated based on analyzed user data and is optimized for the user.

[1606] The provision means refers to a method or device for delivering the generated customized content to users, such as a delivery system via a network.

[1607] Feedback refers to the user's conscious or unconscious evaluation or reaction to the content provided.

[1608] Real-time adjustments refer to algorithms and systems that instantly change the content or difficulty level based on collected feedback and sentiment data.

[1609] The system that realizes this application example is a content distribution system using smart glasses and a cloud server. This system provides personalized content based on the user's viewing history and preferences, as well as analyzing real-time emotional data.

[1610] 1. System Program

[1611] The system uses the smart glasses' camera and microphone to analyze facial expressions and voices to collect emotional data as users watch content. The collected data is then transmitted over a network to a cloud server. The cloud server then analyzes the data using machine learning algorithms to identify the user's viewing style, preferences, and emotional state. The cloud server then generates customized content based on the analysis results and delivers the content data to the user. The system also collects user feedback and emotional data in real time while watching content and dynamically adjusts the content accordingly.

[1612] 2. Details of the processing

[1613] The server uses Google Cloud Platform (GCP) and TensorFlow to analyze the collected data. This analysis uses a clustering algorithm to identify the user's viewing style, preferences, and emotional state. After analysis, the server generates customized content data and transmits it to the smart glasses over the network.

[1614] The smart glasses use Android Wear OS and OpenCV to collect data from the camera and microphone to recognize emotions in real time, which is then sent to a cloud server that tailors content accordingly.

[1615] 3. Specific Examples

[1616] For example, suppose user B logs in wearing smart glasses. Data shows that in the past, user B mainly watches documentaries and likes to relax. The camera and microphone in the smart glasses analyze user B's facial expressions and voice, and the emotion engine recognizes that user B is in a relaxed state. The cloud server then delivers relaxing music and videos of natural scenery for user B. While watching, user B's expression clouds over for a moment, indicating that he is feeling stressed. This data is immediately sent to the cloud server. The cloud server analyzes the feedback and provides user B with the latest relaxation content.

[1617] 4. Examples of prompts

[1618] "Please suggest the most suitable relaxation content based on the user's past viewing history and real-time emotional data. The user is currently in a state of acute stress, so please select content that delivers music and videos that will help alleviate that stress."

[1619] As a result, the system can instantly adapt content to the user's real-time emotional state, enabling a more personalized viewing experience.

[1620] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1621] Step 1:

[1622] The user puts on the smart glasses and logs in. The device collects past viewing history data and preference information. At this time, the camera and microphone are activated, and the user's facial expressions and voice are analyzed to obtain real-time emotional data. The input includes viewing history data and real-time emotional data, and the output compiles this data for transmission to the server.

[1623] Step 2:

[1624] The viewing history data, preference information, and emotional data collected from the device are sent to a server via a network. The server receives the data and begins analyzing it using a machine learning algorithm. The input includes the data sent from the device, and the output is the analysis result, which identifies the user's viewing style, preferences, and emotional state.

[1625] Step 3:

[1626] The server generates customized content data based on the analysis results, including the user's preferred themes, genres, video composition, music, etc., and adjusts it to take into account the user's real-time emotional state. The input includes the user's viewing style, preferences, and emotional data analysis results, and the output includes the generated customized content data.

[1627] Step 4:

[1628] The server transmits the generated customized content data to the terminal via the network. The terminal receives this data and provides the content to the user. The input includes the customized content data, and the output includes the content provided as a user viewing experience.

[1629] Step 5:

[1630] While watching content, the device continuously collects user feedback and real-time emotion data. The device then transmits this data back to the server for real-time analysis. The input includes the continuously collected feedback data and emotion data, and the output includes the data sent to the server.

[1631] Step 6:

[1632] The server analyzes the feedback and real-time emotion data to dynamically adjust the difficulty and content of the content. It then sends the adjusted new content data to the device. The input includes the feedback data and emotion data, and the output includes the adjusted content data.

[1633] Step 7:

[1634] The terminal then provides the user with new, customized content again, optimizing the viewing experience so that the user can always enjoy content that best suits their emotional state. The input includes tailored content data, and the output includes an optimized viewing experience.

[1635] The above are the specific processing steps for carrying out the present invention.

[1636] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1637] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1638] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1639] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1640] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1641] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1642] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1643] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1644] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1645] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1646] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1647] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1648] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1649] 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.

[1650] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1651] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1652] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1653] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1654] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1655] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1656] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1657] The following is further disclosed regarding the above embodiment.

[1658] (Claim 1)

[1659] means for collecting historical gameplay data and game preferences from users;

[1660] A means for analyzing collected data to identify users' play styles and preferences;

[1661] means for generating customized game data based on the identified user's play style and preferences;

[1662] A means for providing the generated game data to users;

[1663] A means of collecting user feedback and adjusting game data in real time;

[1664] A system including:

[1665] (Claim 2)

[1666] 10. The system of claim 1, further comprising means for customizing story, game mechanics, and level design based on the analyzed user data.

[1667] (Claim 3)

[1668] 10. The system of claim 1, further comprising means for dynamically adjusting the difficulty and content of the game based on real-time feedback obtained while the user is playing the game.

[1669] "Example 1"

[1670] (Claim 1)

[1671] means for collecting historical gameplay data and game preferences from users;

[1672] A means for analyzing collected data to identify users' play styles and preferences;

[1673] means for generating customized game data based on the identified user's play style and preferences;

[1674] A means for providing the generated game data to users;

[1675] A means of collecting user feedback and adjusting game data in real time;

[1676] A system including means for generating game scenarios and character settings based on the use of a generative model and prompt sentences.

[1677] (Claim 2)

[1678] 10. The system of claim 1, further comprising means for customizing the story, game mechanics, and level design based on the analyzed user data.

[1679] (Claim 3)

[1680] 10. The system of claim 1, further comprising means for dynamically adjusting the difficulty and content of the game based on real-time feedback obtained while the user is playing the game.

[1681] "Application Example 1"

[1682] (Claim 1)

[1683] means for collecting viewing history data and content preferences from users;

[1684] means for analyzing the collected data to identify users' viewing styles and preferences;

[1685] means for generating customized content data based on the identified user's viewing style and preferences;

[1686] A means for providing the generated content data to a user;

[1687] a means of collecting user feedback and adjusting content data in real time;

[1688] A system including:

[1689] (Claim 2)

[1690] 10. The system of claim 1, further comprising means for customizing genres, stories, and viewing content based on the analyzed user data.

[1691] (Claim 3)

[1692] 10. The system of claim 1, further comprising means for dynamically adjusting content recommendations and content based on real-time feedback obtained while a user is viewing the content.

[1693] "Example 2: Combining Emotion Engines"

[1694] (Claim 1)

[1695] means for collecting historical gameplay data and game preferences from users;

[1696] means for analyzing the collected data and identifying the user's emotional state using facial expression recognition and voice analysis techniques;

[1697] means for generating customized game data based on the identified user's play style and emotional state;

[1698] A means for transmitting the generated game data to a user's device to provide a customized game experience;

[1699] A means of collecting real-time feedback and emotional data from users and dynamically adjusting the difficulty and content of the game in real time;

[1700] A system including:

[1701] (Claim 2)

[1702] 10. The system of claim 1, further comprising means for customizing story, game mechanics, character progression system, and level design based on the analyzed user data.

[1703] (Claim 3)

[1704] 10. The system of claim 1, further comprising means for dynamically adjusting the difficulty and content of the game based on real-time feedback and emotional data obtained while a user is playing the game.

[1705] "Application example 2 when combining emotion engines"

[1706] (Claim 1)

[1707] means for collecting content viewing history data and content preferences from users;

[1708] means for analyzing the collected data and emotional data to identify a user's viewing style and preferences and emotional state;

[1709] means for generating customized content data based on the identified user's viewing style and preferences and emotional state;

[1710] A means for providing the generated content data to a user;

[1711] a means for collecting user feedback and sentiment data and adjusting content data in real time;

[1712] A system including:

[1713] (Claim 2)

[1714] 10. The system of claim 1, further comprising means for customizing story, content mechanics, and video composition based on the analyzed user data.

[1715] (Claim 3)

[1716] 10. The system of claim 1, further comprising means for dynamically adjusting the difficulty level and content of the content based on real-time feedback and emotional data obtained while the user is viewing the content. [Explanation of symbols]

[1717] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting historical gameplay data and game preferences from users; A means for analyzing collected data to identify users' play styles and preferences; means for generating customized game data based on the identified user's play style and preferences; A means for providing the generated game data to users; A means of collecting user feedback and adjusting game data in real time; A system including:

2. 10. The system of claim 1, further comprising means for customizing story, game mechanics, and level design based on the analyzed user data.

3. 10. The system of claim 1, further comprising means for dynamically adjusting the difficulty and content of the game based on real-time feedback obtained while the user is playing the game.

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