system

Abstract

Provide a system. 【Solution means】 Means for generating the development of a story based on a user's request using a large language model, Means for generating the design of characters with specified features using image generation technology, Means for designing the actions of a game by applying a reinforcement learning algorithm and providing optimal variables, Means for constructing a virtual reality environment and enabling early-stage trials, Means for collecting and analyzing opinions from users and presenting improvement plans, Means for providing an interface that allows users to access with a smart device and spontaneously generate and edit stories, Means for presenting in real time the content generated based on the above means in an environment where it can be interactively experienced, A system including the above.

Description

Technical Field

[0004] , ,

[0005] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Game developers are faced with the problems of coming up with new stories and character designs, conducting efficient prototype tests, and improving development speed to quickly respond to market changes. In order to solve these problems, means for maintaining creativity while streamlining the development process are required.

Means for Solving the Problems

[0005] This invention provides a development support system utilizing AI technology. This system includes a means for generating storylines using a large-scale language model, a means for generating character designs using image generation technology, a means for designing game mechanics using reinforcement learning algorithms, and a prototype test play environment using virtual reality. Furthermore, by collecting and analyzing user feedback and presenting improvement measures in real time, it achieves increased efficiency in the development process and improved creative quality.

[0006] A "large-scale language model" is an AI model trained on vast amounts of text data, possessing the ability to generate natural-sounding sentences like a human.

[0007] "Image generation technology" is an artificial intelligence technology that automatically creates visual content based on specified features, and is used when generating prototypes for character designs.

[0008] A "reinforcement learning algorithm" is an artificial intelligence technology that learns the optimal action through trial and error, and is applied to adjusting the parameters of game mechanics.

[0009] A "virtual reality environment" is a technology that provides experiences and activities within a computer-generated three-dimensional space, and is used to create a space for testing prototypes.

[0010] "Prototype test play" is the process of actually running a prototype of a game under development to check its operation and evaluate the features and game mechanics being developed.

[0011] "Collecting and analyzing feedback" is the process of gathering opinions and reactions from users and analyzing them to help improve the development process and system. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] This invention is a system that utilizes AI technology to streamline the game development process and support creativity. The specific implementation of this system is described below.

[0034] This system consists of servers, terminals, and users. The servers are equipped with software for building large-scale language models, image generation techniques, reinforcement learning algorithms, and virtual reality environments.

[0035] First, the user connects to the system via a terminal and inputs requests regarding the storyline, character design, and game mechanics. The terminal then sends this information to the server.

[0036] The server receives these requests and generates storylines using a large-scale language model. For example, a request such as "I want to create an adventure story set in a fantasy world" can automatically generate a detailed plot.

[0037] Next, the server utilizes image generation technology to create a character design based on the features specified by the user. For example, if the user specifies "young wizard, blue robe," a character image with those characteristics will be generated.

[0038] Furthermore, the server uses reinforcement learning algorithms to design game mechanics. For example, it automatically designs the optimal battle system based on information such as "turn-based battle, adjustable difficulty."

[0039] Once the prototype development is complete, the server will set up a virtual reality environment, providing users with an environment where they can experience actual gameplay through a VR headset. Through this experience, users can evaluate the realism and playability of the game.

[0040] Users send feedback to the server via their device after playing. The server analyzes this feedback and suggests improvements to the user. By repeating this feedback loop, the quality of the game can be improved.

[0041] This system provides powerful support for game developers to quickly develop high-quality games while saving time and resources.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user logs into the system via their device and requests the generation of a storyline. The user then sends information to the server by entering a specific genre or theme.

[0045] Step 2:

[0046] The server receives requests from users and generates storylines using a large-scale language model. This model creates relevant plots and character outlines based on the genres and themes specified by the user.

[0047] Step 3:

[0048] The server sends the generated storyline to the device and presents it to the user. The user reviews the story on the screen and decides whether they are satisfied. At this point, the user can provide feedback.

[0049] Step 4:

[0050] If a user wishes to generate a character design, they send a request to the server via their device, specifying the character's characteristics and style.

[0051] Step 5:

[0052] The server utilizes image generation technology to generate character designs based on features specified by the user. The generated designs can include multiple variations.

[0053] Step 6:

[0054] The server sends the generated character design to the user's device and presents them with multiple options. The user reviews the designs on their device and selects their preferred character.

[0055] Step 7:

[0056] If a user wishes to design game mechanics, they specify the mechanic requirements in detail via a terminal. This information is then sent to the server.

[0057] Step 8:

[0058] The server uses a reinforcement learning algorithm to design game mechanics based on user requirements. The designed mechanics may include improvements over existing settings.

[0059] Step 9:

[0060] The game mechanics designed on the server are delivered to the terminal, and the user is allowed to review them. The user provides feedback as needed.

[0061] Step 10:

[0062] If a user requests to test play the prototype in VR, the device will inform the server of this request.

[0063] Step 11:

[0064] The server builds the virtual reality environment and prepares a prototype based on an integrated storyline, character designs, and game mechanics.

[0065] Step 12:

[0066] The VR prototype is sent from the server to the terminal, and the user can perform a test play using a VR headset.

[0067] Step 13:

[0068] Users send feedback to the server via their device after playing. The server analyzes the feedback and presents the user with suggestions for improvement in real time.

[0069] This step-by-step process enables efficient and creative game development.

[0070] (Example 1)

[0071] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0072] Traditional game development processes require significant time and effort for story construction, character design, and game mechanics design. Furthermore, they present challenges in efficiently creating prototypes based on user requests and implementing improvement cycles that incorporate feedback.

[0073] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0074] In this invention, the server includes means for generating a story based on user requests using a large-scale data processing model, means for generating a character with specified features using an image generation algorithm, and means for designing the mechanism of an electronic game and providing an optimal configuration by applying a machine learning algorithm. This makes it possible to create prototypes quickly and efficiently and to realize a continuous improvement cycle that utilizes user feedback.

[0075] A "large-scale data processing model" is an algorithm that performs natural language processing and uses large amounts of data to generate stories and texts.

[0076] An "image generation algorithm" is a method of creating visual representations of people or objects using a computer based on input instructions.

[0077] A "machine learning algorithm" is a method for learning features from vast amounts of data and automatically optimizing the mechanical design of electronic games.

[0078] A "virtual representation environment" is a computer-generated three-dimensional space that allows users to have an immersive experience through digital devices.

[0079] A "mediating device" is an interface device that transmits user input information to a processing unit in an appropriate format.

[0080] This invention is a system designed to streamline the game development process and support creativity. The system consists of a server, terminals, and users, with each device working in coordination. Specific embodiments of the invention are described below.

[0081] The server is equipped with software for building large-scale data processing models, image generation algorithms, machine learning algorithms, and virtual representation environments. The large-scale data processing model generates stories based on user requests. Users input detailed requests regarding storylines, character designs, and game mechanics as prompts via a terminal and send them to the server.

[0082] For example, if a user enters the prompt, "Create an adventure story set in a medieval fantasy world. The protagonist is a young wizard wearing a blue robe. The story should also include a scene where the protagonist fights a dragon," the server will generate a story based on this information.

[0083] The image generation algorithm generates a visual representation of a character based on the characteristics specified by the user. For example, if the user specifies "brave knight, red shield," an image of a character matching those characteristics will be generated.

[0084] Machine learning algorithms design the mechanisms of electronic games according to the game mechanics specified by the user, and automatically determine the optimal configuration. Complex mechanics, such as turn-based battle systems, are also designed efficiently.

[0085] Finally, the server creates a virtual representation environment, allowing users to experience the game prototype through a VR headset. This provides users with an immersive gameplay experience, enabling them to evaluate the game's realism and playability. This system streamlines the development cycle and supports the rapid development of high-quality games.

[0086] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0087] Step 1:

[0088] The user inputs requests regarding the storyline, character design, and game mechanics as prompts through the terminal. These prompts serve as initial data for building the narrative, visual elements, and game mechanics. The terminal then prepares to send these prompts to the server.

[0089] Step 2:

[0090] The server generates a story using a large-scale data processing model based on the prompt message received from the terminal. Specifically, it analyzes the context of the prompt message and constructs a new storyline by referring to relevant data. The output is a detailed story plot that aligns with the user's request. For example, a prompt like "The adventures of a young wizard" would lead to a scenario in which the wizard battles a dragon.

[0091] Step 3:

[0092] The server then uses an image generation algorithm to design the character. It extracts the character's visual characteristics from the prompt text (e.g., "brave knight, red shield") and generates an image based on them. The output is a visual representation of the character that meets the user's requirements. The generated image is used as the design for the character that will appear in the story.

[0093] Step 4:

[0094] The server uses machine learning algorithms to design the game mechanics. It takes the play style and difficulty settings specified in the prompt (e.g., "turn-based battle") as input to derive the optimal design. Based on patterns learned from vast amounts of data, the server determines a structure that produces an efficient gameplay experience. The output is a play system that matches the user's intentions.

[0095] Step 5:

[0096] The server finally constructs the virtual representation environment and integrates the designed storyline, characters, and game mechanics. These elements interact within the VR environment, allowing the user to experience the prototype game through a VR headset. The output is an integrated game experience that the user can intuitively test.

[0097] Step 6:

[0098] Users input feedback on their VR gameplay experience via their device and send it to the server. The server analyzes this feedback and generates suggestions for improvements regarding the story, visual design, and mechanics. This provides users with concrete guidelines for improving the quality of the game.

[0099] (Application Example 1)

[0100] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0101] In game development, constructing storylines, designing characters, and designing gameplay mechanics are extremely time-consuming and labor-intensive processes. Furthermore, the limited opportunities for users to creatively generate and edit games themselves make it difficult to provide the joy of interactive content creation to a wider audience. Moreover, achieving a real-time interactive experience presents significant technical challenges.

[0102] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0103] In this invention, the server includes means for generating the development of a story using a large-scale language model, means for generating character designs using image generation technology, means for designing game actions and providing optimal variables by applying a reinforcement learning algorithm, means for providing an interface that users can access via smart devices to spontaneously generate and edit stories, and means for presenting an interactive experience environment in real time. This makes it possible to stimulate creativity by enabling users to easily generate new content and have a real-time interactive experience.

[0104] A "large-scale language model" is an AI technology that enables natural language processing, and it is a model that has the ability to generate and understand text based on a large amount of text data.

[0105] "Story development" refers to the flow and plot of a story that is automatically generated by AI based on user requests.

[0106] "Image generation technology" is a technique in which AI generates new images based on specified features, and is used in the creation of character designs and background designs.

[0107] "Character design" refers to the design of characters with unique characteristics, either specified by the user or automatically generated by AI.

[0108] A "reinforcement learning algorithm" is a method in which AI learns the optimal action through trial and error and efficiently designs in-game mechanics and other elements.

[0109] "Game actions" refer to actions and rules related to the operation and progression of the game, and are set based on player interaction.

[0110] "Smart devices" refer to multi-functional electronic devices such as mobile devices and handheld terminals.

[0111] An "interface" refers to the screens, input methods, and other elements that allow a user to interact directly with a system.

[0112] An "interactive experience environment" refers to an environment that enables real-time, two-way communication between the user and the system.

[0113] This system is designed to support user creativity and efficiently generate content. The server combines large-scale language models, image generation technologies, and reinforcement learning algorithms to generate storylines, character designs, and gameplay based on user requests.

[0114] When a user accesses the system using a smart device, they can input initial story and character requests through the interface. For example, they might enter a prompt such as, "Generate an adventure story where a knight fights a dragon in a medieval fantasy world." The server then receives these requests and begins processing them.

[0115] The server uses a large-scale language model to automatically generate a detailed narrative development from the input prompts. Image generation technology generates character designs that reflect the features specified by the user. For example, based on the request "knights should wear blue armor and dragons should have red and gold markings," a specific character image is generated.

[0116] Furthermore, the server utilizes reinforcement learning algorithms to design gameplay and build an optimal game system. This process involves learning variables through trial and error to optimize the user experience.

[0117] Ultimately, the server displays the generated content in real time, providing an interactive environment for users. In this environment, users can experience the progression of the story and the actions of the characters, enjoying content that aligns with their own creative intentions.

[0118] This system allows users to spontaneously generate new stories and characters, and to have a real-time interactive experience.

[0119] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0120] Step 1:

[0121] The user enters prompt messages through the interface of a smart device. These prompts concern requests related to the story's theme and character traits. This information is collected by the terminal and sent to the server. The user's requests become the basis for subsequent processing within the system.

[0122] Step 2:

[0123] The server parses the received prompt message. In this step, natural language processing techniques are used to convert the user's request into structured data. Key keywords and context are extracted from the prompt message, and the data is prepared in a format suitable for the story generator.

[0124] Step 3:

[0125] The server uses a large-scale language model to generate the narrative flow. Utilizing the structured data received as input, the large-scale language model automatically generates a detailed storyline. This generated storyline is then used as foundational information for subsequent character design.

[0126] Step 4:

[0127] The server uses image generation technology to generate character designs based on the features specified in the prompt. In this step, the AI ​​generates realistic character images based on the output of the previous storyline. For example, the specific designs of knights and dragons are finalized here.

[0128] Step 5:

[0129] The server uses a reinforcement learning algorithm to design the game's actions. In this process, it sets optimal action parameters for the game based on the generated storyline and character designs. The server searches for the optimal solution by trying various simulations, learning actions that are suitable for the interactive environment the user will experience.

[0130] Step 6:

[0131] The server integrates the generated story, character designs, and gameplay to create an interactive experience environment. In this step, the server sets up the virtual reality environment and provides the user with a real-time interactive experience.

[0132] Step 7:

[0133] Users experience content generated in an interactive environment and send feedback to the server. The server analyzes this feedback and identifies areas for improvement. Based on user feedback, the system evolves further.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] This invention relates to a system that recognizes user emotions and dynamically adjusts the game development process based on those emotions. This system comprises a server, a terminal, a user, and an emotion engine.

[0136] The server is equipped with a large-scale language model, image generation technology, reinforcement learning algorithms, software for building virtual reality environments, and an emotion engine. The emotion engine analyzes the user's emotions in real time and uses the results in conjunction with other modules.

[0137] First, the user accesses the system from their device and enters their request for generating a storyline. For example, if the user enters "I would like a suspenseful story," the device sends that information to the server. The server then uses a large-scale language model to generate a storyline, referencing the user's input and the user's emotional state as determined by the emotion engine.

[0138] Next, the user sends a request for a character design via the terminal. For example, in response to a request such as "a tense detective character," the server generates a character design using image generation technology, while taking into account the results of the emotion engine's analysis.

[0139] Furthermore, when a user sends information about their desired game mechanics from their device to the server, the server applies a reinforcement learning algorithm to design the game mechanics. In doing so, the server uses user feedback obtained by the emotion engine to make optimal adjustments tailored to the player.

[0140] Then, when a user tests the prototype, the server creates a virtual reality environment. The prototype is delivered in a way that is optimized for the user's emotional state. For example, when the user is relaxed, it is possible to generate an environment that includes more challenging elements.

[0141] Users can send their impressions and feedback after playing to the server via their device. The server analyzes the feedback using an emotion engine and generates suggestions for further improvement, which are then presented to the user.

[0142] In this way, leveraging user emotions allows for a more personalized game development process. This system enables faster development and maximizes user satisfaction.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user logs into the system via their device and requests storyline generation. The user enters their desired theme and genre, and this information is sent to the server.

[0146] Step 2:

[0147] Based on the received request, the server initiates a process to generate a storyline using a large-scale language model. During this process, the server uses an emotion engine to analyze the user's current emotional state and reflects that state in the generated story.

[0148] Step 3:

[0149] The generated storyline data is sent from the server to the terminal and presented to the user. The user reviews the story content on the screen and provides feedback as needed.

[0150] Step 4:

[0151] If a user wishes to design a character, they enter the characteristics of the specific character on their device and send the information to the server.

[0152] Step 5:

[0153] The server uses image generation technology to generate visuals based on character design requests received from the terminal. The emotion engine considers the user's emotions during this process and reflects them in the design.

[0154] Step 6:

[0155] The generated character designs are sent to the terminal via the server and presented to the user as multiple design options. The user can then select their preferred design.

[0156] Step 7:

[0157] If a user requests to design game mechanics, the details are sent from the terminal to the server.

[0158] Step 8:

[0159] The server uses reinforcement learning algorithms to design game mechanics that satisfy user requests. In doing so, the server incorporates user feedback from previous stages and considers the user's emotional state, analyzed by the emotion engine, to make optimal suggestions.

[0160] Step 9:

[0161] The designed game mechanics are transmitted to the device in real time and reviewed by the user. The user can then provide additional feedback.

[0162] Step 10:

[0163] When a user requests to test play a prototype, that request is sent from the device to the server.

[0164] Step 11:

[0165] The server builds a virtual reality environment for the prototype and uses an emotion engine to adjust it based on the user's emotional state. This adjustment is made to enrich the user experience.

[0166] Step 12:

[0167] The prototype is sent from the server to the terminal, and the user uses a VR headset to test play and deepen their experience.

[0168] Step 13:

[0169] Users input their impressions after the test via their device and send the feedback to the server. The server uses an emotion engine to analyze the feedback, generate improvement suggestions, and present them to the user.

[0170] This system enables a dynamic, emotion-driven game development process.

[0171] (Example 2)

[0172] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0173] In modern game development processes, creating content that reflects individual user emotions and immediate feedback is a challenging task. Traditional technologies fail to adequately adapt to user emotions, resulting in a lack of personalized game experiences. To address this challenge, it is necessary to analyze user emotions in real time and dynamically adjust the game development process based on the results.

[0174] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0175] In this invention, the server includes means for generating a narrative flow based on user requests using large-scale language processing technology, means for generating character designs with specified features using an image generation method, and means for designing a game structure and providing optimal components by applying a learning algorithm. This makes it possible to provide a individually tailored game experience in real time based on user sentiment analysis.

[0176] "Large-scale language processing technology" refers to techniques that analyze large amounts of text data and generate natural language.

[0177] An "image generation method" is a technology for creating visual information based on input information.

[0178] A "learning algorithm" is a computational method in which a machine learns from data and adaptively solves problems based on the results.

[0179] A "virtual reality space" is a computer-generated environment that closely resembles reality, allowing users to immerse themselves in and experience it.

[0180] "User emotional state" refers to the emotional reactions and psychological state of the user during gameplay.

[0181] An "analysis mechanism" is a system or device used to analyze input data and identify its characteristics.

[0182] A "regulation mechanism" is a device or function that dynamically changes a system or environment based on the information obtained.

[0183] This invention relates to a system that analyzes user emotions and dynamically adjusts the game development process based on those emotions. This system consists of a server, terminals, an emotion engine, and the like.

[0184] The server is equipped with software that incorporates large-scale language processing technology and image generation methods, as well as software for building learning algorithms and virtual reality environments. These technologies enable the server to generate diverse content based on user input and dynamically adjust it accordingly.

[0185] Users access the system using a terminal and enter prompts to create their desired storyline. For example, if a user enters "I want a suspenseful story" into the terminal, the terminal transmits this information to the server. The server then uses large-scale language processing technology to generate a storyline based on the results of an emotion engine analysis of the user's emotional state. Emotion analysis allows the system to provide content that matches the atmosphere and story development the user desires.

[0186] Next, the user's specified character design request is transmitted via the terminal. For example, if the request is for a "tense detective character," the server generates an image of the character using an image generation method that reflects the analysis results of the emotion engine. In this way, a character design that responds to user feedback is provided.

[0187] Furthermore, when a user sends their desired game mechanics from their device to the server, the server uses a learning algorithm to design the gamification. In this process, the server designs the optimal game structure based on the user's emotional feedback. This approach provides a game experience tailored to each individual user.

[0188] Furthermore, when a user tests the prototype, the server creates a virtual reality space within which the prototype operates. This makes it possible to create an optimal playing environment tailored to the user's emotional state. For example, if the user is relaxed, an environment with challenging content can be provided.

[0189] Finally, users send their post-game impressions and feedback from their device to the server. The server uses an emotion engine to analyze the feedback and generates suggestions for further improvement, which are then presented to the user.

[0190] A concrete example of a prompt message would be, "Generate a story and characters to be used in the next project. The theme is fantasy." This invention improves the efficiency of game development work and user satisfaction.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] The user accesses the system using a terminal and enters prompt text describing the desired storyline. The entered prompt text is then sent from the terminal to the server.

[0194] Step 2:

[0195] Based on the received prompt, the server uses an emotion engine to analyze the user's emotional state. This analysis reveals the atmosphere and theme of the story the user is seeking. The analysis results are then used in the next step.

[0196] Step 3:

[0197] The server uses a large-scale language model to generate storylines based on analyzed sentiment states. In this process, it takes prompts and sentiment analysis results as input and outputs a coherent narrative flow.

[0198] Step 4:

[0199] Users write character design requests from their devices and send them to the server. These design requests include details about the character's features and style.

[0200] Step 5:

[0201] The server uses image generation technology, taking into account the results of the emotion engine, to generate a character design with specified features. It receives the character's requests and emotional state as input and outputs a visually concrete character design.

[0202] Step 6:

[0203] The user sends information about their desired game mechanics from their device to the server. This information includes details such as the structure of the interaction and the difficulty level.

[0204] Step 7:

[0205] The server applies a reinforcement learning algorithm to design the game structure based on the information received and the user's emotional state. In this process, it takes feedback and interaction information as input and outputs optimized game mechanics.

[0206] Step 8:

[0207] When a user requests to test a prototype, the server constructs a virtual reality space. The prototype is then run in an environment that is adjusted based on the user's emotional state.

[0208] Step 9:

[0209] Users send their impressions and feedback from their device to the server after playing the test version.

[0210] Step 10:

[0211] The server utilizes an emotion engine to analyze user feedback in detail, generating and presenting suggestions for further improvement. Based on the feedback information, it outputs specific ideas for improvement.

[0212] (Application Example 2)

[0213] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0214] In traditional virtual stores, it was difficult to personalize the shopping experience in real time based on user emotions and behavior. This meant that it was not possible to make optimal suggestions to users, potentially leading to lower satisfaction. Furthermore, there was a lack of dynamic environmental adjustments that directly reflected user emotions, making it difficult to provide a more immersive experience.

[0215] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0216] In this invention, the server includes means for generating information based on user requests using a large-scale language model, means for generating visual elements with specified features using image generation technology, means for providing optimization of the designed mechanism by applying a reinforcement learning algorithm, means for constructing a virtual environment and making the visualized prototype verifiable, means for collecting and analyzing user feedback and suggesting improvements, and means for analyzing the user's emotional state and dynamically adjusting environmental elements and suggestions based on the results. This makes it possible to provide users with a personalized, emotionally responsive shopping experience in real time.

[0217] A "large-scale language model" is a machine learning model that learns from vast amounts of text data and has the ability to understand and generate human language.

[0218] "Image generation technology" is a technology that uses computer algorithms to create new visual content and images.

[0219] A "reinforcement learning algorithm" is an algorithm that learns the optimal course of action through trial and error involving actions and rewards.

[0220] A "virtual environment" is a virtual world or setting created through computer simulation that differs from reality.

[0221] "User emotional state" refers to information that indicates the psychological or emotional state experienced by the user.

[0222] "Visual elements" refer to features or components that are visually recognizable and play an important role in a particular design or layout.

[0223] "Mechanism optimization" refers to improving functions or processes in order to achieve specific goals or conditions.

[0224] "Feedback" refers to information collected from users, including their opinions and impressions, which is used to improve systems and processes.

[0225] "Environmental elements" refer to individual components or settings used within a specific environment that influence the user experience.

[0226] The system used to realize this application recognizes the user's emotions and dynamically adjusts the shopping experience in a virtual store. The user uses a smartphone or smart glasses to launch an app, which initiates the collection of emotional data. This involves using sensors such as the device's camera and microphone to analyze the user's emotional state from their facial expressions and voice.

[0227] The server analyzes collected emotional data in real time using an emotion engine. Then, a large-scale language model generates information based on the user's preferences and emotions, which determines the visual elements and suggested products. The server further utilizes image generation technology and reinforcement learning algorithms to dynamically design optimal visual elements and interactions in response to user reactions. For example, prompts such as "For a relaxed user, suggest a calming landscape image" are input into the AI ​​model, and the generated visual elements provide the user with the most suitable experience.

[0228] The virtual environment is built on a server and runs on the user's device. This allows the store layout and product suggestions to be displayed in a way that is optimized for the user. Through this process, a personalized and emotionally resonant shopping experience is provided to the user.

[0229] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0230] Step 1:

[0231] The device uses its camera and microphone to collect facial expressions and voice data from the user who launched the application. This data is the initial input necessary for emotion analysis.

[0232] Step 2:

[0233] The device sends the collected facial and audio data to the server. The server uses an emotion engine to analyze this data and determine the user's current emotional state. The output of this analysis is an emotion label, such as the user being relaxed or excited.

[0234] Step 3:

[0235] The server receives the user's emotional state as an analysis result and uses a large-scale language model to generate prompts that respond to the user's emotions and input requests. For example, for a relaxed user, it might create a prompt such as "Suggest a calming landscape painting." This prompt then serves as an instruction for generating the next visual element.

[0236] Step 4:

[0237] The server uses image generation technology to create visual elements based on the generated prompt text. This results in the output of appropriate designs and product proposals that respond to the user's emotions. The generated visual elements then serve as input for the next step.

[0238] Step 5:

[0239] The server applies a reinforcement learning algorithm to optimize the generated visual elements and configure the optimal environment for the user. User feedback is also considered during this process, leading to improvements for future interactions. The optimized environment configuration is the output.

[0240] Step 6:

[0241] Users access a virtual environment through their device and experience a real-time virtual store built by the server. The generated prompts and visual elements create a seamless shopping experience. Feedback is continuously collected to improve future interactions.

[0242] This entire process provides a dynamic and personalized experience tailored to the user's emotional state.

[0243] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0244] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0245] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0249] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0250] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0251] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0252] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0253] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0254] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0255] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0256] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0257] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0258] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0259] This invention is a system that utilizes AI technology to streamline the game development process and support creativity. The specific implementation of this system is described below.

[0260] This system consists of servers, terminals, and users. The servers are equipped with software for building large-scale language models, image generation techniques, reinforcement learning algorithms, and virtual reality environments.

[0261] First, the user connects to the system via a terminal and inputs requests regarding the storyline, character design, and game mechanics. The terminal then sends this information to the server.

[0262] The server receives these requests and generates storylines using a large-scale language model. For example, a request such as "I want to create an adventure story set in a fantasy world" can automatically generate a detailed plot.

[0263] Next, the server utilizes image generation technology to create a character design based on the features specified by the user. For example, if the user specifies "young wizard, blue robe," a character image with those characteristics will be generated.

[0264] Furthermore, the server uses reinforcement learning algorithms to design game mechanics. For example, it automatically designs the optimal battle system based on information such as "turn-based battle, adjustable difficulty."

[0265] Once the prototype development is complete, the server will set up a virtual reality environment, providing users with an environment where they can experience actual gameplay through a VR headset. Through this experience, users can evaluate the realism and playability of the game.

[0266] Users send feedback to the server via their device after playing. The server analyzes this feedback and suggests improvements to the user. By repeating this feedback loop, the quality of the game can be improved.

[0267] This system provides powerful support for game developers to quickly develop high-quality games while saving time and resources.

[0268] The following describes the processing flow.

[0269] Step 1:

[0270] The user logs into the system via their device and requests the generation of a storyline. The user then sends information to the server by entering a specific genre or theme.

[0271] Step 2:

[0272] The server receives requests from users and generates storylines using a large-scale language model. This model creates relevant plots and character outlines based on the genres and themes specified by the user.

[0273] Step 3:

[0274] The server sends the generated storyline to the device and presents it to the user. The user reviews the story on the screen and decides whether they are satisfied. At this point, the user can provide feedback.

[0275] Step 4:

[0276] If a user wishes to generate a character design, they send a request to the server via their device, specifying the character's characteristics and style.

[0277] Step 5:

[0278] The server utilizes image generation technology to create character designs based on features specified by the user. The generated designs can include multiple variations.

[0279] Step 6:

[0280] The server sends the generated character design to the terminal and presents the user with multiple options. The user reviews the designs on the terminal and selects their preferred character.

[0281] Step 7:

[0282] If the user wishes to design game mechanics, the user specifies the requirements for the mechanics specifically through the terminal and sends this information to the server.

[0283] Step 8:

[0284] The server uses a reinforcement learning algorithm to design game mechanics based on the requirements from the user. The designed mechanics can include improvements over the existing settings.

[0285] Step 9:

[0286] The server distributes the designed game mechanics to the terminal and allows the user to check the content. The user provides feedback if necessary.

[0287] Step 10:

[0288] If the user requests to test-play the prototype in VR, the terminal conveys this to the server.

[0289] Step 11:

[0290] The server constructs a virtual reality environment and prepares a prototype based on the integrated storyline, character design, and game mechanics.

[0291] Step 12:

[0292] The VR prototype is sent from the server to the terminal, and the user can perform a test-play using a VR headset.

[0293] Step 13:

[0294] The user sends post-play feedback to the server through the terminal. The server analyzes the feedback and presents the improvement points to the user in real time.

[0295] This step-by-step process enables efficient and creative game development.

[0296] (Example 1)

[0297] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0298] Traditional game development processes require significant time and effort for story construction, character design, and game mechanics design. Furthermore, they present challenges in efficiently creating prototypes based on user requests and implementing improvement cycles that incorporate feedback.

[0299] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0300] In this invention, the server includes means for generating a story based on user requests using a large-scale data processing model, means for generating a character with specified features using an image generation algorithm, and means for designing the mechanism of an electronic game and providing an optimal configuration by applying a machine learning algorithm. This makes it possible to create prototypes quickly and efficiently and to realize a continuous improvement cycle that utilizes user feedback.

[0301] A "large-scale data processing model" is an algorithm that performs natural language processing and uses large amounts of data to generate stories and texts.

[0302] An "image generation algorithm" is a method of creating visual representations of people or objects using a computer based on input instructions.

[0303] A "machine learning algorithm" is a method for learning features from vast amounts of data and automatically optimizing the mechanical design of electronic games.

[0304] The "virtual representation environment" is a computer-generated three-dimensional space that enables users to have an immersive experience through digital devices.

[0305] The "intermediate device" is an interface device for transmitting the input information of the user to the processing device in an appropriate form.

[0306] This invention is a system for streamlining the game development process and supporting creativity. This system consists of a server, a terminal, and a user, and each device works in cooperation. The following shows specific embodiments of the invention.

[0307] The server is installed with a large-scale data processing model, an image generation algorithm, a machine learning algorithm, and software for constructing a virtual representation environment. The large-scale data processing model generates a story based on the user's request. The user inputs detailed requests regarding the story line, character design, and game mechanics as prompt texts via the terminal and sends them to the server.

[0308] For example, when the user inputs a prompt text such as "Please create an adventure story set in a medieval fantasy world. The protagonist is a young wizard wearing a blue robe. Also, please include a scene of fighting a dragon in the story.", the server generates a story based on this information.

[0309] The image generation algorithm generates a visual representation of a character based on the features specified by the user. For example, if "a brave knight, a red shield" is specified, a character image matching those features is generated.

[0310] The machine learning algorithm designs the mechanism of an electronic game according to the game mechanics specified by the user and automatically determines the optimal configuration. Even complex mechanics such as a turn-based battle system are efficiently designed.

[0311] Finally, the server creates a virtual representation environment, allowing users to experience the game prototype through a VR headset. This provides users with an immersive gameplay experience, enabling them to evaluate the game's realism and playability. This system streamlines the development cycle and supports the rapid development of high-quality games.

[0312] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0313] Step 1:

[0314] The user inputs requests regarding the storyline, character design, and game mechanics as prompts through the terminal. These prompts serve as initial data for building the narrative, visual elements, and game mechanics. The terminal then prepares to send these prompts to the server.

[0315] Step 2:

[0316] The server generates a story using a large-scale data processing model based on the prompt message received from the terminal. Specifically, it analyzes the context of the prompt message and constructs a new storyline by referring to relevant data. The output is a detailed story plot that aligns with the user's request. For example, a prompt like "The adventures of a young wizard" would lead to a scenario in which the wizard battles a dragon.

[0317] Step 3:

[0318] The server then uses an image generation algorithm to design the character. It extracts the character's visual characteristics from the prompt text (e.g., "brave knight, red shield") and generates an image based on them. The output is a visual representation of the character that meets the user's requirements. The generated image is used as the design for the character that will appear in the story.

[0319] Step 4:

[0320] The server uses machine learning algorithms to design the game mechanics. It takes the play style and difficulty settings specified in the prompt (e.g., "turn-based battle") as input to derive the optimal design. Based on patterns learned from vast amounts of data, the server determines a structure that produces an efficient gameplay experience. The output is a play system that matches the user's intentions.

[0321] Step 5:

[0322] The server finally constructs the virtual representation environment and integrates the designed storyline, characters, and game mechanics. These elements interact within the VR environment, allowing the user to experience the prototype game through a VR headset. The output is an integrated game experience that the user can intuitively test.

[0323] Step 6:

[0324] Users input feedback on their VR gameplay experience via their device and send it to the server. The server analyzes this feedback and generates suggestions for improvements regarding the story, visual design, and mechanics. This provides users with concrete guidelines for improving the quality of the game.

[0325] (Application Example 1)

[0326] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0327] In game development, constructing storylines, designing characters, and designing gameplay mechanics are extremely time-consuming and labor-intensive processes. Furthermore, the limited opportunities for users to creatively generate and edit games themselves make it difficult to provide the joy of interactive content creation to a wider audience. Moreover, achieving a real-time interactive experience presents significant technical challenges.

[0328] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0329] In this invention, the server includes means for generating the development of a story using a large-scale language model, means for generating character designs using image generation technology, means for designing game actions and providing optimal variables by applying a reinforcement learning algorithm, means for providing an interface that users can access via smart devices to spontaneously generate and edit stories, and means for presenting an interactive experience environment in real time. This makes it possible to stimulate creativity by enabling users to easily generate new content and have a real-time interactive experience.

[0330] A "large-scale language model" is an AI technology that enables natural language processing, and it is a model that has the ability to generate and understand text based on a large amount of text data.

[0331] "Story development" refers to the flow and plot of a story that is automatically generated by AI based on user requests.

[0332] "Image generation technology" is a technique in which AI generates new images based on specified features, and is used in the creation of character designs and background designs.

[0333] "Character design" refers to the design of characters with unique characteristics, either specified by the user or automatically generated by AI.

[0334] A "reinforcement learning algorithm" is a method in which AI learns the optimal action through trial and error and efficiently designs in-game mechanics and other elements.

[0335] "Game actions" refer to actions and rules related to the operation and progression of the game, and are set based on player interaction.

[0336] "Smart devices" refer to multi-functional electronic devices such as mobile devices and handheld terminals.

[0337] An "interface" refers to the screens, input methods, and other elements that allow a user to interact directly with a system.

[0338] An "interactive experience environment" refers to an environment that enables real-time, two-way communication between the user and the system.

[0339] This system is designed to support user creativity and efficiently generate content. The server combines large-scale language models, image generation technologies, and reinforcement learning algorithms to generate storylines, character designs, and gameplay based on user requests.

[0340] When a user accesses the system using a smart device, they can input initial story and character requests through the interface. For example, they might enter a prompt such as, "Generate an adventure story where a knight fights a dragon in a medieval fantasy world." The server then receives these requests and begins processing them.

[0341] The server uses a large-scale language model to automatically generate a detailed narrative development from the input prompts. Image generation technology generates character designs that reflect the features specified by the user. For example, based on the request "knights should wear blue armor and dragons should have red and gold markings," a specific character image is generated.

[0342] Furthermore, the server utilizes reinforcement learning algorithms to design gameplay and build an optimal game system. This process involves learning variables through trial and error to optimize the user experience.

[0343] Ultimately, the server displays the generated content in real time, providing an interactive environment for users. In this environment, users can experience the progression of the story and the actions of the characters, enjoying content that aligns with their own creative intentions.

[0344] This system allows users to spontaneously generate new stories and characters, and to have a real-time interactive experience.

[0345] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0346] Step 1:

[0347] The user enters prompt messages through the interface of a smart device. These prompts concern requests related to the story's theme and character traits. This information is collected by the terminal and sent to the server. The user's requests become the basis for subsequent processing within the system.

[0348] Step 2:

[0349] The server parses the received prompt message. In this step, natural language processing techniques are used to convert the user's request into structured data. Key keywords and context are extracted from the prompt message, and the data is prepared in a format suitable for the story generator.

[0350] Step 3:

[0351] The server uses a large-scale language model to generate the narrative flow. Utilizing the structured data received as input, the large-scale language model automatically generates a detailed storyline. This generated storyline is then used as foundational information for subsequent character design.

[0352] Step 4:

[0353] The server uses image generation technology to generate character designs based on the features specified in the prompt. In this step, the AI ​​generates realistic character images based on the output of the previous storyline. For example, the specific designs of knights and dragons are finalized here.

[0354] Step 5:

[0355] The server uses a reinforcement learning algorithm to design the game's actions. In this process, it sets optimal action parameters for the game based on the generated storyline and character designs. The server searches for the optimal solution by trying various simulations, learning actions that are suitable for the interactive environment the user will experience.

[0356] Step 6:

[0357] The server integrates the generated story, character designs, and gameplay to create an interactive experience environment. In this step, the server sets up the virtual reality environment and provides the user with a real-time interactive experience.

[0358] Step 7:

[0359] Users experience content generated in an interactive environment and send feedback to the server. The server analyzes this feedback and identifies areas for improvement. Based on user feedback, the system evolves further.

[0360] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0361] This invention relates to a system that recognizes user emotions and dynamically adjusts the game development process based on those emotions. This system comprises a server, a terminal, a user, and an emotion engine.

[0362] The server is equipped with a large-scale language model, image generation technology, reinforcement learning algorithms, software for building virtual reality environments, and an emotion engine. The emotion engine analyzes the user's emotions in real time and uses the results in conjunction with other modules.

[0363] First, the user accesses the system from their device and enters their request for generating a storyline. For example, if the user enters "I would like a suspenseful story," the device sends that information to the server. The server then uses a large-scale language model to generate a storyline, referencing the user's input and the user's emotional state as determined by the emotion engine.

[0364] Next, the user sends a request for a character design via the terminal. For example, in response to a request such as "a tense detective character," the server generates a character design using image generation technology, while taking into account the results of the emotion engine's analysis.

[0365] Furthermore, when a user sends information about their desired game mechanics from their device to the server, the server applies a reinforcement learning algorithm to design the game mechanics. In doing so, the server uses user feedback obtained by the emotion engine to make optimal adjustments tailored to the player.

[0366] Then, when a user tests the prototype, the server creates a virtual reality environment. The prototype is delivered in a way that is optimized for the user's emotional state. For example, when the user is relaxed, it is possible to generate an environment that includes more challenging elements.

[0367] Users can send their impressions and feedback after playing to the server via their device. The server analyzes the feedback using an emotion engine and generates suggestions for further improvement, which are then presented to the user.

[0368] In this way, leveraging user emotions allows for a more personalized game development process. This system enables faster development and maximizes user satisfaction.

[0369] The following describes the processing flow.

[0370] Step 1:

[0371] The user logs into the system via their device and requests storyline generation. The user enters their desired theme and genre, and this information is sent to the server.

[0372] Step 2:

[0373] Based on the received request, the server initiates a process to generate a storyline using a large-scale language model. During this process, the server uses an emotion engine to analyze the user's current emotional state and reflects that state in the generated story.

[0374] Step 3:

[0375] The generated storyline data is sent from the server to the terminal and presented to the user. The user reviews the story content on the screen and provides feedback as needed.

[0376] Step 4:

[0377] If a user wishes to design a character, they enter the characteristics of the specific character on their device and send the information to the server.

[0378] Step 5:

[0379] The server uses image generation technology to generate visuals based on character design requests received from the terminal. The emotion engine considers the user's emotions during this process and reflects them in the design.

[0380] Step 6:

[0381] The generated character designs are sent to the terminal via the server and presented to the user as multiple design options. The user can then select their preferred design.

[0382] Step 7:

[0383] If a user requests to design game mechanics, the details are sent from the terminal to the server.

[0384] Step 8:

[0385] The server uses reinforcement learning algorithms to design game mechanics that satisfy user requests. In doing so, the server incorporates user feedback from previous stages and considers the user's emotional state, analyzed by the emotion engine, to make optimal suggestions.

[0386] Step 9:

[0387] The designed game mechanics are transmitted to the device in real time and reviewed by the user. The user can then provide additional feedback.

[0388] Step 10:

[0389] When a user requests to test play a prototype, that request is sent from the device to the server.

[0390] Step 11:

[0391] The server builds a virtual reality environment for the prototype and uses an emotion engine to adjust it based on the user's emotional state. This adjustment is made to enrich the user experience.

[0392] Step 12:

[0393] The prototype is sent from the server to the terminal, and the user uses a VR headset to test play and deepen their experience.

[0394] Step 13:

[0395] Users input their impressions after the test via their device and send the feedback to the server. The server uses an emotion engine to analyze the feedback, generate improvement suggestions, and present them to the user.

[0396] This system enables a dynamic, emotion-driven game development process.

[0397] (Example 2)

[0398] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0399] In modern game development processes, creating content that reflects individual user emotions and immediate feedback is a challenging task. Traditional technologies fail to adequately adapt to user emotions, resulting in a lack of personalized game experiences. To address this challenge, it is necessary to analyze user emotions in real time and dynamically adjust the game development process based on the results.

[0400] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0401] In this invention, the server includes means for generating a narrative flow based on user requests using large-scale language processing technology, means for generating character designs with specified features using an image generation method, and means for designing a game structure and providing optimal components by applying a learning algorithm. This makes it possible to provide a individually tailored game experience in real time based on user sentiment analysis.

[0402] "Large-scale language processing technology" refers to techniques that analyze large amounts of text data and generate natural language.

[0403] An "image generation method" is a technology for creating visual information based on input information.

[0404] A "learning algorithm" is a computational method in which a machine learns from data and adaptively solves problems based on the results.

[0405] A "virtual reality space" is a computer-generated environment that closely resembles reality, allowing users to immerse themselves in and experience it.

[0406] "User emotional state" refers to the emotional reactions and psychological state of the user during gameplay.

[0407] An "analysis mechanism" is a system or device used to analyze input data and identify its characteristics.

[0408] A "regulation mechanism" is a device or function that dynamically changes a system or environment based on the information obtained.

[0409] This invention relates to a system that analyzes user emotions and dynamically adjusts the game development process based on those emotions. This system consists of a server, terminals, an emotion engine, and the like.

[0410] The server is equipped with software that incorporates large-scale language processing technology and image generation methods, as well as software for building learning algorithms and virtual reality environments. These technologies enable the server to generate diverse content based on user input and dynamically adjust it accordingly.

[0411] Users access the system using a terminal and enter prompts to create their desired storyline. For example, if a user enters "I want a suspenseful story" into the terminal, the terminal transmits this information to the server. The server then uses large-scale language processing technology to generate a storyline based on the results of an emotion engine analysis of the user's emotional state. Emotion analysis allows the system to provide content that matches the atmosphere and story development the user desires.

[0412] Next, the user's specified character design request is transmitted via the terminal. For example, if the request is for a "tense detective character," the server generates an image of the character using an image generation method that reflects the analysis results of the emotion engine. In this way, a character design that responds to user feedback is provided.

[0413] Furthermore, when a user sends their desired game mechanics from their device to the server, the server uses a learning algorithm to design the gamification. In this process, the server designs the optimal game structure based on the user's emotional feedback. This approach provides a game experience tailored to each individual user.

[0414] Furthermore, when a user tests the prototype, the server creates a virtual reality space within which the prototype operates. This makes it possible to create an optimal playing environment tailored to the user's emotional state. For example, if the user is relaxed, an environment with challenging content can be provided.

[0415] Finally, users send their post-game impressions and feedback from their device to the server. The server uses an emotion engine to analyze the feedback and generates suggestions for further improvement, which are then presented to the user.

[0416] A concrete example of a prompt message would be, "Generate a story and characters to be used in the next project. The theme is fantasy." This invention improves the efficiency of game development work and user satisfaction.

[0417] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0418] Step 1:

[0419] The user accesses the system using a terminal and enters prompt text describing the desired storyline. The entered prompt text is then sent from the terminal to the server.

[0420] Step 2:

[0421] Based on the received prompt, the server uses an emotion engine to analyze the user's emotional state. This analysis reveals the atmosphere and theme of the story the user is seeking. The analysis results are then used in the next step.

[0422] Step 3:

[0423] The server uses a large-scale language model to generate storylines based on analyzed sentiment states. In this process, it takes prompts and sentiment analysis results as input and outputs a coherent narrative flow.

[0424] Step 4:

[0425] Users write character design requests from their devices and send them to the server. These design requests include details about the character's features and style.

[0426] Step 5:

[0427] The server uses image generation technology, taking into account the results of the emotion engine, to generate a character design with specified features. It receives the character's requests and emotional state as input and outputs a visually concrete character design.

[0428] Step 6:

[0429] The user sends information about their desired game mechanics from their device to the server. This information includes details such as the structure of the interaction and the difficulty level.

[0430] Step 7:

[0431] The server applies a reinforcement learning algorithm to design the game structure based on the information received and the user's emotional state. In this process, it takes feedback and interaction information as input and outputs optimized game mechanics.

[0432] Step 8:

[0433] When a user requests to test a prototype, the server constructs a virtual reality space. The prototype is then run in an environment that is adjusted based on the user's emotional state.

[0434] Step 9:

[0435] Users send their impressions and feedback from their device to the server after playing the test version.

[0436] Step 10:

[0437] The server utilizes an emotion engine to analyze user feedback in detail, generating and presenting suggestions for further improvement. Based on the feedback information, it outputs specific ideas for improvement.

[0438] (Application Example 2)

[0439] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0440] In traditional virtual stores, it was difficult to personalize the shopping experience in real time based on user emotions and behavior. This meant that it was not possible to make optimal suggestions to users, potentially leading to lower satisfaction. Furthermore, there was a lack of dynamic environmental adjustments that directly reflected user emotions, making it difficult to provide a more immersive experience.

[0441] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0442] In this invention, the server includes means for generating information based on user requests using a large-scale language model, means for generating visual elements with specified features using image generation technology, means for providing optimization of the designed mechanism by applying a reinforcement learning algorithm, means for constructing a virtual environment and making the visualized prototype verifiable, means for collecting and analyzing user feedback and suggesting improvements, and means for analyzing the user's emotional state and dynamically adjusting environmental elements and suggestions based on the results. This makes it possible to provide users with a personalized, emotionally responsive shopping experience in real time.

[0443] A "large-scale language model" is a machine learning model that learns from vast amounts of text data and has the ability to understand and generate human language.

[0444] "Image generation technology" is a technology that uses computer algorithms to create new visual content and images.

[0445] A "reinforcement learning algorithm" is an algorithm that learns the optimal course of action through trial and error involving actions and rewards.

[0446] A "virtual environment" is a virtual world or setting created through computer simulation that differs from reality.

[0447] "User emotional state" refers to information that indicates the psychological or emotional state experienced by the user.

[0448] "Visual elements" refer to features or components that are visually recognizable and play an important role in a particular design or layout.

[0449] "Mechanism optimization" refers to improving functions or processes in order to achieve specific goals or conditions.

[0450] "Feedback" refers to information collected from users, including their opinions and impressions, which is used to improve systems and processes.

[0451] "Environmental elements" refer to individual components or settings used within a specific environment that influence the user experience.

[0452] The system used to realize this application recognizes the user's emotions and dynamically adjusts the shopping experience in a virtual store. The user uses a smartphone or smart glasses to launch an app, which initiates the collection of emotional data. This involves using sensors such as the device's camera and microphone to analyze the user's emotional state from their facial expressions and voice.

[0453] The server analyzes collected emotional data in real time using an emotion engine. Then, a large-scale language model generates information based on the user's preferences and emotions, which determines the visual elements and suggested products. The server further utilizes image generation technology and reinforcement learning algorithms to dynamically design optimal visual elements and interactions in response to user reactions. For example, prompts such as "For a relaxed user, suggest a calming landscape image" are input into the AI ​​model, and the generated visual elements provide the user with the most suitable experience.

[0454] The virtual environment is built on a server and runs on the user's device. This allows the store layout and product suggestions to be displayed in a way that is optimized for the user. Through this process, a personalized and emotionally resonant shopping experience is provided to the user.

[0455] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0456] Step 1:

[0457] The device uses its camera and microphone to collect facial expressions and voice data from the user who launched the application. This data is the initial input necessary for emotion analysis.

[0458] Step 2:

[0459] The device sends the collected facial and audio data to the server. The server uses an emotion engine to analyze this data and determine the user's current emotional state. The output of this analysis is an emotion label, such as the user being relaxed or excited.

[0460] Step 3:

[0461] The server receives the user's emotional state as an analysis result and uses a large-scale language model to generate prompts that respond to the user's emotions and input requests. For example, for a relaxed user, it might create a prompt such as "Suggest a calming landscape painting." This prompt then serves as an instruction for generating the next visual element.

[0462] Step 4:

[0463] The server uses image generation technology to create visual elements based on the generated prompt text. This results in the output of appropriate designs and product proposals that respond to the user's emotions. The generated visual elements then serve as input for the next step.

[0464] Step 5:

[0465] The server applies a reinforcement learning algorithm to optimize the generated visual elements and configure the optimal environment for the user. User feedback is also considered during this process, leading to improvements for future interactions. The optimized environment configuration is the output.

[0466] Step 6:

[0467] Users access a virtual environment through their device and experience a real-time virtual store built by the server. The generated prompts and visual elements create a seamless shopping experience. Feedback is continuously collected to improve future interactions.

[0468] This entire process provides a dynamic and personalized experience tailored to the user's emotional state.

[0469] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0470] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0471] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0472] [Third Embodiment]

[0473] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0474] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0475] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0476] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0477] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0478] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0479] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0480] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0481] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0482] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0483] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0484] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0485] This invention is a system that utilizes AI technology to streamline the game development process and support creativity. The specific implementation of this system is described below.

[0486] This system consists of servers, terminals, and users. The servers are equipped with software for building large-scale language models, image generation techniques, reinforcement learning algorithms, and virtual reality environments.

[0487] First, the user connects to the system via a terminal and inputs requests regarding the storyline, character design, and game mechanics. The terminal then sends this information to the server.

[0488] The server receives these requests and generates storylines using a large-scale language model. For example, a request such as "I want to create an adventure story set in a fantasy world" can automatically generate a detailed plot.

[0489] Next, the server utilizes image generation technology to create a character design based on the features specified by the user. For example, if the user specifies "young wizard, blue robe," a character image with those characteristics will be generated.

[0490] Furthermore, the server uses reinforcement learning algorithms to design game mechanics. For example, it automatically designs the optimal battle system based on information such as "turn-based battle, adjustable difficulty."

[0491] Once the prototype development is complete, the server will set up a virtual reality environment, providing users with an environment where they can experience actual gameplay through a VR headset. Through this experience, users can evaluate the realism and playability of the game.

[0492] Users send feedback to the server via their device after playing. The server analyzes this feedback and suggests improvements to the user. By repeating this feedback loop, the quality of the game can be improved.

[0493] This system provides powerful support for game developers to quickly develop high-quality games while saving time and resources.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The user logs into the system via their device and requests the generation of a storyline. The user then sends information to the server by entering a specific genre or theme.

[0497] Step 2:

[0498] The server receives requests from users and generates storylines using a large-scale language model. This model creates relevant plots and character outlines based on the genres and themes specified by the user.

[0499] Step 3:

[0500] The server sends the generated storyline to the device and presents it to the user. The user reviews the story on the screen and decides whether they are satisfied. At this point, the user can provide feedback.

[0501] Step 4:

[0502] If a user wishes to generate a character design, they send a request to the server via their device, specifying the character's characteristics and style.

[0503] Step 5:

[0504] The server utilizes image generation technology to create character designs based on features specified by the user. The generated designs can include multiple variations.

[0505] Step 6:

[0506] The server sends the generated character design to the terminal and presents the user with multiple options. The user reviews the designs on the terminal and selects their preferred character.

[0507] Step 7:

[0508] If a user wishes to design game mechanics, they specify the mechanic requirements in detail via a terminal. This information is then sent to the server.

[0509] Step 8:

[0510] The server uses a reinforcement learning algorithm to design game mechanics based on user requirements. The designed mechanics may include improvements over existing settings.

[0511] Step 9:

[0512] The game mechanics designed on the server are delivered to the terminal, and the user is allowed to review them. The user provides feedback as needed.

[0513] Step 10:

[0514] If a user requests to test play the prototype in VR, the device will inform the server of this request.

[0515] Step 11:

[0516] The server builds the virtual reality environment and prepares a prototype based on an integrated storyline, character designs, and game mechanics.

[0517] Step 12:

[0518] The VR prototype is sent from the server to the terminal, and the user can perform a test play using a VR headset.

[0519] Step 13:

[0520] Users send feedback to the server via their device after playing. The server analyzes the feedback and presents the user with suggestions for improvement in real time.

[0521] This step-by-step process enables efficient and creative game development.

[0522] (Example 1)

[0523] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Traditional game development processes require significant time and effort for story construction, character design, and game mechanics design. Furthermore, they present challenges in efficiently creating prototypes based on user requests and implementing improvement cycles that incorporate feedback.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0526] In this invention, the server includes means for generating a story based on user requests using a large-scale data processing model, means for generating a character with specified features using an image generation algorithm, and means for designing the mechanism of an electronic game and providing an optimal configuration by applying a machine learning algorithm. This makes it possible to create prototypes quickly and efficiently and to realize a continuous improvement cycle that utilizes user feedback.

[0527] A "large-scale data processing model" is an algorithm that performs natural language processing and uses large amounts of data to generate stories and texts.

[0528] An "image generation algorithm" is a method of creating visual representations of people or objects using a computer based on input instructions.

[0529] A "machine learning algorithm" is a method for learning features from vast amounts of data and automatically optimizing the mechanical design of electronic games.

[0530] A "virtual representation environment" is a computer-generated three-dimensional space that allows users to have an immersive experience through digital devices.

[0531] A "mediating device" is an interface device that transmits user input information to a processing unit in an appropriate format.

[0532] This invention is a system designed to streamline the game development process and support creativity. The system consists of a server, terminals, and users, with each device working in coordination. Specific embodiments of the invention are described below.

[0533] The server is equipped with software for building large-scale data processing models, image generation algorithms, machine learning algorithms, and virtual representation environments. The large-scale data processing model generates stories based on user requests. Users input detailed requests regarding storylines, character designs, and game mechanics as prompts via a terminal and send them to the server.

[0534] For example, if a user enters the prompt, "Create an adventure story set in a medieval fantasy world. The protagonist is a young wizard wearing a blue robe. The story should also include a scene where the protagonist fights a dragon," the server will generate a story based on this information.

[0535] The image generation algorithm generates a visual representation of a character based on the characteristics specified by the user. For example, if the user specifies "brave knight, red shield," an image of a character matching those characteristics will be generated.

[0536] Machine learning algorithms design the mechanisms of electronic games according to the game mechanics specified by the user, and automatically determine the optimal configuration. Complex mechanics, such as turn-based battle systems, are also designed efficiently.

[0537] Finally, the server creates a virtual representation environment, allowing users to experience the game prototype through a VR headset. This provides users with an immersive gameplay experience, enabling them to evaluate the game's realism and playability. This system streamlines the development cycle and supports the rapid development of high-quality games.

[0538] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0539] Step 1:

[0540] The user inputs requests regarding the storyline, character design, and game mechanics as prompts through the terminal. These prompts serve as initial data for building the narrative, visual elements, and game mechanics. The terminal then prepares to send these prompts to the server.

[0541] Step 2:

[0542] The server generates a story using a large-scale data processing model based on the prompt message received from the terminal. Specifically, it analyzes the context of the prompt message and constructs a new storyline by referring to relevant data. The output is a detailed story plot that aligns with the user's request. For example, a prompt like "The adventures of a young wizard" would lead to a scenario in which the wizard battles a dragon.

[0543] Step 3:

[0544] The server then uses an image generation algorithm to design the character. It extracts the character's visual characteristics from the prompt text (e.g., "brave knight, red shield") and generates an image based on them. The output is a visual representation of the character that meets the user's requirements. The generated image is used as the design for the character that will appear in the story.

[0545] Step 4:

[0546] The server uses machine learning algorithms to design the game mechanics. It takes the play style and difficulty settings specified in the prompt (e.g., "turn-based battle") as input to derive the optimal design. Based on patterns learned from vast amounts of data, the server determines a structure that produces an efficient gameplay experience. The output is a play system that matches the user's intentions.

[0547] Step 5:

[0548] The server finally constructs the virtual representation environment and integrates the designed storyline, characters, and game mechanics. These elements interact within the VR environment, allowing the user to experience the prototype game through a VR headset. The output is an integrated game experience that the user can intuitively test.

[0549] Step 6:

[0550] Users input feedback on their VR gameplay experience via their device and send it to the server. The server analyzes this feedback and generates suggestions for improvements regarding the story, visual design, and mechanics. This provides users with concrete guidelines for improving the quality of the game.

[0551] (Application Example 1)

[0552] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0553] In game development, constructing storylines, designing characters, and designing gameplay mechanics are extremely time-consuming and labor-intensive processes. Furthermore, the limited opportunities for users to creatively generate and edit games themselves make it difficult to provide the joy of interactive content creation to a wider audience. Moreover, achieving a real-time interactive experience presents significant technical challenges.

[0554] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0555] In this invention, the server includes means for generating the development of a story using a large-scale language model, means for generating character designs using image generation technology, means for designing game actions and providing optimal variables by applying a reinforcement learning algorithm, means for providing an interface that users can access via smart devices to spontaneously generate and edit stories, and means for presenting an interactive experience environment in real time. This makes it possible to stimulate creativity by enabling users to easily generate new content and have a real-time interactive experience.

[0556] A "large-scale language model" is an AI technology that enables natural language processing, and it is a model that has the ability to generate and understand text based on a large amount of text data.

[0557] "Story development" refers to the flow and plot of a story that is automatically generated by AI based on user requests.

[0558] "Image generation technology" is a technique in which AI generates new images based on specified features, and is used in the creation of character designs and background designs.

[0559] "Character design" refers to the design of characters with unique characteristics, either specified by the user or automatically generated by AI.

[0560] A "reinforcement learning algorithm" is a method in which AI learns the optimal action through trial and error and efficiently designs in-game mechanics and other elements.

[0561] "Game actions" refer to actions and rules related to the operation and progression of the game, and are set based on player interaction.

[0562] "Smart devices" refer to multi-functional electronic devices such as mobile devices and handheld terminals.

[0563] An "interface" refers to the screens, input methods, and other elements that allow a user to interact directly with a system.

[0564] An "interactive experience environment" refers to an environment that enables real-time, two-way communication between the user and the system.

[0565] This system is designed to support user creativity and efficiently generate content. The server combines large-scale language models, image generation technologies, and reinforcement learning algorithms to generate storylines, character designs, and gameplay based on user requests.

[0566] When a user accesses the system using a smart device, they can input initial story and character requests through the interface. For example, they might enter a prompt such as, "Generate an adventure story where a knight fights a dragon in a medieval fantasy world." The server then receives these requests and begins processing them.

[0567] The server uses a large-scale language model to automatically generate a detailed narrative development from the input prompts. Image generation technology generates character designs that reflect the features specified by the user. For example, based on the request "knights should wear blue armor and dragons should have red and gold markings," a specific character image is generated.

[0568] Furthermore, the server utilizes reinforcement learning algorithms to design gameplay and build an optimal game system. This process involves learning variables through trial and error to optimize the user experience.

[0569] Ultimately, the server displays the generated content in real time, providing an interactive environment for users. In this environment, users can experience the progression of the story and the actions of the characters, enjoying content that aligns with their own creative intentions.

[0570] This system allows users to spontaneously generate new stories and characters, and to have a real-time interactive experience.

[0571] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0572] Step 1:

[0573] The user enters prompt messages through the interface of a smart device. These prompts concern requests related to the story's theme and character traits. This information is collected by the terminal and sent to the server. The user's requests become the basis for subsequent processing within the system.

[0574] Step 2:

[0575] The server parses the received prompt message. In this step, natural language processing techniques are used to convert the user's request into structured data. Key keywords and context are extracted from the prompt message, and the data is prepared in a format suitable for the story generator.

[0576] Step 3:

[0577] The server uses a large-scale language model to generate the narrative flow. Utilizing the structured data received as input, the large-scale language model automatically generates a detailed storyline. This generated storyline is then used as foundational information for subsequent character design.

[0578] Step 4:

[0579] The server uses image generation technology to generate character designs based on the features specified in the prompt. In this step, the AI ​​generates realistic character images based on the output of the previous storyline. For example, the specific designs of knights and dragons are finalized here.

[0580] Step 5:

[0581] The server uses a reinforcement learning algorithm to design the game's actions. In this process, it sets optimal action parameters for the game based on the generated storyline and character designs. The server searches for the optimal solution by trying various simulations, learning actions that are suitable for the interactive environment the user will experience.

[0582] Step 6:

[0583] The server integrates the generated story, character designs, and gameplay to create an interactive experience environment. In this step, the server sets up the virtual reality environment and provides the user with a real-time interactive experience.

[0584] Step 7:

[0585] Users experience content generated in an interactive environment and send feedback to the server. The server analyzes this feedback and identifies areas for improvement. Based on user feedback, the system evolves further.

[0586] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0587] This invention relates to a system that recognizes user emotions and dynamically adjusts the game development process based on those emotions. This system comprises a server, a terminal, a user, and an emotion engine.

[0588] The server is equipped with a large-scale language model, image generation technology, reinforcement learning algorithms, software for building virtual reality environments, and an emotion engine. The emotion engine analyzes the user's emotions in real time and uses the results in conjunction with other modules.

[0589] First, the user accesses the system from their device and enters their request for generating a storyline. For example, if the user enters "I would like a suspenseful story," the device sends that information to the server. The server then uses a large-scale language model to generate a storyline, referencing the user's input and the user's emotional state as determined by the emotion engine.

[0590] Next, the user sends a request for a character design via the terminal. For example, in response to a request such as "a tense detective character," the server generates a character design using image generation technology, while taking into account the results of the emotion engine's analysis.

[0591] Furthermore, when a user sends information about their desired game mechanics from their device to the server, the server applies a reinforcement learning algorithm to design the game mechanics. In doing so, the server uses user feedback obtained by the emotion engine to make optimal adjustments tailored to the player.

[0592] Then, when a user tests the prototype, the server creates a virtual reality environment. The prototype is delivered in a way that is optimized for the user's emotional state. For example, when the user is relaxed, it is possible to generate an environment that includes more challenging elements.

[0593] Users can send their impressions and feedback after playing to the server via their device. The server analyzes the feedback using an emotion engine and generates suggestions for further improvement, which are then presented to the user.

[0594] In this way, leveraging user emotions allows for a more personalized game development process. This system enables faster development and maximizes user satisfaction.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The user logs into the system via their device and requests storyline generation. The user enters their desired theme and genre, and this information is sent to the server.

[0598] Step 2:

[0599] Based on the received request, the server initiates a process to generate a storyline using a large-scale language model. During this process, the server uses an emotion engine to analyze the user's current emotional state and reflects that state in the generated story.

[0600] Step 3:

[0601] The generated storyline data is sent from the server to the terminal and presented to the user. The user reviews the story content on the screen and provides feedback as needed.

[0602] Step 4:

[0603] If a user wishes to design a character, they enter the characteristics of the specific character on their device and send the information to the server.

[0604] Step 5:

[0605] The server uses image generation technology to generate visuals based on character design requests received from the terminal. The emotion engine considers the user's emotions during this process and reflects them in the design.

[0606] Step 6:

[0607] The generated character designs are sent to the terminal via the server and presented to the user as multiple design options. The user can then select their preferred design.

[0608] Step 7:

[0609] If a user requests to design game mechanics, the details are sent from the terminal to the server.

[0610] Step 8:

[0611] The server uses reinforcement learning algorithms to design game mechanics that satisfy user requests. In doing so, the server incorporates user feedback from previous stages and considers the user's emotional state, analyzed by the emotion engine, to make optimal suggestions.

[0612] Step 9:

[0613] The designed game mechanics are transmitted to the device in real time and reviewed by the user. The user can then provide additional feedback.

[0614] Step 10:

[0615] When a user requests to test play a prototype, that request is sent from the device to the server.

[0616] Step 11:

[0617] The server builds a virtual reality environment for the prototype and uses an emotion engine to adjust it based on the user's emotional state. This adjustment is made to enrich the user experience.

[0618] Step 12:

[0619] The prototype is sent from the server to the terminal, and the user uses a VR headset to test play and deepen their experience.

[0620] Step 13:

[0621] Users input their impressions after the test via their device and send the feedback to the server. The server uses an emotion engine to analyze the feedback, generate improvement suggestions, and present them to the user.

[0622] This system enables a dynamic, emotion-driven game development process.

[0623] (Example 2)

[0624] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0625] In modern game development processes, creating content that reflects individual user emotions and immediate feedback is a challenging task. Traditional technologies fail to adequately adapt to user emotions, resulting in a lack of personalized game experiences. To address this challenge, it is necessary to analyze user emotions in real time and dynamically adjust the game development process based on the results.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0627] In this invention, the server includes means for generating a narrative flow based on user requests using large-scale language processing technology, means for generating character designs with specified features using an image generation method, and means for designing a game structure and providing optimal components by applying a learning algorithm. This makes it possible to provide a individually tailored game experience in real time based on user sentiment analysis.

[0628] "Large-scale language processing technology" refers to techniques that analyze large amounts of text data and generate natural language.

[0629] An "image generation method" is a technology for creating visual information based on input information.

[0630] A "learning algorithm" is a computational method in which a machine learns from data and adaptively solves problems based on the results.

[0631] A "virtual reality space" is a computer-generated environment that closely resembles reality, allowing users to immerse themselves in and experience it.

[0632] "User emotional state" refers to the emotional reactions and psychological state of the user during gameplay.

[0633] An "analysis mechanism" is a system or device used to analyze input data and identify its characteristics.

[0634] A "regulation mechanism" is a device or function that dynamically changes a system or environment based on the information obtained.

[0635] This invention relates to a system that analyzes user emotions and dynamically adjusts the game development process based on those emotions. This system consists of a server, terminals, an emotion engine, and the like.

[0636] The server is equipped with software that incorporates large-scale language processing technology and image generation methods, as well as software for building learning algorithms and virtual reality environments. These technologies enable the server to generate diverse content based on user input and dynamically adjust it accordingly.

[0637] Users access the system using a terminal and enter prompts to create their desired storyline. For example, if a user enters "I want a suspenseful story" into the terminal, the terminal transmits this information to the server. The server then uses large-scale language processing technology to generate a storyline based on the results of an emotion engine analysis of the user's emotional state. Emotion analysis allows the system to provide content that matches the atmosphere and story development the user desires.

[0638] Next, the user's specified character design request is transmitted via the terminal. For example, if the request is for a "tense detective character," the server generates an image of the character using an image generation method that reflects the analysis results of the emotion engine. In this way, a character design that responds to user feedback is provided.

[0639] Furthermore, when a user sends their desired game mechanics from their device to the server, the server uses a learning algorithm to design the gamification. In this process, the server designs the optimal game structure based on the user's emotional feedback. This approach provides a game experience tailored to each individual user.

[0640] Furthermore, when a user tests the prototype, the server creates a virtual reality space within which the prototype operates. This makes it possible to create an optimal playing environment tailored to the user's emotional state. For example, if the user is relaxed, an environment with challenging content can be provided.

[0641] Finally, users send their post-game impressions and feedback from their device to the server. The server uses an emotion engine to analyze the feedback and generates suggestions for further improvement, which are then presented to the user.

[0642] A concrete example of a prompt message would be, "Generate a story and characters to be used in the next project. The theme is fantasy." This invention improves the efficiency of game development work and user satisfaction.

[0643] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0644] Step 1:

[0645] The user accesses the system using a terminal and enters prompt text describing the desired storyline. The entered prompt text is then sent from the terminal to the server.

[0646] Step 2:

[0647] Based on the received prompt, the server uses an emotion engine to analyze the user's emotional state. This analysis reveals the atmosphere and theme of the story the user is seeking. The analysis results are then used in the next step.

[0648] Step 3:

[0649] The server uses a large-scale language model to generate storylines based on analyzed sentiment states. In this process, it takes prompts and sentiment analysis results as input and outputs a coherent narrative flow.

[0650] Step 4:

[0651] Users write character design requests from their devices and send them to the server. These design requests include details about the character's features and style.

[0652] Step 5:

[0653] The server uses image generation technology, taking into account the results of the emotion engine, to generate a character design with specified features. It receives the character's requests and emotional state as input and outputs a visually concrete character design.

[0654] Step 6:

[0655] The user sends information about their desired game mechanics from their device to the server. This information includes details such as the structure of the interaction and the difficulty level.

[0656] Step 7:

[0657] The server applies a reinforcement learning algorithm to design the game structure based on the information received and the user's emotional state. In this process, it takes feedback and interaction information as input and outputs optimized game mechanics.

[0658] Step 8:

[0659] When a user requests to test a prototype, the server constructs a virtual reality space. The prototype is then run in an environment that is adjusted based on the user's emotional state.

[0660] Step 9:

[0661] Users send their impressions and feedback from their device to the server after playing the test version.

[0662] Step 10:

[0663] The server utilizes an emotion engine to analyze user feedback in detail, generating and presenting suggestions for further improvement. Based on the feedback information, it outputs specific ideas for improvement.

[0664] (Application Example 2)

[0665] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0666] In traditional virtual stores, it was difficult to personalize the shopping experience in real time based on user emotions and behavior. This meant that it was not possible to make the best suggestions for the user, potentially leading to lower satisfaction. Furthermore, there was a lack of dynamic environmental adjustments that directly reflected user emotions, making it difficult to provide a more immersive experience.

[0667] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0668] In this invention, the server includes means for generating information based on user requests using a large-scale language model, means for generating visual elements with specified features using image generation technology, means for providing optimization of the designed mechanism by applying a reinforcement learning algorithm, means for constructing a virtual environment and making the visualized prototype verifiable, means for collecting and analyzing user feedback and suggesting improvements, and means for analyzing the user's emotional state and dynamically adjusting environmental elements and suggestions based on the results. This makes it possible to provide users with a personalized, emotionally responsive shopping experience in real time.

[0669] A "large-scale language model" is a machine learning model that learns from vast amounts of text data and has the ability to understand and generate human language.

[0670] "Image generation technology" is a technology that uses computer algorithms to create new visual content and images.

[0671] A "reinforcement learning algorithm" is an algorithm that learns the optimal course of action through trial and error involving actions and rewards.

[0672] A "virtual environment" is a virtual world or setting created through computer simulation that differs from reality.

[0673] "User emotional state" refers to information that indicates the psychological or emotional state experienced by the user.

[0674] "Visual elements" refer to features or components that are visually recognizable and play an important role in a particular design or layout.

[0675] "Mechanism optimization" refers to improving functions or processes in order to achieve specific goals or conditions.

[0676] "Feedback" refers to information collected from users, including their opinions and impressions, which is used to improve systems and processes.

[0677] "Environmental elements" refer to individual components or settings used within a specific environment that influence the user experience.

[0678] The system used to realize this application recognizes the user's emotions and dynamically adjusts the shopping experience in a virtual store. The user uses a smartphone or smart glasses to launch an app, which initiates the collection of emotional data. This involves using sensors such as the device's camera and microphone to analyze the user's emotional state from their facial expressions and voice.

[0679] The server analyzes collected emotional data in real time using an emotion engine. Then, a large-scale language model generates information based on the user's preferences and emotions, which determines the visual elements and suggested products. The server further utilizes image generation technology and reinforcement learning algorithms to dynamically design optimal visual elements and interactions in response to user reactions. For example, prompts such as "For a relaxed user, suggest a calming landscape image" are input into the AI ​​model, and the generated visual elements provide the user with the most suitable experience.

[0680] The virtual environment is built on a server and runs on the user's device. This allows the store layout and product suggestions to be displayed in a way that is optimized for the user. Through this process, a personalized and emotionally resonant shopping experience is provided to the user.

[0681] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0682] Step 1:

[0683] The device uses its camera and microphone to collect facial expressions and voice data from the user who launched the application. This data is the initial input necessary for emotion analysis.

[0684] Step 2:

[0685] The device sends the collected facial and audio data to the server. The server uses an emotion engine to analyze this data and determine the user's current emotional state. The output of this analysis is an emotion label, such as the user being relaxed or excited.

[0686] Step 3:

[0687] The server receives the user's emotional state as an analysis result and uses a large-scale language model to generate prompts that respond to the user's emotions and input requests. For example, for a relaxed user, it might create a prompt such as "Suggest a calming landscape painting." This prompt then serves as an instruction for generating the next visual element.

[0688] Step 4:

[0689] The server uses image generation technology to create visual elements based on the generated prompt text. This results in the output of appropriate designs and product proposals that respond to the user's emotions. The generated visual elements then serve as input for the next step.

[0690] Step 5:

[0691] The server applies a reinforcement learning algorithm to optimize the generated visual elements and configure the optimal environment for the user. User feedback is also considered during this process, leading to improvements for future interactions. The optimized environment configuration is the output.

[0692] Step 6:

[0693] Users access a virtual environment through their device and experience a real-time virtual store built by the server. The generated prompts and visual elements create a seamless shopping experience. Feedback is continuously collected to improve future interactions.

[0694] This entire process provides a dynamic and personalized experience tailored to the user's emotional state.

[0695] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0696] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0697] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0698] [Fourth Embodiment]

[0699] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0700] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0701] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0702] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0703] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0704] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0705] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0706] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0707] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0708] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0709] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0710] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0711] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0712] This invention is a system that utilizes AI technology to streamline the game development process and support creativity. The specific implementation of this system is described below.

[0713] This system consists of servers, terminals, and users. The servers are equipped with software for building large-scale language models, image generation techniques, reinforcement learning algorithms, and virtual reality environments.

[0714] First, the user connects to the system via a terminal and inputs requests regarding the storyline, character design, and game mechanics. The terminal then sends this information to the server.

[0715] The server receives these requests and generates storylines using a large-scale language model. For example, a request such as "I want to create an adventure story set in a fantasy world" can automatically generate a detailed plot.

[0716] Next, the server utilizes image generation technology to create a character design based on the features specified by the user. For example, if the user specifies "young wizard, blue robe," a character image with those characteristics will be generated.

[0717] Furthermore, the server uses reinforcement learning algorithms to design game mechanics. For example, it automatically designs the optimal battle system based on information such as "turn-based battle, adjustable difficulty."

[0718] Once the prototype development is complete, the server will set up a virtual reality environment, providing users with an environment where they can experience actual gameplay through a VR headset. Through this experience, users can evaluate the realism and playability of the game.

[0719] Users send feedback to the server via their device after playing. The server analyzes this feedback and suggests improvements to the user. By repeating this feedback loop, the quality of the game can be improved.

[0720] This system provides powerful support for game developers to quickly develop high-quality games while saving time and resources.

[0721] The following describes the processing flow.

[0722] Step 1:

[0723] The user logs into the system via their device and requests the generation of a storyline. The user then sends information to the server by entering a specific genre or theme.

[0724] Step 2:

[0725] The server receives requests from users and generates storylines using a large-scale language model. This model creates relevant plots and character outlines based on the genres and themes specified by the user.

[0726] Step 3:

[0727] The server sends the generated storyline to the device and presents it to the user. The user reviews the story on the screen and decides whether they are satisfied. At this point, the user can provide feedback.

[0728] Step 4:

[0729] If a user wishes to generate a character design, they send a request to the server via their device, specifying the character's characteristics and style.

[0730] Step 5:

[0731] The server utilizes image generation technology to create character designs based on features specified by the user. The generated designs can include multiple variations.

[0732] Step 6:

[0733] The server sends the generated character design to the terminal and presents the user with multiple options. The user reviews the designs on the terminal and selects their preferred character.

[0734] Step 7:

[0735] If a user wishes to design game mechanics, they specify the mechanic requirements in detail via a terminal. This information is then sent to the server.

[0736] Step 8:

[0737] The server uses a reinforcement learning algorithm to design game mechanics based on user requirements. The designed mechanics may include improvements over existing settings.

[0738] Step 9:

[0739] The game mechanics designed on the server are delivered to the terminal, and the user is allowed to review them. The user provides feedback as needed.

[0740] Step 10:

[0741] If a user requests to test play the prototype in VR, the device will inform the server of this request.

[0742] Step 11:

[0743] The server builds the virtual reality environment and prepares a prototype based on an integrated storyline, character designs, and game mechanics.

[0744] Step 12:

[0745] The VR prototype is sent from the server to the terminal, and the user can perform a test play using a VR headset.

[0746] Step 13:

[0747] Users send feedback to the server via their device after playing. The server analyzes the feedback and presents the user with suggestions for improvement in real time.

[0748] This step-by-step process enables efficient and creative game development.

[0749] (Example 1)

[0750] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0751] Traditional game development processes require significant time and effort for story construction, character design, and game mechanics design. Furthermore, they present challenges in efficiently creating prototypes based on user requests and implementing improvement cycles that incorporate feedback.

[0752] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0753] In this invention, the server includes means for generating a story based on user requests using a large-scale data processing model, means for generating a character with specified features using an image generation algorithm, and means for designing the mechanism of an electronic game and providing an optimal configuration by applying a machine learning algorithm. This makes it possible to create prototypes quickly and efficiently and to realize a continuous improvement cycle that utilizes user feedback.

[0754] A "large-scale data processing model" is an algorithm that performs natural language processing and uses large amounts of data to generate stories and texts.

[0755] An "image generation algorithm" is a method of creating visual representations of people or objects using a computer based on input instructions.

[0756] A "machine learning algorithm" is a method for learning features from vast amounts of data and automatically optimizing the mechanical design of electronic games.

[0757] A "virtual representation environment" is a computer-generated three-dimensional space that allows users to have an immersive experience through digital devices.

[0758] A "mediating device" is an interface device that transmits user input information to a processing unit in an appropriate format.

[0759] This invention is a system designed to streamline the game development process and support creativity. The system consists of a server, terminals, and users, with each device working in coordination. Specific embodiments of the invention are described below.

[0760] The server is equipped with software for building large-scale data processing models, image generation algorithms, machine learning algorithms, and virtual representation environments. The large-scale data processing model generates stories based on user requests. Users input detailed requests regarding storylines, character designs, and game mechanics as prompts via a terminal and send them to the server.

[0761] For example, if a user enters the prompt, "Create an adventure story set in a medieval fantasy world. The protagonist is a young wizard wearing a blue robe. The story should also include a scene where the protagonist fights a dragon," the server will generate a story based on this information.

[0762] The image generation algorithm generates a visual representation of a character based on the characteristics specified by the user. For example, if the user specifies "brave knight, red shield," an image of a character matching those characteristics will be generated.

[0763] Machine learning algorithms design the mechanisms of electronic games according to the game mechanics specified by the user, and automatically determine the optimal configuration. Complex mechanics, such as turn-based battle systems, are also designed efficiently.

[0764] Finally, the server creates a virtual representation environment, allowing users to experience the game prototype through a VR headset. This provides users with an immersive gameplay experience, enabling them to evaluate the game's realism and playability. This system streamlines the development cycle and supports the rapid development of high-quality games.

[0765] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0766] Step 1:

[0767] The user inputs requests regarding the storyline, character design, and game mechanics as prompts through the terminal. These prompts serve as initial data for building the narrative, visual elements, and game mechanics. The terminal then prepares to send these prompts to the server.

[0768] Step 2:

[0769] The server generates a story using a large-scale data processing model based on the prompt message received from the terminal. Specifically, it analyzes the context of the prompt message and constructs a new storyline by referring to relevant data. The output is a detailed story plot that aligns with the user's request. For example, a prompt like "The adventures of a young wizard" would lead to a scenario in which the wizard battles a dragon.

[0770] Step 3:

[0771] The server then uses an image generation algorithm to design the character. It extracts the character's visual characteristics from the prompt text (e.g., "brave knight, red shield") and generates an image based on them. The output is a visual representation of the character that meets the user's requirements. The generated image is used as the design for the character that will appear in the story.

[0772] Step 4:

[0773] The server uses machine learning algorithms to design the game mechanics. It takes the play style and difficulty settings specified in the prompt (e.g., "turn-based battle") as input to derive the optimal design. Based on patterns learned from vast amounts of data, the server determines a structure that produces an efficient gameplay experience. The output is a play system that matches the user's intentions.

[0774] Step 5:

[0775] The server finally constructs the virtual representation environment and integrates the designed storyline, characters, and game mechanics. These elements interact within the VR environment, allowing the user to experience the prototype game through a VR headset. The output is an integrated game experience that the user can intuitively test.

[0776] Step 6:

[0777] Users input feedback on their VR gameplay experience via their device and send it to the server. The server analyzes this feedback and generates suggestions for improvements regarding the story, visual design, and mechanics. This provides users with concrete guidelines for improving the quality of the game.

[0778] (Application Example 1)

[0779] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] In game development, constructing storylines, designing characters, and designing gameplay mechanics are extremely time-consuming and labor-intensive processes. Furthermore, the limited opportunities for users to creatively generate and edit games themselves make it difficult to provide the joy of interactive content creation to a wider audience. Moreover, achieving a real-time interactive experience presents significant technical challenges.

[0781] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0782] In this invention, the server includes means for generating the development of a story using a large-scale language model, means for generating character designs using image generation technology, means for designing game actions and providing optimal variables by applying a reinforcement learning algorithm, means for providing an interface that users can access via smart devices to spontaneously generate and edit stories, and means for presenting an interactive experience environment in real time. This makes it possible to stimulate creativity by enabling users to easily generate new content and have a real-time interactive experience.

[0783] A "large-scale language model" is an AI technology that enables natural language processing, and it is a model that has the ability to generate and understand text based on a large amount of text data.

[0784] "Story development" refers to the flow and plot of a story that is automatically generated by AI based on user requests.

[0785] "Image generation technology" is a technique in which AI generates new images based on specified features, and is used in the creation of character designs and background designs.

[0786] "Character design" refers to the design of characters with unique characteristics, either specified by the user or automatically generated by AI.

[0787] A "reinforcement learning algorithm" is a method in which AI learns the optimal action through trial and error and efficiently designs in-game mechanics and other elements.

[0788] "Game actions" refer to actions and rules related to the operation and progression of the game, and are set based on player interaction.

[0789] "Smart devices" refer to multi-functional electronic devices such as mobile devices and handheld terminals.

[0790] An "interface" refers to the screens, input methods, and other elements that allow a user to interact directly with a system.

[0791] An "interactive experience environment" refers to an environment that enables real-time, two-way communication between the user and the system.

[0792] This system is designed to support user creativity and efficiently generate content. The server combines large-scale language models, image generation technologies, and reinforcement learning algorithms to generate storylines, character designs, and gameplay based on user requests.

[0793] When a user accesses the system using a smart device, they can input initial story and character requests through the interface. For example, they might enter a prompt such as, "Generate an adventure story where a knight fights a dragon in a medieval fantasy world." The server then receives these requests and begins processing them.

[0794] The server uses a large-scale language model to automatically generate a detailed narrative development from the input prompts. Image generation technology generates character designs that reflect the features specified by the user. For example, based on the request "knights should wear blue armor and dragons should have red and gold markings," a specific character image is generated.

[0795] Furthermore, the server utilizes reinforcement learning algorithms to design gameplay and build an optimal game system. This process involves learning variables through trial and error to optimize the user experience.

[0796] Ultimately, the server displays the generated content in real time, providing an interactive environment for users. In this environment, users can experience the progression of the story and the actions of the characters, enjoying content that aligns with their own creative intentions.

[0797] This system allows users to spontaneously generate new stories and characters, and to have a real-time interactive experience.

[0798] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0799] Step 1:

[0800] The user enters prompt messages through the interface of a smart device. These prompts concern requests related to the story's theme and character traits. This information is collected by the terminal and sent to the server. The user's requests become the basis for subsequent processing within the system.

[0801] Step 2:

[0802] The server parses the received prompt message. In this step, natural language processing techniques are used to convert the user's request into structured data. Key keywords and context are extracted from the prompt message, and the data is prepared in a format suitable for the story generator.

[0803] Step 3:

[0804] The server uses a large-scale language model to generate the narrative flow. Utilizing the structured data received as input, the large-scale language model automatically generates a detailed storyline. This generated storyline is then used as foundational information for subsequent character design.

[0805] Step 4:

[0806] The server uses image generation technology to generate character designs based on the features specified in the prompt. In this step, the AI ​​generates realistic character images based on the output of the previous storyline. For example, the specific designs of knights and dragons are finalized here.

[0807] Step 5:

[0808] The server uses a reinforcement learning algorithm to design the game's actions. In this process, it sets optimal action parameters for the game based on the generated storyline and character designs. The server searches for the optimal solution by trying various simulations, learning actions that are suitable for the interactive environment the user will experience.

[0809] Step 6:

[0810] The server integrates the generated story, character designs, and gameplay to create an interactive experience environment. In this step, the server sets up the virtual reality environment and provides the user with a real-time interactive experience.

[0811] Step 7:

[0812] Users experience content generated in an interactive environment and send feedback to the server. The server analyzes this feedback and identifies areas for improvement. Based on user feedback, the system evolves further.

[0813] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0814] This invention relates to a system that recognizes user emotions and dynamically adjusts the game development process based on those emotions. This system comprises a server, a terminal, a user, and an emotion engine.

[0815] The server is equipped with a large-scale language model, image generation technology, reinforcement learning algorithms, software for building virtual reality environments, and an emotion engine. The emotion engine analyzes the user's emotions in real time and uses the results in conjunction with other modules.

[0816] First, the user accesses the system from their device and enters their request for generating a storyline. For example, if the user enters "I would like a suspenseful story," the device sends that information to the server. The server then uses a large-scale language model to generate a storyline, referencing the user's input and the user's emotional state as determined by the emotion engine.

[0817] Next, the user sends a request for a character design via the terminal. For example, in response to a request such as "a tense detective character," the server generates a character design using image generation technology, while taking into account the results of the emotion engine's analysis.

[0818] Furthermore, when a user sends information about their desired game mechanics from their device to the server, the server applies a reinforcement learning algorithm to design the game mechanics. In doing so, the server uses user feedback obtained by the emotion engine to make optimal adjustments tailored to the player.

[0819] When a user tests the prototype, the server creates a virtual reality environment. The prototype is delivered in a way that is optimized for the user's emotional state. For example, when the user is relaxed, it is possible to generate an environment that includes more challenging elements.

[0820] Users can send their impressions and feedback after playing to the server via their device. The server analyzes the feedback using an emotion engine and generates suggestions for further improvement, which are then presented to the user.

[0821] In this way, leveraging user emotions allows for a more personalized game development process. This system enables faster development and maximizes user satisfaction.

[0822] The following describes the processing flow.

[0823] Step 1:

[0824] The user logs into the system via their device and requests storyline generation. The user enters their desired theme and genre, and this information is sent to the server.

[0825] Step 2:

[0826] Based on the received request, the server initiates a process to generate a storyline using a large-scale language model. During this process, the server uses an emotion engine to analyze the user's current emotional state and reflects that state in the generated story.

[0827] Step 3:

[0828] The generated storyline data is sent from the server to the terminal and presented to the user. The user reviews the story content on the screen and provides feedback as needed.

[0829] Step 4:

[0830] If a user wishes to design a character, they enter the characteristics of the specific character on their device and send the information to the server.

[0831] Step 5:

[0832] The server uses image generation technology to generate visuals based on character design requests received from the terminal. The emotion engine considers the user's emotions during this process and reflects them in the design.

[0833] Step 6:

[0834] The generated character designs are sent to the terminal via the server and presented to the user as multiple design options. The user can then select their preferred design.

[0835] Step 7:

[0836] If a user requests to design game mechanics, the details are sent from the terminal to the server.

[0837] Step 8:

[0838] The server uses reinforcement learning algorithms to design game mechanics that satisfy user requests. In doing so, the server incorporates user feedback from previous stages and considers the user's emotional state, analyzed by the emotion engine, to make optimal suggestions.

[0839] Step 9:

[0840] The designed game mechanics are transmitted to the device in real time and reviewed by the user. The user can then provide additional feedback.

[0841] Step 10:

[0842] When a user requests to test play a prototype, that request is sent from the device to the server.

[0843] Step 11:

[0844] The server builds a virtual reality environment for the prototype and uses an emotion engine to adjust it based on the user's emotional state. This adjustment is made to enrich the user experience.

[0845] Step 12:

[0846] The prototype is sent from the server to the terminal, and the user uses a VR headset to test play and deepen their experience.

[0847] Step 13:

[0848] Users input their impressions after the test via their device and send the feedback to the server. The server uses an emotion engine to analyze the feedback, generate improvement suggestions, and present them to the user.

[0849] This system enables a dynamic, emotion-driven game development process.

[0850] (Example 2)

[0851] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0852] In modern game development processes, creating content that reflects individual user emotions and immediate feedback is a challenging task. Traditional technologies fail to adequately adapt to user emotions, resulting in a lack of personalized game experiences. To address this challenge, it is necessary to analyze user emotions in real time and dynamically adjust the game development process based on the results.

[0853] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0854] In this invention, the server includes means for generating a narrative flow based on user requests using large-scale language processing technology, means for generating character designs with specified features using an image generation method, and means for designing a game structure and providing optimal components by applying a learning algorithm. This makes it possible to provide a individually tailored game experience in real time based on user sentiment analysis.

[0855] "Large-scale language processing technology" refers to techniques that analyze large amounts of text data and generate natural language.

[0856] An "image generation method" is a technology for creating visual information based on input information.

[0857] A "learning algorithm" is a computational method in which a machine learns from data and adaptively solves problems based on the results.

[0858] A "virtual reality space" is a computer-generated environment that closely resembles reality, allowing users to immerse themselves in and experience it.

[0859] "User emotional state" refers to the emotional reactions and psychological state of the user during gameplay.

[0860] An "analysis mechanism" is a system or device used to analyze input data and identify its characteristics.

[0861] A "regulation mechanism" is a device or function that dynamically changes a system or environment based on the information obtained.

[0862] This invention relates to a system that analyzes user emotions and dynamically adjusts the game development process based on those emotions. This system consists of a server, terminals, an emotion engine, and the like.

[0863] The server is equipped with software that incorporates large-scale language processing technology and image generation methods, as well as software for building learning algorithms and virtual reality environments. These technologies enable the server to generate diverse content based on user input and dynamically adjust it accordingly.

[0864] Users access the system using a terminal and enter prompts to create their desired storyline. For example, if a user enters "I want a suspenseful story" into the terminal, the terminal transmits this information to the server. The server then uses large-scale language processing technology to generate a storyline based on the results of an emotion engine analysis of the user's emotional state. Emotion analysis allows the system to provide content that matches the atmosphere and story development the user desires.

[0865] Next, the user's specified character design request is transmitted via the terminal. For example, if the request is for a "tense detective character," the server generates an image of the character using an image generation method that reflects the analysis results of the emotion engine. In this way, a character design that responds to user feedback is provided.

[0866] Furthermore, when a user sends their desired game mechanics from their device to the server, the server uses a learning algorithm to design the gamification. In this process, the server designs the optimal game structure based on the user's emotional feedback. This approach provides a game experience tailored to each individual user.

[0867] Furthermore, when a user tests the prototype, the server creates a virtual reality space within which the prototype operates. This makes it possible to create an optimal playing environment tailored to the user's emotional state. For example, if the user is relaxed, an environment with challenging content can be provided.

[0868] Finally, users send their post-game impressions and feedback from their device to the server. The server uses an emotion engine to analyze the feedback and generates suggestions for further improvement, which are then presented to the user.

[0869] A concrete example of a prompt message would be, "Generate a story and characters to be used in the next project. The theme is fantasy." This invention improves the efficiency of game development work and user satisfaction.

[0870] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0871] Step 1:

[0872] The user accesses the system using a terminal and enters prompt text describing the desired storyline. The entered prompt text is then sent from the terminal to the server.

[0873] Step 2:

[0874] Based on the received prompt, the server uses an emotion engine to analyze the user's emotional state. This analysis reveals the atmosphere and theme of the story the user is seeking. The analysis results are then used in the next step.

[0875] Step 3:

[0876] The server uses a large-scale language model to generate storylines based on analyzed sentiment states. In this process, it takes prompts and sentiment analysis results as input and outputs a coherent narrative flow.

[0877] Step 4:

[0878] Users write character design requests from their devices and send them to the server. These design requests include details about the character's features and style.

[0879] Step 5:

[0880] The server uses image generation technology, taking into account the results of the emotion engine, to generate a character design with specified features. It receives the character's requests and emotional state as input and outputs a visually concrete character design.

[0881] Step 6:

[0882] The user sends information about their desired game mechanics from their device to the server. This information includes details such as the structure of the interaction and the difficulty level.

[0883] Step 7:

[0884] The server applies a reinforcement learning algorithm to design the game structure based on the information received and the user's emotional state. In this process, it takes feedback and interaction information as input and outputs optimized game mechanics.

[0885] Step 8:

[0886] When a user requests to test a prototype, the server constructs a virtual reality space. The prototype is then run in an environment that is adjusted based on the user's emotional state.

[0887] Step 9:

[0888] Users send their impressions and feedback from their device to the server after playing the test version.

[0889] Step 10:

[0890] The server utilizes an emotion engine to analyze user feedback in detail, generating and presenting suggestions for further improvement. Based on the feedback information, it outputs specific ideas for improvement.

[0891] (Application Example 2)

[0892] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0893] In traditional virtual stores, it was difficult to personalize the shopping experience in real time based on user emotions and behavior. This meant that it was not possible to make the best suggestions for the user, potentially leading to lower satisfaction. Furthermore, there was a lack of dynamic environmental adjustments that directly reflected user emotions, making it difficult to provide a more immersive experience.

[0894] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0895] In this invention, the server includes means for generating information based on user requests using a large-scale language model, means for generating visual elements with specified features using image generation technology, means for providing optimization of the designed mechanism by applying a reinforcement learning algorithm, means for constructing a virtual environment and making the visualized prototype verifiable, means for collecting and analyzing user feedback and suggesting improvements, and means for analyzing the user's emotional state and dynamically adjusting environmental elements and suggestions based on the results. This makes it possible to provide users with a personalized, emotionally responsive shopping experience in real time.

[0896] A "large-scale language model" is a machine learning model that learns from vast amounts of text data and has the ability to understand and generate human language.

[0897] "Image generation technology" is a technology that uses computer algorithms to create new visual content and images.

[0898] A "reinforcement learning algorithm" is an algorithm that learns the optimal course of action through trial and error involving actions and rewards.

[0899] A "virtual environment" is a virtual world or setting created through computer simulation that differs from reality.

[0900] "User emotional state" refers to information that indicates the psychological or emotional state experienced by the user.

[0901] "Visual elements" refer to features or components that are visually recognizable and play an important role in a particular design or layout.

[0902] "Mechanism optimization" refers to improving functions or processes in order to achieve specific goals or conditions.

[0903] "Feedback" refers to information collected from users, including their opinions and impressions, which is used to improve systems and processes.

[0904] "Environmental elements" refer to individual components or settings used within a specific environment that influence the user experience.

[0905] The system used to realize this application recognizes the user's emotions and dynamically adjusts the shopping experience in a virtual store. The user uses a smartphone or smart glasses to launch an app, which initiates the collection of emotional data. This involves using sensors such as the device's camera and microphone to analyze the user's emotional state from their facial expressions and voice.

[0906] The server analyzes collected emotional data in real time using an emotion engine. Then, a large-scale language model generates information based on the user's preferences and emotions, which determines the visual elements and suggested products. The server further utilizes image generation technology and reinforcement learning algorithms to dynamically design optimal visual elements and interactions in response to user reactions. For example, prompts such as "For a relaxed user, suggest a calming landscape image" are input into the AI ​​model, and the generated visual elements provide the user with the most suitable experience.

[0907] The virtual environment is built on a server and runs on the user's device. This allows the store layout and product suggestions to be displayed in a way that is optimized for the user. Through this process, a personalized and emotionally resonant shopping experience is provided to the user.

[0908] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0909] Step 1:

[0910] The device uses its camera and microphone to collect facial expressions and voice data from the user who launched the application. This data is the initial input necessary for emotion analysis.

[0911] Step 2:

[0912] The device sends the collected facial and audio data to the server. The server uses an emotion engine to analyze this data and determine the user's current emotional state. The output of this analysis is an emotion label, such as the user being relaxed or excited.

[0913] Step 3:

[0914] The server receives the user's emotional state as an analysis result and uses a large-scale language model to generate prompts that respond to the user's emotions and input requests. For example, for a relaxed user, it might create a prompt such as "Suggest a calming landscape painting." This prompt then serves as an instruction for generating the next visual element.

[0915] Step 4:

[0916] The server uses image generation technology to create visual elements based on the generated prompt text. This results in the output of appropriate designs and product proposals that respond to the user's emotions. The generated visual elements then serve as input for the next step.

[0917] Step 5:

[0918] The server applies a reinforcement learning algorithm to optimize the generated visual elements and configure the optimal environment for the user. User feedback is also considered during this process, leading to improvements for future interactions. The optimized environment configuration is the output.

[0919] Step 6:

[0920] Users access a virtual environment through their device and experience a real-time virtual store built by the server. The generated prompts and visual elements create a seamless shopping experience. Feedback is continuously collected to improve future interactions.

[0921] This entire process provides a dynamic and personalized experience tailored to the user's emotional state.

[0922] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0923] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0924] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0925] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0926] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0927] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0928] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0929] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0930] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0931] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0932] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0933] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0934] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0935] 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.

[0936] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0937] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0938] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0939] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0940] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0941] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0942] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0943] The following is further disclosed regarding the embodiments described above.

[0944] (Claim 1)

[0945] A means of generating storylines based on user requests using a large-scale language model,

[0946] A means for generating a character design with specified features using image generation technology,

[0947] A means of designing game mechanics by applying reinforcement learning algorithms and providing optimal parameters,

[0948] A means to build a virtual reality environment and enable prototype testing,

[0949] A system that includes means for collecting and analyzing user feedback and proposing improvement suggestions.

[0950] (Claim 2)

[0951] The system according to claim 1, comprising an interface for receiving user requests and sending them to a server.

[0952] (Claim 3)

[0953] The system according to claim 1, comprising means for combining the generated storyline, character designs, and game mechanics and presenting them to the user in real time.

[0954] "Example 1"

[0955] (Claim 1)

[0956] A means of generating narratives based on user requests using a large-scale data processing model,

[0957] A means of generating a character with specified features using an image generation algorithm,

[0958] A means of designing the mechanism of an electronic game by applying machine learning algorithms and providing an optimal configuration,

[0959] A means to build a virtual representation environment and enable testing of prototypes,

[0960] A system that includes means for collecting and analyzing user feedback and proposing improvement suggestions.

[0961] (Claim 2)

[0962] The system according to claim 1, comprising an intermediary device for receiving user requests and transmitting them to a data processing device.

[0963] (Claim 3)

[0964] The system according to claim 1, comprising means for integrating a generated story, characters, and electronic game mechanisms and presenting them immediately to a user.

[0965] "Application Example 1"

[0966] (Claim 1)

[0967] A means of generating narrative developments based on user requests using a large-scale language model,

[0968] A means for generating a character design with specified characteristics using image generation technology,

[0969] A means of designing game behavior by applying a reinforcement learning algorithm and providing optimal variables,

[0970] A means to build a virtual reality environment and enable initial trials,

[0971] A means of collecting and analyzing user feedback and proposing improvement plans,

[0972] A means of providing an interface that users can access on smart devices and spontaneously generate and edit stories,

[0973] A means for presenting the content generated based on the aforementioned means in real time in an interactively experienceable environment,

[0974] A system that includes this.

[0975] (Claim 2)

[0976] The system according to claim 1, comprising connection means for receiving user input and transmitting it to a processing unit.

[0977] (Claim 3)

[0978] The system according to claim 1, comprising means for providing a user with an interactive experience environment by combining the development of a generated story, the design of characters, and the actions of the game.

[0979] "Example 2 of combining an emotion engine"

[0980] (Claim 1)

[0981] A means of generating a narrative flow based on user requests using large-scale language processing technology,

[0982] A means for generating character designs with specified characteristics using an image generation method,

[0983] A means for designing a game structure by applying a learning algorithm and providing optimal components,

[0984] A means to construct a virtual reality space and enable the test operation of a prototype,

[0985] An analytical mechanism for analyzing the emotional state of users,

[0986] An adjustment mechanism to adjust the difficulty and atmosphere of the game based on the user's emotions,

[0987] A system that includes means for collecting and analyzing user feedback and suggesting improvements.

[0988] (Claim 2)

[0989] The system according to claim 1, comprising a connection device for receiving user instructions and transmitting them to an information processing device.

[0990] (Claim 3)

[0991] The system according to claim 1, comprising means for combining the generated narrative flow, character designs, and game structure and presenting them to the user in real time.

[0992] "Application example 2 when combining with an emotional engine"

[0993] (Claim 1)

[0994] A means of generating information based on user requests using a large-scale language model,

[0995] A means for generating visual elements with specified features using image generation technology,

[0996] A means for providing optimization of a mechanism designed by applying a reinforcement learning algorithm,

[0997] A means to build a virtual environment and make the visualized prototype verifiable,

[0998] A means of collecting and analyzing user feedback and proposing improvement suggestions,

[0999] A system that includes means for analyzing the user's emotional state and dynamically adjusting environmental elements and suggestions based on the results.

[1000] (Claim 2)

[1001] The system according to claim 1, comprising an interface for receiving user requests and sending them to a server.

[1002] (Claim 3)

[1003] The system according to claim 1, comprising means for combining a generated storyline, visual elements, and designed mechanisms, presenting them to the user in real time, and dynamically adjusting the content based on the user's sentiment analysis results. [Explanation of Symbols]

[1004] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of generating narrative developments based on user requests using a large-scale language model, A means for generating a character design with specified characteristics using image generation technology, A means of designing game behavior by applying a reinforcement learning algorithm and providing optimal variables, A means to build a virtual reality environment and enable initial trials, A means of collecting and analyzing user feedback and proposing improvement plans, A means of providing an interface that users can access on smart devices and spontaneously generate and edit stories, A means for presenting the content generated based on the aforementioned means in real time in an interactively experienceable environment, A system that includes this.

2. The system according to claim 1, comprising connection means for receiving user input and transmitting it to a processing unit.

3. The system according to claim 1, comprising means for providing an interactive experience environment to a user by combining the development of a generated story, the design of characters, and the actions of the game.

Images