system

Abstract

We provide the system. [Solution] A means of accepting natural language input, A means for analyzing the user's intent based on the aforementioned natural language input, Based on the analyzed intent, means for generating game elements, A means for automatically acquiring disaster prevention information and incorporating it into the game elements, A system that includes means for presenting generated game elements to the user in real time.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional disaster prevention education is mainly classroom-based learning, which has the problems of being difficult to arouse children's interest and difficult to firmly establish practical disaster prevention knowledge. There is also a problem that it is difficult for children without programming knowledge to create disaster prevention games based on their own creative ideas. To solve these problems, there is a demand for providing a new educational platform that allows children to learn disaster prevention knowledge actively and enjoyably.

Means for Solving the Problems

[0005] This invention solves the above problems by providing an educational system that utilizes natural language processing and a multimodal learning model. Specifically, when a user inputs instructions in natural language, their intent is analyzed and appropriate game elements are automatically generated. Furthermore, by dynamically acquiring disaster prevention information and incorporating it into the game elements, it becomes possible to create games with high educational effectiveness. In addition, the system displays the generated game elements in real time, readjusts the game elements in response to additional instructions from the user, and provides a mechanism for saving and downloading the completed version.

[0006] "Natural language input" is a method of input where users provide instructions and information to a system using language they use in their daily lives.

[0007] "Analyzing user intent" refers to the process of identifying the information and actions a user is seeking from instructions and information entered in natural language.

[0008] "Generating game elements" means creating the basic elements that make up a game, such as characters, scenarios, and obstacles, based on the user's intentions.

[0009] "Disaster prevention information" refers to methods and knowledge for protecting oneself from natural disasters, and this information is obtained from reliable data sources.

[0010] "Integrating into game elements" refers to integrating elements generated based on disaster prevention information and user intent into the game's scenarios and settings.

[0011] "Presenting in real time" means displaying the generated game elements to the user immediately without any delay.

[0012] "Readjusting" means modifying or changing already generated game elements in response to additional instructions from the user.

[0013] "Saving the final version" means retaining the data of the final game that was created so that it can be retrieved and used later.

[0014] "Providing a download link" means giving the user a link to retrieve the data so that they can save the completed game to their device. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides a system for users to create disaster prevention games using natural language, and its implementation is carried out in the following configuration: When a user inputs "I want to create a disaster prevention game" in natural language via a terminal, the terminal transmits this input information to a server. The server analyzes this information and operates a natural language processing engine to identify the user's intent. This engine understands the game's theme, scenario, and necessary elements from the input text, and uses a multimodal learning model to begin generating appropriate game elements.

[0037] The generated game elements are supplemented by incorporating necessary disaster prevention knowledge based on a disaster prevention information database. This creates a game with high educational value. The server sends this generated content to the user's terminal in real time, and the user can check the preview and issue further instructions for modifications and additions, such as "I want to add more characters" or "I want to adjust the scenario." The server receives these new instructions and readjusts the dynamically generated game elements.

[0038] Finally, once the user is satisfied with the completed game, the server saves the final data and provides a link that the user can download via their device. This system allows users to easily create disaster prevention games that reflect their own ideas and learn disaster prevention knowledge while having fun, without requiring any specialized programming skills.

[0039] For example, if a primary school student user instructs the system to "create a game about protecting a town from a tsunami," the server will generate a realistic town model based on a tsunami scenario and incorporate necessary evacuation information and warning systems into the game. The user can then monitor the game's progress, adjust character dialogue and the town's layout, and complete their ideal game. In this way, a fun and enriching learning experience can be provided.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user enters their request for a disaster prevention game using natural language input via their device. This input is in the format of "I want to create a game about evacuation during an earthquake."

[0043] Step 2:

[0044] The terminal sends user input to the server, which then uses a natural language processing engine to analyze the input. Here, the server identifies the game's theme (e.g., earthquake, evacuation) and necessary elements.

[0045] Step 3:

[0046] Based on the information analyzed by the server, game elements are generated using a multimodal model. This includes scenario development, character design, and evacuation route design.

[0047] Step 4:

[0048] The server accesses a disaster prevention information database and incorporates official recommendations and action guidelines regarding earthquake evacuation as in-game information.

[0049] Step 5:

[0050] The server sends the generated game elements and disaster prevention information to the user's terminal in real time and displays it as a preview. The user can review this and make any necessary changes.

[0051] Step 6:

[0052] If a user gives instructions to add or modify elements, such as "add an alarm sound" or "adjust the difficulty of evacuation," the server will receive these instructions and readjust the existing game elements.

[0053] Step 7:

[0054] Once the user is finally satisfied with the game's content, the server saves the completed game data and provides a downloadable link to the user's device, allowing them to acquire and play the game.

[0055] (Example 1)

[0056] 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."

[0057] In order for many users to deepen their knowledge of disaster prevention, there is a need for a system that allows them to easily design and adjust educational and interactive games based on their own ideas, even without specialized programming skills.

[0058] 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.

[0059] In this invention, the server includes a device for receiving natural language input, a device for analyzing the user's intent based on the natural language input, and a device for generating game elements based on the analyzed intent. This makes it possible for users to design their own disaster prevention-related games that reflect their own ideas, without requiring specialized technical knowledge, and to learn while having fun.

[0060] "Natural language input" is a method of conveying instructions and information to a system using the language that users use in their daily lives.

[0061] A "device" is a machine or electronic device designed to perform a specific function.

[0062] "User intent" refers to the user's goals and desires regarding the system, such as what they want to achieve or what elements they are looking for.

[0063] "Analyzing" is the process of deciphering given information or data and revealing its meaning and intent.

[0064] "Gameplay elements" refer to all the elements that make up a game, such as items, characters, and scenarios used during gameplay.

[0065] "Disaster information" refers to data and knowledge related to natural disasters and sudden events.

[0066] "Presenting immediately" means that the results are displayed quickly without any waiting time after input or processing has been completed.

[0067] This invention is a system that enables users to design games related to disaster prevention without requiring specialized technical knowledge, and to learn while having fun.

[0068] First, the user enters natural language input via their device, such as "I want to create a disaster prevention game." The device then forwards this input data to a server to understand the user's intent. The server analyzes this input using a natural language processing engine. Tools such as NLTK and spaCy are often used for this engine.

[0069] Based on the analysis results, the server activates a generative AI model to generate game elements. This model utilizes a general-purpose generative AI tool available on a specific platform (e.g., GPT). The generated game elements are further enhanced by referencing a disaster information database and incorporating necessary disaster prevention knowledge.

[0070] The game elements created in this way are immediately presented to the user's device. The user can review this preview and give additional instructions, such as "I want to add more characters" or "I want to change the scenario." The server receives these instructions and readjusts the game elements in real time. Finally, when the user is satisfied with the completed game, the server saves the completed data and provides a download link to the device.

[0071] For example, if an elementary school student inputs "I want to create a game about protecting a town from a tsunami," the server will generate a tsunami-themed scenario. Furthermore, this scenario will incorporate specific evacuation information and warning systems related to disaster prevention. Based on this, users can adjust the game's progression and character movements to complete their ideal game.

[0072] An example of a prompt message would be: "The theme of this disaster prevention game is tsunamis. We want to incorporate evacuation routes and warning systems as learning elements necessary for players to protect the city. To enhance replayability, we want to provide multiple characters and scenario variations." This allows users to deepen their disaster prevention knowledge while having fun playing the game.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user uses a terminal to input natural language instructions, such as "I want to create a disaster prevention game." This input includes the user's intentions and desired game theme. The terminal formats this input as text data and sends it to the server.

[0076] Step 2:

[0077] The server analyzes natural language input data received from the terminal. First, a natural language processing engine tokenizes the input text and performs syntactic analysis. This analysis extracts the game themes and elements desired by the user. The input is the user's instruction, and the output is the analyzed intent and keywords.

[0078] Step 3:

[0079] The server activates a generating AI model based on the analysis results. Specifically, the AI ​​model generates game elements from the analyzed themes and keywords. For example, if the user selects "tsunami" as the theme, the AI ​​model will generate related game scenarios and characters. In this step, the input is the analysis results, and the output is the generated game elements.

[0080] Step 4:

[0081] The server compares the generated game elements with a disaster information database. It retrieves necessary disaster prevention knowledge from this database and incorporates it into the game elements. The input is the generated game elements, and the output is the game elements with the disaster prevention information incorporated.

[0082] Step 5:

[0083] The server sends the completed game elements to the user's device in real time. The user can view a preview of the game on their device. At this time, an interface is displayed that accepts user feedback and additional instructions. The input is the generated game elements, and the output is the real-time display on the user's device.

[0084] Step 6:

[0085] The user checks the game preview and gives additional instructions via the terminal, such as "I want to add more characters." The terminal then sends these instructions back to the server. The input is the additional instructions from the user, and the output is the update instructions sent to the server.

[0086] Step 7:

[0087] The server receives additional instructions from the user and readjusts the game elements using the generated AI model again. The readjusted elements are then cross-referenced with the disaster prevention database to complete the content. The input for this step is the user's additional instructions, and the output is the readjusted game elements.

[0088] Step 8:

[0089] When the user is satisfied with the gameplay, the server saves the final game data and provides a download link to the device. The user can then download the game using this link. The input is the final decision instruction, and the output is the download link.

[0090] (Application Example 1)

[0091] 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."

[0092] There is a need for a system that allows users to easily create and share disaster prevention educational content with others. However, conventional systems require specialized knowledge, making it difficult for ordinary users to easily create and share disaster prevention games that reflect their own ideas. Furthermore, the lack of real-time editing and sharing functions means that the system lacks the flexibility needed to enhance its educational effectiveness.

[0093] 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.

[0094] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for generating game elements, means for automatically acquiring disaster prevention data and incorporating it into the game, means for presenting the generated game elements to the user in a real-time editable state, and means for sharing the generated game with others via an information and communication network. This makes it possible for users to create, modify, and share disaster prevention games in natural language without requiring specialized knowledge.

[0095] "Natural language input" refers to text that users speak or write as human language, rather than in a programming language.

[0096] "Methods for analyzing user intent" refer to technologies that interpret what the user wants from the input natural language and extract specific instructions or objectives.

[0097] "Means for generating game elements" refers to a process that automatically creates the necessary components of a game (characters, scenarios, rules, etc.) based on the user's intentions.

[0098] "Disaster prevention data" refers to information related to disasters, including knowledge and awareness-raising information about natural disasters such as earthquakes and tsunamis.

[0099] "A means of presenting information in a real-time, editable state" refers to a method of displaying information in a way that allows users to instantly view and modify the generated content.

[0100] "Means of sharing with others through information and communication networks" refers to methods of transmitting created content to other users using the internet or other communication networks, making it available for use or viewing.

[0101] This system allows users to easily create and share disaster prevention games. The system primarily consists of a server and the user's device (e.g., a smartphone).

[0102] First, the user inputs the concept for the disaster prevention game in natural language using the interface on their device. Input can be either text or voice. For example, the instruction might be, "Please create a disaster prevention game for flood control."

[0103] The server operates in the cloud and analyzes user intent using a natural language processing engine (e.g., Google® NLP API). The analysis reveals the game themes and elements the user desires. Next, the server uses a multimodal learning model (e.g., OpenAI® GPT series) to generate the necessary game elements, including characters, scenarios, and rules.

[0104] The generated game elements are supplemented with disaster prevention information data (e.g., public databases on earthquakes and tsunamis) and incorporated as necessary educational content. The server then sends the generated content to the user's device in real time, providing an interactive environment where the user can immediately preview and make adjustments.

[0105] Furthermore, the completed game edited by the user can be shared with other users via a server and information and communication network (e.g., the internet). This sharing function allows for efficient use in educational institutions and individual learning environments.

[0106] For example, if a user instructs the system to "create a simulation game for natural disasters that are likely to occur in the region," the system will analyze disaster data for that region and automatically generate a game that includes appropriate scenarios and evacuation instructions. An example of a prompt in this case would be, "Please create a flood prevention game. I want to create a scenario for safe evacuation when a flood occurs in the city."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user inputs the concept of the disaster prevention game in natural language via their device. The device sends this input as text data to the server. An example of input is "I would like you to create a disaster prevention game for flood control."

[0110] Step 2:

[0111] The server sends the received input to a natural language processing engine (e.g., Google NLP API) to analyze the user's intent. The analysis process tokenizes the input string, performs semantic analysis, and extracts themes and necessary elements. The output includes game themes and scenario candidates.

[0112] Step 3:

[0113] The server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate game elements based on the themes extracted in step 2. Specifically, it automatically generates character settings and scenario details. During this generation process, prompts are input to the generating AI model, and the corresponding game elements are output.

[0114] Step 4:

[0115] The server compares the generated game elements with a disaster prevention database (e.g., local flood data) and incorporates appropriate disaster prevention information. This ensures that the generated elements are both educational and practical. The output is integrated disaster prevention game data.

[0116] Step 5:

[0117] The server sends the completed game data to the user's device in real time. The device analyzes the data and displays an interactive preview screen to the user. The user can then edit characters and scenarios on the screen.

[0118] Step 6:

[0119] Users can choose whether to share their created and edited games with others via an information and communication network (e.g., the internet). If sharing is selected, the server uploads the game data to the specified platform and generates a link that other users can access. The output will be either a sharing URL or access rights to the file.

[0120] 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.

[0121] This invention provides a system for users to create disaster prevention games using natural language, and further incorporates a configuration that recognizes the user's emotions and utilizes that information to generate and adjust the game. When a user inputs instructions such as "I want to create an evacuation game for when an earthquake occurs" in natural language on a terminal, the terminal sends this to a server. The server uses a natural language processing engine to analyze the user's intent. At the same time, an emotion engine recognizes the user's emotions from the input and uses this information to complement the analysis results.

[0122] Based on user instructions and emotional data, the server can generate game elements (characters, scenarios, evacuation routes, etc.) and adjust the game's tone and difficulty depending on whether the user is experiencing positive or negative emotions. Furthermore, when accessing the disaster prevention information database and incorporating necessary disaster preparedness knowledge into the game, the server can consider the recognized user's emotions and adjust the way information is presented and the depth of its content accordingly.

[0123] The generated game elements are sent to the user's device in real time, and a basic preview is provided. Users can view this preview and provide feedback, such as "The evacuation route is too complicated; please simplify it." The server then readjusts the game elements based on the user's feedback.

[0124] Once a user is satisfied with the completed game, the server saves it and provides a download link to their device. In this way, users can freely create disaster prevention games while gaining a learning experience that takes their emotions into consideration. For example, if an elementary school student instructs the server to "add a character that is evacuating while feeling worried," the server will generate a character expression that matches that emotion, creating a game that more richly expresses the user's intentions. This allows users to experience interactive learning that reflects their emotions.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] The user uses their device to input instructions in natural language, such as "I want to create a tsunami evacuation game." The device immediately sends this input to the server.

[0128] Step 2:

[0129] The server runs a natural language processing engine to analyze the natural language input it receives. Through this analysis, it identifies the game's theme and objective, while an emotion engine recognizes the user's emotions from the input. For example, if the user is worried, that emotion data is extracted.

[0130] Step 3:

[0131] The server generates appropriate game elements (e.g., character expressions, game difficulty settings) based on the identified game theme and recognized emotions. If the emotion is positive, adventurous elements are added; if the emotion is negative, the theme emphasizes safety and security.

[0132] Step 4:

[0133] The server accesses a disaster prevention information database to obtain official guidelines regarding tsunami evacuation. This information is then incorporated into the game, with the presentation method adjusted according to the perceived emotions of the user.

[0134] Step 5:

[0135] The server transmits generated game elements and coordinated disaster prevention information to the user's terminal in real time. The terminal first presents the user with a basic preview, which the user then views to confirm the initial game experience.

[0136] Step 6:

[0137] If a user provides additional instructions, such as "I want to change the character's perspective to be more tense," the server will readjust the game elements accordingly. The scenario and presentation will be modified to better align with the user's intentions, taking emotional data into consideration.

[0138] Step 7:

[0139] Once the user is satisfied with the final content of the game, the server saves the completed game data and sends a download link to the device. This allows the user to download the game at home and enjoy an emotionally responsive learning experience.

[0140] (Example 2)

[0141] 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".

[0142] In disaster prevention education, there is a need for systems that allow users to easily create interactive games that reflect their own intentions and promote learning through real-world experience. However, conventional systems have difficulty accurately reflecting user intentions in game elements, and adjustments that take emotional elements into consideration have not been made. Furthermore, there have been challenges in providing real-time feedback and making readjustments.

[0143] 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.

[0144] In this invention, the server includes means for receiving input data via a device that accepts natural language input, means for processing the input data and analyzing the user's intentions and emotions, and means for generating and adjusting game elements using generative technology based on the analyzed intentions and emotions. This allows users to not only create disaster prevention games that reflect their own intentions, but also to adjust the game experience according to their emotions, thereby providing more personalized learning.

[0145] "Natural language input" is an input method that uses the language a user uses on a daily basis, and is a way of transmitting information to a system in a form that can be analyzed by a machine.

[0146] "Input data" refers to information that a user provides to the system, and includes all digital information, such as character data and numerical data based on natural language input.

[0147] "User intent" is a concept that refers to the purpose or desired outcome that a user tries to achieve through natural language input.

[0148] "Emotion analysis" is a technology that identifies and analyzes a user's emotional state based on the content of their input data.

[0149] "Generative techniques" refer to algorithms and methodologies for systems to create new data based on specified conditions or prompts.

[0150] "Game elements" refer to individual components included in a game created by a user, and include characters, scenarios, evacuation routes, etc.

[0151] "Real-time presentation" refers to the process of instantly processing generated information and providing it to the user on the spot.

[0152] "Readjustment" refers to the process of reviewing and adjusting the content and difficulty level of a game based on user feedback on its initial settings.

[0153] "Tone" is a term that refers to the overall atmosphere or emotion conveyed to the user within a game, and it usually has positive or negative attributes.

[0154] A "download link" is an internet link used by users to obtain digital content, and clicking it copies the specified file to the device.

[0155] This invention relates to a system that allows users to create interactive games related to disaster prevention using natural language. The system functions by using a terminal that receives user input and a server that analyzes and processes the data. The terminal is a device that accepts natural language input, thereby transmitting text information from the user to the server.

[0156] The server uses a specific analysis engine to determine the user's intent. Specifically, it uses a natural language processing engine, commonly known as "natural language processing software," to analyze the game elements the user desires. In addition, it utilizes "sentiment analysis software" for sentiment analysis, extracting the user's emotional state from the input text.

[0157] The results obtained from these analyses are reflected in each element of the game using an AI model that utilizes generative technology. These generated game elements include characters, scenarios, and evacuation routes, and are adjusted according to the user's intentions and emotions. Furthermore, relevant disaster prevention information can be obtained from external databases and incorporated into the game. This allows users to receive disaster prevention education while experiencing the game in an emotionally engaging way.

[0158] For example, if a primary school student inputs in natural language, "I want to add a character that evacuates while feeling worried," the server will generate a character that reflects that emotion based on this instruction and provide a scenario in which that character plays an active role. An example of a prompt message would be, "I want to create an evacuation game for when an earthquake occurs. Please simplify the evacuation routes." The server will then receive this instruction and perform appropriate data processing.

[0159] Thus, the present invention enables users to create educational and interactive games that reflect their own emotions.

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] The user inputs game instructions into the device using natural language. For example, they might input the instruction, "I want to create a game about evacuation during an earthquake." The device prepares this text data as input and proceeds with the transmission process.

[0163] Step 2:

[0164] The terminal sends the input natural language data to the server. The HTTPS protocol is used for transmission to ensure data security. This process receives the user's natural language data as input and sends it to the server as output.

[0165] Step 3:

[0166] The server uses natural language processing software to analyze the received natural language data. Specifically, it analyzes the received text data to identify the user's intent. In this process, the server receives user instructions as input and obtains the intent analysis results as output.

[0167] Step 4:

[0168] Based on the analyzed user intent, the server performs sentiment analysis using sentiment analysis software. It analyzes what emotions the user is feeling from keywords and context within the text. In this step, natural language data is received as input and sentiment data is obtained as output.

[0169] Step 5:

[0170] The server uses a generative AI model to generate game elements based on analysis results and sentiment data. This generation utilizes prompts to create characters and scenarios that are suitable for the specified conditions. In this step, intention and sentiment data are taken as input, and the generated game elements are obtained as output.

[0171] Step 6:

[0172] The server accesses a disaster prevention information database to retrieve relevant disaster information. This allows for the incorporation of realistic and educational information into the generated game. This process takes necessary information as input and provides related information as output.

[0173] Step 7:

[0174] The generated game elements are sent to the user's device in real time, and a preview is provided to the user. In this step, the generated game data is received as input, and data that is visually displayed on the device is provided as output.

[0175] Step 8:

[0176] The user reviews the preview and provides additional instructions (e.g., "The evacuation route is too complicated; please simplify it") via their device. The user sends feedback as input to their device and provides it to the server as output.

[0177] Step 9:

[0178] The server readjusts the generated game elements based on additional user instructions. It redesigns the game structure to reflect the feedback. In this step, it receives feedback as input and generates the adjusted game elements as output.

[0179] Step 10:

[0180] Once the user is satisfied with the game, the server saves the final version and provides the user with a download link. This process stores the final game data as input and provides a downloadable link as output.

[0181] (Application Example 2)

[0182] 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".

[0183] The goal is to provide a system for creating disaster prevention education games that allows users to generate game elements using natural language while personalizing the game content based on the user's emotions. Specifically, the challenge is to provide a more appropriate educational experience by dynamically adjusting the tone and difficulty of the game according to the user's emotional state.

[0184] 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.

[0185] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for recognizing emotional information, means for generating or adjusting game elements using emotional information, means for acquiring disaster prevention information and incorporating it into the game, and means for presenting the generated game in real time. This makes it possible to generate a disaster prevention education game based on instructions given by the user in natural language, and further enables a personalized game experience that takes the user's emotions into consideration.

[0186] A "means for accepting natural language input" refers to a function that allows the system to receive natural language text entered by a user and process its content.

[0187] "Means for analyzing user intent" refers to technologies that recognize what a user wants from the natural language input received and extract that information.

[0188] "Means for generating game elements" refers to a function that develops necessary game components such as characters, scenarios, and evacuation routes based on the analyzed user intent.

[0189] "Means of recognizing emotional information" refers to technologies that analyze a user's natural language input and voice intonation to determine what emotional state the user is in.

[0190] "Means for generating or adjusting game elements using emotional information" refers to a function that dynamically controls the tone, difficulty level, and information presentation method of the generated game using the results of emotional recognition.

[0191] The "means of acquiring disaster prevention information and incorporating it into games" refers to a function that provides realistic educational content by acquiring information from the latest disaster prevention-related databases and reflecting it in the game's content.

[0192] "Means of presenting generated games in real time" refers to technologies that instantly display created game elements to users in order to enable interaction with them.

[0193] In a form for carrying out the invention, this system is specifically designed for users to create disaster prevention education games using natural language. Users use devices such as smartphones or tablets to input their instructions in natural language.

[0194] The terminal sends user instructions to the server as natural language text. The server uses a natural language processing engine to analyze the user's intent. Furthermore, it uses an emotion recognition engine to identify the user's emotions and incorporate them into the analyzed intent. In this process, specific program libraries (e.g., the emotion_recognition library and the language_processor library) run on the server, analyzing the user's input data based on their tone of voice and word choice tendencies.

[0195] The server designs game elements using a game generation engine based on the user's intent and emotional information. Here, necessary information is retrieved from a disaster prevention information database and reflected in the game content. The generated game elements are then adjusted to match the user's emotional state in terms of tone and difficulty.

[0196] Once game elements are created, they are sent to the user's device in real time. The user reviews the game content through a preview screen and provides feedback by offering additional instructions if necessary. This feedback is received by the server and used to fine-tune the game elements. Finally, once the user confirms the game is complete, the server saves the final version and generates a link for content delivery to the user's device.

[0197] For example, when an elementary school teacher instructs the app to "simulate the experience of children evacuating safely," the server generates a game incorporating a gentle-looking character and a simple evacuation route.

[0198] An example of an input prompt for the generating AI model would be: "Generate a calming emergency evacuation game scenario for elementary school students. Include supportive and motivational messages that can help students understand safety procedures while feeling reassured." This system enables the provision of personalized disaster prevention education content tailored to the user's needs.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The user inputs instructions in natural language using a terminal. The entered natural language text is saved as data on the terminal and prepared to be sent to the server for the next processing step.

[0202] Step 2:

[0203] The terminal sends natural language text to the server. The server begins processing the received natural language text as input. The server's natural language processing engine starts up, analyzes it, and extracts the user's intent. The user's intent obtained from the analysis is prepared for the next processing step.

[0204] Step 3:

[0205] Based on the user's intent, the server uses an emotion recognition engine to re-analyze the natural language text. This analysis extracts the user's emotional information. The server then processes this emotional information and converts it into a format usable for generating game elements.

[0206] Step 4:

[0207] The server uses a game generation engine, taking user intent and emotional information as input. It retrieves the latest data from a disaster prevention information database and generates game elements. These generated game elements include characters, scenarios, and evacuation routes. The server prepares this data.

[0208] Step 5:

[0209] The server automatically adjusts the generated game elements based on the user's emotional information. The game's tone and difficulty are customized to the user. The adjusted game elements are then prepared for real-time display.

[0210] Step 6:

[0211] The server sends the adjusted game elements to the device. The device receives this and displays a real-time preview to the user. The user can then check the displayed game content.

[0212] Step 7:

[0213] Users provide feedback using their devices. The device receives user feedback as input and sends it to the server. This feedback may include additional instructions or requests for improvements from the user.

[0214] Step 8:

[0215] The server readjusts game elements based on the feedback received. It analyzes the feedback as data, makes necessary corrections, and generates the final game elements.

[0216] Step 9:

[0217] If the user agrees to complete the game, the server saves the completed game and generates a content distribution link. This link is sent to the device, and the user can obtain the game through it.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention provides a system for users to create disaster prevention games using natural language, and its implementation is carried out in the following configuration: When a user inputs "I want to create a disaster prevention game" in natural language via a terminal, the terminal transmits this input information to a server. The server analyzes this information and operates a natural language processing engine to identify the user's intent. This engine understands the game's theme, scenario, and necessary elements from the input text, and uses a multimodal learning model to begin generating appropriate game elements.

[0235] The generated game elements are supplemented by incorporating necessary disaster prevention knowledge based on a disaster prevention information database. This creates a game with high educational value. The server sends this generated content to the user's terminal in real time, and the user can check the preview and issue further instructions for modifications and additions, such as "I want to add more characters" or "I want to adjust the scenario." The server receives these new instructions and readjusts the dynamically generated game elements.

[0236] Finally, once the user is satisfied with the completed game, the server saves the final data and provides a link that the user can download via their device. This system allows users to easily create disaster prevention games that reflect their own ideas and learn disaster prevention knowledge while having fun, without requiring any specialized programming skills.

[0237] For example, if a primary school student user instructs the system to "create a game about protecting a town from a tsunami," the server will generate a realistic town model based on a tsunami scenario and incorporate necessary evacuation information and warning systems into the game. The user can then monitor the game's progress, adjust character dialogue and the town's layout, and complete their ideal game. In this way, a fun and enriching learning experience can be provided.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user enters their request for a disaster prevention game using natural language input via their device. This input is in the format of "I want to create a game about evacuation during an earthquake."

[0241] Step 2:

[0242] The terminal sends user input to the server, which then uses a natural language processing engine to analyze the input. Here, the server identifies the game's theme (e.g., earthquake, evacuation) and necessary elements.

[0243] Step 3:

[0244] Based on the information analyzed by the server, game elements are generated using a multimodal model. This includes scenario development, character design, and evacuation route design.

[0245] Step 4:

[0246] The server accesses a disaster prevention information database and incorporates official recommendations and action guidelines regarding earthquake evacuation as in-game information.

[0247] Step 5:

[0248] The server sends the generated game elements and disaster prevention information to the user's terminal in real time and displays it as a preview. The user can review this and make any necessary changes.

[0249] Step 6:

[0250] If a user gives instructions to add or modify elements, such as "add an alarm sound" or "adjust the difficulty of evacuation," the server will receive these instructions and readjust the existing game elements.

[0251] Step 7:

[0252] Once the user is finally satisfied with the game's content, the server saves the completed game data and provides a downloadable link to the user's device, allowing them to acquire and play the game.

[0253] (Example 1)

[0254] 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."

[0255] In order for many users to deepen their knowledge of disaster prevention, there is a need for a system that allows them to easily design and adjust educational and interactive games based on their own ideas, even without specialized programming skills.

[0256] 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.

[0257] In this invention, the server includes a device for receiving natural language input, a device for analyzing the user's intent based on the natural language input, and a device for generating game elements based on the analyzed intent. This makes it possible for users to design their own disaster prevention-related games that reflect their own ideas, without requiring specialized technical knowledge, and to learn while having fun.

[0258] "Natural language input" is a method of conveying instructions and information to a system using the language that users use in their daily lives.

[0259] A "device" is a machine or electronic device designed to perform a specific function.

[0260] "User intent" refers to the user's goals and desires regarding the system, such as what they want to achieve or what elements they are looking for.

[0261] "Analyzing" is the process of deciphering given information or data and revealing its meaning and intent.

[0262] "Gameplay elements" refer to all the elements that make up a game, such as items, characters, and scenarios used during gameplay.

[0263] "Disaster information" refers to data and knowledge related to natural disasters and sudden events.

[0264] "Presenting immediately" means that the results are displayed quickly without any waiting time after input or processing has been completed.

[0265] This invention is a system that enables users to design games related to disaster prevention without requiring specialized technical knowledge, and to learn while having fun.

[0266] First, the user enters natural language input via their device, such as "I want to create a disaster prevention game." The device then forwards this input data to a server to understand the user's intent. The server analyzes this input using a natural language processing engine. Tools such as NLTK and spaCy are often used for this engine.

[0267] Based on the analysis results, the server activates a generative AI model to generate game elements. This model utilizes a general-purpose generative AI tool available on a specific platform (e.g., GPT). The generated game elements are further enhanced by referencing a disaster information database and incorporating necessary disaster prevention knowledge.

[0268] The game elements created in this way are immediately presented to the user's device. The user can review this preview and give additional instructions, such as "I want to add more characters" or "I want to change the scenario." The server receives these instructions and readjusts the game elements in real time. Finally, when the user is satisfied with the completed game, the server saves the completed data and provides a download link to the device.

[0269] For example, if an elementary school student inputs "I want to create a game about protecting a town from a tsunami," the server will generate a tsunami-themed scenario. Furthermore, this scenario will incorporate specific evacuation information and warning systems related to disaster prevention. Based on this, users can adjust the game's progression and character movements to complete their ideal game.

[0270] An example of a prompt message would be: "The theme of this disaster prevention game is tsunamis. We want to incorporate evacuation routes and warning systems as learning elements necessary for players to protect the city. To enhance replayability, we want to provide multiple characters and scenario variations." This allows users to deepen their disaster prevention knowledge while having fun playing the game.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The user uses a terminal to input natural language instructions, such as "I want to create a disaster prevention game." This input includes the user's intentions and desired game theme. The terminal formats this input as text data and sends it to the server.

[0274] Step 2:

[0275] The server analyzes natural language input data received from the terminal. First, a natural language processing engine tokenizes the input text and performs syntactic analysis. This analysis extracts the game themes and elements desired by the user. The input is the user's instruction, and the output is the analyzed intent and keywords.

[0276] Step 3:

[0277] The server activates a generating AI model based on the analysis results. Specifically, the AI ​​model generates game elements from the analyzed themes and keywords. For example, if the user selects "tsunami" as the theme, the AI ​​model will generate related game scenarios and characters. In this step, the input is the analysis results, and the output is the generated game elements.

[0278] Step 4:

[0279] The server matches the generated game elements with the disaster information database. It retrieves the necessary disaster prevention knowledge from this database and incorporates it into the game elements. The input is the generated game elements, and the output is the game elements incorporated with disaster prevention information.

[0280] Step 5:

[0281] The server transmits the completed game elements to the user's terminal in real time. The user can view a preview of the game on the terminal. At this time, an interface for receiving evaluations and additional instructions from the user is displayed. The input is the generated game elements, and the output is the real-time display on the user's terminal.

[0282] Step 6:

[0283] The user views the game preview and gives additional instructions such as "want to increase the characters" through the terminal. The terminal transmits these instructions to the server again. The input is the additional instructions from the user, and the output is the update instructions to the server.

[0284] Step 7:

[0285] The server receives the additional instructions from the user and readjusts the game elements using the generation AI model again. The readjusted elements are compared with the disaster prevention database again to complement the content. The input for this step is the additional instructions from the user, and the output is the readjusted game elements.

[0286] Step 8:

[0287] When the user is satisfied with the content of the game, the server saves the final game data and provides a download link to the terminal. Using this link, the user can download the game. The input is the instruction of the final decision, and the output is the download link.

[0288] (Application Example 1)

[0289] 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."

[0290] There is a need for a system that allows users to easily create and share disaster prevention educational content with others. However, conventional systems require specialized knowledge, making it difficult for ordinary users to easily create and share disaster prevention games that reflect their own ideas. Furthermore, the lack of real-time editing and sharing functions means that the system lacks the flexibility needed to enhance its educational effectiveness.

[0291] 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.

[0292] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for generating game elements, means for automatically acquiring disaster prevention data and incorporating it into the game, means for presenting the generated game elements to the user in a real-time editable state, and means for sharing the generated game with others via an information and communication network. This makes it possible for users to create, modify, and share disaster prevention games in natural language without requiring specialized knowledge.

[0293] "Natural language input" refers to text that users speak or write as human language, rather than in a programming language.

[0294] "Methods for analyzing user intent" refer to technologies that interpret what the user wants from the input natural language and extract specific instructions or objectives.

[0295] "Means for generating game elements" refers to a process that automatically creates the necessary components of a game (characters, scenarios, rules, etc.) based on the user's intentions.

[0296] "Disaster prevention data" refers to information related to disasters, including knowledge and awareness-raising information about natural disasters such as earthquakes and tsunamis.

[0297] "A means of presenting information in a real-time, editable state" refers to a method of displaying information in a way that allows users to instantly view and modify the generated content.

[0298] "Means of sharing with others through information and communication networks" refers to methods of transmitting created content to other users using the internet or other communication networks, making it available for use or viewing.

[0299] This system allows users to easily create and share disaster prevention games. The system primarily consists of a server and the user's device (e.g., a smartphone).

[0300] First, the user inputs the concept for the disaster prevention game in natural language using the interface on their device. Input can be either text or voice. For example, the instruction might be, "Please create a disaster prevention game for flood control."

[0301] The server runs on the cloud and analyzes user intent using a natural language processing engine (e.g., Google NLP API). The analysis reveals the game themes and elements the user is looking for. Next, the server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate the necessary game elements, including characters, scenarios, and rules.

[0302] The generated game elements are supplemented with disaster prevention information data (e.g., public databases on earthquakes and tsunamis) and incorporated as necessary educational content. The server then sends the generated content to the user's device in real time, providing an interactive environment where the user can immediately preview and make adjustments.

[0303] Furthermore, the completed game edited by the user can be shared with other users via the information communication network (e.g., the Internet) through the server. With this sharing function, it can be efficiently utilized in educational institutions and individual learning environments.

[0304] As a specific example, when a user gives an instruction such as "I want to create a simulation game for natural disasters likely to occur in the region", the system analyzes the disaster data of that region and automatically generates a game including appropriate scenarios and evacuation guidance. An example of the prompt text at this time could be in the form of "Create a disaster prevention game for floods. When a flood occurs in the city, I want to create a scenario for safe evacuation".

[0305] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0306] Step 1: [[ID=Z14]]

[0307] The user inputs the concept of the disaster prevention game in natural language via the terminal. The terminal sends this input to the server as text data. An example of the input is "I want to create a disaster prevention game for flood countermeasures".

[0308] Step 2:

[0309] The server sends the received input to a natural language processing engine (e.g., Google NLP API) to analyze the user's intention. In the analysis process, the input string is tokenized, semantic analysis is performed, and themes and necessary elements are extracted. As output, the theme and scenario candidates of the game are obtained.

[0310] Step 3:

[0311] The server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate game elements based on the themes extracted in step 2. Specifically, it automatically generates character settings and scenario details. During this generation process, prompts are input to the generating AI model, and the corresponding game elements are output.

[0312] Step 4:

[0313] The server compares the generated game elements with a disaster prevention database (e.g., local flood data) and incorporates appropriate disaster prevention information. This ensures that the generated elements are both educational and practical. The output is integrated disaster prevention game data.

[0314] Step 5:

[0315] The server sends the completed game data to the user's device in real time. The device analyzes the data and displays an interactive preview screen to the user. The user can then edit characters and scenarios on the screen.

[0316] Step 6:

[0317] Users can choose whether to share their created and edited games with others via an information and communication network (e.g., the internet). If sharing is selected, the server uploads the game data to the specified platform and generates a link that other users can access. The output will be either a sharing URL or access rights to the file.

[0318] 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.

[0319] This invention provides a system for users to create disaster prevention games using natural language, and further incorporates a configuration that recognizes the user's emotions and utilizes that information to generate and adjust the game. When a user inputs instructions such as "I want to create an evacuation game for when an earthquake occurs" in natural language on a terminal, the terminal sends this to a server. The server uses a natural language processing engine to analyze the user's intent. At the same time, an emotion engine recognizes the user's emotions from the input and uses this information to complement the analysis results.

[0320] Based on user instructions and emotional data, the server can generate game elements (characters, scenarios, evacuation routes, etc.) and adjust the game's tone and difficulty depending on whether the user is experiencing positive or negative emotions. Furthermore, when accessing the disaster prevention information database and incorporating necessary disaster preparedness knowledge into the game, the server can consider the recognized user's emotions and adjust the way information is presented and the depth of its content accordingly.

[0321] The generated game elements are sent to the user's device in real time, and a basic preview is provided. Users can view this preview and provide feedback, such as "The evacuation route is too complicated; please simplify it." The server then readjusts the game elements based on the user's feedback.

[0322] Once a user is satisfied with the completed game, the server saves it and provides a download link to their device. In this way, users can freely create disaster prevention games while gaining a learning experience that takes their emotions into consideration. For example, if an elementary school student instructs the server to "add a character that is evacuating while feeling worried," the server will generate a character expression that matches that emotion, creating a game that more richly expresses the user's intentions. This allows users to experience interactive learning that reflects their emotions.

[0323] The following describes the processing flow.

[0324] Step 1:

[0325] The user uses their device to input instructions in natural language, such as "I want to create a tsunami evacuation game." The device immediately sends this input to the server.

[0326] Step 2:

[0327] The server runs a natural language processing engine to analyze the natural language input it receives. Through this analysis, it identifies the game's theme and objective, while an emotion engine recognizes the user's emotions from the input. For example, if the user is worried, that emotion data is extracted.

[0328] Step 3:

[0329] The server generates appropriate game elements (e.g., character expressions, game difficulty settings) based on the identified game theme and recognized emotions. If the emotion is positive, adventurous elements are added; if the emotion is negative, the theme emphasizes safety and security.

[0330] Step 4:

[0331] The server accesses a disaster prevention information database to obtain official guidelines regarding tsunami evacuation. This information is then incorporated into the game, with the presentation method adjusted according to the perceived emotions of the user.

[0332] Step 5:

[0333] The server transmits generated game elements and coordinated disaster prevention information to the user's terminal in real time. The terminal first presents the user with a basic preview, which the user then views to confirm the initial game experience.

[0334] Step 6:

[0335] If a user provides additional instructions, such as "I want to change the character's perspective to be more tense," the server will readjust the game elements accordingly. The scenario and presentation will be modified to better align with the user's intentions, taking emotional data into consideration.

[0336] Step 7:

[0337] Once the user is satisfied with the final content of the game, the server saves the completed game data and sends a download link to the device. This allows the user to download the game at home and enjoy an emotionally responsive learning experience.

[0338] (Example 2)

[0339] 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".

[0340] In disaster prevention education, there is a need for systems that allow users to easily create interactive games that reflect their own intentions and promote learning through real-world experience. However, conventional systems have difficulty accurately reflecting user intentions in game elements, and adjustments that take emotional elements into consideration have not been made. Furthermore, there have been challenges in providing real-time feedback and making readjustments.

[0341] 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.

[0342] In this invention, the server includes means for receiving input data via a device that accepts natural language input, means for processing the input data and analyzing the user's intentions and emotions, and means for generating and adjusting game elements using generative technology based on the analyzed intentions and emotions. This allows users to not only create disaster prevention games that reflect their own intentions, but also to adjust the game experience according to their emotions, thereby providing more personalized learning.

[0343] "Natural language input" is an input method that uses the language a user uses on a daily basis, and is a way of transmitting information to a system in a form that can be analyzed by a machine.

[0344] "Input data" refers to information that a user provides to the system, and includes all digital information, such as character data and numerical data based on natural language input.

[0345] "User intent" is a concept that refers to the purpose or desired outcome that a user tries to achieve through natural language input.

[0346] "Emotion analysis" is a technology that identifies and analyzes a user's emotional state based on the content of their input data.

[0347] "Generative techniques" refer to algorithms and methodologies for systems to create new data based on specified conditions or prompts.

[0348] "Game elements" refer to individual components included in a game created by a user, and include characters, scenarios, evacuation routes, etc.

[0349] "Real-time presentation" refers to the process of instantly processing generated information and providing it to the user on the spot.

[0350] "Readjustment" refers to the process of reviewing and adjusting the content and difficulty level of a game based on user feedback on its initial settings.

[0351] "Tone" is a term that refers to the overall atmosphere or emotion conveyed to the user within a game, and it usually has positive or negative attributes.

[0352] A "download link" is an internet link used by users to obtain digital content, and clicking it copies the specified file to the device.

[0353] This invention relates to a system that allows users to create interactive games related to disaster prevention using natural language. The system functions by using a terminal that receives user input and a server that analyzes and processes the data. The terminal is a device that accepts natural language input, thereby transmitting text information from the user to the server.

[0354] The server uses a specific analysis engine to determine the user's intent. Specifically, it uses a natural language processing engine, commonly known as "natural language processing software," to analyze the game elements the user desires. In addition, it utilizes "sentiment analysis software" for sentiment analysis, extracting the user's emotional state from the input text.

[0355] The results obtained from these analyses are reflected in each element of the game using an AI model that utilizes generative technology. These generated game elements include characters, scenarios, and evacuation routes, and are adjusted according to the user's intentions and emotions. Furthermore, relevant disaster prevention information can be obtained from external databases and incorporated into the game. This allows users to receive disaster prevention education while experiencing the game in an emotionally engaging way.

[0356] For example, if a primary school student inputs in natural language, "I want to add a character that evacuates while feeling worried," the server will generate a character that reflects that emotion based on this instruction and provide a scenario in which that character plays an active role. An example of a prompt message would be, "I want to create an evacuation game for when an earthquake occurs. Please simplify the evacuation routes." The server will then receive this instruction and perform appropriate data processing.

[0357] Thus, the present invention enables users to create educational and interactive games that reflect their own emotions.

[0358] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0359] Step 1:

[0360] The user inputs game instructions into the device using natural language. For example, they might input the instruction, "I want to create a game about evacuation during an earthquake." The device prepares this text data as input and proceeds with the transmission process.

[0361] Step 2:

[0362] The terminal sends the input natural language data to the server. The HTTPS protocol is used for transmission to ensure data security. This process receives the user's natural language data as input and sends it to the server as output.

[0363] Step 3:

[0364] The server uses natural language processing software to analyze the received natural language data. Specifically, it analyzes the received text data to identify the user's intent. In this process, the server receives user instructions as input and obtains the intent analysis results as output.

[0365] Step 4:

[0366] Based on the analyzed user intent, the server performs sentiment analysis using sentiment analysis software. It analyzes what emotions the user is feeling from keywords and context within the text. In this step, natural language data is received as input and sentiment data is obtained as output.

[0367] Step 5:

[0368] The server uses a generative AI model to generate game elements based on analysis results and sentiment data. This generation utilizes prompts to create characters and scenarios that are suitable for the specified conditions. In this step, intention and sentiment data are taken as input, and the generated game elements are obtained as output.

[0369] Step 6:

[0370] The server accesses a disaster prevention information database to retrieve relevant disaster information. This allows for the incorporation of realistic and educational information into the generated game. This process takes necessary information as input and provides related information as output.

[0371] Step 7:

[0372] The generated game elements are sent to the user's device in real time, and a preview is provided to the user. In this step, the generated game data is received as input, and data that is visually displayed on the device is provided as output.

[0373] Step 8:

[0374] The user reviews the preview and provides additional instructions (e.g., "The evacuation route is too complicated; please simplify it") via their device. The user sends feedback as input to their device and provides it to the server as output.

[0375] Step 9:

[0376] The server readjusts the generated game elements based on additional user instructions. It redesigns the game structure to reflect the feedback. In this step, it receives feedback as input and generates the adjusted game elements as output.

[0377] Step 10:

[0378] Once the user is satisfied with the game, the server saves the final version and provides the user with a download link. This process stores the final game data as input and provides a downloadable link as output.

[0379] (Application Example 2)

[0380] 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."

[0381] The goal is to provide a system for creating disaster prevention education games that allows users to generate game elements using natural language while personalizing the game content based on the user's emotions. Specifically, the challenge is to provide a more appropriate educational experience by dynamically adjusting the tone and difficulty of the game according to the user's emotional state.

[0382] 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.

[0383] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for recognizing emotional information, means for generating or adjusting game elements using emotional information, means for acquiring disaster prevention information and incorporating it into the game, and means for presenting the generated game in real time. This makes it possible to generate a disaster prevention education game based on instructions given by the user in natural language, and further enables a personalized game experience that takes the user's emotions into consideration.

[0384] A "means for accepting natural language input" refers to a function that allows the system to receive natural language text entered by a user and process its content.

[0385] "Means for analyzing user intent" refers to technologies that recognize what a user wants from the natural language input received and extract that information.

[0386] "Means for generating game elements" refers to a function that develops necessary game components such as characters, scenarios, and evacuation routes based on the analyzed user intent.

[0387] "Means of recognizing emotional information" refers to technologies that analyze a user's natural language input and voice intonation to determine what emotional state the user is in.

[0388] "Means for generating or adjusting game elements using emotional information" refers to a function that dynamically controls the tone, difficulty level, and information presentation method of the generated game using the results of emotional recognition.

[0389] The "means of acquiring disaster prevention information and incorporating it into games" refers to a function that provides realistic educational content by acquiring information from the latest disaster prevention-related databases and reflecting it in the game's content.

[0390] "Means of presenting generated games in real time" refers to technologies that instantly display created game elements to users in order to enable interaction with them.

[0391] In a form for carrying out the invention, this system is specifically designed for users to create disaster prevention education games using natural language. Users use devices such as smartphones or tablets to input their instructions in natural language.

[0392] The terminal sends user instructions to the server as natural language text. The server uses a natural language processing engine to analyze the user's intent. Furthermore, it uses an emotion recognition engine to identify the user's emotions and incorporate them into the analyzed intent. In this process, specific program libraries (e.g., the emotion_recognition library and the language_processor library) run on the server, analyzing the user's input data based on their tone of voice and word choice tendencies.

[0393] The server designs game elements using a game generation engine based on the user's intent and emotional information. Here, necessary information is retrieved from a disaster prevention information database and reflected in the game content. The generated game elements are then adjusted to match the user's emotional state in terms of tone and difficulty.

[0394] Once game elements are created, they are sent to the user's device in real time. The user reviews the game content through a preview screen and provides feedback by offering additional instructions if necessary. This feedback is received by the server and used to fine-tune the game elements. Finally, once the user confirms the game is complete, the server saves the final version and generates a link for content delivery to the user's device.

[0395] For example, when an elementary school teacher instructs the app to "simulate the experience of children evacuating safely," the server generates a game incorporating a gentle-looking character and a simple evacuation route.

[0396] An example of an input prompt for the generating AI model would be: "Generate a calming emergency evacuation game scenario for elementary school students. Include supportive and motivational messages that can help students understand safety procedures while feeling reassured." This system enables the provision of personalized disaster prevention education content tailored to the user's needs.

[0397] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0398] Step 1:

[0399] The user inputs instructions in natural language using a terminal. The entered natural language text is saved as data on the terminal and prepared to be sent to the server for the next processing step.

[0400] Step 2:

[0401] The terminal sends natural language text to the server. The server begins processing the received natural language text as input. The server's natural language processing engine starts up, analyzes it, and extracts the user's intent. The user's intent obtained from the analysis is prepared for the next processing step.

[0402] Step 3:

[0403] Based on the user's intent, the server uses an emotion recognition engine to re-analyze the natural language text. This analysis extracts the user's emotional information. The server then processes this emotional information and converts it into a format usable for generating game elements.

[0404] Step 4:

[0405] The server uses a game generation engine, taking user intent and emotional information as input. It retrieves the latest data from a disaster prevention information database and generates game elements. These generated game elements include characters, scenarios, and evacuation routes. The server prepares this data.

[0406] Step 5:

[0407] The server automatically adjusts the generated game elements based on the user's emotional information. The game's tone and difficulty are customized to the user. The adjusted game elements are then prepared for real-time display.

[0408] Step 6:

[0409] The server sends the adjusted game elements to the device. The device receives this and displays a real-time preview to the user. The user can then check the displayed game content.

[0410] Step 7:

[0411] Users provide feedback using their devices. The device receives user feedback as input and sends it to the server. This feedback may include additional instructions or requests for improvements from the user.

[0412] Step 8:

[0413] The server readjusts game elements based on the feedback received. It analyzes the feedback as data, makes necessary corrections, and generates the final game elements.

[0414] Step 9:

[0415] If the user agrees to complete the game, the server saves the completed game and generates a content distribution link. This link is sent to the device, and the user can obtain the game through it.

[0416] 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.

[0417] 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention provides a system for users to create disaster prevention games using natural language, and its implementation is carried out in the following configuration: When a user inputs "I want to create a disaster prevention game" in natural language via a terminal, the terminal transmits this input information to a server. The server analyzes this information and operates a natural language processing engine to identify the user's intent. This engine understands the game's theme, scenario, and necessary elements from the input text, and uses a multimodal learning model to begin generating appropriate game elements.

[0433] The generated game elements are supplemented by incorporating necessary disaster prevention knowledge based on a disaster prevention information database. This creates a game with high educational value. The server sends this generated content to the user's terminal in real time, and the user can check the preview and issue further instructions for modifications and additions, such as "I want to add more characters" or "I want to adjust the scenario." The server receives these new instructions and readjusts the dynamically generated game elements.

[0434] Finally, once the user is satisfied with the completed game, the server saves the final data and provides a link that the user can download via their device. This system allows users to easily create disaster prevention games that reflect their own ideas and learn disaster prevention knowledge while having fun, without requiring any specialized programming skills.

[0435] For example, if a primary school student user instructs the system to "create a game about protecting a town from a tsunami," the server will generate a realistic town model based on a tsunami scenario and incorporate necessary evacuation information and warning systems into the game. The user can then monitor the game's progress, adjust character dialogue and the town's layout, and complete their ideal game. In this way, a fun and enriching learning experience can be provided.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user enters their request for a disaster prevention game using natural language input via their device. This input is in the format of "I want to create a game about evacuation during an earthquake."

[0439] Step 2:

[0440] The terminal sends user input to the server, which then uses a natural language processing engine to analyze the input. Here, the server identifies the game's theme (e.g., earthquake, evacuation) and necessary elements.

[0441] Step 3:

[0442] Based on the information analyzed by the server, game elements are generated using a multimodal model. This includes scenario development, character design, and evacuation route design.

[0443] Step 4:

[0444] The server accesses a disaster prevention information database and incorporates official recommendations and action guidelines regarding earthquake evacuation as in-game information.

[0445] Step 5:

[0446] The server sends the generated game elements and disaster prevention information to the user's terminal in real time and displays it as a preview. The user can review this and make any necessary changes.

[0447] Step 6:

[0448] If a user gives instructions to add or modify elements, such as "add an alarm sound" or "adjust the difficulty of evacuation," the server will receive these instructions and readjust the existing game elements.

[0449] Step 7:

[0450] Once the user is finally satisfied with the game's content, the server saves the completed game data and provides a downloadable link to the user's device, allowing them to acquire and play the game.

[0451] (Example 1)

[0452] 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."

[0453] In order for many users to deepen their knowledge of disaster prevention, there is a need for a system that allows them to easily design and adjust educational and interactive games based on their own ideas, even without specialized programming skills.

[0454] 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.

[0455] In this invention, the server includes a device for receiving natural language input, a device for analyzing the user's intent based on the natural language input, and a device for generating game elements based on the analyzed intent. This makes it possible for users to design their own disaster prevention-related games that reflect their own ideas, without requiring specialized technical knowledge, and to learn while having fun.

[0456] "Natural language input" is a method of conveying instructions and information to a system using the language that users use in their daily lives.

[0457] A "device" is a machine or electronic device designed to perform a specific function.

[0458] "User intent" refers to the user's goals and desires regarding the system, such as what they want to achieve or what elements they are looking for.

[0459] "Analyzing" is the process of deciphering given information or data and revealing its meaning and intent.

[0460] "Gameplay elements" refer to all the elements that make up a game, such as items, characters, and scenarios used during gameplay.

[0461] "Disaster information" refers to data and knowledge related to natural disasters and sudden events.

[0462] "Presenting immediately" means that the results are displayed quickly without any waiting time after input or processing has been completed.

[0463] This invention is a system that enables users to design games related to disaster prevention without requiring specialized technical knowledge, and to learn while having fun.

[0464] First, the user enters natural language input via their device, such as "I want to create a disaster prevention game." The device then forwards this input data to a server to understand the user's intent. The server analyzes this input using a natural language processing engine. Tools such as NLTK and spaCy are often used for this engine.

[0465] Based on the analysis results, the server activates a generative AI model to generate game elements. This model utilizes a general-purpose generative AI tool available on a specific platform (e.g., GPT). The generated game elements are further enhanced by referencing a disaster information database and incorporating necessary disaster prevention knowledge.

[0466] The game elements created in this way are immediately presented to the user's device. The user can review this preview and give additional instructions, such as "I want to add more characters" or "I want to change the scenario." The server receives these instructions and readjusts the game elements in real time. Finally, when the user is satisfied with the completed game, the server saves the completed data and provides a download link to the device.

[0467] For example, if an elementary school student inputs "I want to create a game about protecting a town from a tsunami," the server will generate a tsunami-themed scenario. Furthermore, this scenario will incorporate specific evacuation information and warning systems related to disaster prevention. Based on this, users can adjust the game's progression and character movements to complete their ideal game.

[0468] An example of a prompt message would be: "The theme of this disaster prevention game is tsunamis. We want to incorporate evacuation routes and warning systems as learning elements necessary for players to protect the city. To enhance replayability, we want to provide multiple characters and scenario variations." This allows users to deepen their disaster prevention knowledge while having fun playing the game.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] The user uses a terminal to input natural language instructions, such as "I want to create a disaster prevention game." This input includes the user's intentions and desired game theme. The terminal formats this input as text data and sends it to the server.

[0472] Step 2:

[0473] The server analyzes natural language input data received from the terminal. First, a natural language processing engine tokenizes the input text and performs syntactic analysis. This analysis extracts the game themes and elements desired by the user. The input is the user's instruction, and the output is the analyzed intent and keywords.

[0474] Step 3:

[0475] The server activates a generating AI model based on the analysis results. Specifically, the AI ​​model generates game elements from the analyzed themes and keywords. For example, if the user selects "tsunami" as the theme, the AI ​​model will generate related game scenarios and characters. In this step, the input is the analysis results, and the output is the generated game elements.

[0476] Step 4:

[0477] The server compares the generated game elements with a disaster information database. It retrieves necessary disaster prevention knowledge from this database and incorporates it into the game elements. The input is the generated game elements, and the output is the game elements with the disaster prevention information incorporated.

[0478] Step 5:

[0479] The server sends the completed game elements to the user's device in real time. The user can view a preview of the game on their device. At this time, an interface is displayed that accepts user feedback and additional instructions. The input is the generated game elements, and the output is the real-time display on the user's device.

[0480] Step 6:

[0481] The user checks the game preview and gives additional instructions via the terminal, such as "I want to add more characters." The terminal then sends these instructions back to the server. The input is the additional instructions from the user, and the output is the update instructions sent to the server.

[0482] Step 7:

[0483] The server receives additional instructions from the user and readjusts the game elements using the generated AI model again. The readjusted elements are then cross-referenced with the disaster prevention database to complete the content. The input for this step is the user's additional instructions, and the output is the readjusted game elements.

[0484] Step 8:

[0485] When the user is satisfied with the gameplay, the server saves the final game data and provides a download link to the device. The user can then download the game using this link. The input is the final decision instruction, and the output is the download link.

[0486] (Application Example 1)

[0487] 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."

[0488] There is a need for a system that allows users to easily create and share disaster prevention educational content with others. However, conventional systems require specialized knowledge, making it difficult for ordinary users to easily create and share disaster prevention games that reflect their own ideas. Furthermore, the lack of real-time editing and sharing functions means that the system lacks the flexibility needed to enhance its educational effectiveness.

[0489] 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.

[0490] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for generating game elements, means for automatically acquiring disaster prevention data and incorporating it into the game, means for presenting the generated game elements to the user in a real-time editable state, and means for sharing the generated game with others via an information and communication network. This makes it possible for users to create, modify, and share disaster prevention games in natural language without requiring specialized knowledge.

[0491] "Natural language input" refers to text that users speak or write as human language, rather than in a programming language.

[0492] "Methods for analyzing user intent" refer to technologies that interpret what the user wants from the input natural language and extract specific instructions or objectives.

[0493] "Means for generating game elements" refers to a process that automatically creates the necessary components of a game (characters, scenarios, rules, etc.) based on the user's intentions.

[0494] "Disaster prevention data" refers to information related to disasters, including knowledge and awareness-raising information about natural disasters such as earthquakes and tsunamis.

[0495] "A means of presenting information in a real-time, editable state" refers to a method of displaying information in a way that allows users to instantly view and modify the generated content.

[0496] "Means of sharing with others through information and communication networks" refers to methods of transmitting created content to other users using the internet or other communication networks, making it available for use or viewing.

[0497] This system allows users to easily create and share disaster prevention games. The system primarily consists of a server and the user's device (e.g., a smartphone).

[0498] First, the user inputs the concept for the disaster prevention game in natural language using the interface on their device. Input can be either text or voice. For example, the instruction might be, "Please create a disaster prevention game for flood control."

[0499] The server runs on the cloud and analyzes user intent using a natural language processing engine (e.g., Google NLP API). The analysis reveals the game themes and elements the user is looking for. Next, the server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate the necessary game elements, including characters, scenarios, and rules.

[0500] The generated game elements are supplemented with disaster prevention information data (e.g., public databases on earthquakes and tsunamis) and incorporated as necessary educational content. The server then sends the generated content to the user's device in real time, providing an interactive environment where the user can immediately preview and make adjustments.

[0501] Furthermore, the completed game edited by the user can be shared with other users via a server and information and communication network (e.g., the internet). This sharing function allows for efficient use in educational institutions and individual learning environments.

[0502] For example, if a user instructs the system to "create a simulation game for natural disasters that are likely to occur in the region," the system will analyze disaster data for that region and automatically generate a game that includes appropriate scenarios and evacuation instructions. An example of a prompt in this case would be, "Please create a flood prevention game. I want to create a scenario for safe evacuation when a flood occurs in the city."

[0503] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0504] Step 1:

[0505] The user inputs the concept of the disaster prevention game in natural language via their device. The device sends this input as text data to the server. An example of input is "I would like you to create a disaster prevention game for flood control."

[0506] Step 2:

[0507] The server sends the received input to a natural language processing engine (e.g., Google NLP API) to analyze the user's intent. The analysis process tokenizes the input string, performs semantic analysis, and extracts themes and necessary elements. The output includes game themes and scenario candidates.

[0508] Step 3:

[0509] The server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate game elements based on the themes extracted in step 2. Specifically, it automatically generates character settings and scenario details. During this generation process, prompts are input to the generating AI model, and the corresponding game elements are output.

[0510] Step 4:

[0511] The server compares the generated game elements with a disaster prevention database (e.g., local flood data) and incorporates appropriate disaster prevention information. This ensures that the generated elements are both educational and practical. The output is integrated disaster prevention game data.

[0512] Step 5:

[0513] The server sends the completed game data to the user's device in real time. The device analyzes the data and displays an interactive preview screen to the user. The user can then edit characters and scenarios on the screen.

[0514] Step 6:

[0515] Users can choose whether to share their created and edited games with others via an information and communication network (e.g., the internet). If sharing is selected, the server uploads the game data to the specified platform and generates a link that other users can access. The output will be either a sharing URL or access rights to the file.

[0516] 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.

[0517] This invention provides a system for users to create disaster prevention games using natural language, and further incorporates a configuration that recognizes the user's emotions and utilizes that information to generate and adjust the game. When a user inputs instructions such as "I want to create an evacuation game for when an earthquake occurs" in natural language on a terminal, the terminal sends this to a server. The server uses a natural language processing engine to analyze the user's intent. At the same time, an emotion engine recognizes the user's emotions from the input and uses this information to complement the analysis results.

[0518] Based on user instructions and emotional data, the server can generate game elements (characters, scenarios, evacuation routes, etc.) and adjust the game's tone and difficulty depending on whether the user is experiencing positive or negative emotions. Furthermore, when accessing the disaster prevention information database and incorporating necessary disaster preparedness knowledge into the game, the server can consider the recognized user's emotions and adjust the way information is presented and the depth of its content accordingly.

[0519] The generated game elements are sent to the user's device in real time, and a basic preview is provided. Users can view this preview and provide feedback, such as "The evacuation route is too complicated; please simplify it." The server then readjusts the game elements based on the user's feedback.

[0520] Once a user is satisfied with the completed game, the server saves it and provides a download link to their device. In this way, users can freely create disaster prevention games while gaining a learning experience that takes their emotions into consideration. For example, if an elementary school student instructs the server to "add a character that is evacuating while feeling worried," the server will generate a character expression that matches that emotion, creating a game that more richly expresses the user's intentions. This allows users to experience interactive learning that reflects their emotions.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The user uses their device to input instructions in natural language, such as "I want to create a tsunami evacuation game." The device immediately sends this input to the server.

[0524] Step 2:

[0525] The server runs a natural language processing engine to analyze the natural language input it receives. Through this analysis, it identifies the game's theme and objective, while an emotion engine recognizes the user's emotions from the input. For example, if the user is worried, that emotion data is extracted.

[0526] Step 3:

[0527] The server generates appropriate game elements (e.g., character expressions, game difficulty settings) based on the identified game theme and recognized emotions. If the emotion is positive, adventurous elements are added; if the emotion is negative, the theme emphasizes safety and security.

[0528] Step 4:

[0529] The server accesses a disaster prevention information database to obtain official guidelines regarding tsunami evacuation. This information is then incorporated into the game, with the presentation method adjusted according to the perceived emotions of the user.

[0530] Step 5:

[0531] The server transmits generated game elements and coordinated disaster prevention information to the user's terminal in real time. The terminal first presents the user with a basic preview, which the user then views to confirm the initial game experience.

[0532] Step 6:

[0533] If a user provides additional instructions, such as "I want to change the character's perspective to be more tense," the server will readjust the game elements accordingly. The scenario and presentation will be modified to better align with the user's intentions, taking emotional data into consideration.

[0534] Step 7:

[0535] Once the user is satisfied with the final content of the game, the server saves the completed game data and sends a download link to the device. This allows the user to download the game at home and enjoy an emotionally responsive learning experience.

[0536] (Example 2)

[0537] 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."

[0538] In disaster prevention education, there is a need for systems that allow users to easily create interactive games that reflect their own intentions and promote learning through real-world experience. However, conventional systems have difficulty accurately reflecting user intentions in game elements, and adjustments that take emotional elements into consideration have not been made. Furthermore, there have been challenges in providing real-time feedback and making readjustments.

[0539] 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.

[0540] In this invention, the server includes means for receiving input data via a device that accepts natural language input, means for processing the input data and analyzing the user's intentions and emotions, and means for generating and adjusting game elements using generative technology based on the analyzed intentions and emotions. This allows users to not only create disaster prevention games that reflect their own intentions, but also to adjust the game experience according to their emotions, thereby providing more personalized learning.

[0541] "Natural language input" is an input method that uses the language a user uses on a daily basis, and is a way of transmitting information to a system in a form that can be analyzed by a machine.

[0542] "Input data" refers to information that a user provides to the system, and includes all digital information, such as character data and numerical data based on natural language input.

[0543] "User intent" is a concept that refers to the purpose or desired outcome that a user tries to achieve through natural language input.

[0544] "Emotion analysis" is a technology that identifies and analyzes a user's emotional state based on the content of their input data.

[0545] "Generative techniques" refer to algorithms and methodologies for systems to create new data based on specified conditions or prompts.

[0546] "Game elements" refer to individual components included in a game created by a user, and include characters, scenarios, evacuation routes, etc.

[0547] "Real-time presentation" refers to the process of instantly processing generated information and providing it to the user on the spot.

[0548] "Readjustment" refers to the process of reviewing and adjusting the content and difficulty level of a game based on user feedback on its initial settings.

[0549] "Tone" is a term that refers to the overall atmosphere or emotion conveyed to the user within a game, and it usually has positive or negative attributes.

[0550] A "download link" is an internet link used by users to obtain digital content, and clicking it copies the specified file to the device.

[0551] This invention relates to a system that allows users to create interactive games related to disaster prevention using natural language. The system functions by using a terminal that receives user input and a server that analyzes and processes the data. The terminal is a device that accepts natural language input, thereby transmitting text information from the user to the server.

[0552] The server uses a specific analysis engine to determine the user's intent. Specifically, it uses a natural language processing engine, commonly known as "natural language processing software," to analyze the game elements the user desires. In addition, it utilizes "sentiment analysis software" for sentiment analysis, extracting the user's emotional state from the input text.

[0553] The results obtained from these analyses are reflected in each element of the game using an AI model that utilizes generative technology. These generated game elements include characters, scenarios, and evacuation routes, and are adjusted according to the user's intentions and emotions. Furthermore, relevant disaster prevention information can be obtained from external databases and incorporated into the game. This allows users to receive disaster prevention education while experiencing the game in an emotionally engaging way.

[0554] For example, if a primary school student inputs in natural language, "I want to add a character that evacuates while feeling worried," the server will generate a character that reflects that emotion based on this instruction and provide a scenario in which that character plays an active role. An example of a prompt message would be, "I want to create an evacuation game for when an earthquake occurs. Please simplify the evacuation routes." The server will then receive this instruction and perform appropriate data processing.

[0555] Thus, the present invention enables users to create educational and interactive games that reflect their own emotions.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] The user inputs game instructions into the device using natural language. For example, they might input the instruction, "I want to create a game about evacuation during an earthquake." The device prepares this text data as input and proceeds with the transmission process.

[0559] Step 2:

[0560] The terminal sends the input natural language data to the server. The HTTPS protocol is used for transmission to ensure data security. This process receives the user's natural language data as input and sends it to the server as output.

[0561] Step 3:

[0562] The server uses natural language processing software to analyze the received natural language data. Specifically, it analyzes the received text data to identify the user's intent. In this process, the server receives user instructions as input and obtains the intent analysis results as output.

[0563] Step 4:

[0564] Based on the analyzed user intent, the server performs sentiment analysis using sentiment analysis software. It analyzes what emotions the user is feeling from keywords and context within the text. In this step, natural language data is received as input and sentiment data is obtained as output.

[0565] Step 5:

[0566] The server uses a generative AI model to generate game elements based on analysis results and sentiment data. This generation utilizes prompts to create characters and scenarios that are suitable for the specified conditions. In this step, intention and sentiment data are taken as input, and the generated game elements are obtained as output.

[0567] Step 6:

[0568] The server accesses a disaster prevention information database to retrieve relevant disaster information. This allows for the incorporation of realistic and educational information into the generated game. This process takes necessary information as input and provides related information as output.

[0569] Step 7:

[0570] The generated game elements are sent to the user's device in real time, and a preview is provided to the user. In this step, the generated game data is received as input, and data that is visually displayed on the device is provided as output.

[0571] Step 8:

[0572] The user reviews the preview and provides additional instructions (e.g., "The evacuation route is too complicated; please simplify it") via their device. The user sends feedback as input to their device and provides it to the server as output.

[0573] Step 9:

[0574] The server readjusts the generated game elements based on additional user instructions. It redesigns the game structure to reflect the feedback. In this step, it receives feedback as input and generates the adjusted game elements as output.

[0575] Step 10:

[0576] Once the user is satisfied with the game, the server saves the final version and provides the user with a download link. This process stores the final game data as input and provides a downloadable link as output.

[0577] (Application Example 2)

[0578] 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."

[0579] The goal is to provide a system for creating disaster prevention education games that allows users to generate game elements using natural language while personalizing the game content based on the user's emotions. Specifically, the challenge is to provide a more appropriate educational experience by dynamically adjusting the tone and difficulty of the game according to the user's emotional state.

[0580] 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.

[0581] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for recognizing emotional information, means for generating or adjusting game elements using emotional information, means for acquiring disaster prevention information and incorporating it into the game, and means for presenting the generated game in real time. This makes it possible to generate a disaster prevention education game based on instructions given by the user in natural language, and further enables a personalized game experience that takes the user's emotions into consideration.

[0582] A "means for accepting natural language input" refers to a function that allows the system to receive natural language text entered by a user and process its content.

[0583] "Means for analyzing user intent" refers to technologies that recognize what a user wants from the natural language input received and extract that information.

[0584] "Means for generating game elements" refers to a function that develops necessary game components such as characters, scenarios, and evacuation routes based on the analyzed user intent.

[0585] "Means of recognizing emotional information" refers to technologies that analyze a user's natural language input and voice intonation to determine what emotional state the user is in.

[0586] "Means for generating or adjusting game elements using emotional information" refers to a function that dynamically controls the tone, difficulty level, and information presentation method of the generated game using the results of emotional recognition.

[0587] The "means of acquiring disaster prevention information and incorporating it into games" refers to a function that provides realistic educational content by acquiring information from the latest disaster prevention-related databases and reflecting it in the game's content.

[0588] "Means of presenting generated games in real time" refers to technologies that instantly display created game elements to users in order to enable interaction with them.

[0589] In a form for carrying out the invention, this system is specifically designed for users to create disaster prevention education games using natural language. Users use devices such as smartphones or tablets to input their instructions in natural language.

[0590] The terminal sends user instructions to the server as natural language text. The server uses a natural language processing engine to analyze the user's intent. Furthermore, it uses an emotion recognition engine to identify the user's emotions and incorporate them into the analyzed intent. In this process, specific program libraries (e.g., the emotion_recognition library and the language_processor library) run on the server, analyzing the user's input data based on their tone of voice and word choice tendencies.

[0591] The server designs game elements using a game generation engine based on the user's intent and emotional information. Here, necessary information is retrieved from a disaster prevention information database and reflected in the game content. The generated game elements are then adjusted to match the user's emotional state in terms of tone and difficulty.

[0592] Once game elements are created, they are sent to the user's device in real time. The user reviews the game content through a preview screen and provides feedback by offering additional instructions if necessary. This feedback is received by the server and used to fine-tune the game elements. Finally, once the user confirms the game is complete, the server saves the final version and generates a link for content delivery to the user's device.

[0593] For example, when an elementary school teacher instructs the app to "simulate the experience of children evacuating safely," the server generates a game incorporating a gentle-looking character and a simple evacuation route.

[0594] An example of an input prompt for the generating AI model would be: "Generate a calming emergency evacuation game scenario for elementary school students. Include supportive and motivational messages that can help students understand safety procedures while feeling reassured." This system enables the provision of personalized disaster prevention education content tailored to the user's needs.

[0595] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0596] Step 1:

[0597] The user inputs instructions in natural language using a terminal. The entered natural language text is saved as data on the terminal and prepared to be sent to the server for the next processing step.

[0598] Step 2:

[0599] The terminal sends natural language text to the server. The server begins processing the received natural language text as input. The server's natural language processing engine starts up, analyzes it, and extracts the user's intent. The user's intent obtained from the analysis is prepared for the next processing step.

[0600] Step 3:

[0601] Based on the user's intent, the server uses an emotion recognition engine to re-analyze the natural language text. This analysis extracts the user's emotional information. The server then processes this emotional information and converts it into a format usable for generating game elements.

[0602] Step 4:

[0603] The server uses a game generation engine, taking user intent and emotional information as input. It retrieves the latest data from a disaster prevention information database and generates game elements. These generated game elements include characters, scenarios, and evacuation routes. The server prepares this data.

[0604] Step 5:

[0605] The server automatically adjusts the generated game elements based on the user's emotional information. The game's tone and difficulty are customized to the user. The adjusted game elements are then prepared for real-time display.

[0606] Step 6:

[0607] The server sends the adjusted game elements to the device. The device receives this and displays a real-time preview to the user. The user can then check the displayed game content.

[0608] Step 7:

[0609] Users provide feedback using their devices. The device receives user feedback as input and sends it to the server. This feedback may include additional instructions or requests for improvements from the user.

[0610] Step 8:

[0611] The server readjusts game elements based on the feedback received. It analyzes the feedback as data, makes necessary corrections, and generates the final game elements.

[0612] Step 9:

[0613] If the user agrees to complete the game, the server saves the completed game and generates a content distribution link. This link is sent to the device, and the user can obtain the game through it.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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".

[0631] This invention provides a system for users to create disaster prevention games using natural language, and its implementation is carried out in the following configuration: When a user inputs "I want to create a disaster prevention game" in natural language via a terminal, the terminal transmits this input information to a server. The server analyzes this information and operates a natural language processing engine to identify the user's intent. This engine understands the game's theme, scenario, and necessary elements from the input text, and uses a multimodal learning model to begin generating appropriate game elements.

[0632] The generated game elements are supplemented by incorporating necessary disaster prevention knowledge based on a disaster prevention information database. This creates a game with high educational value. The server sends this generated content to the user's terminal in real time, and the user can check the preview and issue further instructions for modifications and additions, such as "I want to add more characters" or "I want to adjust the scenario." The server receives these new instructions and readjusts the dynamically generated game elements.

[0633] Finally, once the user is satisfied with the completed game, the server saves the final data and provides a link that the user can download via their device. This system allows users to easily create disaster prevention games that reflect their own ideas and learn disaster prevention knowledge while having fun, without requiring any specialized programming skills.

[0634] For example, if a primary school student user instructs the system to "create a game about protecting a town from a tsunami," the server will generate a realistic town model based on a tsunami scenario and incorporate necessary evacuation information and warning systems into the game. The user can then monitor the game's progress, adjust character dialogue and the town's layout, and complete their ideal game. In this way, a fun and enriching learning experience can be provided.

[0635] The following describes the processing flow.

[0636] Step 1:

[0637] The user enters their request for a disaster prevention game using natural language input via their device. This input is in the format of "I want to create a game about evacuation during an earthquake."

[0638] Step 2:

[0639] The terminal sends user input to the server, which then uses a natural language processing engine to analyze the input. Here, the server identifies the game's theme (e.g., earthquake, evacuation) and necessary elements.

[0640] Step 3:

[0641] Based on the information analyzed by the server, game elements are generated using a multimodal model. This includes scenario development, character design, and evacuation route design.

[0642] Step 4:

[0643] The server accesses a disaster prevention information database and incorporates official recommendations and action guidelines regarding earthquake evacuation as in-game information.

[0644] Step 5:

[0645] The server sends the generated game elements and disaster prevention information to the user's terminal in real time and displays it as a preview. The user can review this and make any necessary changes.

[0646] Step 6:

[0647] If a user gives instructions to add or modify elements, such as "add an alarm sound" or "adjust the difficulty of evacuation," the server will receive these instructions and readjust the existing game elements.

[0648] Step 7:

[0649] Once the user is finally satisfied with the game's content, the server saves the completed game data and provides a downloadable link to the user's device, allowing them to acquire and play the game.

[0650] (Example 1)

[0651] 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".

[0652] In order for many users to deepen their knowledge of disaster prevention, there is a need for a system that allows them to easily design and adjust educational and interactive games based on their own ideas, even without specialized programming skills.

[0653] 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.

[0654] In this invention, the server includes a device for receiving natural language input, a device for analyzing the user's intent based on the natural language input, and a device for generating game elements based on the analyzed intent. This makes it possible for users to design their own disaster prevention-related games that reflect their own ideas, without requiring specialized technical knowledge, and to learn while having fun.

[0655] "Natural language input" is a method of conveying instructions and information to a system using the language that users use in their daily lives.

[0656] A "device" is a machine or electronic device designed to perform a specific function.

[0657] "User intent" refers to the user's goals and desires regarding the system, such as what they want to achieve or what elements they are looking for.

[0658] "Analyzing" is the process of deciphering given information or data and revealing its meaning and intent.

[0659] "Gameplay elements" refer to all the elements that make up a game, such as items, characters, and scenarios used during gameplay.

[0660] "Disaster information" refers to data and knowledge related to natural disasters and sudden events.

[0661] "Presenting immediately" means that the results are displayed quickly without any waiting time after input or processing has been completed.

[0662] This invention is a system that enables users to design games related to disaster prevention without requiring specialized technical knowledge, and to learn while having fun.

[0663] First, the user enters natural language input via their device, such as "I want to create a disaster prevention game." The device then forwards this input data to a server to understand the user's intent. The server analyzes this input using a natural language processing engine. Tools such as NLTK and spaCy are often used for this engine.

[0664] Based on the analysis results, the server activates a generative AI model to generate game elements. This model utilizes a general-purpose generative AI tool available on a specific platform (e.g., GPT). The generated game elements are further enhanced by referencing a disaster information database and incorporating necessary disaster prevention knowledge.

[0665] The game elements created in this way are immediately presented to the user's device. The user can review this preview and give additional instructions, such as "I want to add more characters" or "I want to change the scenario." The server receives these instructions and readjusts the game elements in real time. Finally, when the user is satisfied with the completed game, the server saves the completed data and provides a download link to the device.

[0666] For example, if an elementary school student inputs "I want to create a game about protecting a town from a tsunami," the server will generate a tsunami-themed scenario. Furthermore, this scenario will incorporate specific evacuation information and warning systems related to disaster prevention. Based on this, users can adjust the game's progression and character movements to complete their ideal game.

[0667] An example of a prompt message would be: "The theme of this disaster prevention game is tsunamis. We want to incorporate evacuation routes and warning systems as learning elements necessary for players to protect the city. To enhance replayability, we want to provide multiple characters and scenario variations." This allows users to deepen their disaster prevention knowledge while having fun playing the game.

[0668] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0669] Step 1:

[0670] The user uses a terminal to input natural language instructions, such as "I want to create a disaster prevention game." This input includes the user's intentions and desired game theme. The terminal formats this input as text data and sends it to the server.

[0671] Step 2:

[0672] The server analyzes natural language input data received from the terminal. First, a natural language processing engine tokenizes the input text and performs syntactic analysis. This analysis extracts the game themes and elements desired by the user. The input is the user's instruction, and the output is the analyzed intent and keywords.

[0673] Step 3:

[0674] The server activates a generating AI model based on the analysis results. Specifically, the AI ​​model generates game elements from the analyzed themes and keywords. For example, if the user selects "tsunami" as the theme, the AI ​​model will generate related game scenarios and characters. In this step, the input is the analysis results, and the output is the generated game elements.

[0675] Step 4:

[0676] The server compares the generated game elements with a disaster information database. It retrieves necessary disaster prevention knowledge from this database and incorporates it into the game elements. The input is the generated game elements, and the output is the game elements with the disaster prevention information incorporated.

[0677] Step 5:

[0678] The server sends the completed game elements to the user's device in real time. The user can view a preview of the game on their device. At this time, an interface is displayed that accepts user feedback and additional instructions. The input is the generated game elements, and the output is the real-time display on the user's device.

[0679] Step 6:

[0680] The user checks the game preview and gives additional instructions via the terminal, such as "I want to add more characters." The terminal then sends these instructions back to the server. The input is the additional instructions from the user, and the output is the update instructions sent to the server.

[0681] Step 7:

[0682] The server receives additional instructions from the user and readjusts the game elements using the generated AI model again. The readjusted elements are then cross-referenced with the disaster prevention database to complete the content. The input for this step is the user's additional instructions, and the output is the readjusted game elements.

[0683] Step 8:

[0684] When the user is satisfied with the gameplay, the server saves the final game data and provides a download link to the device. The user can then download the game using this link. The input is the final decision instruction, and the output is the download link.

[0685] (Application Example 1)

[0686] 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".

[0687] There is a need for a system that allows users to easily create and share disaster prevention educational content with others. However, conventional systems require specialized knowledge, making it difficult for ordinary users to easily create and share disaster prevention games that reflect their own ideas. Furthermore, the lack of real-time editing and sharing functions means that the system lacks the flexibility needed to enhance its educational effectiveness.

[0688] 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.

[0689] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for generating game elements, means for automatically acquiring disaster prevention data and incorporating it into the game, means for presenting the generated game elements to the user in a real-time editable state, and means for sharing the generated game with others via an information and communication network. This makes it possible for users to create, modify, and share disaster prevention games in natural language without requiring specialized knowledge.

[0690] "Natural language input" refers to text that users speak or write as human language, rather than in a programming language.

[0691] "Methods for analyzing user intent" refer to technologies that interpret what the user wants from the input natural language and extract specific instructions or objectives.

[0692] "Means for generating game elements" refers to a process that automatically creates the necessary components of a game (characters, scenarios, rules, etc.) based on the user's intentions.

[0693] "Disaster prevention data" refers to information related to disasters, including knowledge and awareness-raising information about natural disasters such as earthquakes and tsunamis.

[0694] "A means of presenting information in a real-time, editable state" refers to a method of displaying information in a way that allows users to instantly view and modify the generated content.

[0695] "Means of sharing with others through information and communication networks" refers to methods of transmitting created content to other users using the internet or other communication networks, making it available for use or viewing.

[0696] This system allows users to easily create and share disaster prevention games. The system primarily consists of a server and the user's device (e.g., a smartphone).

[0697] First, the user inputs the concept for the disaster prevention game in natural language using the interface on their device. Input can be either text or voice. For example, the instruction might be, "Please create a disaster prevention game for flood control."

[0698] The server runs on the cloud and analyzes user intent using a natural language processing engine (e.g., Google NLP API). The analysis reveals the game themes and elements the user is looking for. Next, the server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate the necessary game elements, including characters, scenarios, and rules.

[0699] The generated game elements are supplemented with disaster prevention information data (e.g., public databases on earthquakes and tsunamis) and incorporated as necessary educational content. The server then sends the generated content to the user's device in real time, providing an interactive environment where the user can immediately preview and make adjustments.

[0700] Furthermore, the completed game edited by the user can be shared with other users via a server and information and communication network (e.g., the internet). This sharing function allows for efficient use in educational institutions and individual learning environments.

[0701] For example, if a user instructs the system to "create a simulation game for natural disasters that are likely to occur in the region," the system will analyze disaster data for that region and automatically generate a game that includes appropriate scenarios and evacuation instructions. An example of a prompt in this case would be, "Please create a flood prevention game. I want to create a scenario for safe evacuation when a flood occurs in the city."

[0702] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0703] Step 1:

[0704] The user inputs the concept of the disaster prevention game in natural language via their device. The device sends this input as text data to the server. An example of input is "I would like you to create a disaster prevention game for flood control."

[0705] Step 2:

[0706] The server sends the received input to a natural language processing engine (e.g., Google NLP API) to analyze the user's intent. The analysis process tokenizes the input string, performs semantic analysis, and extracts themes and necessary elements. The output includes game themes and scenario candidates.

[0707] Step 3:

[0708] The server uses a multimodal learning model (e.g., OpenAI's GPT series) to generate game elements based on the themes extracted in step 2. Specifically, it automatically generates character settings and scenario details. During this generation process, prompts are input to the generating AI model, and the corresponding game elements are output.

[0709] Step 4:

[0710] The server compares the generated game elements with a disaster prevention database (e.g., local flood data) and incorporates appropriate disaster prevention information. This ensures that the generated elements are both educational and practical. The output is integrated disaster prevention game data.

[0711] Step 5:

[0712] The server sends the completed game data to the user's device in real time. The device analyzes the data and displays an interactive preview screen to the user. The user can then edit characters and scenarios on the screen.

[0713] Step 6:

[0714] Users can choose whether to share their created and edited games with others via an information and communication network (e.g., the internet). If sharing is selected, the server uploads the game data to the specified platform and generates a link that other users can access. The output will be either a sharing URL or access rights to the file.

[0715] 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.

[0716] This invention provides a system for users to create disaster prevention games using natural language, and further incorporates a configuration that recognizes the user's emotions and utilizes that information to generate and adjust the game. When a user inputs instructions such as "I want to create an evacuation game for when an earthquake occurs" in natural language on a terminal, the terminal sends this to a server. The server uses a natural language processing engine to analyze the user's intent. At the same time, an emotion engine recognizes the user's emotions from the input and uses this information to complement the analysis results.

[0717] Based on user instructions and emotional data, the server can generate game elements (characters, scenarios, evacuation routes, etc.) and adjust the game's tone and difficulty depending on whether the user is experiencing positive or negative emotions. Furthermore, when accessing the disaster prevention information database and incorporating necessary disaster preparedness knowledge into the game, the server can consider the recognized user's emotions and adjust the way information is presented and the depth of its content accordingly.

[0718] The generated game elements are sent to the user's device in real time, and a basic preview is provided. Users can view this preview and provide feedback, such as "The evacuation route is too complicated; please simplify it." The server then readjusts the game elements based on the user's feedback.

[0719] Once a user is satisfied with the completed game, the server saves it and provides a download link to their device. In this way, users can freely create disaster prevention games while gaining a learning experience that takes their emotions into consideration. For example, if an elementary school student instructs the server to "add a character that is evacuating while feeling worried," the server will generate a character expression that matches that emotion, creating a game that more richly expresses the user's intentions. This allows users to experience interactive learning that reflects their emotions.

[0720] The following describes the processing flow.

[0721] Step 1:

[0722] The user uses their device to input instructions in natural language, such as "I want to create a tsunami evacuation game." The device immediately sends this input to the server.

[0723] Step 2:

[0724] The server runs a natural language processing engine to analyze the natural language input it receives. Through this analysis, it identifies the game's theme and objective, while an emotion engine recognizes the user's emotions from the input. For example, if the user is worried, that emotion data is extracted.

[0725] Step 3:

[0726] The server generates appropriate game elements (e.g., character expressions, game difficulty settings) based on the identified game theme and recognized emotions. If the emotion is positive, adventurous elements are added; if the emotion is negative, the theme emphasizes safety and security.

[0727] Step 4:

[0728] The server accesses a disaster prevention information database to obtain official guidelines regarding tsunami evacuation. This information is then incorporated into the game, with the presentation method adjusted according to the perceived emotions of the user.

[0729] Step 5:

[0730] The server transmits generated game elements and coordinated disaster prevention information to the user's terminal in real time. The terminal first presents the user with a basic preview, which the user then views to confirm the initial game experience.

[0731] Step 6:

[0732] If a user provides additional instructions, such as "I want to change the character's perspective to be more tense," the server will readjust the game elements accordingly. The scenario and presentation will be modified to better align with the user's intentions, taking emotional data into consideration.

[0733] Step 7:

[0734] Once the user is satisfied with the final content of the game, the server saves the completed game data and sends a download link to the device. This allows the user to download the game at home and enjoy an emotionally responsive learning experience.

[0735] (Example 2)

[0736] 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".

[0737] In disaster prevention education, there is a need for systems that allow users to easily create interactive games that reflect their own intentions and promote learning through real-world experience. However, conventional systems have difficulty accurately reflecting user intentions in game elements, and adjustments that take emotional elements into consideration have not been made. Furthermore, there have been challenges in providing real-time feedback and making readjustments.

[0738] 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.

[0739] In this invention, the server includes means for receiving input data via a device that accepts natural language input, means for processing the input data and analyzing the user's intentions and emotions, and means for generating and adjusting game elements using generative technology based on the analyzed intentions and emotions. This allows users to not only create disaster prevention games that reflect their own intentions, but also to adjust the game experience according to their emotions, thereby providing more personalized learning.

[0740] "Natural language input" is an input method that uses the language a user uses on a daily basis, and is a way of transmitting information to a system in a form that can be analyzed by a machine.

[0741] "Input data" refers to information that a user provides to the system, and includes all digital information, such as character data and numerical data based on natural language input.

[0742] "User intent" is a concept that refers to the purpose or desired outcome that a user tries to achieve through natural language input.

[0743] "Emotion analysis" is a technology that identifies and analyzes a user's emotional state based on the content of their input data.

[0744] "Generative techniques" refer to algorithms and methodologies for systems to create new data based on specified conditions or prompts.

[0745] "Game elements" refer to individual components included in a game created by a user, and include characters, scenarios, evacuation routes, etc.

[0746] "Real-time presentation" refers to the process of instantly processing generated information and providing it to the user on the spot.

[0747] "Readjustment" refers to the process of reviewing and adjusting the content and difficulty level of a game based on user feedback on its initial settings.

[0748] "Tone" is a term that refers to the overall atmosphere or emotion conveyed to the user within a game, and it usually has positive or negative attributes.

[0749] A "download link" is an internet link used by users to obtain digital content, and clicking it copies the specified file to the device.

[0750] This invention relates to a system that allows users to create interactive games related to disaster prevention using natural language. The system functions by using a terminal that receives user input and a server that analyzes and processes the data. The terminal is a device that accepts natural language input, thereby transmitting text information from the user to the server.

[0751] The server uses a specific analysis engine to determine the user's intent. Specifically, it uses a natural language processing engine, commonly known as "natural language processing software," to analyze the game elements the user desires. In addition, it utilizes "sentiment analysis software" for sentiment analysis, extracting the user's emotional state from the input text.

[0752] The results obtained from these analyses are reflected in each element of the game using an AI model that utilizes generative technology. These generated game elements include characters, scenarios, and evacuation routes, and are adjusted according to the user's intentions and emotions. Furthermore, relevant disaster prevention information can be obtained from external databases and incorporated into the game. This allows users to receive disaster prevention education while experiencing the game in an emotionally engaging way.

[0753] For example, if a primary school student inputs in natural language, "I want to add a character that evacuates while feeling worried," the server will generate a character that reflects that emotion based on this instruction and provide a scenario in which that character plays an active role. An example of a prompt message would be, "I want to create an evacuation game for when an earthquake occurs. Please simplify the evacuation routes." The server will then receive this instruction and perform appropriate data processing.

[0754] Thus, the present invention enables users to create educational and interactive games that reflect their own emotions.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The user inputs game instructions into the device using natural language. For example, they might input the instruction, "I want to create a game about evacuation during an earthquake." The device prepares this text data as input and proceeds with the transmission process.

[0758] Step 2:

[0759] The terminal sends the input natural language data to the server. The HTTPS protocol is used for transmission to ensure data security. This process receives the user's natural language data as input and sends it to the server as output.

[0760] Step 3:

[0761] The server uses natural language processing software to analyze the received natural language data. Specifically, it analyzes the received text data to identify the user's intent. In this process, the server receives user instructions as input and obtains the intent analysis results as output.

[0762] Step 4:

[0763] Based on the analyzed user intent, the server performs sentiment analysis using sentiment analysis software. It analyzes what emotions the user is feeling from keywords and context within the text. In this step, natural language data is received as input and sentiment data is obtained as output.

[0764] Step 5:

[0765] The server uses a generative AI model to generate game elements based on analysis results and sentiment data. This generation utilizes prompts to create characters and scenarios that are suitable for the specified conditions. In this step, intention and sentiment data are taken as input, and the generated game elements are obtained as output.

[0766] Step 6:

[0767] The server accesses a disaster prevention information database to retrieve relevant disaster information. This allows for the incorporation of realistic and educational information into the generated game. This process takes necessary information as input and provides related information as output.

[0768] Step 7:

[0769] The generated game elements are sent to the user's device in real time, and a preview is provided to the user. In this step, the generated game data is received as input, and data that is visually displayed on the device is provided as output.

[0770] Step 8:

[0771] The user reviews the preview and provides additional instructions (e.g., "The evacuation route is too complicated; please simplify it") via their device. The user sends feedback as input to their device and provides it to the server as output.

[0772] Step 9:

[0773] The server readjusts the generated game elements based on additional user instructions. It redesigns the game structure to reflect the feedback. In this step, it receives feedback as input and generates the adjusted game elements as output.

[0774] Step 10:

[0775] Once the user is satisfied with the game, the server saves the final version and provides the user with a download link. This process stores the final game data as input and provides a downloadable link as output.

[0776] (Application Example 2)

[0777] 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".

[0778] The goal is to provide a system for creating disaster prevention education games that allows users to generate game elements using natural language while personalizing the game content based on the user's emotions. Specifically, the challenge is to provide a more appropriate educational experience by dynamically adjusting the tone and difficulty of the game according to the user's emotional state.

[0779] 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.

[0780] In this invention, the server includes means for receiving natural language input, means for analyzing the user's intent, means for recognizing emotional information, means for generating or adjusting game elements using emotional information, means for acquiring disaster prevention information and incorporating it into the game, and means for presenting the generated game in real time. This makes it possible to generate a disaster prevention education game based on instructions given by the user in natural language, and further enables a personalized game experience that takes the user's emotions into consideration.

[0781] A "means for accepting natural language input" refers to a function that allows the system to receive natural language text entered by a user and process its content.

[0782] "Means for analyzing user intent" refers to technologies that recognize what a user wants from the natural language input received and extract that information.

[0783] "Means for generating game elements" refers to a function that develops necessary game components such as characters, scenarios, and evacuation routes based on the analyzed user intent.

[0784] "Means of recognizing emotional information" refers to technologies that analyze a user's natural language input and voice intonation to determine what emotional state the user is in.

[0785] "Means for generating or adjusting game elements using emotional information" refers to a function that dynamically controls the tone, difficulty level, and information presentation method of the generated game using the results of emotional recognition.

[0786] The "means of acquiring disaster prevention information and incorporating it into games" refers to a function that provides realistic educational content by acquiring information from the latest disaster prevention-related databases and reflecting it in the game's content.

[0787] "Means of presenting generated games in real time" refers to technologies that instantly display created game elements to users in order to enable interaction with them.

[0788] In a form for carrying out the invention, this system is specifically designed for users to create disaster prevention education games using natural language. Users use devices such as smartphones or tablets to input their instructions in natural language.

[0789] The terminal sends user instructions to the server as natural language text. The server uses a natural language processing engine to analyze the user's intent. Furthermore, it uses an emotion recognition engine to identify the user's emotions and incorporate them into the analyzed intent. In this process, specific program libraries (e.g., the emotion_recognition library and the language_processor library) run on the server, analyzing the user's input data based on their tone of voice and word choice tendencies.

[0790] The server designs game elements using a game generation engine based on the user's intent and emotional information. Here, necessary information is retrieved from a disaster prevention information database and reflected in the game content. The generated game elements are then adjusted to match the user's emotional state in terms of tone and difficulty.

[0791] Once game elements are created, they are sent to the user's device in real time. The user reviews the game content through a preview screen and provides feedback by offering additional instructions if necessary. This feedback is received by the server and used to fine-tune the game elements. Finally, once the user confirms the game is complete, the server saves the final version and generates a link for content delivery to the user's device.

[0792] For example, when an elementary school teacher instructs the app to "simulate the experience of children evacuating safely," the server generates a game incorporating a gentle-looking character and a simple evacuation route.

[0793] An example of an input prompt for the generating AI model would be: "Generate a calming emergency evacuation game scenario for elementary school students. Include supportive and motivational messages that can help students understand safety procedures while feeling reassured." This system enables the provision of personalized disaster prevention education content tailored to the user's needs.

[0794] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0795] Step 1:

[0796] The user inputs instructions in natural language using a terminal. The entered natural language text is saved as data on the terminal and prepared to be sent to the server for the next processing step.

[0797] Step 2:

[0798] The terminal sends natural language text to the server. The server begins processing the received natural language text as input. The server's natural language processing engine starts up, analyzes it, and extracts the user's intent. The user's intent obtained from the analysis is prepared for the next processing step.

[0799] Step 3:

[0800] Based on the user's intent, the server uses an emotion recognition engine to re-analyze the natural language text. This analysis extracts the user's emotional information. The server then processes this emotional information and converts it into a format usable for generating game elements.

[0801] Step 4:

[0802] The server uses a game generation engine, taking user intent and emotional information as input. It retrieves the latest data from a disaster prevention information database and generates game elements. These generated game elements include characters, scenarios, and evacuation routes. The server prepares this data.

[0803] Step 5:

[0804] The server automatically adjusts the generated game elements based on the user's emotional information. The game's tone and difficulty are customized to the user. The adjusted game elements are then prepared for real-time display.

[0805] Step 6:

[0806] The server sends the adjusted game elements to the device. The device receives this and displays a real-time preview to the user. The user can then check the displayed game content.

[0807] Step 7:

[0808] Users provide feedback using their devices. The device receives user feedback as input and sends it to the server. This feedback may include additional instructions or requests for improvements from the user.

[0809] Step 8:

[0810] The server readjusts game elements based on the feedback received. It analyzes the feedback as data, makes necessary corrections, and generates the final game elements.

[0811] Step 9:

[0812] If the user agrees to complete the game, the server saves the completed game and generates a content distribution link. This link is sent to the device, and the user can obtain the game through it.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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."

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] The following is further disclosed regarding the embodiments described above.

[0835] (Claim 1)

[0836] A means of accepting natural language input,

[0837] A means for analyzing the user's intent based on the aforementioned natural language input,

[0838] Based on the analyzed intent, means for generating game elements,

[0839] A means for automatically acquiring disaster prevention information and incorporating it into the game elements,

[0840] A system that includes means for presenting generated game elements to the user in real time.

[0841] (Claim 2)

[0842] The system according to claim 1, further comprising means for readjusting the game elements in response to additional instructions from the user.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for saving the completed version of the generated game and providing a download link to the terminal.

[0845] "Example 1"

[0846] (Claim 1)

[0847] A device that accepts natural language input,

[0848] A device that analyzes the user's intent based on the aforementioned natural language input,

[0849] Based on the analyzed intent, a device for generating game elements,

[0850] A device that automatically acquires disaster information and incorporates it into the aforementioned game elements,

[0851] A system including a device that immediately presents generated game elements to the user.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising a device for readjusting the game elements in response to additional instructions from the user.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising a device for saving the completed version of the generated game and providing a download link to an information processing device.

[0856] "Application Example 1"

[0857] (Claim 1)

[0858] A means of accepting natural language input,

[0859] A means for analyzing the user's intent based on the aforementioned natural language input,

[0860] Based on the analyzed intent, means for generating game elements,

[0861] A means for automatically acquiring disaster prevention data and incorporating it into the game elements,

[0862] A means of presenting generated game elements to the user in real time and in a state that allows the user to edit them,

[0863] A system that includes means for sharing generated games with others via an information and communication network.

[0864] (Claim 2)

[0865] The system according to claim 1, further comprising means for readjusting the game elements in response to additional instructions from the user and for sharing the adjusted game with other users.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising means for saving the completed version of the generated game, providing a download link to a terminal, and enabling online access.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] A means for receiving input data via a device that accepts natural language input,

[0871] A means for processing the aforementioned input data and analyzing the user's intentions and emotions,

[0872] A means for generating and adjusting game elements using generation technology based on the analyzed intentions and emotions,

[0873] A means for obtaining disaster information from an external source and incorporating it into the game elements,

[0874] A means of presenting generated game elements to users in real time and obtaining their evaluations,

[0875] A system including means for readjusting the game elements based on the aforementioned evaluation.

[0876] (Claim 2)

[0877] The system according to claim 1, further comprising means for adjusting the tone and difficulty of the game in consideration of the user's emotional state.

[0878] (Claim 3)

[0879] The system according to claim 1, further comprising means for saving the completed version of the generated game and providing a download link to a user device.

[0880] "Application example 2 when combining with an emotional engine"

[0881] (Claim 1)

[0882] A means of accepting natural language input,

[0883] A means for analyzing the user's intent based on the aforementioned natural language input,

[0884] Based on the analyzed intent, means for generating game elements,

[0885] A means for recognizing emotional information and utilizing that information in the game generation,

[0886] A means for automatically acquiring disaster prevention information and incorporating it into the game elements,

[0887] A system that includes means for presenting generated game elements to the user in real time.

[0888] (Claim 2)

[0889] The system according to claim 1, further comprising means for adjusting the tone or difficulty of the game in accordance with the user's emotional state.

[0890] (Claim 3)

[0891] The system according to claim 1, further comprising means for saving the completed version of the generated game and providing a content distribution link to a terminal. [Explanation of Symbols]

[0892] 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 accepting natural language input, A means for analyzing the user's intent based on the aforementioned natural language input, Based on the analyzed intent, means for generating game elements, A means for automatically acquiring disaster prevention information and incorporating it into the game elements, A system that includes means for presenting generated game elements to the user in real time.

2. The system according to claim 1, further comprising means for readjusting the game elements in response to additional instructions from the user.

3. The system according to claim 1, further comprising means for saving the completed version of the generated game and providing a download link to the terminal.

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