System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving textual data input by a user; means including a generative AI module for parsing the textual data and generating a corresponding response; means for transmitting the response generated by the generative AI module to the user's device; and means for storing the user's input and the generated AI response as a log.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional puzzle-solving games are dominated by choice-based dialogue, limiting the player's self-expression. Furthermore, few games offer a lively communication experience, resulting in a lack of immersion. Therefore, there was a demand for a system that allows players to freely interact in their own words and experience a story that progresses dynamically in real time. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving text data entered by a user, a means including a generation AI module that analyzes the text data and generates a corresponding response, a means for transmitting the response generated by the generation AI module to a user terminal, and a means for saving the user's input and the generation AI's response as a log. Furthermore, the system solves these problems by including a means for requesting the generation AI module to generate an initial scenario, a means for transmitting the initial scenario to the user terminal, a means for generating clues and events based on the user's input, a means for transmitting the generated clues and events to the user terminal, a means for saving the clue and event information, a means for analyzing the user's input related to solving the case, a means for evaluating the user's performance based on the analysis results, and a means for transmitting the evaluation results to the user terminal.

[0006] A "user" is a player or user who operates the system.

[0007] "Text data" refers to sentence or character string data entered by the user.

[0008] A "generative AI module" is an artificial intelligence software component that analyzes user input and generates a corresponding response.

[0009] A "means" is a method or apparatus provided for performing a particular function or process.

[0010] A "response" is a reply or message that the generative AI module generates based on user input.

[0011] A "terminal" is a device operated by a user, and includes, for example, a computer, a smartphone, a tablet, and the like.

[0012] A "log" is a collection of data in which the system records user input and the responses of the generating AI module.

[0013] The "initial scenario" is the initial part of the story created by the generation AI module at the start of the game.

[0014] "Clues" are information or hints that the user needs to progress through the game.

[0015] An "event" is a specific occurrence or action that occurs within the game.

[0016] A "game session ID" is a unique identifier for each time a user plays a game.

[0017] "Evaluation" is the result of analyzing user behavior and results related to solving the case. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

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

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0039] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. The system consists of a server, a user terminal, and a generative AI module.

[0040] 1. User login and initial settings

[0041] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[0042] 2. Initializing the game scenario

[0043] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[0044] 3. Real-time interaction with users

[0045] The user enters the content they want to search for, for example, "Search the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. The generation AI module analyzes the data and sends the response data back to the server. The server then sends the response data to the device, which can then display it on the screen.

[0046] 4. New clues discovered

[0047] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains in more detail." The device sends this input data to the server again. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the server saves the new clue information in the game progress log.

[0048] 5. Manage and save your progress

[0049] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server.

[0050] 6. Ending the Game and Evaluation

[0051] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0052] Specific examples

[0053] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[0054] 1. User: Type "Check out letter openers" and submit.

[0055] 2. Terminal: Receives this text data and sends it to the server.

[0056] 3. Server: Sends text data to the generation AI module.

[0057] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[0058] 5. Server: Sends the response to the terminal, which displays it to the user.

[0059] Thus, the present invention is a system that allows users to freely input text and generates dynamic responses in response to that text, thereby providing a highly immersive gaming experience.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The user enters the username and password and clicks the login button.

[0063] Step 2:

[0064] The terminal receives this user input data and sends it to the server as a login request.

[0065] Step 3:

[0066] The server receives this login request and performs authentication by checking the user database.

[0067] Step 4:

[0068] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[0069] Step 5:

[0070] The terminal receives the initial setting data, initializes the game screen, and displays it.

[0071] Step 6:

[0072] The user clicks the "Start Game" button on the game screen.

[0073] Step 7:

[0074] The terminal sends a game start request to the server.

[0075] Step 8:

[0076] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[0077] Step 9:

[0078] The generation AI module generates an initial scenario and sends it back to the server.

[0079] Step 10:

[0080] The server transmits the initial scenario data to the terminal.

[0081] Step 11:

[0082] The terminal receives the scenario data and displays it on the game screen.

[0083] Step 12:

[0084] The user enters what they want to find out in the text input field and clicks the submit button.

[0085] Step 13:

[0086] The terminal receives the user's input text data and transmits it to the server.

[0087] Step 14:

[0088] The server sends the user input data to the generation AI module, requesting it to generate a response.

[0089] Step 15:

[0090] The generative AI module analyzes the user's input and generates a corresponding response, which is sent back to the server.

[0091] Step 16:

[0092] The server transmits the generated response data to the terminal.

[0093] Step 17:

[0094] The terminal receives the response data and displays it on the game screen.

[0095] Step 18:

[0096] The server saves the user's input and the generated AI's responses in a game progress log.

[0097] Step 19:

[0098] When clues or important events occur, the server adds and saves the information to the game progress database.

[0099] Step 20:

[0100] When the user reaches a solution to the case, he / she inputs "decide on a solution method" and submits it.

[0101] Step 21:

[0102] The terminal receives this user input data and transmits it to the server.

[0103] Step 22:

[0104] The server analyzes the game progress log and evaluates the user's performance.

[0105] Step 23:

[0106] The server generates an evaluation result and transmits it to the terminal.

[0107] Step 24:

[0108] The terminal receives the evaluation results and displays them to the user.

[0109] Step 25:

[0110] The server saves all game session data and ends the game session.

[0111] Example 1

[0112] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] Conventional mystery game systems provide users with fixed stories and responses, preventing dynamic game progression based on the user's free ideas and actions. Furthermore, they lack a mechanism for responding appropriately to user inputs on the fly and managing and saving progress in real time, making it difficult to enhance the user's immersion. This invention aims to improve the user experience by allowing users to freely input text and generating dynamic responses and scenarios using a generation AI module.

[0114] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0115] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for receiving and verifying user authentication information; and means for generating a game session ID and initial setting data and transmitting them to the terminal. This allows the user to freely enter text and obtain dynamic responses, manages the game progress in real time, and provides a highly immersive gaming experience.

[0116] A "user" is a person who interacts with the system to provide input and receive responses from the generative AI module.

[0117] A "terminal" is a device through which a user accesses the system, provides input via an interface, and displays responses from the server and generating AI module.

[0118] The "server" is a central computing device that receives requests from users, operates the generative AI module accordingly, and sends the final response to the terminal.

[0119] A "generative AI module" is artificial intelligence software that analyzes user input and generates appropriate responses and scenarios.

[0120] "Text data" is character string information used when a user inputs a survey or instruction.

[0121] A "response" is a reply or explanation generated by the generative AI module based on user input.

[0122] A "log" is data that records and saves user input and the responses of the generation AI module.

[0123] "Authentication information" refers to information such as a username and password required to verify the identity of a user.

[0124] A "game session ID" is a unique identifier generated by the server to identify a specific user's game progress.

[0125] "Initial setting data" is data that is generated by the server at the start of the game and sent to the terminal, and includes the initial state of the game and user profile information.

[0126] A "scenario" is data that indicates the game's progress plan and story development generated by the generation AI module.

[0127] A "clue" is important information or an item that a user discovers in the game to solve a case.

[0128] An "event" is an occurrence that occurs within the game based on the user's actions and choices.

[0129] "Evaluation" is the result calculated by the server after analyzing the user's actions and results leading up to the resolution of the case.

[0130] This invention is a mystery game system that uses generative AI. The user plays the role of a detective and solves the case using their own words. This system consists of a server, a user terminal, and a generative AI module.

[0131] The user first logs in by entering their username and password. The device sends this authentication information to the server, which then references the user database and performs authentication. If authentication is successful, the server generates a game session ID and initial setting data and sends them to the device. The device receives this, initializes the game screen, and displays it to the user.

[0132] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server requests the generation AI module to generate an initial scenario and sends the generated scenario data to the terminal. The terminal receives this scenario data and displays it to the user.

[0133] The user enters the content they want to search into the text input field, for example, "Search the front door." The device sends this text data to the server. The server sends this data to the generation AI module, which generates a response. The generation AI module analyzes the input content, generates a response, and sends it back to the server. The server sends this response data to the device, which displays it on the screen.

[0134] Furthermore, if the user inputs "investigate the bloodstains in more detail," the device will send this data to the server again. The server will send it to the generation AI module again, asking it to generate a detailed response. The generation AI module will generate a detailed response, and the server will send it to the device. The device will display this information on the screen. The server will save the new clue information in the game progress log.

[0135] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also stored in a game progress database on the server.

[0136] Finally, when the user reaches a solution to the case and inputs "Decide on the solution method", the terminal sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. The evaluation result is sent to the terminal and presented to the user. Finally, the server saves the game session data and ends the game session.

[0137] As a concrete example, consider the following prompt sentence:

[0138] example:

[0139] User: "Check out letter openers"

[0140] Terminal: Send text data to server

[0141] Server: Sends text data to the generation AI module

[0142] Generative AI module: Sends the response "There is blood on the letter opener" back to the server

[0143] Server: Sends response to device

[0144] Terminal: Display the response to the user

[0145] These prompt examples allow users to ask questions or give instructions in natural language through the system interface, with responses returned in real time, providing a highly immersive gaming experience.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] The user enters the username and password and clicks the login button.

[0149] Input: Username, Password

[0150] How it works: The device sends this authentication information to the server.

[0151] Output: User authentication request data

[0152] Step 2:

[0153] The server authenticates the user by checking the received authentication information against a user database.

[0154] Input: User authentication request data

[0155] What it does: The server looks up the database and performs authentication. If successful, it generates a game session ID and initial setup data.

[0156] Output: Game session ID, initial configuration data

[0157] Step 3:

[0158] The server sends the generated game session ID and initial setting data to the terminal.

[0159] Input: Game session ID, initial setup data

[0160] Operation: The server sends data to the device.

[0161] Output: Game session ID, initial setting data sent

[0162] Step 4:

[0163] The terminal receives the initial setting data, initializes the game screen, and displays it to the user.

[0164] Input: Game session ID, initial setting data to be sent

[0165] Operation: The device initializes and displays the game screen based on the initial setting data.

[0166] Output: Initialized game screen

[0167] Step 5:

[0168] The user clicks the "Start Game" button.

[0169] Input: User request to start a game

[0170] Action: The device sends this request to the server.

[0171] Output: Game start request data

[0172] Step 6:

[0173] The server receives a request to start a game from the terminal and requests the generation AI module to generate an initial scenario.

[0174] Input: Game start request data

[0175] Operation: The server asks the generation AI module to generate an initial scenario.

[0176] Output: Initial scenario generation request

[0177] Step 7:

[0178] The generation AI module generates an initial scenario and sends it back to the server.

[0179] Input: Initial scenario generation request

[0180] How it works: The Generative AI module performs the necessary data analysis and generates an initial scenario.

[0181] Output: Initial scenario data

[0182] Step 8:

[0183] The server transmits the generated initial scenario data to the terminal.

[0184] Input: Initial scenario data

[0185] Operation: The server sends the initial scenario data to the device.

[0186] Output: Initial scenario transmission data

[0187] Step 9:

[0188] The terminal receives the initial scenario data and displays it on the game screen.

[0189] Input: Initial scenario transmission data

[0190] Action: The terminal displays the initial scenario on the screen.

[0191] Output: Scenario screen displayed

[0192] Step 10:

[0193] The user inputs the content to be searched for, for example, "search the entrance" into the text input field and submits it.

[0194] Input: Survey text

[0195] Action: The device sends this text data to the server.

[0196] Output: Survey content text data

[0197] Step 11:

[0198] The server sends the user's input data to the generation AI module and requests it to generate a corresponding response.

[0199] Input: Survey content text data

[0200] How it works: The server sends this data to the generation AI module.

[0201] Output: Response-generating request data

[0202] Step 12:

[0203] The generative AI module analyzes the input and generates an appropriate response to send back to the server.

[0204] Input: Response generation request data

[0205] How it works: The generative AI module analyzes the content and generates a response.

[0206] Output: Response data

[0207] Step 13:

[0208] The server transmits the generated response data to the terminal.

[0209] Input: Response data

[0210] Operation: The server sends the response data to the terminal.

[0211] Output: Response send data

[0212] Step 14:

[0213] The terminal displays the received response data on the game screen.

[0214] Input: Response send data

[0215] Action: The terminal displays the response on the screen.

[0216] Output: The displayed response

[0217] Step 15:

[0218] The user further inputs specific details of the investigation, for example, sending "investigate the bloodstains in detail."

[0219] Input: Text of detailed investigation content

[0220] Action: The device sends this input data back to the server.

[0221] Output: Detailed survey data

[0222] Step 16:

[0223] The server sends the detailed investigation results to the generation AI module, requesting it to generate more detailed information.

[0224] Input: Detailed survey data

[0225] How it works: The server sends data to the generation AI module.

[0226] Output: Detailed response generation request data

[0227] Step 17:

[0228] The generative AI module generates a detailed response based on the detailed investigation and sends it back to the server.

[0229] Input: Detailed response generation request data

[0230] How it works: The generative AI module generates a detailed response.

[0231] Output: Detailed response data

[0232] Step 18:

[0233] The server sends detailed response data to the terminal, which displays it on the game screen.

[0234] Input: Detailed response data

[0235] Operation: The server sends detailed response data to the terminal, which displays it on the screen.

[0236] Output: Detailed response displayed

[0237] Step 19:

[0238] The server stores the new clue information in a game progress log.

[0239] Input: User input and generated AI response

[0240] How it works: The server stores clue information in a database.

[0241] Output: Saved clue log

[0242] Step 20:

[0243] A game progress log is kept and the user's progress is updated as needed.

[0244] Input: User progress data

[0245] Action: The server updates the game progress database.

[0246] Output: Updated progress log

[0247] Step 21:

[0248] The user reaches a resolution to the case and enters "Determine resolution" and submits.

[0249] Input: Case resolution text

[0250] Action: The device sends this data to the server.

[0251] Output: Incident resolution data

[0252] Step 22:

[0253] The server analyzes the game progress log based on the received data and evaluates the user's performance.

[0254] Input: Case resolution data, game progress log

[0255] Operation: The server analyzes the game progress log and generates an evaluation result.

[0256] Output: Evaluation result data

[0257] Step 23:

[0258] The server transmits the evaluation result data to the terminal, which displays it to the user.

[0259] Input: Evaluation result data

[0260] Operation: The server sends the data to the terminal, and the terminal displays the evaluation results on the screen.

[0261] Output: The displayed evaluation results

[0262] Step 24:

[0263] The server saves the game session data and ends the game session.

[0264] Input: Game session data, evaluation result data

[0265] Action: The server saves the data and ends the session.

[0266] Output: Saved game session data

[0267] (Application example 1)

[0268] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0269] Autonomous vehicles are required to analyze data collected in real time from numerous sensors and to propose prompt and appropriate countermeasures when abnormalities or problems occur. However, conventional systems have had difficulty efficiently performing the entire process of detecting abnormalities, identifying their causes, and proposing countermeasures. Therefore, a system that enables more advanced analysis and rapid response is needed to maintain user safety and vehicle performance.

[0270] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0271] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI response as a log; means for receiving data collected from various sensors in the autonomous vehicle; means including a generation AI module that analyzes, identifies causes, and generates countermeasure proposals based on the sensor data; and means for transmitting the analysis and countermeasure proposals generated by the generation AI module to an in-vehicle display or audio system. This enables the autonomous vehicle to identify abnormalities in real time and present prompt and appropriate countermeasures.

[0272] "Means for receiving text data entered by a user" refers to a function for receiving and processing information entered by a user in text format.

[0273] "Means including a generative AI module" refers to functionality that includes an AI model for analyzing text data and generating a corresponding response.

[0274] "Means for sending to the user's terminal" refers to a function for transferring the generated response to the user's device.

[0275] "Means of saving as a log" refers to the function of recording user input and the generated AI's response and saving it in a format that can be referenced later.

[0276] "Means for receiving data collected from various sensors in an autonomous vehicle" refers to the function of receiving various data obtained from the vehicle's sensors.

[0277] "Means including a generation AI module that analyzes sensor data, identifies the cause, and generates proposed countermeasures based on the sensor data" refers to a function that includes an AI model that analyzes sensor data, identifies the cause of the problem, and proposes countermeasures.

[0278] "Means for sending to a display or audio system" refers to a function for transferring the generated countermeasure proposal to a display device or audio device in the vehicle.

[0279] The present invention is a system that applies a mystery game system using generative AI to perform real-time analysis and propose countermeasures inside an autonomous vehicle. Specific embodiments are shown below.

[0280] First, the user accesses the system from a user device such as a display in the vehicle or a smartphone and enters the abnormality or problem situation in text format. For example, the user enters "abnormal vibration detected." The user device then sends this text data to the server.

[0281] The server receives input data from the user and sends it to the generation AI module. The generation AI module analyzes this data and generates an appropriate response. For example, it generates a response such as, "Your front tire pressure is low. We recommend that you visit a nearby service station." The generated response is sent back to the user's device via the server and displayed or notified on the user's display or audio system.

[0282] Autonomous vehicles are equipped with numerous sensors, including cameras, LiDAR, microphones, and GPS. Data collected from these sensors is aggregated in an on-board computer. This data is sent to a server in real time. The server uses a generative AI module to analyze this sensor data, identify the cause of the problem, and generate appropriate countermeasures.

[0283] For example, if a vehicle detects abnormal vibrations, it sends the vibration data, speed data, tire pressure data, etc. to the generation AI module. The generation AI module comprehensively analyzes this data and creates a specific countermeasure suggestion, such as "The front tire pressure is low. We recommend you visit a nearby service station." This suggestion is then displayed on the vehicle's display and audio system via the server.

[0284] The hardware used includes the vehicle's on-board computer and various sensors (camera, LiDAR, microphone, GPS, etc.), while the software uses a server using a web framework such as Django or Flask, an HTTP request library using Python (such as requests), and a generative AI model such as GPT-4.

[0285] As a concrete example, a scenario where abnormal vibrations are detected inside a vehicle will be shown.

[0286] 1. User: Enters the text "Abnormal vibration detected."

[0287] 2. Terminal: Sends input data to the server.

[0288] 3. Server: Sends data to the generation AI module and analyzes causes and countermeasures.

[0289] 4. Generative AI module: Generates a response such as, "Your front tire pressure is low. We recommend you visit a nearby service station."

[0290] 5. Server: Sends the response to the terminal and displays it to the user.

[0291] An example of a prompt sentence is "If abnormal vibration is detected, please provide the cause and countermeasure."

[0292] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0293] Step 1: User enters anomaly text

[0294] The user inputs the abnormality or problem situation in text format from a display in the autonomous vehicle or a terminal such as a smartphone. For example, they might input "abnormal vibration detected." This input data is received by the terminal.

[0295] Step 2: The device sends the input data to the server

[0296] The terminal sends the text data entered by the user to the server. The input data is sent via an HTTP request and received by the server.

[0297] Step 3: The server sends the data to the generation AI module

[0298] The server sends the received text data to the generation AI module, where it is converted into an appropriate format and input to the generation AI as a prompt.

[0299] Step 4: The generative AI module analyzes

[0300] The generative AI module analyzes the data based on the received prompt and generates an appropriate response, such as "Your front tire pressure is low. We recommend you visit a nearby service station." This response is then sent back to the server.

[0301] Step 5: The server sends the response data to the device

[0302] The server receives the response data returned from the generation AI module and sends it to the user's device. The data is sent as an HTTP response and received by the device.

[0303] Step 6: The device displays the response data

[0304] The device displays or notifies the user of the received response data on a display or audio system, allowing the user to understand the appropriate measures.

[0305] Step 7: Collect and transmit sensor data

[0306] Various sensors in autonomous vehicles (cameras, LiDAR, microphones, GPS, etc.) collect data in real time and send it to the on-board computer, which also simultaneously sends the collected data to a server.

[0307] Step 8: The server sends the sensor data to the generation AI module

[0308] The server sends the received sensor data to the generative AI module, which uses this data to identify the cause of the anomaly and create a prompt that generates a countermeasure proposal.

[0309] Step 9: Generative AI module analyzes sensor data

[0310] The generative AI module analyzes the sensor data and generates corresponding countermeasure suggestions, such as "The front tire pressure is low. We recommend you visit a nearby service station" based on vibration data and tire pressure data.

[0311] Step 10: The server sends the countermeasure proposal to the device.

[0312] The server receives the countermeasure proposals returned by the generation AI module and sends them to the vehicle's display or audio system. The data is sent as an HTTP response and received by the terminal.

[0313] Step 11: Your device will display suggested solutions

[0314] The device then displays or notifies the user of the received countermeasure proposals on a display or audio system, allowing the user to take the necessary countermeasures.

[0315] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0316] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[0317] 1. User login and initial settings

[0318] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[0319] 2. Initializing the game scenario

[0320] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[0321] 3. Real-time interaction with users

[0322] The user enters the content they want to search for, for example, "Check the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes it and sends the response data back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and submits it back to the server. The server then submits the response data to the device, which can then display it on the screen.

[0323] 4. New clues discovered

[0324] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains more closely." The device sends this input data back to the server. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the generated responses and clues. The server saves the new clue information in the game progress log.

[0325] 5. Manage and save your progress

[0326] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[0327] 6. Ending the Game and Evaluation

[0328] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0329] Specific examples

[0330] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[0331] 1. User: Type "Check out letter openers" and submit.

[0332] 2. Terminal: Receives this text data and sends it to the server.

[0333] 3. Server: Sends text data to the generation AI module.

[0334] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[0335] 5. Emotion engine: Recognizes emotions such as "excitement" or "surprise" from user input.

[0336] 6. Generative AI module: Takes emotional information into account to tailor the response and send it back to the server (e.g., "This bloodstain is recent and may hold some important clues").

[0337] 7. Server: Sends the response to the terminal, which displays it to the user.

[0338] Thus, the present invention is a system that provides a more immersive gaming experience by recognizing a user's emotions and generating dynamic responses based on those emotions.

[0339] The processing flow will be explained below.

[0340] Step 1:

[0341] The user enters the username and password and clicks the login button.

[0342] Step 2:

[0343] The terminal receives this user input data and sends it to the server as a login request.

[0344] Step 3:

[0345] The server receives this login request and performs authentication by checking the user database.

[0346] Step 4:

[0347] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[0348] Step 5:

[0349] The terminal receives the initial setting data, initializes the game screen, and displays it.

[0350] Step 6:

[0351] The user clicks the "Start Game" button on the game screen.

[0352] Step 7:

[0353] The terminal sends a game start request to the server.

[0354] Step 8:

[0355] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[0356] Step 9:

[0357] The generation AI module generates an initial scenario and sends it back to the server.

[0358] Step 10:

[0359] The server transmits the initial scenario data to the terminal.

[0360] Step 11:

[0361] The terminal receives the scenario data and displays it on the game screen.

[0362] Step 12:

[0363] The user enters what they want to find out in the text input field and clicks the submit button.

[0364] Step 13:

[0365] The terminal receives this user input data and transmits it to the server.

[0366] Step 14:

[0367] The server sends the user input data to the generation AI module, requesting it to generate a response.

[0368] Step 15:

[0369] The emotion engine analyzes the user's input text and recognizes the user's emotion (e.g., "surprise" or "excitement").

[0370] Step 16:

[0371] The generative AI module generates tailored responses based on emotional information from the emotion engine.

[0372] Step 17:

[0373] The generation AI module sends the generated response back to the server.

[0374] Step 18:

[0375] The server transmits the generated response data to the terminal.

[0376] Step 19:

[0377] The terminal receives the response data and displays it on the game screen.

[0378] Step 20:

[0379] The server stores the user's input, the generated AI's response, and the emotional information recognized by the emotion engine in a game progress log.

[0380] Step 21:

[0381] The emotion engine instructs the generative AI module to adjust the generation of cues and events based on the user's emotional information.

[0382] Step 22:

[0383] The user may use the clue to investigate further (for example, enter "investigate the blood trail").

[0384] Step 23:

[0385] The terminal sends this new input to the server.

[0386] Step 24:

[0387] The server sends new input to the generation AI module, asking it to generate a more detailed response.

[0388] Step 25:

[0389] The generation AI module generates a new response and sends it back to the server.

[0390] Step 26:

[0391] The emotion engine again recognizes the user's emotions and provides them to the generative AI module.

[0392] Step 27:

[0393] The generative AI module takes emotional information into account to tailor a detailed response and send it back to the server.

[0394] Step 28:

[0395] The server sends the generated response to the terminal.

[0396] Step 29:

[0397] The terminal displays the generated response on its screen.

[0398] Step 30:

[0399] The server stores the progress and emotion information in a log and database.

[0400] Step 31:

[0401] When the user has gathered the information necessary to solve the case, he / she inputs "Decide on a solution method."

[0402] Step 32:

[0403] The terminal sends the user input to the server.

[0404] Step 33:

[0405] The server analyzes the game progress log and evaluates the user's performance, including the emotion information from the emotion engine.

[0406] Step 34:

[0407] The server transmits the generated evaluation results to the terminal.

[0408] Step 35:

[0409] The terminal displays the evaluation results to the user.

[0410] Step 36:

[0411] The server saves all game session data and ends the game session.

[0412] Example 2

[0413] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0414] Conventional mystery game systems have a problem of low immersion because they respond to user inputs in a fixed manner and do not generate dynamic responses that take the user's emotions into account. Furthermore, it is difficult to generate clues and events that reflect the user's emotions, and to properly manage and evaluate the game progress.

[0415] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means including an emotion engine that analyzes and recognizes the user's emotions; and means for adjusting the response of the generation AI module based on the emotions recognized by the emotion engine. This enables dynamic generation of responses that take the user's emotions into consideration, providing a greater sense of immersion. Furthermore, it also enables appropriate initialization of the game scenario, dynamic generation of clues and events, progress management, and user performance evaluation.

[0416] A "user" is a person who uses a terminal to play a mystery game using the system.

[0417] "Text data" is character string information related to surveys or instructions that a user inputs into a terminal and sends to a server.

[0418] A "generative AI module" is an artificial intelligence component that analyzes text data entered by a user and generates a corresponding response.

[0419] The "emotion engine" is a component that analyzes and recognizes emotions from the user's input text and provides that information to the generative AI module.

[0420] A "scenario" is data that contains an outline of the story and events necessary for the game to progress, and is generated by the generation AI module.

[0421] A "clue" is important information or an item that the user discovers in the game to solve the problem.

[0422] The "server" is a central computer system that receives user input, controls the generative AI module and emotion engine, and transmits response data and scenario data to the user's device.

[0423] A "terminal" is a device operated by a user that provides a game interface through communication with a server.

[0424] A "log" is recorded data that stores information such as the game's progress, user input, and the responses of the generated AI.

[0425] "Performance evaluation" is the process of evaluating in-game progress and achievements based on user input and actions, and providing feedback.

[0426] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[0427] The server receives a login request from the user device and checks it against the user database for authentication. If authentication is successful, it generates a game session ID and initial setting data and sends them to the device. The database used may be MySQL.

[0428] The user terminal receives the initial setting data, initializes and displays the game screen, and sends text data entered by the user to the server and displays a response on the screen.

[0429] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server passes this request to a generation AI module (e.g., GPT-3) to request the generation of an initial scenario. The generated initial scenario is sent to the device via the server. The device displays the received scenario data on the game screen.

[0430] The user enters the content they want to search for (e.g., "Check the front door") into the text input field and submits it. The device transmits this data to the server, which then requests the generative AI module to generate a response. The generative AI module analyzes the user's input text and generates an appropriate response. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and generates the final response. The server submits this response to the device, which displays it on the screen.

[0431] For example, if a user types "examine the letter opener," the emotion engine recognizes emotions such as "excitement" or "surprise," and the AI ​​module adjusts the response (e.g., "the letter opener has bloodstains"). This response is then displayed on the device via the server.

[0432] The server logs user input and the generated AI's responses, and also stores the user's progress and important events in a game progress database. Additionally, emotions recognized by the emotion engine are also recorded.

[0433] When the user solves the case and clicks "Decide on the solution," the device sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. This evaluation also uses emotional information from the emotion engine, and the results are sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0434] Prompt Sentence Examples

[0435] Generate an example response for the input "Check for activity outside the window." Please also include adjustments for when the emotion engine recognizes "anxiety."

[0436] In this way, by reflecting the user's emotions and generating dynamic, real-time responses, a more immersive gaming experience can be provided.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] Step 1: User Login and Initial Setup

[0439] The user enters their username and password on the login screen. The device sends the input data to the server. The server authenticates the received user data by checking it against the MySQL database. If authentication is successful, it generates a game session ID and constructs a response including initial setup data (JSON format) and sends it to the device. The device receives the response data, initializes the game screen based on the initial setup data, and displays it to the user.

[0440] Input: Username, Password

[0441] Output: Game session ID, initial configuration data

[0442] Data processing: User database matching, session ID generation, initial setting data JSON construction

[0443] Step 2: Initializing the game scenario

[0444] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and requests the generation AI module (e.g., GPT-3) to generate an initial scenario. The generated initial scenario is sent back to the server, which then sends it to the device. The device displays the received scenario data on the game screen.

[0445] Input: Game start request

[0446] Output: Initial scenario data

[0447] Data processing: Initial scenario generation request, scenario generation by generation AI module

[0448] Step 3: Real-time interaction with the user

[0449] The user enters the information they want to search in the text input field and submits it. The device sends this text data to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes the text data, generates an appropriate response, and sends it back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response, incorporating this emotional information. The server then sends the final response data to the device, which displays it on the screen.

[0450] Input: Text data of the content you want to search

[0451] Output: Response data

[0452] Data processing: text data analysis, response generation, emotion recognition, response adjustment

[0453] Step 4: Discover new clues

[0454] The user inputs a more detailed investigation, for example, "Examine the bloodstains in more detail." The device then sends this input data back to the server. The server then sends it to the generation AI module, requesting it to generate a more detailed response. The generation AI module generates a detailed response and sends it back to the server. The server then sends this to the device, which displays it on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the responses and clues that are generated. The server then saves the new clue information in the game progress log.

[0455] Input: Text data from detailed survey

[0456] Output: Detailed response data

[0457] Data processing: detailed response generation requests, detailed response generation, emotion recognition, response adjustment, cue storage

[0458] Step 5: Manage and save your progress

[0459] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[0460] Input: User input, AI generation response, emotional information

[0461] Output: Game progress log

[0462] Data processing: log storage, clue storage, emotional information storage

[0463] Step 6: Ending the game and evaluating

[0464] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0465] Input: Text data of the solution

[0466] Output: Evaluation results, final response data

[0467] Data processing: Analysis of game progress logs, performance evaluation, evaluation and use of emotional information, presentation of evaluation results, session data storage

[0468] (Application example 2)

[0469] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0470] Conventional mystery game systems have had the problem of making it difficult for users to become immersed in the story. One of the reasons for this is that dynamic responses that reflect the user's emotions are not generated. Therefore, the objective of the present invention is to provide an interactive game system that allows users to become deeply involved in the progress of the game with their emotions.

[0471] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0472] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for combining response generation by the generation AI module with an emotion engine that recognizes the user's emotions; and means for adjusting the generated response based on the emotion recognition information. This enables dynamic response generation that reflects the user's emotions, resulting in an interactive game experience that provides a deeper immersion.

[0473] - "Means for receiving text data entered by a user" means a device or program that obtains information from the text entered by a user and receives it as initial data for processing within the system.

[0474] A "generative AI module" is artificial intelligence-based software that analyzes text data and automatically generates requested responses and scenarios.

[0475] "Means for sending to the user's device" refers to the communication protocols and applications used to send information such as responses and scenarios from the generation AI module to the device used by the user.

[0476] The "means for saving as a log" is a database or recording device for storing the user's input data and generated responses as a history.

[0477] An "emotion engine" is software that uses natural language processing technology to analyze emotions from text data entered by the user and identify those emotions.

[0478] "Means for adjusting responses generated based on emotion recognition information" refers to functions and algorithms for appropriately adjusting responses generated by the generative AI based on the emotion information recognized by the emotion engine.

[0479] "Means for requesting generation of an initial scenario" refers to commands or communication methods used to request the generation AI module to create the foundation for the story or scenario at the start of the game.

[0480] The "means for generating clues and events" refers to a function or module for generating new clues and events within the game based on user input data.

[0481] This invention is a mystery game system in which users play the role of detectives and solve various cases. The system combines a server, user terminals, a generative AI module, and an emotion engine that recognizes the user's emotions.

[0482] First, the user logs in from a device such as a smartphone. The server receives the user name and password and performs authentication. If authentication is successful, a game session ID is generated and sent to the device along with initial setup data. The device receives this and initializes and displays the game screen.

[0483] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and sends it back to the server. The server sends this scenario data to the terminal, which receives the scenario data and displays it on the game screen.

[0484] When a user enters survey content into the text field, the device sends the text data to the server. The server then sends the text data along with the emotional information recognized by the emotion engine to the generation AI module. The generation AI module analyzes this and generates response data. The response is adjusted based on the emotional information and sent back to the server. The server then sends the response data to the device, which displays it on the screen.

[0485] For example, if a user types "examine the letter opener," the input data is sent to the server. The server sends this data to the generative AI module, which generates a response such as "the letter opener has bloodstains." The emotion engine recognizes emotions such as "surprise" from the user's input and adjusts the generated response, such as "the bloodstains are recent and may hold important clues." This provides a dynamic response that also reflects the user's emotions.

[0486] Similarly, if a user types "investigate the blood stains," that input is sent to the generative AI module to generate more detailed information, while the emotion engine recognizes the user's emotions and tailors responses and cues based on that information.

[0487] The software used includes generative AI models (e.g., OpenAI GPT), emotion recognition engines (NLP analysis), server applications (e.g., Flask, Django), and databases (e.g., PostgreSQL, MySQL). The hardware includes smartphones and servers.

[0488] Below are some examples of specific prompt sentences.

[0489] Example prompt sentence:

[0490] User input: "Examine the blood trail."

[0491] Prompt to generative AI model: "The user is examining a blood stain. What details can they find? Please describe the scene in detail."

[0492] User input: "Check letter opener"

[0493] Prompt to generative AI model: "A user is examining a letter opener. What clues can they find about this item? The emotion is surprise."

[0494] In this way, the present invention realizes an interactive mystery game system that reflects the user's emotions and provides a deeper sense of immersion.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] The user enters their username and password from a device such as a smartphone and sends a login request to the server. The server analyzes the received login request and performs user authentication using a database. If authentication is successful, the server generates a game session ID and initial setting data and sends this to the device. The device receives the initial setting data, initializes the game screen, and displays it.

[0498] Input: Username, Password

[0499] Output: Game session ID, initial configuration data

[0500] Specific operations: Database matching, session ID generation

[0501] Step 2:

[0502] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and asks the generation AI module to generate an initial scenario. The generation AI module generates an initial scenario based on the prompt and sends it back to the server. The server then sends the generated initial scenario to the device, which receives it and displays it on the game screen.

[0503] Input: Game start request

[0504] Output: Initial scenario

[0505] Specific operations: prompt generation, scenario generation

[0506] Step 3:

[0507] The user enters the information they want to find in a text field on the game screen and submits it. The device receives this and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. At the same time, the emotion engine recognizes emotions from the user's input and provides this information to the generation AI module. The generation AI module adjusts the response based on the emotion information and sends the final response back to the server. The server then submits the response data to the device, which displays it on the screen.

[0508] Input: User-entered text

[0509] Output: The generated response

[0510] Specific behaviors: emotion recognition, response adjustment, data transmission

[0511] Step 4:

[0512] If the user wishes to continue with a more detailed investigation, they enter the details of their investigation again into the text field and submit it. The device receives the input data and sends it to the server. The server again sends this request to the generation AI module, asking it to generate more detailed information based on the content. The generation AI module generates a more detailed response and sends it back to the server. The emotion engine again recognizes emotions from the user's input, and the generation AI module adjusts the response based on that. The server sends the generated detailed information to the device, which displays it on the screen.

[0513] Input: Further investigation details

[0514] Output: Detailed response

[0515] Specific actions: detailed scenario generation, emotion recognition, and response adjustment

[0516] Step 5:

[0517] All user input and all responses from the generated AI are logged and saved by the server. The server also records clues and important events discovered by the user and adds them to the game progress database. At the same time, emotional information recognized by the emotion engine is also saved.

[0518] Input: User input, generative AI response, emotional information

[0519] Output: Game progress log

[0520] Specific operations: saving to database, logging

[0521] Step 6:

[0522] When the user reaches the final stage of the game and decides on a solution, they send the input data to the server. The server receives it, analyzes the game progress log, and evaluates the user's performance. This evaluation process also takes into account the emotional information recognized by the emotion engine. The evaluation results are sent from the server to the terminal and displayed to the user. Finally, the server saves the game session data and ends the game session.

[0523] Input: Solution input

[0524] Output: Performance evaluation results

[0525] Specific operations: log analysis, evaluation result generation, data storage

[0526] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0527] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0528] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0529] [Second embodiment]

[0530] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0531] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0532] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0533] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0534] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0535] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0536] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0537] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0538] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0539] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0540] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0541] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0542] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. The system consists of a server, a user terminal, and a generative AI module.

[0543] 1. User login and initial settings

[0544] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[0545] 2. Initializing the game scenario

[0546] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[0547] 3. Real-time interaction with users

[0548] The user enters the content they want to search for, for example, "Search the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. The generation AI module analyzes the data and sends the response data back to the server. The server then sends the response data to the device, which can then display it on the screen.

[0549] 4. New clues discovered

[0550] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains in more detail." The device sends this input data to the server again. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the server saves the new clue information in the game progress log.

[0551] 5. Manage and save your progress

[0552] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server.

[0553] 6. Ending the Game and Evaluation

[0554] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0555] Specific examples

[0556] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[0557] 1. User: Type "Check out letter openers" and submit.

[0558] 2. Terminal: Receives this text data and sends it to the server.

[0559] 3. Server: Sends text data to the generation AI module.

[0560] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[0561] 5. Server: Sends the response to the terminal, which displays it to the user.

[0562] Thus, the present invention is a system that allows users to freely input text and generates dynamic responses in response to that text, thereby providing a highly immersive gaming experience.

[0563] The processing flow will be explained below.

[0564] Step 1:

[0565] The user enters the username and password and clicks the login button.

[0566] Step 2:

[0567] The terminal receives this user input data and sends it to the server as a login request.

[0568] Step 3:

[0569] The server receives this login request and performs authentication by checking the user database.

[0570] Step 4:

[0571] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[0572] Step 5:

[0573] The terminal receives the initial setting data, initializes the game screen, and displays it.

[0574] Step 6:

[0575] The user clicks the "Start Game" button on the game screen.

[0576] Step 7:

[0577] The terminal sends a game start request to the server.

[0578] Step 8:

[0579] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[0580] Step 9:

[0581] The generation AI module generates an initial scenario and sends it back to the server.

[0582] Step 10:

[0583] The server transmits the initial scenario data to the terminal.

[0584] Step 11:

[0585] The terminal receives the scenario data and displays it on the game screen.

[0586] Step 12:

[0587] The user enters what they want to find out in the text input field and clicks the submit button.

[0588] Step 13:

[0589] The terminal receives the user's input text data and transmits it to the server.

[0590] Step 14:

[0591] The server sends the user input data to the generation AI module, requesting it to generate a response.

[0592] Step 15:

[0593] The generative AI module analyzes the user's input and generates a corresponding response, which is sent back to the server.

[0594] Step 16:

[0595] The server transmits the generated response data to the terminal.

[0596] Step 17:

[0597] The terminal receives the response data and displays it on the game screen.

[0598] Step 18:

[0599] The server saves the user's input and the generated AI's responses in a game progress log.

[0600] Step 19:

[0601] When clues or important events occur, the server adds and saves the information to the game progress database.

[0602] Step 20:

[0603] When the user reaches a solution to the case, he / she inputs "decide on a solution method" and submits it.

[0604] Step 21:

[0605] The terminal receives this user input data and transmits it to the server.

[0606] Step 22:

[0607] The server analyzes the game progress log and evaluates the user's performance.

[0608] Step 23:

[0609] The server generates an evaluation result and transmits it to the terminal.

[0610] Step 24:

[0611] The terminal receives the evaluation results and displays them to the user.

[0612] Step 25:

[0613] The server saves all game session data and ends the game session.

[0614] Example 1

[0615] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0616] Conventional mystery game systems provide users with fixed stories and responses, preventing dynamic game progression based on the user's free ideas and actions. Furthermore, they lack a mechanism for responding appropriately to user inputs on the fly and managing and saving progress in real time, making it difficult to enhance the user's immersion. This invention aims to improve the user experience by allowing users to freely input text and generating dynamic responses and scenarios using a generation AI module.

[0617] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0618] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for receiving and verifying user authentication information; and means for generating a game session ID and initial setting data and transmitting them to the terminal. This allows the user to freely enter text and obtain dynamic responses, manages the game progress in real time, and provides a highly immersive gaming experience.

[0619] A "user" is a person who interacts with the system to provide input and receive responses from the generative AI module.

[0620] A "terminal" is a device through which a user accesses the system, provides input via an interface, and displays responses from the server and generating AI module.

[0621] The "server" is a central computing device that receives requests from users, operates the generative AI module accordingly, and sends the final response to the terminal.

[0622] A "generative AI module" is artificial intelligence software that analyzes user input and generates appropriate responses and scenarios.

[0623] "Text data" is character string information used when a user inputs a survey or instruction.

[0624] A "response" is a reply or explanation generated by the generative AI module based on user input.

[0625] A "log" is data that records and saves user input and the responses of the generation AI module.

[0626] "Authentication information" refers to information such as a username and password required to verify the identity of a user.

[0627] A "game session ID" is a unique identifier generated by the server to identify a specific user's game progress.

[0628] "Initial setting data" is data that is generated by the server at the start of the game and sent to the terminal, and includes the initial state of the game and user profile information.

[0629] A "scenario" is data that indicates the game's progress plan and story development generated by the generation AI module.

[0630] A "clue" is important information or an item that a user discovers in the game to solve a case.

[0631] An "event" is an occurrence that occurs within the game based on the user's actions and choices.

[0632] "Evaluation" is the result calculated by the server after analyzing the user's actions and results leading up to the resolution of the case.

[0633] This invention is a mystery game system that uses generative AI. The user plays the role of a detective and solves the case using their own words. This system consists of a server, a user terminal, and a generative AI module.

[0634] The user first logs in by entering their username and password. The device sends this authentication information to the server, which then references the user database and performs authentication. If authentication is successful, the server generates a game session ID and initial setting data and sends them to the device. The device receives this, initializes the game screen, and displays it to the user.

[0635] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server requests the generation AI module to generate an initial scenario and sends the generated scenario data to the terminal. The terminal receives this scenario data and displays it to the user.

[0636] The user enters the content they want to search into the text input field, for example, "Search the front door." The device sends this text data to the server. The server sends this data to the generation AI module, which generates a response. The generation AI module analyzes the input content, generates a response, and sends it back to the server. The server sends this response data to the device, which displays it on the screen.

[0637] Furthermore, if the user inputs "investigate the bloodstains in more detail," the device will send this data to the server again. The server will send it to the generation AI module again, asking it to generate a detailed response. The generation AI module will generate a detailed response, and the server will send it to the device. The device will display this information on the screen. The server will save the new clue information in the game progress log.

[0638] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also stored in a game progress database on the server.

[0639] Finally, when the user reaches a solution to the case and inputs "Decide on the solution method", the terminal sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. The evaluation result is sent to the terminal and presented to the user. Finally, the server saves the game session data and ends the game session.

[0640] As a concrete example, consider the following prompt sentence:

[0641] example:

[0642] User: "Check out letter openers"

[0643] Terminal: Send text data to server

[0644] Server: Sends text data to the generation AI module

[0645] Generative AI module: Sends the response "There is blood on the letter opener" back to the server

[0646] Server: Sends response to device

[0647] Terminal: Display the response to the user

[0648] These prompt examples allow users to ask questions or give instructions in natural language through the system interface, with responses returned in real time, providing a highly immersive gaming experience.

[0649] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0650] Step 1:

[0651] The user enters the username and password and clicks the login button.

[0652] Input: Username, Password

[0653] How it works: The device sends this authentication information to the server.

[0654] Output: User authentication request data

[0655] Step 2:

[0656] The server authenticates the user by checking the received authentication information against a user database.

[0657] Input: User authentication request data

[0658] What it does: The server looks up the database and performs authentication. If successful, it generates a game session ID and initial setup data.

[0659] Output: Game session ID, initial configuration data

[0660] Step 3:

[0661] The server sends the generated game session ID and initial setting data to the terminal.

[0662] Input: Game session ID, initial setup data

[0663] Operation: The server sends data to the device.

[0664] Output: Game session ID, initial setting data sent

[0665] Step 4:

[0666] The terminal receives the initial setting data, initializes the game screen, and displays it to the user.

[0667] Input: Game session ID, initial setting data to be sent

[0668] Operation: The device initializes and displays the game screen based on the initial setting data.

[0669] Output: Initialized game screen

[0670] Step 5:

[0671] The user clicks the "Start Game" button.

[0672] Input: User request to start a game

[0673] Action: The device sends this request to the server.

[0674] Output: Game start request data

[0675] Step 6:

[0676] The server receives a request to start a game from the terminal and requests the generation AI module to generate an initial scenario.

[0677] Input: Game start request data

[0678] Operation: The server asks the generation AI module to generate an initial scenario.

[0679] Output: Initial scenario generation request

[0680] Step 7:

[0681] The generation AI module generates an initial scenario and sends it back to the server.

[0682] Input: Initial scenario generation request

[0683] How it works: The Generative AI module performs the necessary data analysis and generates an initial scenario.

[0684] Output: Initial scenario data

[0685] Step 8:

[0686] The server transmits the generated initial scenario data to the terminal.

[0687] Input: Initial scenario data

[0688] Operation: The server sends the initial scenario data to the device.

[0689] Output: Initial scenario transmission data

[0690] Step 9:

[0691] The terminal receives the initial scenario data and displays it on the game screen.

[0692] Input: Initial scenario transmission data

[0693] Action: The terminal displays the initial scenario on the screen.

[0694] Output: Scenario screen displayed

[0695] Step 10:

[0696] The user inputs the content to be searched for, for example, "search the entrance" into the text input field and submits it.

[0697] Input: Survey text

[0698] Action: The device sends this text data to the server.

[0699] Output: Survey content text data

[0700] Step 11:

[0701] The server sends the user's input data to the generation AI module and requests it to generate a corresponding response.

[0702] Input: Survey content text data

[0703] How it works: The server sends this data to the generation AI module.

[0704] Output: Response-generating request data

[0705] Step 12:

[0706] The generative AI module analyzes the input and generates an appropriate response to send back to the server.

[0707] Input: Response generation request data

[0708] How it works: The generative AI module analyzes the content and generates a response.

[0709] Output: Response data

[0710] Step 13:

[0711] The server transmits the generated response data to the terminal.

[0712] Input: Response data

[0713] Operation: The server sends the response data to the terminal.

[0714] Output: Response send data

[0715] Step 14:

[0716] The terminal displays the received response data on the game screen.

[0717] Input: Response send data

[0718] Action: The terminal displays the response on the screen.

[0719] Output: The displayed response

[0720] Step 15:

[0721] The user further inputs specific details of the investigation, for example, sending "investigate the bloodstains in detail."

[0722] Input: Text of detailed investigation content

[0723] Action: The device sends this input data back to the server.

[0724] Output: Detailed survey data

[0725] Step 16:

[0726] The server sends the detailed investigation results to the generation AI module, requesting it to generate more detailed information.

[0727] Input: Detailed survey data

[0728] How it works: The server sends data to the generation AI module.

[0729] Output: Detailed response generation request data

[0730] Step 17:

[0731] The generative AI module generates a detailed response based on the detailed investigation and sends it back to the server.

[0732] Input: Detailed response generation request data

[0733] How it works: The generative AI module generates a detailed response.

[0734] Output: Detailed response data

[0735] Step 18:

[0736] The server sends detailed response data to the terminal, which displays it on the game screen.

[0737] Input: Detailed response data

[0738] Operation: The server sends detailed response data to the terminal, which displays it on the screen.

[0739] Output: Detailed response displayed

[0740] Step 19:

[0741] The server stores the new clue information in a game progress log.

[0742] Input: User input and generated AI response

[0743] How it works: The server stores clue information in a database.

[0744] Output: Saved clue log

[0745] Step 20:

[0746] A game progress log is kept and the user's progress is updated as needed.

[0747] Input: User progress data

[0748] Action: The server updates the game progress database.

[0749] Output: Updated progress log

[0750] Step 21:

[0751] The user reaches a resolution to the case and enters "Determine resolution" and submits.

[0752] Input: Case resolution text

[0753] Action: The device sends this data to the server.

[0754] Output: Incident resolution data

[0755] Step 22:

[0756] The server analyzes the game progress log based on the received data and evaluates the user's performance.

[0757] Input: Case resolution data, game progress log

[0758] Operation: The server analyzes the game progress log and generates an evaluation result.

[0759] Output: Evaluation result data

[0760] Step 23:

[0761] The server transmits the evaluation result data to the terminal, which displays it to the user.

[0762] Input: Evaluation result data

[0763] Operation: The server sends the data to the terminal, and the terminal displays the evaluation results on the screen.

[0764] Output: The displayed evaluation results

[0765] Step 24:

[0766] The server saves the game session data and ends the game session.

[0767] Input: Game session data, evaluation result data

[0768] Action: The server saves the data and ends the session.

[0769] Output: Saved game session data

[0770] (Application example 1)

[0771] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0772] Autonomous vehicles are required to analyze data collected in real time from numerous sensors and to propose prompt and appropriate countermeasures when abnormalities or problems occur. However, conventional systems have had difficulty efficiently performing the entire process of detecting abnormalities, identifying their causes, and proposing countermeasures. Therefore, a system that enables more advanced analysis and rapid response is needed to maintain user safety and vehicle performance.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0774] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI response as a log; means for receiving data collected from various sensors in the autonomous vehicle; means including a generation AI module that analyzes, identifies causes, and generates countermeasure proposals based on the sensor data; and means for transmitting the analysis and countermeasure proposals generated by the generation AI module to an in-vehicle display or audio system. This enables the autonomous vehicle to identify abnormalities in real time and present prompt and appropriate countermeasures.

[0775] "Means for receiving text data entered by a user" refers to a function for receiving and processing information entered by a user in text format.

[0776] "Means including a generative AI module" refers to functionality that includes an AI model for analyzing text data and generating a corresponding response.

[0777] "Means for sending to the user's terminal" refers to a function for transferring the generated response to the user's device.

[0778] "Means of saving as a log" refers to the function of recording user input and the generated AI's response and saving it in a format that can be referenced later.

[0779] "Means for receiving data collected from various sensors in an autonomous vehicle" refers to the function of receiving various data obtained from the vehicle's sensors.

[0780] "Means including a generation AI module that analyzes sensor data, identifies the cause, and generates proposed countermeasures based on the sensor data" refers to a function that includes an AI model that analyzes sensor data, identifies the cause of the problem, and proposes countermeasures.

[0781] "Means for sending to a display or audio system" refers to a function for transferring the generated countermeasure proposal to a display device or audio device in the vehicle.

[0782] The present invention is a system that applies a mystery game system using generative AI to perform real-time analysis and propose countermeasures inside an autonomous vehicle. Specific embodiments are shown below.

[0783] First, the user accesses the system from a user device such as a display in the vehicle or a smartphone and enters the abnormality or problem situation in text format. For example, the user enters "abnormal vibration detected." The user device then sends this text data to the server.

[0784] The server receives input data from the user and sends it to the generation AI module. The generation AI module analyzes this data and generates an appropriate response. For example, it generates a response such as, "Your front tire pressure is low. We recommend that you visit a nearby service station." The generated response is sent back to the user's device via the server and displayed or notified on the user's display or audio system.

[0785] Autonomous vehicles are equipped with numerous sensors, including cameras, LiDAR, microphones, and GPS. Data collected from these sensors is aggregated in an on-board computer. This data is sent to a server in real time. The server uses a generative AI module to analyze this sensor data, identify the cause of the problem, and generate appropriate countermeasures.

[0786] For example, if a vehicle detects abnormal vibrations, it sends the vibration data, speed data, tire pressure data, etc. to the generation AI module. The generation AI module comprehensively analyzes this data and creates a specific countermeasure suggestion, such as "The front tire pressure is low. We recommend you visit a nearby service station." This suggestion is then displayed on the vehicle's display and audio system via the server.

[0787] The hardware used includes the vehicle's on-board computer and various sensors (camera, LiDAR, microphone, GPS, etc.), while the software uses a server using a web framework such as Django or Flask, an HTTP request library using Python (such as requests), and a generative AI model such as GPT-4.

[0788] As a concrete example, a scenario where abnormal vibrations are detected inside a vehicle will be shown.

[0789] 1. User: Enters the text "Abnormal vibration detected."

[0790] 2. Terminal: Sends input data to the server.

[0791] 3. Server: Sends data to the generation AI module and analyzes causes and countermeasures.

[0792] 4. Generative AI module: Generates a response such as, "Your front tire pressure is low. We recommend you visit a nearby service station."

[0793] 5. Server: Sends the response to the terminal and displays it to the user.

[0794] An example of a prompt sentence is "If abnormal vibration is detected, please provide the cause and countermeasure."

[0795] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0796] Step 1: User enters anomaly text

[0797] The user inputs the abnormality or problem situation in text format from a display in the autonomous vehicle or a terminal such as a smartphone. For example, they might input "abnormal vibration detected." This input data is received by the terminal.

[0798] Step 2: The device sends the input data to the server

[0799] The terminal sends the text data entered by the user to the server. The input data is sent via an HTTP request and received by the server.

[0800] Step 3: The server sends the data to the generation AI module

[0801] The server sends the received text data to the generation AI module, where it is converted into an appropriate format and input to the generation AI as a prompt.

[0802] Step 4: The generative AI module analyzes

[0803] The generative AI module analyzes the data based on the received prompt and generates an appropriate response, such as "Your front tire pressure is low. We recommend you visit a nearby service station." This response is then sent back to the server.

[0804] Step 5: The server sends the response data to the device

[0805] The server receives the response data returned from the generation AI module and sends it to the user's device. The data is sent as an HTTP response and received by the device.

[0806] Step 6: The device displays the response data

[0807] The device displays or notifies the user of the received response data on a display or audio system, allowing the user to understand the appropriate measures.

[0808] Step 7: Collect and transmit sensor data

[0809] Various sensors in autonomous vehicles (cameras, LiDAR, microphones, GPS, etc.) collect data in real time and send it to the on-board computer, which also simultaneously sends the collected data to a server.

[0810] Step 8: The server sends the sensor data to the generation AI module

[0811] The server sends the received sensor data to the generative AI module, which uses this data to identify the cause of the anomaly and create a prompt that generates a countermeasure proposal.

[0812] Step 9: Generative AI module analyzes sensor data

[0813] The generative AI module analyzes the sensor data and generates corresponding countermeasure suggestions, such as "The front tire pressure is low. We recommend you visit a nearby service station" based on vibration data and tire pressure data.

[0814] Step 10: The server sends the countermeasure proposal to the device.

[0815] The server receives the countermeasure proposals returned by the generation AI module and sends them to the vehicle's display or audio system. The data is sent as an HTTP response and received by the terminal.

[0816] Step 11: Your device will display suggested solutions

[0817] The device then displays or notifies the user of the received countermeasure proposals on a display or audio system, allowing the user to take the necessary countermeasures.

[0818] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0819] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[0820] 1. User login and initial settings

[0821] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[0822] 2. Initializing the game scenario

[0823] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[0824] 3. Real-time interaction with users

[0825] The user enters the content they want to search for, for example, "Check the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes it and sends the response data back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and submits it back to the server. The server then submits the response data to the device, which can then display it on the screen.

[0826] 4. New clues discovered

[0827] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains more closely." The device sends this input data back to the server. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the generated responses and clues. The server saves the new clue information in the game progress log.

[0828] 5. Manage and save your progress

[0829] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[0830] 6. Ending the Game and Evaluation

[0831] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0832] Specific examples

[0833] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[0834] 1. User: Type "Check out letter openers" and submit.

[0835] 2. Terminal: Receives this text data and sends it to the server.

[0836] 3. Server: Sends text data to the generation AI module.

[0837] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[0838] 5. Emotion engine: Recognizes emotions such as "excitement" or "surprise" from user input.

[0839] 6. Generative AI module: Takes emotional information into account to tailor the response and send it back to the server (e.g., "This bloodstain is recent and may hold some important clues").

[0840] 7. Server: Sends the response to the terminal, which displays it to the user.

[0841] Thus, the present invention is a system that provides a more immersive gaming experience by recognizing a user's emotions and generating dynamic responses based on those emotions.

[0842] The processing flow will be explained below.

[0843] Step 1:

[0844] The user enters the username and password and clicks the login button.

[0845] Step 2:

[0846] The terminal receives this user input data and sends it to the server as a login request.

[0847] Step 3:

[0848] The server receives this login request and performs authentication by checking the user database.

[0849] Step 4:

[0850] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[0851] Step 5:

[0852] The terminal receives the initial setting data, initializes the game screen, and displays it.

[0853] Step 6:

[0854] The user clicks the "Start Game" button on the game screen.

[0855] Step 7:

[0856] The terminal sends a game start request to the server.

[0857] Step 8:

[0858] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[0859] Step 9:

[0860] The generation AI module generates an initial scenario and sends it back to the server.

[0861] Step 10:

[0862] The server transmits the initial scenario data to the terminal.

[0863] Step 11:

[0864] The terminal receives the scenario data and displays it on the game screen.

[0865] Step 12:

[0866] The user enters what they want to find out in the text input field and clicks the submit button.

[0867] Step 13:

[0868] The terminal receives this user input data and transmits it to the server.

[0869] Step 14:

[0870] The server sends the user input data to the generation AI module, requesting it to generate a response.

[0871] Step 15:

[0872] The emotion engine analyzes the user's input text and recognizes the user's emotion (e.g., "surprise" or "excitement").

[0873] Step 16:

[0874] The generative AI module generates tailored responses based on emotional information from the emotion engine.

[0875] Step 17:

[0876] The generation AI module sends the generated response back to the server.

[0877] Step 18:

[0878] The server transmits the generated response data to the terminal.

[0879] Step 19:

[0880] The terminal receives the response data and displays it on the game screen.

[0881] Step 20:

[0882] The server stores the user's input, the generated AI's response, and the emotional information recognized by the emotion engine in a game progress log.

[0883] Step 21:

[0884] The emotion engine instructs the generative AI module to adjust the generation of cues and events based on the user's emotional information.

[0885] Step 22:

[0886] The user may use the clue to investigate further (for example, enter "investigate the blood trail").

[0887] Step 23:

[0888] The terminal sends this new input to the server.

[0889] Step 24:

[0890] The server sends new input to the generation AI module, asking it to generate a more detailed response.

[0891] Step 25:

[0892] The generation AI module generates a new response and sends it back to the server.

[0893] Step 26:

[0894] The emotion engine again recognizes the user's emotions and provides them to the generative AI module.

[0895] Step 27:

[0896] The generative AI module takes emotional information into account to tailor a detailed response and send it back to the server.

[0897] Step 28:

[0898] The server sends the generated response to the terminal.

[0899] Step 29:

[0900] The terminal displays the generated response on its screen.

[0901] Step 30:

[0902] The server stores the progress and emotion information in a log and database.

[0903] Step 31:

[0904] When the user has gathered the information necessary to solve the case, he / she inputs "Decide on a solution method."

[0905] Step 32:

[0906] The terminal sends the user input to the server.

[0907] Step 33:

[0908] The server analyzes the game progress log and evaluates the user's performance, including the emotion information from the emotion engine.

[0909] Step 34:

[0910] The server transmits the generated evaluation results to the terminal.

[0911] Step 35:

[0912] The terminal displays the evaluation results to the user.

[0913] Step 36:

[0914] The server saves all game session data and ends the game session.

[0915] Example 2

[0916] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0917] Conventional mystery game systems have a problem of low immersion because they respond to user inputs in a fixed manner and do not generate dynamic responses that take the user's emotions into account. Furthermore, it is difficult to generate clues and events that reflect the user's emotions, and to properly manage and evaluate the game progress.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means including an emotion engine that analyzes and recognizes the user's emotions; and means for adjusting the response of the generation AI module based on the emotions recognized by the emotion engine. This enables dynamic generation of responses that take the user's emotions into consideration, providing a greater sense of immersion. Furthermore, it also enables appropriate initialization of the game scenario, dynamic generation of clues and events, progress management, and user performance evaluation.

[0919] A "user" is a person who uses a terminal to play a mystery game using the system.

[0920] "Text data" is character string information related to surveys or instructions that a user inputs into a terminal and sends to a server.

[0921] A "generative AI module" is an artificial intelligence component that analyzes text data entered by a user and generates a corresponding response.

[0922] The "emotion engine" is a component that analyzes and recognizes emotions from the user's input text and provides that information to the generative AI module.

[0923] A "scenario" is data that contains an outline of the story and events necessary for the game to progress, and is generated by the generation AI module.

[0924] A "clue" is important information or an item that the user discovers in the game to solve the problem.

[0925] The "server" is a central computer system that receives user input, controls the generative AI module and emotion engine, and transmits response data and scenario data to the user's device.

[0926] A "terminal" is a device operated by a user that provides a game interface through communication with a server.

[0927] A "log" is recorded data that stores information such as the game's progress, user input, and the responses of the generated AI.

[0928] "Performance evaluation" is the process of evaluating in-game progress and achievements based on user input and actions, and providing feedback.

[0929] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[0930] The server receives a login request from the user device and checks it against the user database for authentication. If authentication is successful, it generates a game session ID and initial setting data and sends them to the device. The database used may be MySQL.

[0931] The user terminal receives the initial setting data, initializes and displays the game screen, and sends text data entered by the user to the server and displays a response on the screen.

[0932] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server passes this request to a generation AI module (e.g., GPT-3) to request the generation of an initial scenario. The generated initial scenario is sent to the device via the server. The device displays the received scenario data on the game screen.

[0933] The user enters the content they want to search for (e.g., "Check the front door") into the text input field and submits it. The device transmits this data to the server, which then requests the generative AI module to generate a response. The generative AI module analyzes the user's input text and generates an appropriate response. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and generates the final response. The server submits this response to the device, which displays it on the screen.

[0934] For example, if a user types "examine the letter opener," the emotion engine recognizes emotions such as "excitement" or "surprise," and the AI ​​module adjusts the response (e.g., "the letter opener has bloodstains"). This response is then displayed on the device via the server.

[0935] The server logs user input and the generated AI's responses, and also stores the user's progress and important events in a game progress database. Additionally, emotions recognized by the emotion engine are also recorded.

[0936] When the user solves the case and clicks "Decide on the solution," the device sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. This evaluation also uses emotional information from the emotion engine, and the results are sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0937] Prompt Sentence Examples

[0938] Generate an example response for the input "Check for activity outside the window." Please also include adjustments for when the emotion engine recognizes "anxiety."

[0939] In this way, by reflecting the user's emotions and generating dynamic, real-time responses, a more immersive gaming experience can be provided.

[0940] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0941] Step 1: User Login and Initial Setup

[0942] The user enters their username and password on the login screen. The device sends the input data to the server. The server authenticates the received user data by checking it against the MySQL database. If authentication is successful, it generates a game session ID and constructs a response including initial setup data (JSON format) and sends it to the device. The device receives the response data, initializes the game screen based on the initial setup data, and displays it to the user.

[0943] Input: Username, Password

[0944] Output: Game session ID, initial configuration data

[0945] Data processing: User database matching, session ID generation, initial setting data JSON construction

[0946] Step 2: Initializing the game scenario

[0947] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and requests the generation AI module (e.g., GPT-3) to generate an initial scenario. The generated initial scenario is sent back to the server, which then sends it to the device. The device displays the received scenario data on the game screen.

[0948] Input: Game start request

[0949] Output: Initial scenario data

[0950] Data processing: Initial scenario generation request, scenario generation by generation AI module

[0951] Step 3: Real-time interaction with the user

[0952] The user enters the information they want to search in the text input field and submits it. The device sends this text data to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes the text data, generates an appropriate response, and sends it back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response, incorporating this emotional information. The server then sends the final response data to the device, which displays it on the screen.

[0953] Input: Text data of the content you want to search

[0954] Output: Response data

[0955] Data processing: text data analysis, response generation, emotion recognition, response adjustment

[0956] Step 4: Discover new clues

[0957] The user inputs a more detailed investigation, for example, "Examine the bloodstains in more detail." The device then sends this input data back to the server. The server then sends it to the generation AI module, requesting it to generate a more detailed response. The generation AI module generates a detailed response and sends it back to the server. The server then sends this to the device, which displays it on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the responses and clues that are generated. The server then saves the new clue information in the game progress log.

[0958] Input: Text data from detailed survey

[0959] Output: Detailed response data

[0960] Data processing: detailed response generation requests, detailed response generation, emotion recognition, response adjustment, cue storage

[0961] Step 5: Manage and save your progress

[0962] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[0963] Input: User input, AI generation response, emotional information

[0964] Output: Game progress log

[0965] Data processing: log storage, clue storage, emotional information storage

[0966] Step 6: Ending the game and evaluating

[0967] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[0968] Input: Text data of the solution

[0969] Output: Evaluation results, final response data

[0970] Data processing: Analysis of game progress logs, performance evaluation, evaluation and use of emotional information, presentation of evaluation results, session data storage

[0971] (Application example 2)

[0972] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0973] Conventional mystery game systems have had the problem of making it difficult for users to become immersed in the story. One of the reasons for this is that dynamic responses that reflect the user's emotions are not generated. Therefore, the objective of the present invention is to provide an interactive game system that allows users to become deeply involved in the progress of the game with their emotions.

[0974] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0975] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for combining response generation by the generation AI module with an emotion engine that recognizes the user's emotions; and means for adjusting the generated response based on the emotion recognition information. This enables dynamic response generation that reflects the user's emotions, resulting in an interactive game experience that provides a deeper immersion.

[0976] - "Means for receiving text data entered by a user" means a device or program that obtains information from the text entered by a user and receives it as initial data for processing within the system.

[0977] A "generative AI module" is artificial intelligence-based software that analyzes text data and automatically generates requested responses and scenarios.

[0978] "Means for sending to the user's device" refers to the communication protocols and applications used to send information such as responses and scenarios from the generation AI module to the device used by the user.

[0979] The "means for saving as a log" is a database or recording device for storing the user's input data and generated responses as a history.

[0980] An "emotion engine" is software that uses natural language processing technology to analyze emotions from text data entered by the user and identify those emotions.

[0981] "Means for adjusting responses generated based on emotion recognition information" refers to functions and algorithms for appropriately adjusting responses generated by the generative AI based on the emotion information recognized by the emotion engine.

[0982] "Means for requesting generation of an initial scenario" refers to commands or communication methods used to request the generation AI module to create the foundation for the story or scenario at the start of the game.

[0983] The "means for generating clues and events" refers to a function or module for generating new clues and events within the game based on user input data.

[0984] This invention is a mystery game system in which users play the role of detectives and solve various cases. The system combines a server, user terminals, a generative AI module, and an emotion engine that recognizes the user's emotions.

[0985] First, the user logs in from a device such as a smartphone. The server receives the user name and password and performs authentication. If authentication is successful, a game session ID is generated and sent to the device along with initial setup data. The device receives this and initializes and displays the game screen.

[0986] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and sends it back to the server. The server sends this scenario data to the terminal, which receives the scenario data and displays it on the game screen.

[0987] When a user enters survey content into the text field, the device sends the text data to the server. The server then sends the text data along with the emotional information recognized by the emotion engine to the generation AI module. The generation AI module analyzes this and generates response data. The response is adjusted based on the emotional information and sent back to the server. The server then sends the response data to the device, which displays it on the screen.

[0988] For example, if a user types "examine the letter opener," the input data is sent to the server. The server sends this data to the generative AI module, which generates a response such as "the letter opener has bloodstains." The emotion engine recognizes emotions such as "surprise" from the user's input and adjusts the generated response, such as "the bloodstains are recent and may hold important clues." This provides a dynamic response that also reflects the user's emotions.

[0989] Similarly, if a user types "investigate the blood stains," that input is sent to the generative AI module to generate more detailed information, while the emotion engine recognizes the user's emotions and tailors responses and cues based on that information.

[0990] The software used includes generative AI models (e.g., OpenAI GPT), emotion recognition engines (NLP analysis), server applications (e.g., Flask, Django), and databases (e.g., PostgreSQL, MySQL). The hardware includes smartphones and servers.

[0991] Below are some examples of specific prompt sentences.

[0992] Example prompt sentence:

[0993] User input: "Examine the blood trail."

[0994] Prompt to generative AI model: "The user is examining a blood stain. What details can they find? Please describe the scene in detail."

[0995] User input: "Check letter opener"

[0996] Prompt to generative AI model: "A user is examining a letter opener. What clues can they find about this item? The emotion is surprise."

[0997] In this way, the present invention realizes an interactive mystery game system that reflects the user's emotions and provides a deeper sense of immersion.

[0998] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0999] Step 1:

[1000] The user enters their username and password from a device such as a smartphone and sends a login request to the server. The server analyzes the received login request and performs user authentication using a database. If authentication is successful, the server generates a game session ID and initial setting data and sends this to the device. The device receives the initial setting data, initializes the game screen, and displays it.

[1001] Input: Username, Password

[1002] Output: Game session ID, initial configuration data

[1003] Specific operations: Database matching, session ID generation

[1004] Step 2:

[1005] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and asks the generation AI module to generate an initial scenario. The generation AI module generates an initial scenario based on the prompt and sends it back to the server. The server then sends the generated initial scenario to the device, which receives it and displays it on the game screen.

[1006] Input: Game start request

[1007] Output: Initial scenario

[1008] Specific operations: prompt generation, scenario generation

[1009] Step 3:

[1010] The user enters the information they want to find in a text field on the game screen and submits it. The device receives this and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. At the same time, the emotion engine recognizes emotions from the user's input and provides this information to the generation AI module. The generation AI module adjusts the response based on the emotion information and sends the final response back to the server. The server then submits the response data to the device, which displays it on the screen.

[1011] Input: User-entered text

[1012] Output: The generated response

[1013] Specific behaviors: emotion recognition, response adjustment, data transmission

[1014] Step 4:

[1015] If the user wishes to continue with a more detailed investigation, they enter the details of their investigation again into the text field and submit it. The device receives the input data and sends it to the server. The server again sends this request to the generation AI module, asking it to generate more detailed information based on the content. The generation AI module generates a more detailed response and sends it back to the server. The emotion engine again recognizes emotions from the user's input, and the generation AI module adjusts the response based on that. The server sends the generated detailed information to the device, which displays it on the screen.

[1016] Input: Further investigation details

[1017] Output: Detailed response

[1018] Specific actions: detailed scenario generation, emotion recognition, and response adjustment

[1019] Step 5:

[1020] All user input and all responses from the generated AI are logged and saved by the server. The server also records clues and important events discovered by the user and adds them to the game progress database. At the same time, emotional information recognized by the emotion engine is also saved.

[1021] Input: User input, generative AI response, emotional information

[1022] Output: Game progress log

[1023] Specific operations: saving to database, logging

[1024] Step 6:

[1025] When the user reaches the final stage of the game and decides on a solution, they send the input data to the server. The server receives it, analyzes the game progress log, and evaluates the user's performance. This evaluation process also takes into account the emotional information recognized by the emotion engine. The evaluation results are sent from the server to the terminal and displayed to the user. Finally, the server saves the game session data and ends the game session.

[1026] Input: Solution input

[1027] Output: Performance evaluation results

[1028] Specific operations: log analysis, evaluation result generation, data storage

[1029] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1030] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1031] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1032] [Third embodiment]

[1033] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1034] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1035] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1036] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1037] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1038] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1039] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1040] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1041] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1042] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1043] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1044] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1045] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. The system consists of a server, a user terminal, and a generative AI module.

[1046] 1. User login and initial settings

[1047] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[1048] 2. Initializing the game scenario

[1049] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[1050] 3. Real-time interaction with users

[1051] The user enters the content they want to search for, for example, "Search the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. The generation AI module analyzes the data and sends the response data back to the server. The server then sends the response data to the device, which can then display it on the screen.

[1052] 4. New clues discovered

[1053] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains in more detail." The device sends this input data to the server again. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the server saves the new clue information in the game progress log.

[1054] 5. Manage and save your progress

[1055] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server.

[1056] 6. Ending the Game and Evaluation

[1057] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1058] Specific examples

[1059] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[1060] 1. User: Type "Check out letter openers" and submit.

[1061] 2. Terminal: Receives this text data and sends it to the server.

[1062] 3. Server: Sends text data to the generation AI module.

[1063] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[1064] 5. Server: Sends the response to the terminal, which displays it to the user.

[1065] Thus, the present invention is a system that allows users to freely input text and generates dynamic responses in response to that text, thereby providing a highly immersive gaming experience.

[1066] The processing flow will be explained below.

[1067] Step 1:

[1068] The user enters the username and password and clicks the login button.

[1069] Step 2:

[1070] The terminal receives this user input data and sends it to the server as a login request.

[1071] Step 3:

[1072] The server receives this login request and performs authentication by checking the user database.

[1073] Step 4:

[1074] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[1075] Step 5:

[1076] The terminal receives the initial setting data, initializes the game screen, and displays it.

[1077] Step 6:

[1078] The user clicks the "Start Game" button on the game screen.

[1079] Step 7:

[1080] The terminal sends a game start request to the server.

[1081] Step 8:

[1082] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[1083] Step 9:

[1084] The generation AI module generates an initial scenario and sends it back to the server.

[1085] Step 10:

[1086] The server transmits the initial scenario data to the terminal.

[1087] Step 11:

[1088] The terminal receives the scenario data and displays it on the game screen.

[1089] Step 12:

[1090] The user enters what they want to find out in the text input field and clicks the submit button.

[1091] Step 13:

[1092] The terminal receives the user's input text data and transmits it to the server.

[1093] Step 14:

[1094] The server sends the user input data to the generation AI module, requesting it to generate a response.

[1095] Step 15:

[1096] The generative AI module analyzes the user's input and generates a corresponding response, which is sent back to the server.

[1097] Step 16:

[1098] The server transmits the generated response data to the terminal.

[1099] Step 17:

[1100] The terminal receives the response data and displays it on the game screen.

[1101] Step 18:

[1102] The server saves the user's input and the generated AI's responses in a game progress log.

[1103] Step 19:

[1104] When clues or important events occur, the server adds and saves the information to the game progress database.

[1105] Step 20:

[1106] When the user reaches a solution to the case, he / she inputs "decide on a solution method" and submits it.

[1107] Step 21:

[1108] The terminal receives this user input data and transmits it to the server.

[1109] Step 22:

[1110] The server analyzes the game progress log and evaluates the user's performance.

[1111] Step 23:

[1112] The server generates an evaluation result and transmits it to the terminal.

[1113] Step 24:

[1114] The terminal receives the evaluation results and displays them to the user.

[1115] Step 25:

[1116] The server saves all game session data and ends the game session.

[1117] Example 1

[1118] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1119] Conventional mystery game systems provide users with fixed stories and responses, preventing dynamic game progression based on the user's free ideas and actions. Furthermore, they lack a mechanism for responding appropriately to user inputs on the fly and managing and saving progress in real time, making it difficult to enhance the user's immersion. This invention aims to improve the user experience by allowing users to freely input text and generating dynamic responses and scenarios using a generation AI module.

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

[1121] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for receiving and verifying user authentication information; and means for generating a game session ID and initial setting data and transmitting them to the terminal. This allows the user to freely enter text and obtain dynamic responses, manages the game progress in real time, and provides a highly immersive gaming experience.

[1122] A "user" is a person who interacts with the system to provide input and receive responses from the generative AI module.

[1123] A "terminal" is a device through which a user accesses the system, provides input via an interface, and displays responses from the server and generating AI module.

[1124] The "server" is a central computing device that receives requests from users, operates the generative AI module accordingly, and sends the final response to the terminal.

[1125] A "generative AI module" is artificial intelligence software that analyzes user input and generates appropriate responses and scenarios.

[1126] "Text data" is character string information used when a user inputs a survey or instruction.

[1127] A "response" is a reply or explanation generated by the generative AI module based on user input.

[1128] A "log" is data that records and saves user input and the responses of the generation AI module.

[1129] "Authentication information" refers to information such as a username and password required to verify the identity of a user.

[1130] A "game session ID" is a unique identifier generated by the server to identify a specific user's game progress.

[1131] "Initial setting data" is data that is generated by the server at the start of the game and sent to the terminal, and includes the initial state of the game and user profile information.

[1132] A "scenario" is data that indicates the game's progress plan and story development generated by the generation AI module.

[1133] A "clue" is important information or an item that a user discovers in the game to solve a case.

[1134] An "event" is an occurrence that occurs within the game based on the user's actions and choices.

[1135] "Evaluation" is the result calculated by the server after analyzing the user's actions and results leading up to the resolution of the case.

[1136] This invention is a mystery game system that uses generative AI. The user plays the role of a detective and solves the case using their own words. This system consists of a server, a user terminal, and a generative AI module.

[1137] The user first logs in by entering their username and password. The device sends this authentication information to the server, which then references the user database and performs authentication. If authentication is successful, the server generates a game session ID and initial setting data and sends them to the device. The device receives this, initializes the game screen, and displays it to the user.

[1138] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server requests the generation AI module to generate an initial scenario and sends the generated scenario data to the terminal. The terminal receives this scenario data and displays it to the user.

[1139] The user enters the content they want to search into the text input field, for example, "Search the front door." The device sends this text data to the server. The server sends this data to the generation AI module, which generates a response. The generation AI module analyzes the input content, generates a response, and sends it back to the server. The server sends this response data to the device, which displays it on the screen.

[1140] Furthermore, if the user inputs "investigate the bloodstains in more detail," the device will send this data to the server again. The server will send it to the generation AI module again, asking it to generate a detailed response. The generation AI module will generate a detailed response, and the server will send it to the device. The device will display this information on the screen. The server will save the new clue information in the game progress log.

[1141] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also stored in a game progress database on the server.

[1142] Finally, when the user reaches a solution to the case and inputs "Decide on the solution method", the terminal sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. The evaluation result is sent to the terminal and presented to the user. Finally, the server saves the game session data and ends the game session.

[1143] As a concrete example, consider the following prompt sentence:

[1144] example:

[1145] User: "Check out letter openers"

[1146] Terminal: Send text data to server

[1147] Server: Sends text data to the generation AI module

[1148] Generative AI module: Sends the response "There is blood on the letter opener" back to the server

[1149] Server: Sends response to device

[1150] Terminal: Display the response to the user

[1151] These prompt examples allow users to ask questions or give instructions in natural language through the system interface, with responses returned in real time, providing a highly immersive gaming experience.

[1152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1153] Step 1:

[1154] The user enters the username and password and clicks the login button.

[1155] Input: Username, Password

[1156] How it works: The device sends this authentication information to the server.

[1157] Output: User authentication request data

[1158] Step 2:

[1159] The server authenticates the user by checking the received authentication information against a user database.

[1160] Input: User authentication request data

[1161] What it does: The server looks up the database and performs authentication. If successful, it generates a game session ID and initial setup data.

[1162] Output: Game session ID, initial configuration data

[1163] Step 3:

[1164] The server sends the generated game session ID and initial setting data to the terminal.

[1165] Input: Game session ID, initial setup data

[1166] Operation: The server sends data to the device.

[1167] Output: Game session ID, initial setting data sent

[1168] Step 4:

[1169] The terminal receives the initial setting data, initializes the game screen, and displays it to the user.

[1170] Input: Game session ID, initial setting data to be sent

[1171] Operation: The device initializes and displays the game screen based on the initial setting data.

[1172] Output: Initialized game screen

[1173] Step 5:

[1174] The user clicks the "Start Game" button.

[1175] Input: User request to start a game

[1176] Action: The device sends this request to the server.

[1177] Output: Game start request data

[1178] Step 6:

[1179] The server receives a request to start a game from the terminal and requests the generation AI module to generate an initial scenario.

[1180] Input: Game start request data

[1181] Operation: The server asks the generation AI module to generate an initial scenario.

[1182] Output: Initial scenario generation request

[1183] Step 7:

[1184] The generation AI module generates an initial scenario and sends it back to the server.

[1185] Input: Initial scenario generation request

[1186] How it works: The Generative AI module performs the necessary data analysis and generates an initial scenario.

[1187] Output: Initial scenario data

[1188] Step 8:

[1189] The server transmits the generated initial scenario data to the terminal.

[1190] Input: Initial scenario data

[1191] Operation: The server sends the initial scenario data to the device.

[1192] Output: Initial scenario transmission data

[1193] Step 9:

[1194] The terminal receives the initial scenario data and displays it on the game screen.

[1195] Input: Initial scenario transmission data

[1196] Action: The terminal displays the initial scenario on the screen.

[1197] Output: Scenario screen displayed

[1198] Step 10:

[1199] The user inputs the content to be searched for, for example, "search the entrance" into the text input field and submits it.

[1200] Input: Survey text

[1201] Action: The device sends this text data to the server.

[1202] Output: Survey content text data

[1203] Step 11:

[1204] The server sends the user's input data to the generation AI module and requests it to generate a corresponding response.

[1205] Input: Survey content text data

[1206] How it works: The server sends this data to the generation AI module.

[1207] Output: Response-generating request data

[1208] Step 12:

[1209] The generative AI module analyzes the input and generates an appropriate response to send back to the server.

[1210] Input: Response generation request data

[1211] How it works: The generative AI module analyzes the content and generates a response.

[1212] Output: Response data

[1213] Step 13:

[1214] The server transmits the generated response data to the terminal.

[1215] Input: Response data

[1216] Operation: The server sends the response data to the terminal.

[1217] Output: Response send data

[1218] Step 14:

[1219] The terminal displays the received response data on the game screen.

[1220] Input: Response send data

[1221] Action: The terminal displays the response on the screen.

[1222] Output: The displayed response

[1223] Step 15:

[1224] The user further inputs specific details of the investigation, for example, sending "investigate the bloodstains in detail."

[1225] Input: Text of detailed investigation content

[1226] Action: The device sends this input data back to the server.

[1227] Output: Detailed survey data

[1228] Step 16:

[1229] The server sends the detailed investigation results to the generation AI module, requesting it to generate more detailed information.

[1230] Input: Detailed survey data

[1231] How it works: The server sends data to the generation AI module.

[1232] Output: Detailed response generation request data

[1233] Step 17:

[1234] The generative AI module generates a detailed response based on the detailed investigation and sends it back to the server.

[1235] Input: Detailed response generation request data

[1236] How it works: The generative AI module generates a detailed response.

[1237] Output: Detailed response data

[1238] Step 18:

[1239] The server sends detailed response data to the terminal, which displays it on the game screen.

[1240] Input: Detailed response data

[1241] Operation: The server sends detailed response data to the terminal, which displays it on the screen.

[1242] Output: Detailed response displayed

[1243] Step 19:

[1244] The server stores the new clue information in a game progress log.

[1245] Input: User input and generated AI response

[1246] How it works: The server stores clue information in a database.

[1247] Output: Saved clue log

[1248] Step 20:

[1249] A game progress log is kept and the user's progress is updated as needed.

[1250] Input: User progress data

[1251] Action: The server updates the game progress database.

[1252] Output: Updated progress log

[1253] Step 21:

[1254] The user reaches a resolution to the case and enters "Determine resolution" and submits.

[1255] Input: Case resolution text

[1256] Action: The device sends this data to the server.

[1257] Output: Incident resolution data

[1258] Step 22:

[1259] The server analyzes the game progress log based on the received data and evaluates the user's performance.

[1260] Input: Case resolution data, game progress log

[1261] Operation: The server analyzes the game progress log and generates an evaluation result.

[1262] Output: Evaluation result data

[1263] Step 23:

[1264] The server transmits the evaluation result data to the terminal, which displays it to the user.

[1265] Input: Evaluation result data

[1266] Operation: The server sends the data to the terminal, and the terminal displays the evaluation results on the screen.

[1267] Output: The displayed evaluation results

[1268] Step 24:

[1269] The server saves the game session data and ends the game session.

[1270] Input: Game session data, evaluation result data

[1271] Action: The server saves the data and ends the session.

[1272] Output: Saved game session data

[1273] (Application example 1)

[1274] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1275] Autonomous vehicles are required to analyze data collected in real time from numerous sensors and to propose prompt and appropriate countermeasures when abnormalities or problems occur. However, conventional systems have had difficulty efficiently performing the entire process of detecting abnormalities, identifying their causes, and proposing countermeasures. Therefore, a system that enables more advanced analysis and rapid response is needed to maintain user safety and vehicle performance.

[1276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1277] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI response as a log; means for receiving data collected from various sensors in the autonomous vehicle; means including a generation AI module that analyzes, identifies causes, and generates countermeasure proposals based on the sensor data; and means for transmitting the analysis and countermeasure proposals generated by the generation AI module to an in-vehicle display or audio system. This enables the autonomous vehicle to identify abnormalities in real time and present prompt and appropriate countermeasures.

[1278] "Means for receiving text data entered by a user" refers to a function for receiving and processing information entered by a user in text format.

[1279] "Means including a generative AI module" refers to functionality that includes an AI model for analyzing text data and generating a corresponding response.

[1280] "Means for sending to the user's terminal" refers to a function for transferring the generated response to the user's device.

[1281] "Means of saving as a log" refers to the function of recording user input and the generated AI's response and saving it in a format that can be referenced later.

[1282] "Means for receiving data collected from various sensors in an autonomous vehicle" refers to the function of receiving various data obtained from the vehicle's sensors.

[1283] "Means including a generation AI module that analyzes sensor data, identifies the cause, and generates proposed countermeasures based on the sensor data" refers to a function that includes an AI model that analyzes sensor data, identifies the cause of the problem, and proposes countermeasures.

[1284] "Means for sending to a display or audio system" refers to a function for transferring the generated countermeasure proposal to a display device or audio device in the vehicle.

[1285] The present invention is a system that applies a mystery game system using generative AI to perform real-time analysis and propose countermeasures inside an autonomous vehicle. Specific embodiments are shown below.

[1286] First, the user accesses the system from a user device such as a display in the vehicle or a smartphone and enters the abnormality or problem situation in text format. For example, the user enters "abnormal vibration detected." The user device then sends this text data to the server.

[1287] The server receives input data from the user and sends it to the generation AI module. The generation AI module analyzes this data and generates an appropriate response. For example, it generates a response such as, "Your front tire pressure is low. We recommend that you visit a nearby service station." The generated response is sent back to the user's device via the server and displayed or notified on the user's display or audio system.

[1288] Autonomous vehicles are equipped with numerous sensors, including cameras, LiDAR, microphones, and GPS. Data collected from these sensors is aggregated in an on-board computer. This data is sent to a server in real time. The server uses a generative AI module to analyze this sensor data, identify the cause of the problem, and generate appropriate countermeasures.

[1289] For example, if a vehicle detects abnormal vibrations, it sends the vibration data, speed data, tire pressure data, etc. to the generation AI module. The generation AI module comprehensively analyzes this data and creates a specific countermeasure suggestion, such as "The front tire pressure is low. We recommend you visit a nearby service station." This suggestion is then displayed on the vehicle's display and audio system via the server.

[1290] The hardware used includes the vehicle's on-board computer and various sensors (camera, LiDAR, microphone, GPS, etc.), while the software uses a server using a web framework such as Django or Flask, an HTTP request library using Python (such as requests), and a generative AI model such as GPT-4.

[1291] As a concrete example, a scenario where abnormal vibrations are detected inside a vehicle will be shown.

[1292] 1. User: Enters the text "Abnormal vibration detected."

[1293] 2. Terminal: Sends input data to the server.

[1294] 3. Server: Sends data to the generation AI module and analyzes causes and countermeasures.

[1295] 4. Generative AI module: Generates a response such as, "Your front tire pressure is low. We recommend you visit a nearby service station."

[1296] 5. Server: Sends the response to the terminal and displays it to the user.

[1297] An example of a prompt sentence is "If abnormal vibration is detected, please provide the cause and countermeasure."

[1298] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1299] Step 1: User enters anomaly text

[1300] The user inputs the abnormality or problem situation in text format from a display in the autonomous vehicle or a terminal such as a smartphone. For example, they might input "abnormal vibration detected." This input data is received by the terminal.

[1301] Step 2: The device sends the input data to the server

[1302] The terminal sends the text data entered by the user to the server. The input data is sent via an HTTP request and received by the server.

[1303] Step 3: The server sends the data to the generation AI module

[1304] The server sends the received text data to the generation AI module, where it is converted into an appropriate format and input to the generation AI as a prompt.

[1305] Step 4: The generative AI module analyzes

[1306] The generative AI module analyzes the data based on the received prompt and generates an appropriate response, such as "Your front tire pressure is low. We recommend you visit a nearby service station." This response is then sent back to the server.

[1307] Step 5: The server sends the response data to the device

[1308] The server receives the response data returned from the generation AI module and sends it to the user's device. The data is sent as an HTTP response and received by the device.

[1309] Step 6: The device displays the response data

[1310] The device displays or notifies the user of the received response data on a display or audio system, allowing the user to understand the appropriate measures.

[1311] Step 7: Collect and transmit sensor data

[1312] Various sensors in autonomous vehicles (cameras, LiDAR, microphones, GPS, etc.) collect data in real time and send it to the on-board computer, which also simultaneously sends the collected data to a server.

[1313] Step 8: The server sends the sensor data to the generation AI module

[1314] The server sends the received sensor data to the generative AI module, which uses this data to identify the cause of the anomaly and create a prompt that generates a countermeasure proposal.

[1315] Step 9: Generative AI module analyzes sensor data

[1316] The generative AI module analyzes the sensor data and generates corresponding countermeasure suggestions, such as "The front tire pressure is low. We recommend you visit a nearby service station" based on vibration data and tire pressure data.

[1317] Step 10: The server sends the countermeasure proposal to the device.

[1318] The server receives the countermeasure proposals returned by the generation AI module and sends them to the vehicle's display or audio system. The data is sent as an HTTP response and received by the terminal.

[1319] Step 11: Your device will display suggested solutions

[1320] The device then displays or notifies the user of the received countermeasure proposals on a display or audio system, allowing the user to take the necessary countermeasures.

[1321] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1322] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[1323] 1. User login and initial settings

[1324] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[1325] 2. Initializing the game scenario

[1326] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[1327] 3. Real-time interaction with users

[1328] The user enters the content they want to search for, for example, "Check the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes it and sends the response data back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and submits it back to the server. The server then submits the response data to the device, which can then display it on the screen.

[1329] 4. New clues discovered

[1330] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains more closely." The device sends this input data back to the server. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the generated responses and clues. The server saves the new clue information in the game progress log.

[1331] 5. Manage and save your progress

[1332] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[1333] 6. Ending the Game and Evaluation

[1334] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1335] Specific examples

[1336] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[1337] 1. User: Type "Check out letter openers" and submit.

[1338] 2. Terminal: Receives this text data and sends it to the server.

[1339] 3. Server: Sends text data to the generation AI module.

[1340] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[1341] 5. Emotion engine: Recognizes emotions such as "excitement" or "surprise" from user input.

[1342] 6. Generative AI module: Takes emotional information into account to tailor the response and send it back to the server (e.g., "This bloodstain is recent and may hold some important clues").

[1343] 7. Server: Sends the response to the terminal, which displays it to the user.

[1344] Thus, the present invention is a system that provides a more immersive gaming experience by recognizing a user's emotions and generating dynamic responses based on those emotions.

[1345] The processing flow will be explained below.

[1346] Step 1:

[1347] The user enters the username and password and clicks the login button.

[1348] Step 2:

[1349] The terminal receives this user input data and sends it to the server as a login request.

[1350] Step 3:

[1351] The server receives this login request and performs authentication by checking the user database.

[1352] Step 4:

[1353] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[1354] Step 5:

[1355] The terminal receives the initial setting data, initializes the game screen, and displays it.

[1356] Step 6:

[1357] The user clicks the "Start Game" button on the game screen.

[1358] Step 7:

[1359] The terminal sends a game start request to the server.

[1360] Step 8:

[1361] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[1362] Step 9:

[1363] The generation AI module generates an initial scenario and sends it back to the server.

[1364] Step 10:

[1365] The server transmits the initial scenario data to the terminal.

[1366] Step 11:

[1367] The terminal receives the scenario data and displays it on the game screen.

[1368] Step 12:

[1369] The user enters what they want to find out in the text input field and clicks the submit button.

[1370] Step 13:

[1371] The terminal receives this user input data and transmits it to the server.

[1372] Step 14:

[1373] The server sends the user input data to the generation AI module, requesting it to generate a response.

[1374] Step 15:

[1375] The emotion engine analyzes the user's input text and recognizes the user's emotion (e.g., "surprise" or "excitement").

[1376] Step 16:

[1377] The generative AI module generates tailored responses based on emotional information from the emotion engine.

[1378] Step 17:

[1379] The generation AI module sends the generated response back to the server.

[1380] Step 18:

[1381] The server transmits the generated response data to the terminal.

[1382] Step 19:

[1383] The terminal receives the response data and displays it on the game screen.

[1384] Step 20:

[1385] The server stores the user's input, the generated AI's response, and the emotional information recognized by the emotion engine in a game progress log.

[1386] Step 21:

[1387] The emotion engine instructs the generative AI module to adjust the generation of cues and events based on the user's emotional information.

[1388] Step 22:

[1389] The user may use the clue to investigate further (for example, enter "investigate the blood trail").

[1390] Step 23:

[1391] The terminal sends this new input to the server.

[1392] Step 24:

[1393] The server sends new input to the generation AI module, asking it to generate a more detailed response.

[1394] Step 25:

[1395] The generation AI module generates a new response and sends it back to the server.

[1396] Step 26:

[1397] The emotion engine again recognizes the user's emotions and provides them to the generative AI module.

[1398] Step 27:

[1399] The generative AI module takes emotional information into account to tailor a detailed response and send it back to the server.

[1400] Step 28:

[1401] The server sends the generated response to the terminal.

[1402] Step 29:

[1403] The terminal displays the generated response on its screen.

[1404] Step 30:

[1405] The server stores the progress and emotion information in a log and database.

[1406] Step 31:

[1407] When the user has gathered the information necessary to solve the case, he / she inputs "Decide on a solution method."

[1408] Step 32:

[1409] The terminal sends the user input to the server.

[1410] Step 33:

[1411] The server analyzes the game progress log and evaluates the user's performance, including the emotion information from the emotion engine.

[1412] Step 34:

[1413] The server transmits the generated evaluation results to the terminal.

[1414] Step 35:

[1415] The terminal displays the evaluation results to the user.

[1416] Step 36:

[1417] The server saves all game session data and ends the game session.

[1418] Example 2

[1419] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1420] Conventional mystery game systems have a problem of low immersion because they respond to user inputs in a fixed manner and do not generate dynamic responses that take the user's emotions into account. Furthermore, it is difficult to generate clues and events that reflect the user's emotions, and to properly manage and evaluate the game progress.

[1421] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means including an emotion engine that analyzes and recognizes the user's emotions; and means for adjusting the response of the generation AI module based on the emotions recognized by the emotion engine. This enables dynamic generation of responses that take the user's emotions into consideration, providing a greater sense of immersion. Furthermore, it also enables appropriate initialization of the game scenario, dynamic generation of clues and events, progress management, and user performance evaluation.

[1422] A "user" is a person who uses a terminal to play a mystery game using the system.

[1423] "Text data" is character string information related to surveys or instructions that a user inputs into a terminal and sends to a server.

[1424] A "generative AI module" is an artificial intelligence component that analyzes text data entered by a user and generates a corresponding response.

[1425] The "emotion engine" is a component that analyzes and recognizes emotions from the user's input text and provides that information to the generative AI module.

[1426] A "scenario" is data that contains an outline of the story and events necessary for the game to progress, and is generated by the generation AI module.

[1427] A "clue" is important information or an item that the user discovers in the game to solve the problem.

[1428] The "server" is a central computer system that receives user input, controls the generative AI module and emotion engine, and transmits response data and scenario data to the user's device.

[1429] A "terminal" is a device operated by a user that provides a game interface through communication with a server.

[1430] A "log" is recorded data that stores information such as the game's progress, user input, and the responses of the generated AI.

[1431] "Performance evaluation" is the process of evaluating in-game progress and achievements based on user input and actions, and providing feedback.

[1432] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[1433] The server receives a login request from the user device and checks it against the user database for authentication. If authentication is successful, it generates a game session ID and initial setting data and sends them to the device. The database used may be MySQL.

[1434] The user terminal receives the initial setting data, initializes and displays the game screen, and sends text data entered by the user to the server and displays a response on the screen.

[1435] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server passes this request to a generation AI module (e.g., GPT-3) to request the generation of an initial scenario. The generated initial scenario is sent to the device via the server. The device displays the received scenario data on the game screen.

[1436] The user enters the content they want to search for (e.g., "Check the front door") into the text input field and submits it. The device transmits this data to the server, which then requests the generative AI module to generate a response. The generative AI module analyzes the user's input text and generates an appropriate response. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and generates the final response. The server submits this response to the device, which displays it on the screen.

[1437] For example, if a user types "examine the letter opener," the emotion engine recognizes emotions such as "excitement" or "surprise," and the AI ​​module adjusts the response (e.g., "the letter opener has bloodstains"). This response is then displayed on the device via the server.

[1438] The server logs user input and the generated AI's responses, and also stores the user's progress and important events in a game progress database. Additionally, emotions recognized by the emotion engine are also recorded.

[1439] When the user solves the case and clicks "Decide on the solution," the device sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. This evaluation also uses emotional information from the emotion engine, and the results are sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1440] Prompt Sentence Examples

[1441] Generate an example response for the input "Check for activity outside the window." Please also include adjustments for when the emotion engine recognizes "anxiety."

[1442] In this way, by reflecting the user's emotions and generating dynamic, real-time responses, a more immersive gaming experience can be provided.

[1443] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1444] Step 1: User Login and Initial Setup

[1445] The user enters their username and password on the login screen. The device sends the input data to the server. The server authenticates the received user data by checking it against the MySQL database. If authentication is successful, it generates a game session ID and constructs a response including initial setup data (JSON format) and sends it to the device. The device receives the response data, initializes the game screen based on the initial setup data, and displays it to the user.

[1446] Input: Username, Password

[1447] Output: Game session ID, initial configuration data

[1448] Data processing: User database matching, session ID generation, initial setting data JSON construction

[1449] Step 2: Initializing the game scenario

[1450] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and requests the generation AI module (e.g., GPT-3) to generate an initial scenario. The generated initial scenario is sent back to the server, which then sends it to the device. The device displays the received scenario data on the game screen.

[1451] Input: Game start request

[1452] Output: Initial scenario data

[1453] Data processing: Initial scenario generation request, scenario generation by generation AI module

[1454] Step 3: Real-time interaction with the user

[1455] The user enters the information they want to search in the text input field and submits it. The device sends this text data to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes the text data, generates an appropriate response, and sends it back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response, incorporating this emotional information. The server then sends the final response data to the device, which displays it on the screen.

[1456] Input: Text data of the content you want to search

[1457] Output: Response data

[1458] Data processing: text data analysis, response generation, emotion recognition, response adjustment

[1459] Step 4: Discover new clues

[1460] The user inputs a more detailed investigation, for example, "Examine the bloodstains in more detail." The device then sends this input data back to the server. The server then sends it to the generation AI module, requesting it to generate a more detailed response. The generation AI module generates a detailed response and sends it back to the server. The server then sends this to the device, which displays it on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the responses and clues that are generated. The server then saves the new clue information in the game progress log.

[1461] Input: Text data from detailed survey

[1462] Output: Detailed response data

[1463] Data processing: detailed response generation requests, detailed response generation, emotion recognition, response adjustment, cue storage

[1464] Step 5: Manage and save your progress

[1465] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[1466] Input: User input, AI generation response, emotional information

[1467] Output: Game progress log

[1468] Data processing: log storage, clue storage, emotional information storage

[1469] Step 6: Ending the game and evaluating

[1470] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1471] Input: Text data of the solution

[1472] Output: Evaluation results, final response data

[1473] Data processing: Analysis of game progress logs, performance evaluation, evaluation and use of emotional information, presentation of evaluation results, session data storage

[1474] (Application example 2)

[1475] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1476] Conventional mystery game systems have had the problem of making it difficult for users to become immersed in the story. One of the reasons for this is that dynamic responses that reflect the user's emotions are not generated. Therefore, the objective of the present invention is to provide an interactive game system that allows users to become deeply involved in the progress of the game with their emotions.

[1477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1478] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for combining response generation by the generation AI module with an emotion engine that recognizes the user's emotions; and means for adjusting the generated response based on the emotion recognition information. This enables dynamic response generation that reflects the user's emotions, resulting in an interactive game experience that provides a deeper immersion.

[1479] - "Means for receiving text data entered by a user" means a device or program that obtains information from the text entered by a user and receives it as initial data for processing within the system.

[1480] A "generative AI module" is artificial intelligence-based software that analyzes text data and automatically generates requested responses and scenarios.

[1481] "Means for sending to the user's device" refers to the communication protocols and applications used to send information such as responses and scenarios from the generation AI module to the device used by the user.

[1482] The "means for saving as a log" is a database or recording device for storing the user's input data and generated responses as a history.

[1483] An "emotion engine" is software that uses natural language processing technology to analyze emotions from text data entered by the user and identify those emotions.

[1484] "Means for adjusting responses generated based on emotion recognition information" refers to functions and algorithms for appropriately adjusting responses generated by the generative AI based on the emotion information recognized by the emotion engine.

[1485] "Means for requesting generation of an initial scenario" refers to commands or communication methods used to request the generation AI module to create the foundation for the story or scenario at the start of the game.

[1486] The "means for generating clues and events" refers to a function or module for generating new clues and events within the game based on user input data.

[1487] This invention is a mystery game system in which users play the role of detectives and solve various cases. The system combines a server, user terminals, a generative AI module, and an emotion engine that recognizes the user's emotions.

[1488] First, the user logs in from a device such as a smartphone. The server receives the user name and password and performs authentication. If authentication is successful, a game session ID is generated and sent to the device along with initial setup data. The device receives this and initializes and displays the game screen.

[1489] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and sends it back to the server. The server sends this scenario data to the terminal, which receives the scenario data and displays it on the game screen.

[1490] When a user enters survey content into the text field, the device sends the text data to the server. The server then sends the text data along with the emotional information recognized by the emotion engine to the generation AI module. The generation AI module analyzes this and generates response data. The response is adjusted based on the emotional information and sent back to the server. The server then sends the response data to the device, which displays it on the screen.

[1491] For example, if a user types "examine the letter opener," the input data is sent to the server. The server sends this data to the generative AI module, which generates a response such as "the letter opener has bloodstains." The emotion engine recognizes emotions such as "surprise" from the user's input and adjusts the generated response, such as "the bloodstains are recent and may hold important clues." This provides a dynamic response that also reflects the user's emotions.

[1492] Similarly, if a user types "investigate the blood stains," that input is sent to the generative AI module to generate more detailed information, while the emotion engine recognizes the user's emotions and tailors responses and cues based on that information.

[1493] The software used includes generative AI models (e.g., OpenAI GPT), emotion recognition engines (NLP analysis), server applications (e.g., Flask, Django), and databases (e.g., PostgreSQL, MySQL). The hardware includes smartphones and servers.

[1494] Below are some examples of specific prompt sentences.

[1495] Example prompt sentence:

[1496] User input: "Examine the blood trail."

[1497] Prompt to generative AI model: "The user is examining a blood stain. What details can they find? Please describe the scene in detail."

[1498] User input: "Check letter opener"

[1499] Prompt to generative AI model: "A user is examining a letter opener. What clues can they find about this item? The emotion is surprise."

[1500] In this way, the present invention realizes an interactive mystery game system that reflects the user's emotions and provides a deeper sense of immersion.

[1501] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1502] Step 1:

[1503] The user enters their username and password from a device such as a smartphone and sends a login request to the server. The server analyzes the received login request and performs user authentication using a database. If authentication is successful, the server generates a game session ID and initial setting data and sends this to the device. The device receives the initial setting data, initializes the game screen, and displays it.

[1504] Input: Username, Password

[1505] Output: Game session ID, initial configuration data

[1506] Specific operations: Database matching, session ID generation

[1507] Step 2:

[1508] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and asks the generation AI module to generate an initial scenario. The generation AI module generates an initial scenario based on the prompt and sends it back to the server. The server then sends the generated initial scenario to the device, which receives it and displays it on the game screen.

[1509] Input: Game start request

[1510] Output: Initial scenario

[1511] Specific operations: prompt generation, scenario generation

[1512] Step 3:

[1513] The user enters the information they want to find in a text field on the game screen and submits it. The device receives this and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. At the same time, the emotion engine recognizes emotions from the user's input and provides this information to the generation AI module. The generation AI module adjusts the response based on the emotion information and sends the final response back to the server. The server then submits the response data to the device, which displays it on the screen.

[1514] Input: User-entered text

[1515] Output: The generated response

[1516] Specific behaviors: emotion recognition, response adjustment, data transmission

[1517] Step 4:

[1518] If the user wishes to continue with a more detailed investigation, they enter the details of their investigation again into the text field and submit it. The device receives the input data and sends it to the server. The server again sends this request to the generation AI module, asking it to generate more detailed information based on the content. The generation AI module generates a more detailed response and sends it back to the server. The emotion engine again recognizes emotions from the user's input, and the generation AI module adjusts the response based on that. The server sends the generated detailed information to the device, which displays it on the screen.

[1519] Input: Further investigation details

[1520] Output: Detailed response

[1521] Specific actions: detailed scenario generation, emotion recognition, and response adjustment

[1522] Step 5:

[1523] All user input and all responses from the generated AI are logged and saved by the server. The server also records clues and important events discovered by the user and adds them to the game progress database. At the same time, emotional information recognized by the emotion engine is also saved.

[1524] Input: User input, generative AI response, emotional information

[1525] Output: Game progress log

[1526] Specific operations: saving to database, logging

[1527] Step 6:

[1528] When the user reaches the final stage of the game and decides on a solution, they send the input data to the server. The server receives it, analyzes the game progress log, and evaluates the user's performance. This evaluation process also takes into account the emotional information recognized by the emotion engine. The evaluation results are sent from the server to the terminal and displayed to the user. Finally, the server saves the game session data and ends the game session.

[1529] Input: Solution input

[1530] Output: Performance evaluation results

[1531] Specific operations: log analysis, evaluation result generation, data storage

[1532] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1533] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1534] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1535] [Fourth embodiment]

[1536] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1537] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1538] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1539] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1540] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1541] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1542] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1543] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1544] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1545] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1546] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1547] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1548] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1549] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. The system consists of a server, a user terminal, and a generative AI module.

[1550] 1. User login and initial settings

[1551] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[1552] 2. Initializing the game scenario

[1553] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[1554] 3. Real-time interaction with users

[1555] The user enters the content they want to search for, for example, "Search the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. The generation AI module analyzes the data and sends the response data back to the server. The server then sends the response data to the device, which can then display it on the screen.

[1556] 4. New clues discovered

[1557] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains in more detail." The device sends this input data to the server again. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the server saves the new clue information in the game progress log.

[1558] 5. Manage and save your progress

[1559] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server.

[1560] 6. Ending the Game and Evaluation

[1561] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. The evaluation results are sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1562] Specific examples

[1563] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[1564] 1. User: Type "Check out letter openers" and submit.

[1565] 2. Terminal: Receives this text data and sends it to the server.

[1566] 3. Server: Sends text data to the generation AI module.

[1567] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[1568] 5. Server: Sends the response to the terminal, which displays it to the user.

[1569] Thus, the present invention is a system that allows users to freely input text and generates dynamic responses in response to that text, thereby providing a highly immersive gaming experience.

[1570] The processing flow will be explained below.

[1571] Step 1:

[1572] The user enters the username and password and clicks the login button.

[1573] Step 2:

[1574] The terminal receives this user input data and sends it to the server as a login request.

[1575] Step 3:

[1576] The server receives this login request and performs authentication by checking the user database.

[1577] Step 4:

[1578] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[1579] Step 5:

[1580] The terminal receives the initial setting data, initializes the game screen, and displays it.

[1581] Step 6:

[1582] The user clicks the "Start Game" button on the game screen.

[1583] Step 7:

[1584] The terminal sends a game start request to the server.

[1585] Step 8:

[1586] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[1587] Step 9:

[1588] The generation AI module generates an initial scenario and sends it back to the server.

[1589] Step 10:

[1590] The server transmits the initial scenario data to the terminal.

[1591] Step 11:

[1592] The terminal receives the scenario data and displays it on the game screen.

[1593] Step 12:

[1594] The user enters what they want to find out in the text input field and clicks the submit button.

[1595] Step 13:

[1596] The terminal receives the text data input by the user and transmits it to the server.

[1597] Step 14:

[1598] The server sends the user input data to the generation AI module, requesting it to generate a response.

[1599] Step 15:

[1600] The generative AI module analyzes the user's input and generates a corresponding response, which is sent back to the server.

[1601] Step 16:

[1602] The server transmits the generated response data to the terminal.

[1603] Step 17:

[1604] The terminal receives the response data and displays it on the game screen.

[1605] Step 18:

[1606] The server saves the user's input and the generated AI's responses in a game progress log.

[1607] Step 19:

[1608] When clues or important events occur, the server adds and saves the information to the game progress database.

[1609] Step 20:

[1610] When the user reaches a solution to the case, he / she inputs "decide on a solution method" and submits it.

[1611] Step 21:

[1612] The terminal receives this user input data and transmits it to the server.

[1613] Step 22:

[1614] The server analyzes the game progress log and evaluates the user's performance.

[1615] Step 23:

[1616] The server generates an evaluation result and transmits it to the terminal.

[1617] Step 24:

[1618] The terminal receives the evaluation results and displays them to the user.

[1619] Step 25:

[1620] The server saves all game session data and ends the game session.

[1621] Example 1

[1622] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1623] Conventional mystery game systems provide users with fixed stories and responses, preventing dynamic game progression based on the user's free ideas and actions. Furthermore, they lack a mechanism for responding appropriately to user inputs on the fly and managing and saving progress in real time, making it difficult to enhance the user's immersive experience. This invention aims to improve the user experience by allowing users to freely input text and generating dynamic responses and scenarios using a generation AI module.

[1624] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1625] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for receiving and verifying user authentication information; and means for generating a game session ID and initial setting data and transmitting them to the terminal. This allows the user to freely enter text and obtain dynamic responses, enabling real-time management of the game progress and providing a highly immersive gaming experience.

[1626] A "user" is a person who interacts with the system to provide input and receive responses from the generative AI module.

[1627] A "terminal" is a device through which a user accesses the system, provides input via an interface, and displays responses from the server and generating AI module.

[1628] The "server" is a central computing device that receives requests from users, operates the generative AI module accordingly, and sends the final response to the terminal.

[1629] A "generative AI module" is artificial intelligence software that analyzes user input and generates appropriate responses and scenarios.

[1630] "Text data" is character string information used when a user inputs a survey or instruction.

[1631] A "response" is a reply or explanation generated by the generative AI module based on user input.

[1632] A "log" is data that records and saves user input and the responses of the generation AI module.

[1633] "Authentication information" refers to information such as a username and password required to verify the identity of a user.

[1634] A "game session ID" is a unique identifier generated by the server to identify a specific user's game progress.

[1635] "Initial setting data" is data that is generated by the server at the start of the game and sent to the terminal, and includes the initial state of the game and user profile information.

[1636] A "scenario" is data that indicates the game's progress plan and story development generated by the generation AI module.

[1637] A "clue" is important information or an item that a user discovers in the game to solve a case.

[1638] An "event" is an occurrence that occurs within the game based on the user's actions and choices.

[1639] "Evaluation" is the result calculated by the server after analyzing the user's actions and results leading up to the resolution of the case.

[1640] This invention is a mystery game system that uses generative AI. The user plays the role of a detective and solves the case using their own words. This system consists of a server, a user terminal, and a generative AI module.

[1641] The user first logs in by entering their username and password. The device sends this authentication information to the server, which then references the user database and performs authentication. If authentication is successful, the server generates a game session ID and initial setting data and sends them to the device. The device receives this, initializes the game screen, and displays it to the user.

[1642] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server requests the generation AI module to generate an initial scenario and sends the generated scenario data to the terminal. The terminal receives this scenario data and displays it to the user.

[1643] The user enters the content they want to search into the text input field, for example, "Search the front door." The device sends this text data to the server. The server sends this data to the generation AI module, which generates a response. The generation AI module analyzes the input content, generates a response, and sends it back to the server. The server sends this response data to the device, which displays it on the screen.

[1644] Furthermore, if the user inputs "investigate the bloodstains in more detail," the device will send this data to the server again. The server will send it to the generation AI module again, asking it to generate a detailed response. The generation AI module will generate a detailed response, and the server will send it to the device. The device will display this information on the screen. The server will save the new clue information in the game progress log.

[1645] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also stored in a game progress database on the server.

[1646] Finally, when the user reaches a solution to the case and inputs "Decide on the solution method", the terminal sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. The evaluation result is sent to the terminal and presented to the user. Finally, the server saves the game session data and ends the game session.

[1647] As a concrete example, consider the following prompt sentence:

[1648] example:

[1649] User: "Check out letter openers"

[1650] Terminal: Send text data to server

[1651] Server: Sends text data to the generation AI module

[1652] Generative AI module: Sends the response "There is blood on the letter opener" back to the server

[1653] Server: Sends response to device

[1654] Terminal: Display the response to the user

[1655] These prompt examples allow users to ask questions or give instructions in natural language through the system interface, and receive responses in real time, providing a highly immersive gaming experience.

[1656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] The user enters the username and password and clicks the login button.

[1659] Input: Username, Password

[1660] How it works: The device sends this authentication information to the server.

[1661] Output: User authentication request data

[1662] Step 2:

[1663] The server authenticates the user by checking the received authentication information against a user database.

[1664] Input: User authentication request data

[1665] What it does: The server looks up the database and performs authentication. If successful, it generates a game session ID and initial setup data.

[1666] Output: Game session ID, initial configuration data

[1667] Step 3:

[1668] The server sends the generated game session ID and initial setting data to the terminal.

[1669] Input: Game session ID, initial setup data

[1670] Operation: The server sends data to the device.

[1671] Output: Game session ID, initial setting data sent

[1672] Step 4:

[1673] The terminal receives the initial setting data, initializes the game screen, and displays it to the user.

[1674] Input: Game session ID, initial setting data to be sent

[1675] Operation: The device initializes and displays the game screen based on the initial setting data.

[1676] Output: Initialized game screen

[1677] Step 5:

[1678] The user clicks the "Start Game" button.

[1679] Input: User request to start a game

[1680] Action: The device sends this request to the server.

[1681] Output: Game start request data

[1682] Step 6:

[1683] The server receives a request to start a game from the terminal and requests the generation AI module to generate an initial scenario.

[1684] Input: Game start request data

[1685] Operation: The server asks the generation AI module to generate an initial scenario.

[1686] Output: Initial scenario generation request

[1687] Step 7:

[1688] The generation AI module generates an initial scenario and sends it back to the server.

[1689] Input: Initial scenario generation request

[1690] How it works: The Generative AI module performs the necessary data analysis and generates an initial scenario.

[1691] Output: Initial scenario data

[1692] Step 8:

[1693] The server transmits the generated initial scenario data to the terminal.

[1694] Input: Initial scenario data

[1695] Operation: The server sends the initial scenario data to the device.

[1696] Output: Initial scenario transmission data

[1697] Step 9:

[1698] The terminal receives the initial scenario data and displays it on the game screen.

[1699] Input: Initial scenario transmission data

[1700] Action: The terminal displays the initial scenario on the screen.

[1701] Output: Scenario screen displayed

[1702] Step 10:

[1703] The user inputs the content to be searched for, for example, "search the entrance" into the text input field and submits it.

[1704] Input: Survey text

[1705] Action: The device sends this text data to the server.

[1706] Output: Survey content text data

[1707] Step 11:

[1708] The server sends the user's input data to the generation AI module and requests it to generate a corresponding response.

[1709] Input: Survey content text data

[1710] How it works: The server sends this data to the generation AI module.

[1711] Output: Response-generating request data

[1712] Step 12:

[1713] The generative AI module analyzes the input and generates an appropriate response to send back to the server.

[1714] Input: Response generation request data

[1715] How it works: The generative AI module analyzes the content and generates a response.

[1716] Output: Response data

[1717] Step 13:

[1718] The server transmits the generated response data to the terminal.

[1719] Input: Response data

[1720] Operation: The server sends the response data to the terminal.

[1721] Output: Response send data

[1722] Step 14:

[1723] The terminal displays the received response data on the game screen.

[1724] Input: Response send data

[1725] Action: The terminal displays the response on the screen.

[1726] Output: The displayed response

[1727] Step 15:

[1728] The user further inputs specific details of the investigation, for example, sending "investigate the bloodstains in detail."

[1729] Input: Text of detailed investigation content

[1730] Action: The device sends this input data back to the server.

[1731] Output: Detailed survey data

[1732] Step 16:

[1733] The server sends the detailed investigation results to the generation AI module, requesting it to generate more detailed information.

[1734] Input: Detailed survey data

[1735] How it works: The server sends data to the generation AI module.

[1736] Output: Detailed response generation request data

[1737] Step 17:

[1738] The generative AI module generates a detailed response based on the detailed investigation and sends it back to the server.

[1739] Input: Detailed response generation request data

[1740] How it works: The generative AI module generates a detailed response.

[1741] Output: Detailed response data

[1742] Step 18:

[1743] The server sends detailed response data to the terminal, which displays it on the game screen.

[1744] Input: Detailed response data

[1745] Operation: The server sends detailed response data to the terminal, which displays it on the screen.

[1746] Output: Detailed response displayed

[1747] Step 19:

[1748] The server stores the new clue information in a game progress log.

[1749] Input: User input and generated AI response

[1750] How it works: The server stores clue information in a database.

[1751] Output: Saved clue log

[1752] Step 20:

[1753] A game progress log is kept and the user's progress is updated as needed.

[1754] Input: User progress data

[1755] Action: The server updates the game progress database.

[1756] Output: Updated progress log

[1757] Step 21:

[1758] The user reaches a resolution to the case and enters "Determine resolution" and submits.

[1759] Input: Case resolution text

[1760] Action: The device sends this data to the server.

[1761] Output: Incident resolution data

[1762] Step 22:

[1763] The server analyzes the game progress log based on the received data and evaluates the user's performance.

[1764] Input: Case resolution data, game progress log

[1765] Operation: The server analyzes the game progress log and generates an evaluation result.

[1766] Output: Evaluation result data

[1767] Step 23:

[1768] The server transmits the evaluation result data to the terminal, which displays it to the user.

[1769] Input: Evaluation result data

[1770] Operation: The server sends the data to the terminal, and the terminal displays the evaluation results on the screen.

[1771] Output: The displayed evaluation results

[1772] Step 24:

[1773] The server saves the game session data and ends the game session.

[1774] Input: Game session data, evaluation result data

[1775] Action: The server saves the data and ends the session.

[1776] Output: Saved game session data

[1777] (Application example 1)

[1778] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1779] Autonomous vehicles are required to analyze data collected in real time from numerous sensors and to propose prompt and appropriate countermeasures when abnormalities or problems occur. However, conventional systems have had difficulty efficiently performing the entire process of detecting abnormalities, identifying their causes, and proposing countermeasures. Therefore, a system that enables more advanced analysis and rapid response is needed to maintain user safety and vehicle performance.

[1780] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1781] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI response as a log; means for receiving data collected from various sensors in the autonomous vehicle; means including a generation AI module that analyzes, identifies causes, and generates countermeasure proposals based on the sensor data; and means for transmitting the analysis and countermeasure proposals generated by the generation AI module to an in-vehicle display or audio system. This enables the autonomous vehicle to identify abnormalities in real time and present prompt and appropriate countermeasures.

[1782] "Means for receiving text data entered by a user" refers to a function for receiving and processing information entered by a user in text format.

[1783] "Means including a generative AI module" refers to functionality that includes an AI model for analyzing text data and generating a corresponding response.

[1784] "Means for sending to the user's terminal" refers to a function for transferring the generated response to the user's device.

[1785] "Means of saving as a log" refers to the function of recording user input and the generated AI's response and saving it in a format that can be referenced later.

[1786] "Means for receiving data collected from various sensors in an autonomous vehicle" refers to the function of receiving various data obtained from the vehicle's sensors.

[1787] "Means including a generation AI module that analyzes sensor data, identifies the cause, and generates proposed countermeasures based on the sensor data" refers to a function that includes an AI model that analyzes sensor data, identifies the cause of the problem, and proposes countermeasures.

[1788] "Means for sending to a display or audio system" refers to a function for transferring the generated countermeasure proposal to a display device or audio device in the vehicle.

[1789] The present invention is a system that applies a mystery game system using generative AI to perform real-time analysis and propose countermeasures inside an autonomous vehicle. Specific embodiments are shown below.

[1790] First, the user accesses the system from a user device such as an in-vehicle display or a smartphone and enters the abnormality or problem in text format. For example, the user enters "abnormal vibration detected." The user device then sends this text data to the server.

[1791] The server receives input data from the user and sends it to the generation AI module. The generation AI module analyzes this data and generates an appropriate response. For example, it generates a response such as, "Your front tire pressure is low. We recommend that you visit a nearby service station." The generated response is sent back to the user's device via the server and displayed or notified on the user's display or audio system.

[1792] Autonomous vehicles are equipped with numerous sensors, including cameras, LiDAR, microphones, and GPS. Data collected from these sensors is aggregated in an on-board computer. This data is sent to a server in real time. The server uses a generative AI module to analyze this sensor data, identify the cause of the problem, and generate appropriate countermeasures.

[1793] For example, if a vehicle detects abnormal vibrations, it sends the vibration data, speed data, tire pressure data, etc. to the generation AI module. The generation AI module comprehensively analyzes this data and creates a specific countermeasure suggestion, such as "The front tire pressure is low. We recommend you visit a nearby service station." This suggestion is then displayed on the vehicle's display and audio system via the server.

[1794] The hardware used includes the vehicle's on-board computer and various sensors (camera, LiDAR, microphone, GPS, etc.), while the software uses a server using a web framework such as Django or Flask, an HTTP request library using Python (such as requests), and a generative AI model such as GPT-4.

[1795] As a concrete example, a scenario where abnormal vibrations are detected inside a vehicle will be shown.

[1796] 1. User: Enters the text "Abnormal vibration detected."

[1797] 2. Terminal: Sends input data to the server.

[1798] 3. Server: Sends data to the generation AI module and analyzes causes and countermeasures.

[1799] 4. Generative AI module: Generates a response such as, "Your front tire pressure is low. We recommend you visit a nearby service station."

[1800] 5. Server: Sends the response to the terminal and displays it to the user.

[1801] An example of a prompt sentence is "If abnormal vibration is detected, please provide the cause and countermeasure."

[1802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1803] Step 1: User enters anomaly text

[1804] The user inputs the abnormality or problem situation in text format from a display in the autonomous vehicle or a terminal such as a smartphone. For example, they might input "abnormal vibration detected." This input data is received by the terminal.

[1805] Step 2: The device sends the input data to the server

[1806] The terminal sends the text data entered by the user to the server. The input data is sent via an HTTP request and received by the server.

[1807] Step 3: The server sends the data to the generation AI module

[1808] The server sends the received text data to the generation AI module, where it is converted into an appropriate format and input to the generation AI as a prompt.

[1809] Step 4: The generative AI module analyzes

[1810] The generative AI module analyzes the data based on the received prompt and generates an appropriate response, such as "Your front tire pressure is low. We recommend you visit a nearby service station." This response is then sent back to the server.

[1811] Step 5: The server sends the response data to the device

[1812] The server receives the response data returned from the generation AI module and sends it to the user's device. The data is sent as an HTTP response and received by the device.

[1813] Step 6: The device displays the response data

[1814] The device displays or notifies the user of the received response data on a display or audio system, allowing the user to understand the appropriate measures.

[1815] Step 7: Collect and transmit sensor data

[1816] Various sensors in autonomous vehicles (cameras, LiDAR, microphones, GPS, etc.) collect data in real time and send it to the on-board computer, which also simultaneously sends the collected data to a server.

[1817] Step 8: The server sends the sensor data to the generation AI module

[1818] The server sends the received sensor data to the generative AI module, which uses this data to identify the cause of the anomaly and create a prompt that generates a countermeasure proposal.

[1819] Step 9: Generative AI module analyzes sensor data

[1820] The generative AI module analyzes the sensor data and generates corresponding countermeasure suggestions, such as "The front tire pressure is low. We recommend you visit a nearby service station" based on vibration data and tire pressure data.

[1821] Step 10: The server sends the countermeasure proposal to the device.

[1822] The server receives the countermeasure proposals returned by the generation AI module and sends them to the vehicle's display or audio system. The data is sent as an HTTP response and received by the terminal.

[1823] Step 11: Your device will display suggested solutions

[1824] The device then displays or notifies the user of the received countermeasure proposals on a display or audio system, allowing the user to take the necessary countermeasures.

[1825] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1826] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[1827] 1. User login and initial settings

[1828] The server receives a login request from the user terminal. The user enters a username and password and sends them from the terminal to the server. The server performs authentication by checking them against a user database, and if authentication is successful, generates a game session ID and sends it to the terminal along with initial setup data. The terminal receives the initial setup data and initializes and displays the game screen.

[1829] 2. Initializing the game scenario

[1830] When the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and returns it to the server, and the server sends this scenario data to the terminal. The terminal receives the scenario data and displays it on the game screen.

[1831] 3. Real-time interaction with users

[1832] The user enters the content they want to search for, for example, "Check the front door," into the text input field and submits it. The device receives this text data and sends it to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes it and sends the response data back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and submits it back to the server. The server then submits the response data to the device, which can then display it on the screen.

[1833] 4. New clues discovered

[1834] The user continues the investigation in more detail, for example, by entering "investigate the bloodstains more closely." The device sends this input data back to the server. The server sends it to the generation AI module, requesting it to generate more detailed information. The generation AI module generates a detailed response, which the server sends to the device. The device displays the generated information on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the generated responses and clues. The server saves the new clue information in the game progress log.

[1835] 5. Manage and save your progress

[1836] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[1837] 6. Ending the Game and Evaluation

[1838] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1839] Specific examples

[1840] For example, if a user enters the first room and finds a letter opener, the specific flow would be as follows:

[1841] 1. User: Type "Check out letter openers" and submit.

[1842] 2. Terminal: Receives this text data and sends it to the server.

[1843] 3. Server: Sends text data to the generation AI module.

[1844] 4. Generative AI module: Analyzes and generates a response ("There is blood on the letter opener") and sends it back to the server.

[1845] 5. Emotion engine: Recognizes emotions such as "excitement" or "surprise" from user input.

[1846] 6. Generative AI module: Takes emotional information into account to tailor the response and send it back to the server (e.g., "This bloodstain is recent and may hold some important clues").

[1847] 7. Server: Sends the response to the terminal, which displays it to the user.

[1848] Thus, the present invention is a system that provides a more immersive gaming experience by recognizing a user's emotions and generating dynamic responses based on those emotions.

[1849] The processing flow will be explained below.

[1850] Step 1:

[1851] The user enters the username and password and clicks the login button.

[1852] Step 2:

[1853] The terminal receives this user input data and sends it to the server as a login request.

[1854] Step 3:

[1855] The server receives this login request and performs authentication by checking the user database.

[1856] Step 4:

[1857] If the authentication is successful, the server generates a game session ID and transmits it to the terminal together with the initial setting data.

[1858] Step 5:

[1859] The terminal receives the initial setting data, initializes the game screen, and displays it.

[1860] Step 6:

[1861] The user clicks the "Start Game" button on the game screen.

[1862] Step 7:

[1863] The terminal sends a game start request to the server.

[1864] Step 8:

[1865] The server receives a request to start a game and requests the generation AI module to generate an initial scenario.

[1866] Step 9:

[1867] The generation AI module generates an initial scenario and sends it back to the server.

[1868] Step 10:

[1869] The server transmits the initial scenario data to the terminal.

[1870] Step 11:

[1871] The terminal receives the scenario data and displays it on the game screen.

[1872] Step 12:

[1873] The user enters what they want to find out in the text input field and clicks the submit button.

[1874] Step 13:

[1875] The terminal receives this user input data and transmits it to the server.

[1876] Step 14:

[1877] The server sends the user input data to the generation AI module, requesting it to generate a response.

[1878] Step 15:

[1879] The emotion engine analyzes the user's input text and recognizes the user's emotion (e.g., "surprise" or "excitement").

[1880] Step 16:

[1881] The generative AI module generates tailored responses based on emotional information from the emotion engine.

[1882] Step 17:

[1883] The generation AI module sends the generated response back to the server.

[1884] Step 18:

[1885] The server transmits the generated response data to the terminal.

[1886] Step 19:

[1887] The terminal receives the response data and displays it on the game screen.

[1888] Step 20:

[1889] The server stores the user's input, the generated AI's response, and the emotional information recognized by the emotion engine in a game progress log.

[1890] Step 21:

[1891] The emotion engine instructs the generative AI module to adjust the generation of cues and events based on the user's emotional information.

[1892] Step 22:

[1893] The user may use the clue to investigate further (for example, enter "investigate the blood trail").

[1894] Step 23:

[1895] The terminal sends this new input to the server.

[1896] Step 24:

[1897] The server sends new input to the generation AI module, asking it to generate a more detailed response.

[1898] Step 25:

[1899] The generation AI module generates a new response and sends it back to the server.

[1900] Step 26:

[1901] The emotion engine again recognizes the user's emotions and provides them to the generative AI module.

[1902] Step 27:

[1903] The generative AI module takes emotional information into account to tailor a detailed response and send it back to the server.

[1904] Step 28:

[1905] The server sends the generated response to the terminal.

[1906] Step 29:

[1907] The terminal displays the generated response on its screen.

[1908] Step 30:

[1909] The server stores the progress and emotion information in a log and database.

[1910] Step 31:

[1911] When the user has gathered the information necessary to solve the case, he / she inputs "Decide on a solution method."

[1912] Step 32:

[1913] The terminal sends the user input to the server.

[1914] Step 33:

[1915] The server analyzes the game progress log and evaluates the user's performance, including the emotion information from the emotion engine.

[1916] Step 34:

[1917] The server transmits the generated evaluation results to the terminal.

[1918] Step 35:

[1919] The terminal displays the evaluation results to the user.

[1920] Step 36:

[1921] The server saves all game session data and ends the game session.

[1922] Example 2

[1923] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1924] Conventional mystery game systems have a problem of low immersion because they respond to user inputs in a fixed manner and do not generate dynamic responses that take the user's emotions into account. Furthermore, it is difficult to generate clues and events that reflect the user's emotions, and to properly manage and evaluate the game progress.

[1925] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means including an emotion engine that analyzes and recognizes the user's emotions; and means for adjusting the response of the generation AI module based on the emotions recognized by the emotion engine. This enables dynamic generation of responses that take the user's emotions into consideration, providing a greater sense of immersion. Furthermore, it also enables appropriate initialization of the game scenario, dynamic generation of clues and events, progress management, and user performance evaluation.

[1926] A "user" is a person who uses a terminal to play a mystery game using the system.

[1927] "Text data" is character string information related to surveys or instructions that a user inputs into a terminal and sends to a server.

[1928] A "generative AI module" is an artificial intelligence component that analyzes text data entered by a user and generates a corresponding response.

[1929] The "emotion engine" is a component that analyzes and recognizes emotions from the user's input text and provides that information to the generative AI module.

[1930] A "scenario" is data that contains an outline of the story and events necessary for the game to progress, and is generated by the generation AI module.

[1931] A "clue" is important information or an item that the user discovers in the game to solve the problem.

[1932] The "server" is a central computer system that receives user input, controls the generative AI module and emotion engine, and transmits response data and scenario data to the user's device.

[1933] A "terminal" is a device operated by a user that provides a game interface through communication with a server.

[1934] A "log" is recorded data that stores information such as the game's progress, user input, and the responses of the generated AI.

[1935] "Performance evaluation" is the process of evaluating in-game progress and achievements based on user input and actions, and providing feedback.

[1936] This invention is a mystery game system using generative AI, in which the user plays the role of a detective and solves the case using their own words. This system combines a server, a user terminal, a generative AI module, and an emotion engine that recognizes the user's emotions.

[1937] The server receives a login request from the user device and checks it against the user database for authentication. If authentication is successful, it generates a game session ID and initial setting data and sends them to the device. The database used may be MySQL.

[1938] The user terminal receives the initial setting data, initializes and displays the game screen, and sends text data entered by the user to the server and displays a response on the screen.

[1939] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server passes this request to a generation AI module (e.g., GPT-3) to request the generation of an initial scenario. The generated initial scenario is sent to the device via the server. The device displays the received scenario data on the game screen.

[1940] The user enters the content they want to search for (e.g., "Check the front door") into the text input field and submits it. The device transmits this data to the server, which then requests the generative AI module to generate a response. The generative AI module analyzes the user's input text and generates an appropriate response. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response based on the emotion information and generates the final response. The server submits this response to the device, which displays it on the screen.

[1941] For example, if a user types "examine the letter opener," the emotion engine recognizes emotions such as "excitement" or "surprise," and the AI ​​module adjusts the response (e.g., "the letter opener has bloodstains"). This response is then displayed on the device via the server.

[1942] The server logs user input and the generated AI's responses, and also stores the user's progress and important events in a game progress database. Additionally, emotions recognized by the emotion engine are also recorded.

[1943] When the user solves the case and clicks "Decide on the solution," the device sends this data to the server. The server analyzes the game progress log and evaluates the user's performance. This evaluation also uses emotional information from the emotion engine, and the results are sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1944] Prompt Sentence Examples

[1945] Generate an example response for the input "Check for activity outside the window." Please also include adjustments for when the emotion engine recognizes "anxiety."

[1946] In this way, by reflecting the user's emotions and generating dynamic, real-time responses, a more immersive gaming experience can be provided.

[1947] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1948] Step 1: User Login and Initial Setup

[1949] The user enters their username and password on the login screen. The device sends the input data to the server. The server authenticates the received user data by checking it against the MySQL database. If authentication is successful, it generates a game session ID and constructs a response including initial setup data (JSON format) and sends it to the device. The device receives the response data, initializes the game screen based on the initial setup data, and displays it to the user.

[1950] Input: Username, Password

[1951] Output: Game session ID, initial configuration data

[1952] Data processing: User database matching, session ID generation, initial setting data JSON construction

[1953] Step 2: Initializing the game scenario

[1954] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and requests the generation AI module (e.g., GPT-3) to generate an initial scenario. The generated initial scenario is sent back to the server, which then sends it to the device. The device displays the received scenario data on the game screen.

[1955] Input: Game start request

[1956] Output: Initial scenario data

[1957] Data processing: Initial scenario generation request, scenario generation by generation AI module

[1958] Step 3: Real-time interaction with the user

[1959] The user enters the information they want to search in the text input field and submits it. The device sends this text data to the server. The server then sends the user's input data to the generative AI module, which generates an appropriate response. The generative AI module analyzes the text data, generates an appropriate response, and sends it back to the server. Here, the emotion engine recognizes emotions from the user's input text and provides this information to the generative AI module. The generative AI module adjusts the response, incorporating this emotional information. The server then sends the final response data to the device, which displays it on the screen.

[1960] Input: Text data of the content you want to search

[1961] Output: Response data

[1962] Data processing: text data analysis, response generation, emotion recognition, response adjustment

[1963] Step 4: Discover new clues

[1964] The user inputs a more detailed investigation, for example, "Examine the bloodstains in more detail." The device then sends this input data back to the server. The server then sends it to the generation AI module, requesting it to generate a more detailed response. The generation AI module generates a detailed response and sends it back to the server. The server then sends this to the device, which displays it on the screen. At this time, the emotion engine recognizes emotions from the user's input and uses this information to adjust the responses and clues that are generated. The server then saves the new clue information in the game progress log.

[1965] Input: Text data from detailed survey

[1966] Output: Detailed response data

[1967] Data processing: detailed response generation requests, detailed response generation, emotion recognition, response adjustment, cue storage

[1968] Step 5: Manage and save your progress

[1969] The server stores the user's input and the generated AI's responses as a game progress log. Clues and important events discovered by the user are also added to the game progress database on the server. Furthermore, emotions recognized by the emotion engine are also stored.

[1970] Input: User input, AI generation response, emotional information

[1971] Output: Game progress log

[1972] Data processing: log storage, clue storage, emotional information storage

[1973] Step 6: Ending the game and evaluating

[1974] The user reaches a solution to the case and inputs "Decide on the solution method." The device sends this data to the server, which analyzes the game progress log and evaluates the user's performance. Again, the emotion information recognized by the emotion engine is used in the evaluation process. The evaluation result is sent from the server to the device and presented to the user. Finally, the server saves the game session data and ends the game session.

[1975] Input: Text data of the solution

[1976] Output: Evaluation results, final response data

[1977] Data processing: Analysis of game progress logs, performance evaluation, evaluation and use of emotional information, presentation of evaluation results, session data storage

[1978] (Application example 2)

[1979] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1980] Conventional mystery game systems have had the problem of making it difficult for users to become immersed in the story. One of the reasons for this is that dynamic responses that reflect the user's emotions are not generated. Therefore, the objective of the present invention is to provide an interactive game system that allows users to become deeply involved in the progress of the game with their emotions.

[1981] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1982] In this invention, the server includes: means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to the user's terminal; means for saving the user's input and the generation AI's response as a log; means for combining response generation by the generation AI module with an emotion engine that recognizes the user's emotions; and means for adjusting the generated response based on the emotion recognition information. This enables dynamic response generation that reflects the user's emotions, resulting in an interactive game experience that provides a deeper immersion.

[1983] - "Means for receiving text data entered by a user" means a device or program that obtains information from the text entered by a user and receives it as initial data for processing within the system.

[1984] A "generative AI module" is artificial intelligence-based software that analyzes text data and automatically generates requested responses and scenarios.

[1985] "Means for sending to the user's device" refers to the communication protocols and applications used to send information such as responses and scenarios from the generation AI module to the device used by the user.

[1986] The "means for saving as a log" is a database or recording device for storing the user's input data and generated responses as a history.

[1987] An "emotion engine" is software that uses natural language processing technology to analyze emotions from text data entered by the user and identify those emotions.

[1988] "Means for adjusting responses generated based on emotion recognition information" refers to functions and algorithms for appropriately adjusting responses generated by the generative AI based on the emotion information recognized by the emotion engine.

[1989] "Means for requesting generation of an initial scenario" refers to commands or communication methods used to request the generation AI module to create the foundation for the story or scenario at the start of the game.

[1990] The "means for generating clues and events" refers to a function or module for generating new clues and events within the game based on user input data.

[1991] This invention is a mystery game system in which users play the role of detectives and solve various cases. The system combines a server, user terminals, a generative AI module, and an emotion engine that recognizes the user's emotions.

[1992] First, the user logs in from a device such as a smartphone. The server receives the user name and password and performs authentication. If authentication is successful, a game session ID is generated and sent to the device along with initial setup data. The device receives this and initializes and displays the game screen.

[1993] Next, when the user clicks the "Start Game" button, the terminal sends a game start request to the server. The server receives this request and requests the generation AI module to generate an initial scenario. The generation AI module generates the initial scenario and sends it back to the server. The server sends this scenario data to the terminal, which receives the scenario data and displays it on the game screen.

[1994] When a user enters survey content into the text field, the device sends the text data to the server. The server then sends the text data along with the emotional information recognized by the emotion engine to the generation AI module. The generation AI module analyzes this and generates response data. The response is adjusted based on the emotional information and sent back to the server. The server then sends the response data to the device, which displays it on the screen.

[1995] For example, if a user types "examine the letter opener," the input data is sent to the server. The server sends this data to the generative AI module, which generates a response such as "the letter opener has bloodstains." The emotion engine recognizes emotions such as "surprise" from the user's input and adjusts the generated response, such as "the bloodstains are recent and may hold important clues." This provides a dynamic response that also reflects the user's emotions.

[1996] Similarly, if a user types "investigate the blood stains," that input is sent to the generative AI module to generate more detailed information, while the emotion engine recognizes the user's emotions and tailors responses and cues based on that information.

[1997] The software used includes generative AI models (e.g., OpenAI GPT), emotion recognition engines (NLP analysis), server applications (e.g., Flask, Django), and databases (e.g., PostgreSQL, MySQL). The hardware includes smartphones and servers.

[1998] Below are some examples of specific prompt sentences.

[1999] Example prompt sentence:

[2000] User input: "Examine the blood trail."

[2001] Prompt to generative AI model: "The user is examining a blood stain. What details can they find? Please describe the scene in detail."

[2002] User input: "Check letter opener"

[2003] Prompt to generative AI model: "A user is examining a letter opener. What clues can they find about this item? The emotion is surprise."

[2004] In this way, the present invention realizes an interactive mystery game system that reflects the user's emotions and provides a deeper sense of immersion.

[2005] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2006] Step 1:

[2007] The user enters their username and password from a device such as a smartphone and sends a login request to the server. The server analyzes the received login request and performs user authentication using a database. If authentication is successful, the server generates a game session ID and initial setting data and sends this to the device. The device receives the initial setting data, initializes the game screen, and displays it.

[2008] Input: Username, Password

[2009] Output: Game session ID, initial configuration data

[2010] Specific operations: Database matching, session ID generation

[2011] Step 2:

[2012] When the user clicks the "Start Game" button, the device sends a game start request to the server. The server receives this request and asks the generation AI module to generate an initial scenario. The generation AI module generates an initial scenario based on the prompt and sends it back to the server. The server then sends the generated initial scenario to the device, which receives it and displays it on the game screen.

[2013] Input: Game start request

[2014] Output: Initial scenario

[2015] Specific operations: prompt generation, scenario generation

[2016] Step 3:

[2017] The user enters the information they want to find in a text field on the game screen and submits it. The device receives this and sends it to the server. The server then sends the user's input data to the generation AI module, which generates an appropriate response. At the same time, the emotion engine recognizes emotions from the user's input and provides this information to the generation AI module. The generation AI module adjusts the response based on the emotion information and sends the final response back to the server. The server then submits the response data to the device, which displays it on the screen.

[2018] Input: User-entered text

[2019] Output: The generated response

[2020] Specific behaviors: emotion recognition, response adjustment, data transmission

[2021] Step 4:

[2022] If the user wishes to continue with a more detailed investigation, they enter the details of their investigation again into the text field and submit it. The device receives the input data and sends it to the server. The server again sends this request to the generation AI module, asking it to generate more detailed information based on the content. The generation AI module generates a more detailed response and sends it back to the server. The emotion engine again recognizes emotions from the user's input, and the generation AI module adjusts the response based on that. The server sends the generated detailed information to the device, which displays it on the screen.

[2023] Input: Further investigation details

[2024] Output: Detailed response

[2025] Specific actions: detailed scenario generation, emotion recognition, and response adjustment

[2026] Step 5:

[2027] All user input and all responses from the generated AI are logged and saved by the server. The server also records clues and important events discovered by the user and adds them to the game progress database. At the same time, emotional information recognized by the emotion engine is also saved.

[2028] Input: User input, generative AI response, emotional information

[2029] Output: Game progress log

[2030] Specific operations: saving to database, logging

[2031] Step 6:

[2032] When the user reaches the final stage of the game and decides on a solution, they send the input data to the server. The server receives it, analyzes the game progress log, and evaluates the user's performance. This evaluation process also takes into account the emotional information recognized by the emotion engine. The evaluation results are sent from the server to the terminal and displayed to the user. Finally, the server saves the game session data and ends the game session.

[2033] Input: Solution input

[2034] Output: Performance evaluation results

[2035] Specific operations: log analysis, evaluation result generation, data storage

[2036] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2037] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2038] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2039] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2040] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2041] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2042] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2043] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2044] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2045] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2046] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2047] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2048] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2049] 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.

[2050] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2051] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2052] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2053] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2054] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2055] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2056] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2057] The following is further disclosed regarding the above embodiment.

[2058] (Claim 1)

[2059] means for receiving text data entered by a user;

[2060] means including a generation AI module that analyzes the text data and generates a corresponding response;

[2061] means for transmitting the response generated by the generation AI module to a user's terminal;

[2062] A means of storing user input and generated AI responses as logs;

[2063] A system including:

[2064] (Claim 2)

[2065] a means for requesting the generation AI module to generate an initial scenario;

[2066] means for transmitting the initial scenario to a user terminal;

[2067] The system of claim 1 further comprising:

[2068] (Claim 3)

[2069] means for generating cues and events based on user input;

[2070] means for transmitting the generated clues and events to a user's device;

[2071] means for storing said clues and event information;

[2072] The system of claim 1 further comprising:

[2073] (Claim 4)

[2074] means for analyzing user input related to resolving the case;

[2075] a means for evaluating the user's performance based on the analysis results;

[2076] means for transmitting the evaluation results to a user terminal;

[2077] The system of claim 1 further comprising:

[2078] (Claim 5)

[2079] a means for receiving initial setting data and initializing a game screen in the user's terminal;

[2080] a means for requesting the generation AI module to generate an initial scenario;

[2081] means for transmitting the initial scenario to a user terminal;

[2082] The system of claim 1 further comprising:

[2083] "Example 1"

[2084] (Claim 1)

[2085] means for receiving text data entered by a user;

[2086] means including a generation AI module that analyzes the text data and generates a corresponding response;

[2087] means for transmitting the response generated by the generation AI module to a user's terminal;

[2088] A means of storing user input and generated AI responses as logs;

[2089] means for receiving and verifying user authentication information;

[2090] means for generating and transmitting a game session ID and initial setting data to the terminal;

[2091] A system including:

[2092] (Claim 2)

[2093] a means for requesting the generation AI module to generate an initial scenario;

[2094] means for transmitting the initial scenario to a user terminal;

[2095] means for receiving and transmitting additional input from a user to the generative AI module;

[2096] a means for transmitting a detailed response from the generating AI module to the terminal;

[2097] The system of claim 1 further comprising:

[2098] (Claim 3)

[2099] means for generating cues and events based on user input;

[2100] means for transmitting the generated clues and events to a user's device;

[2101] means for storing said clues and event information;

[2102] a means for recording and storing a user's progress and in-game actions;

[2103] means for evaluating the user's incident resolution method and transmitting the evaluation result to the terminal;

[2104] The system of claim 1 further comprising:

[2105] "Application Example 1"

[2106] (Claim 1)

[2107] means for receiving text data entered by a user;

[2108] means including a generation AI module that analyzes the text data and generates a corresponding response;

[2109] means for transmitting the response generated by the generation AI module to a user's terminal;

[2110] A means of storing user input and generated AI responses as logs;

[2111] A means for receiving data collected from various sensors in the autonomous vehicle;

[2112] A means including a generation AI module that analyzes, identifies causes, and generates countermeasure proposals based on the sensor data;

[2113] means for transmitting the analysis and countermeasure proposals generated by the generating AI module to a display or audio system in the vehicle;

[2114] A system including:

[2115] (Claim 2)

[2116] a means for requesting the generation AI module to generate an initial scenario;

[2117] means for transmitting the initial scenario to a user terminal;

[2118] means for initializing an analysis scenario for sensor data of an autonomous vehicle based on the initial scenario;

[2119] The system of claim 1 further comprising:

[2120] (Claim 3)

[2121] means for generating cues and events based on user input;

[2122] means for transmitting the generated clues and events to a user's device;

[2123] means for storing said clues and event information;

[2124] A means of generating real-time analysis and countermeasures based on sensor data from autonomous vehicles;

[2125] means for transmitting the generated analysis and countermeasures to a display or audio system in the vehicle;

[2126] A means for storing the analysis and countermeasure information;

[2127] The system of claim 1 further comprising:

[2128] "Example 2: Combining Emotion Engines"

[2129] (Claim 1)

[2130] means for receiving text data entered by a user;

[2131] means including a generation AI module that analyzes the text data and generates a corresponding response;

[2132] A means for transmitting the response generated by the generation AI module to the user's terminal;

[2133] A means of storing user input and generated AI responses as logs;

[2134] means including an emotion engine for analyzing and recognizing the emotion of a user;

[2135] means for adjusting the response of the generative AI module based on the emotions recognized by the emotion engine;

[2136] A system including:

[2137] (Claim 2)

[2138] a means for requesting the generation AI module to generate an initial scenario;

[2139] means for transmitting the initial scenario to a user terminal;

[2140] means for receiving a game start request from a user terminal;

[2141] The system of cla...

Claims

1. means for receiving text data entered by a user; means including a generation AI module that analyzes the text data and generates a corresponding response; means for transmitting the response generated by the generation AI module to a user's terminal; A means of storing user input and generated AI responses as logs; A system including:

2. a means for requesting the generation AI module to generate an initial scenario; means for transmitting the initial scenario to a user terminal; The system of claim 1 further comprising:

3. means for generating cues and events based on user input; means for transmitting the generated clues and events to a user's device; means for storing said clues and event information; The system of claim 1 further comprising:

4. means for analyzing user input related to resolving the case; a means for evaluating the user's performance based on the analysis results; means for transmitting the evaluation results to a user terminal; The system of claim 1 further comprising:

5. a means for receiving initial setting data and initializing a game screen in the user's terminal; a means for requesting the generation AI module to generate an initial scenario; means for transmitting the initial scenario to a user terminal; The system of claim 1 further comprising:

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