system

Abstract

We provide the system. [Solution] A terminal device for inputting information about the user's preferred icons, A server means that performs analysis based on user information received from the terminal, A generation module means for creating individual music games that are generated on a server based on user information, A communication module means for sending the generated music game to a terminal and making it playable by the user, A feedback module means that collects user operation data and sends it back to the server, A learning module means for updating the generation module based on collected data, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an aging society with a high proportion of the elderly, especially in urban areas, there are problems such as a significant shortage of caregivers and a lack of diversity in recreational programs, resulting in a loss of the meaning of life for the elderly and insufficient dementia prevention measures. It is difficult for the elderly to engage in activities based on their individual hobbies and interests, and the opportunities for them to feel a sense of purpose in life are decreasing. Moreover, the increasing burden on staff in care facilities is also a serious issue.

Means for Solving the Problems

[0005] This invention provides a system that generates and delivers individually optimized music games based on each user's preferred icons. Specifically, the user inputs information about their preferred icons and music genres using a terminal and sends this information to a server. The server generates an individual music game using a generation AI model and sends the generated game to the terminal, making it playable by the user. Furthermore, the game play data is sent back to the server, and the generation AI model is updated by a learning module. This makes it possible to provide an attractive and effective dementia prevention activity for the elderly, while also reducing the burden on care staff.

[0006] A "terminal" is a device operated by a user, enabling the input of information and the reception and play of games.

[0007] A "server" is a computer system that receives data transmitted from terminals via a network, analyzes it, and generates games.

[0008] A "generation module" is a software function that generates individually optimized music games based on user information received from the device.

[0009] A "communication module" is a network function that sends game data generated on the server to the terminal and user operation data to the server.

[0010] A "feedback module" is a function that collects user operation data and opinions and sends them to the server to improve the game.

[0011] A "learning module" is a function that has an algorithm to update the generation module based on collected data, thereby improving the accuracy of subsequent game generation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

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

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

[0033] The present invention's system supports dementia prevention by providing individually customized music games to each user through the coordinated operation of a terminal and a server. This system mainly includes a terminal, a server, a generation module, a communication module, a feedback module, and a learning module.

[0034] First, the user uses their device to input information about their preferred icons (e.g., favorite idols or characters) and their favorite music genres. The device then sends this user information to the server.

[0035] Based on the received user information, the server uses a generation module to create individual music games. This generation process utilizes AI technology to design games that combine game elements (e.g., visual and auditory elements) according to the user's preferences.

[0036] The generated music game is transmitted to the terminal via a communication module, allowing the user to play the provided game. The game progresses interactively based on the user's actions, and scoring is performed in real time.

[0037] Once a user finishes playing, the device uses a feedback module to send operation data and user feedback back to the server. The server analyzes this data using a learning module and uses it to improve the generation module. This ensures that future game generation will provide more appropriate and effective content.

[0038] As a concrete example, consider a scenario where an elderly user participates. Suppose the user selects a specific singer as their "favorite" icon on their device and specifies a particular genre as their favorite song. The server then uses this information to generate a game that includes visuals representing the singer's image and requires the user to perform tap and swipe actions in time with the selected music's rhythm. In this way, users can engage in activities based on their interests, naturally leading to the maintenance and improvement of their cognitive function.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user activates the device and enters information about their favorite "idol" icon and preferred music genre. The device provides a user interface to accept input, and the submit button becomes active once the user has entered the data.

[0042] Step 2:

[0043] When the user clicks the submit button, the device converts the entered data into JSON format and sends it to the server. The data is encrypted and communicated via a secure protocol.

[0044] Step 3:

[0045] The server analyzes the received data and determines the specifications of the music game to be generated. Using the generation module, the server compiles the game scenario, music, and visual elements best suited to the user based on the input data.

[0046] Step 4:

[0047] The server uses a generation module to create individually optimized music games. At this stage, AI technology is used to generate games that include the specified icons and musical elements.

[0048] Step 5:

[0049] The server sends the generated game data to the terminal via a communication module. The data is compressed into a lightweight format suitable for real-time processing on the terminal.

[0050] Step 6:

[0051] The device presents the received game to the user and initiates gameplay. The user can progress through the game by following visual and auditory instructions for interactive operation.

[0052] Step 7:

[0053] After gameplay ends, the device collects user operation data and feedback, and sends it to the server using a feedback module.

[0054] Step 8:

[0055] The server analyzes the collected data using a learning module and incorporates the findings into the improvement process for future game generation. This ensures that future music games better meet user needs.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] While music games are expected to promote cognitive function, there are challenges in customizing content based on the specific preferences of individual users, and general content often fails to attract user interest. Furthermore, there is a need for mechanisms that effectively incorporate user feedback to improve the game experience.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes an information processing device means that performs analysis based on user data received from a terminal, a generation mechanism means that designs individual musical activities generated on the information processing device based on the user data, and a learning mechanism means that updates the generation mechanism based on the collected data. This makes it possible to provide a customized music game that suits the specific preferences of each individual user and to improve the game content based on interaction data from the user.

[0061] A "terminal" is a device used by users to input and transmit information related to specific preferences.

[0062] An "information processing device" is a computer system that analyzes data received from a terminal and performs subsequent processing based on that data.

[0063] A "generation mechanism" is a module used to design individual musical activities based on data obtained by an information processing device.

[0064] "Communication means" refers to a communication method that transmits generated musical activity to a terminal, enabling the user to control that musical activity.

[0065] A "feedback mechanism" is a method for collecting user interaction data and sending that data back to an information processing device.

[0066] A "learning mechanism" is a computational means used to improve the generation mechanism based on collected data.

[0067] The embodiments for carrying out the present invention will be described in detail below.

[0068] This invention provides a system in which terminals and servers work together to offer customized music games to individual users. This helps to improve and maintain cognitive function.

[0069] First, the user uses a terminal to input information about their preferred characters and music genres. This terminal has a user-friendly interface. The information entered by the user is sent from the terminal to the server.

[0070] The server analyzes the received data using an information processing device and designs personalized musical activities tailored to the user's preferences. This analysis uses a generative AI model, with the specific prompt being "Generate a music game that reflects the user's specific preferences."

[0071] The generated music game is transmitted to the device via a communication method. The user can play the game on this device, and the interactive experience stimulates cognitive function.

[0072] After the game ends, the device uses a feedback mechanism to recollect user interaction data and send it to the server. The server analyzes this data using a learning mechanism to improve the performance of the generation mechanism for future game offerings. This continuous feedback loop is expected to continuously improve the quality of the user experience.

[0073] As a concrete example, suppose a user selects classical music as their favorite genre and wants a rhythm game set to classical music. In this case, the generative AI model would design and provide game elements specifically tailored to classical music via prompt messages.

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user launches a music game app using their device and enters information about their preferred characters and music genres. This information is entered through the user interface in text and selection formats. This input forms the basis for generating a music game that reflects the user's specific preferences. The device then converts this input data into a digital format for preparation.

[0077] Step 2:

[0078] The terminal transmits the collected user input data to the server. A communication module is used during this process, ensuring data security. The transmitted data is then used as input for analysis processing on the server.

[0079] Step 3:

[0080] The server analyzes the received user data using an information processing device. First, it verifies the data content and converts it into a format suitable for the generation AI model. Next, it uses the generation AI model to design a customized music game based on the prompt "Generate a music game that reflects the user's specific preferences." The analyzed data is used to generate individual music activities, and as a result, game data is output.

[0081] Step 4:

[0082] The generated music game is sent from the server to the terminal. The data is delivered to the terminal quickly and securely via communication means. The received game data is converted into an executable format on the terminal and displayed to the user through the user interface.

[0083] Step 5:

[0084] The user plays a music game received on their device. Here, the device collects user input data in real time to provide an interactive experience. User input in the game (e.g., taps and swipes) is processed immediately within the system and reflected on the screen. This input information is stored on the device for use in the next step.

[0085] Step 6:

[0086] After the game ends, the device uses a feedback mechanism to send data and feedback about the user's gameplay to the server. This is done via a communication module, ensuring the accuracy and security of the data. This data is then used as analysis material by the server during the learning process.

[0087] Step 7:

[0088] The server analyzes the received interaction data using a learning mechanism. Based on previous experience, it identifies areas for improvement in the game and adjusts the parameters of the generative AI model. This improved data allows subsequent music games to more accurately meet user needs.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] The challenge lies in providing interactive experiences that effectively utilize the time spent traveling, a period often associated with boredom for the elderly and long-distance travelers, and that help maintain or improve cognitive function. In particular, it is essential to provide content tailored to individual hobbies and preferences to foster sustained interest and enjoyment.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes an information processing system means that performs analysis based on user information received from an information processing device, a generation means that creates individual musical activities based on the user information on the information processing system, and an automated mobile means equipped with a display device that provides activities to help the user maintain cognitive function during long-distance travel. This enables the provision of content based on the user's hobbies and preferences, and allows for meaningful use of time during travel.

[0094] An "information processing device" is a device that allows users to input information about their preferred icons and music genres.

[0095] An "information processing system" is a system that performs analysis based on user information received from an information processing device.

[0096] "Generation means" refers to a system that has the function of creating individual musical activities based on user information on an information processing system.

[0097] "Communication means" refers to a device that has the function of transmitting generated musical activity to an information processing device and making it available for user operation.

[0098] A "feedback mechanism" is a device that has the function of collecting user operation data and sending it back to the information processing system.

[0099] A "learning tool" is a tool that has the function of updating the generation tool based on the collected data.

[0100] An "autonomous mobile vehicle" is a means of transportation equipped with a display device that provides activities to help users maintain cognitive function during long-distance travel.

[0101] The system program that implements this application is designed to allow users to interactively enjoy their preferred musical activities. The system mainly includes an information processing device, an information processing system, generation means, communication means, feedback means, and learning means.

[0102] The server analyzes information received from the information processing device, including the user's icon and music genre, using its information processing system. This analysis result is then used by a generation mechanism to generate personalized musical experiences that combine visual and auditory elements according to the user's preferences. After generation, these musical experiences are transmitted to the information processing device via communication, making them immediately available for user interaction.

[0103] When users experience generated musical activities, the feedback system collects operation data in real time and sends it to the server. The server analyzes the collected data using a learning system and uses it to improve the capabilities of the generation system. This allows for more personalized content to be provided in future sessions.

[0104] For example, imagine an elderly person enjoying specific music while in an autonomous vehicle. In this scenario, the user selects their favorite music genre and associated icons, and the server uses this information to generate a suitable music game, providing the user with entertainment during long-distance travel.

[0105] An example of a prompt message would be, "Generate a scenario in which a user in their 60s experiences an interactive musical activity with their favorite music playing in the background while automatically traveling." In this way, users can utilize their travel time to maintain their cognitive function through fun and stimulating activities.

[0106] Thus, the present invention can provide personalized activities that go beyond mere musical experiences, transforming long journeys into meaningful experiences. The technologies used include app development using Unity, server integration using AWS® Lambda, and AI technology using TENSORFLOW®.

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

[0108] Step 1:

[0109] The device receives information from the user, such as preferred icons and music genres. This input information is sent to the server. The input data includes the user's icon selection and music genre information, and serves as initial data for providing a personalized experience based on this information.

[0110] Step 2:

[0111] The server analyzes user information received from the terminal and generates individual musical activities using a generation mechanism. In this process, an AI model combines visual and acoustic elements based on the user's preferences. The input information consists of the user's icon and music genre, and the design of the generated musical activity is output based on this information.

[0112] Step 3:

[0113] The generated musical activity is transmitted to the terminal via communication, allowing the user to control the game within the automated mobile vehicle. The input data is the digital data of the generated musical activity, which is displayed in a user-operable format, enabling real-time interaction.

[0114] Step 4:

[0115] Users experience music activities on their devices and interact according to the given instructions. During this process, user interaction data is collected in real time and sent to the server via a feedback mechanism. The input is user interaction data, which is transmitted instantly, enabling real-time responses.

[0116] Step 5:

[0117] The server analyzes user interaction data collected by the server using a learning mechanism and provides feedback to improve the generation mechanism. This analysis adjusts the next musical activity based on the interaction data. The input is the interaction data, and the output is the improved musical activity design data. This allows for the provision of content that better meets the individual needs of each user.

[0118] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0119] The system of the present invention consists of a terminal, a server, a generation module, a communication module, a feedback module, a learning module, and an emotion engine. This enables not only the provision of individually optimized music games, but also dynamic game adjustments based on the user's emotions.

[0120] In the system's operation, the user first inputs their preferred icons and music genres using the user interface on their device. This information is then sent from the device to the server. The server utilizes a generation module to create a personalized music game for each user. During this generation process, the game's visual and auditory elements are optimized based on the user's input.

[0121] The emotion engine analyzes the user's facial expressions and movements in real time while they play the music game. This is done via cameras and sensors, acquiring data according to the user's emotional state. The device sends this emotional data to a server, which is used to dynamically adjust the game's difficulty and scenarios.

[0122] When a user exits a game, the device sends operational and emotional data to the server via a feedback module. The server uses a learning module to analyze this data and use it to adjust the generation module for future games. This allows for a more accurate game experience tailored to the user's emotions, further enhancing the overall user experience.

[0123] As a concrete example, suppose an elderly user selects their favorite classical music artist and enters it into the program. The emotion engine detects smiles, serious expressions, and other reactions during gameplay. The system analyzes this data and dynamically adjusts the game's progression, for example, by highlighting moments where positive feedback is received to increase smiles. This allows the user to maintain positive emotions while enjoying the game at their own pace.

[0124] The following describes the processing flow.

[0125] Step 1:

[0126] The user activates the device and enters information about their preferred icons and music genres. The device provides a user interface for this purpose, and once the information is entered, it is ready to send the data.

[0127] Step 2:

[0128] The terminal sends user input data to the server. The data is encrypted in JSON format and sent to the server using a secure communication protocol.

[0129] Step 3:

[0130] The server analyzes the received data and provides information to the generation module to create a personalized music game based on the user's tastes and preferences. The generation module determines the game specifications and creates game data by combining music and visual elements.

[0131] Step 4:

[0132] The server sends the generated music game data to the terminal. The communication module is responsible for this, optimizing the data so that the game can run on the terminal.

[0133] Step 5:

[0134] The user starts a game sent to their device and plays it interactively. The device utilizes an emotion engine and cameras and sensors to monitor the user's facial expressions and movements, collecting emotion data in real time.

[0135] Step 6:

[0136] The device sends emotional data collected by the user during gameplay to the server. Based on this data, the server dynamically adjusts the game's difficulty and progression, providing feedback to optimize the user's experience.

[0137] Step 7:

[0138] When the game ends, the device sends operation data, including the user's play data, to the server via a feedback module. The server analyzes this data and uses a learning module to improve the algorithm for future game generation.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] Traditional music game systems lack individual optimization for each user and dynamic adjustments based on emotions, making it difficult to provide a personalized experience that reflects individual preferences and emotional states. Furthermore, the lack of means to reflect user emotions in game progression limits the quality of entertainment.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] In this invention, the server includes an information processing device for analyzing information acquired from the user, a generation program for generating personalized musical activities based on the analysis results, and an emotion analysis device for acquiring the user's facial expressions and movements in real time and making individual adjustments. This makes it possible to generate a music game that is individually optimized according to the user's preferences and emotional state, and to dynamically adjust it in real time.

[0144] An "information terminal" is a device used by users to input or receive information, and primarily refers to a terminal device.

[0145] An "information processing device" is a computer system that analyzes received information and performs appropriate data processing.

[0146] A "generation program" is an algorithm and software for generating personalized music games on an information processing device.

[0147] "Communication means" refers to the technologies that enable communication, including protocols and physical infrastructure for sending and receiving information.

[0148] A "feedback mechanism" is a device for collecting data related to user operations and sending it back to an information processing device.

[0149] "Learning methods" refer to machine learning and data analysis techniques used to analyze collected data and improve generation programs.

[0150] "Emotional analysis means" refers to a system that includes sensors and analytical technologies to analyze the user's emotions in real time and adjust the game's progress accordingly.

[0151] The system of this invention is centered around a user, a terminal, and a server. In this system, the terminal is equipped with a user interface for receiving input information from the user. Through this interface, the user selects their preferred music genre and icons. This information is sent from the terminal to the server, which then analyzes it.

[0152] The server is equipped with an information processing device for information analysis and generates personalized music games based on user input. The generation program utilizes game engines such as Unity or Unreal Engine. This optimizes the game's visual and auditory elements to match the user's preferences.

[0153] While the user is playing the game, the device uses cameras and sensors to monitor the user's facial expressions and movements in real time. During this process, an emotion analysis system determines the user's emotional state and sends this data to a server. Based on this data, the server dynamically adjusts the game's difficulty and progression.

[0154] Once gameplay ends, the device sends operation and emotional data back to the server. The server analyzes this data through learning mechanisms and incorporates the findings into the next game generation. This learning process utilizes machine learning algorithms, and the generation program is improved based on the data analysis.

[0155] For example, if a user selects the classical music genre, the generation program designs a music game stage based on that genre. If the user's smile is detected through emotion analysis during gameplay, the game is adjusted to amplify this positive reaction. This allows the user to have a more fulfilling gaming experience.

[0156] An example of a prompt might be, "Analyze user emotion data and suggest strategies to optimize game progression." This prompt utilizes a generative AI model to provide ideas for personalizing the user experience.

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] Users input their preferred music genres and icons using the device's user interface. This data is entered into the device by the user tapping and selecting options displayed on the screen. The entered data reflects the user's personal preferences.

[0160] Step 2:

[0161] The terminal sends the information entered by the user to the server. The input here is user preference information, and the output is a data packet containing this information. The terminal transfers the data to the server, and as a result, the server's information processing device confirms receipt.

[0162] Step 3:

[0163] The server generates personalized music games using a generation program based on the received information. The input is user preference information, and the output is music game data configured specifically for the user. The server utilizes a generation AI model to integrate visual and auditory elements and design game content tailored to the user.

[0164] Step 4:

[0165] The generated music game is sent back to the terminal via a communication method. The server sends the generated game data as output, and the terminal receives and displays that data. The user can start the music game at this point.

[0166] Step 5:

[0167] While the user is playing the game, the device's camera and sensors monitor the user's facial expressions and movements in real time. This information is used as input, and emotional state data is generated as output. The device analyzes this data using an emotion analysis system and sends the emotional data to a server.

[0168] Step 6:

[0169] The server adjusts the game's difficulty and progression in real time based on the emotional data it receives. The input is emotional data, and the output is data showing the adjusted game progression. The server modifies the game experience according to the user's emotions, promoting more positive feedback.

[0170] Step 7:

[0171] When a user finishes a game, the device sends final operation and emotion data back to the server through a feedback mechanism. The input consists of all data from the gameplay, and the output is stored on the server as a training dataset. Based on this data, the server's training mechanism analyzes the data for the next game generation and uses it to improve the program.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] In commercial facilities and public spaces, there is a growing need to provide an optimal sound environment tailored to the psychological state of visitors. However, conventional sound systems simply play fixed music playlists and do not respond to the emotions or circumstances of visitors. As a result, there have been limitations in improving customer satisfaction and stimulating purchasing intent.

[0175] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0176] In this invention, the server includes an information processing device means that performs analysis based on visitor information received from an information device, a generation means that creates an acoustic simulation based on the visitor information on the information processing device, and an emotion analysis engine means that estimates the visitor's psychological state and dynamically adjusts the acoustic elements. This makes it possible to dynamically provide an optimal acoustic environment according to the visitor's psychological state.

[0177] "Information equipment" refers to electronic devices used by users to input or manipulate information.

[0178] An "information processing device" is a computer system used to analyze and process data transmitted from information devices.

[0179] "Generation means" refers to a function in an information processing device that creates individual acoustic simulations based on user data.

[0180] "Communication means" refers to a means of transmitting the generated acoustic simulation to an information device, thereby enabling interaction with the user.

[0181] A "feedback mechanism" is a device for collecting user operation information and transmitting it back to the information processing device.

[0182] A "learning mechanism" is a system that updates the generation mechanism and improves its accuracy based on information collected by the feedback mechanism.

[0183] The "emotion analysis engine" is analysis software that estimates the psychological state of visitors and dynamically adjusts acoustic elements based on that estimation.

[0184] The system for carrying out this invention is an acoustic system that dynamically adjusts the acoustic environment based on the emotions of visitors. This system includes an information device, an information processing device, a generation means, a communication means, a feedback means, a learning means, and an emotion analysis engine.

[0185] The server receives visitor facial expressions and movements from information devices and performs real-time analysis using an information processing device. The analysis utilizes cameras and sensors, along with facial recognition libraries such as the Emotion SDK. This allows for the estimation of the visitor's psychological state.

[0186] The information processing device generates an acoustic simulation based on visitor information. This acoustic simulation dynamically selects music using the Spotify API and other means, providing acoustic elements tailored to the visitor's psychological state.

[0187] Using communication methods, the generated acoustic simulation is transmitted to an information device and played back in the physical space where the visitor is located. The impact of the played-back acoustic environment on the visitor is collected as operational information through a feedback mechanism.

[0188] The server adjusts its generation methods through learning mechanisms based on feedback, improving the accuracy of subsequent acoustic simulations. This process enables the continuous provision of a dynamic and optimal acoustic environment.

[0189] For example, if a visitor is looking at products with interest in a store, the emotion analysis engine can detect the visitor's joy and excitement and select and play upbeat music. As a result, the visitor's willingness to purchase can be increased.

[0190] An example of a prompt when using a generative AI model is an instruction such as, "Select background music that emphasizes items X and Y when the customer is smiling."

[0191] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0192] Step 1:

[0193] The server receives data on visitors' facial expressions and movements from information devices. This data is collected from cameras and sensors. Next, the server uses the Emotion SDK to perform facial recognition and analyze the visitor's psychological state in real time. This analysis provides an output that estimates the visitor's emotional state (e.g., joy, interest, concentration).

[0194] Step 2:

[0195] The server uses the emotional state obtained in Step 1 as input data to generate an acoustic simulation using a generative AI model. This model selects acoustic elements according to the user's psychological state and outputs the optimal combination of music and sound effects. Specifically, it utilizes music libraries such as the Spotify API to select songs that are appropriate for the visitor's psychological state.

[0196] Step 3:

[0197] The server transmits the acoustic simulation to the information device using a communication method. The information device then transfers the received acoustic data to a playback device (speaker, etc.) to provide an acoustic environment in the actual space. At this time, the music is set to play at the specified volume and tone.

[0198] Step 4:

[0199] As users (visitors) experience the acoustic environment, information devices use sensors to monitor their reactions and acquire operational information. The Emotion SDK is used again for emotional data analysis. The feedback collected along with the emotional data is sent to the server.

[0200] Step 5:

[0201] The server analyzes feedback data through feedback mechanisms and updates the generated AI model using learning mechanisms. This update optimizes the model based on previously collected feedback, improving the accuracy of subsequent acoustic simulations. This continuous improvement process makes it possible to provide visitors with a better acoustic experience.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] The present invention's system supports dementia prevention by providing individually customized music games to each user through the coordinated operation of a terminal and a server. This system mainly includes a terminal, a server, a generation module, a communication module, a feedback module, and a learning module.

[0219] First, the user uses their device to input information about their preferred icons (e.g., favorite idols or characters) and their favorite music genres. The device then sends this user information to the server.

[0220] Based on the received user information, the server uses a generation module to create individual music games. This generation process utilizes AI technology to design games that combine game elements (e.g., visual and auditory elements) according to the user's preferences.

[0221] The generated music game is transmitted to the terminal via a communication module, allowing the user to play the provided game. The game progresses interactively based on the user's actions, and scoring is performed in real time.

[0222] Once a user finishes playing, the device uses a feedback module to send operation data and user feedback back to the server. The server analyzes this data using a learning module and uses it to improve the generation module. This ensures that future game generation will provide more appropriate and effective content.

[0223] As a concrete example, consider a scenario where an elderly user participates. Suppose the user selects a specific singer as their "favorite" icon on their device and specifies a particular genre as their favorite song. The server then uses this information to generate a game that includes visuals representing the singer's image and requires the user to perform tap and swipe actions in time with the selected music's rhythm. In this way, users can engage in activities based on their interests, naturally leading to the maintenance and improvement of their cognitive function.

[0224] The following describes the processing flow.

[0225] Step 1:

[0226] The user activates the device and enters information about their favorite "idol" icon and preferred music genre. The device provides a user interface to accept input, and the submit button becomes active once the user has entered the data.

[0227] Step 2:

[0228] When the user clicks the submit button, the device converts the entered data into JSON format and sends it to the server. The data is encrypted and communicated via a secure protocol.

[0229] Step 3:

[0230] The server analyzes the received data and determines the specifications of the music game to be generated. Using the generation module, the server compiles the game scenario, music, and visual elements best suited to the user based on the input data.

[0231] Step 4:

[0232] The server uses a generation module to create individually optimized music games. At this stage, AI technology is used to generate games that include the specified icons and musical elements.

[0233] Step 5:

[0234] The server sends the generated game data to the terminal via a communication module. The data is compressed into a lightweight format suitable for real-time processing on the terminal.

[0235] Step 6:

[0236] The device presents the received game to the user and initiates gameplay. The user can progress through the game by following visual and auditory instructions for interactive operation.

[0237] Step 7:

[0238] After gameplay ends, the device collects user operation data and feedback, and sends it to the server using a feedback module.

[0239] Step 8:

[0240] The server analyzes the collected data using a learning module and incorporates the findings into the improvement process for future game generation. This ensures that future music games better meet user needs.

[0241] (Example 1)

[0242] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0243] While music games are expected to promote cognitive function, there are challenges in customizing content based on the specific preferences of individual users, and general content often fails to attract user interest. Furthermore, there is a need for mechanisms that effectively incorporate user feedback to improve the game experience.

[0244] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0245] In this invention, the server includes an information processing device means that performs analysis based on user data received from a terminal, a generation mechanism means that designs individual musical activities generated on the information processing device based on the user data, and a learning mechanism means that updates the generation mechanism based on the collected data. This makes it possible to provide a customized music game that suits the specific preferences of each individual user and to improve the game content based on interaction data from the user.

[0246] A "terminal" is a device used by users to input and transmit information related to specific preferences.

[0247] An "information processing device" is a computer system that analyzes data received from a terminal and performs subsequent processing based on that data.

[0248] A "generation mechanism" is a module used to design individual musical activities based on data obtained by an information processing device.

[0249] "Communication means" refers to a communication method that transmits generated musical activity to a terminal, enabling the user to control that musical activity.

[0250] A "feedback mechanism" is a method for collecting user interaction data and sending that data back to an information processing device.

[0251] A "learning mechanism" is a computational means used to improve the generation mechanism based on collected data.

[0252] The embodiments for carrying out the present invention will be described in detail below.

[0253] This invention provides a system in which terminals and servers work together to offer customized music games to individual users. This helps to improve and maintain cognitive function.

[0254] First, the user uses a terminal to input information about their preferred characters and music genres. This terminal has a user-friendly interface. The information entered by the user is sent from the terminal to the server.

[0255] The server analyzes the received data using an information processing device and designs personalized musical activities tailored to the user's preferences. This analysis uses a generative AI model, with the specific prompt being "Generate a music game that reflects the user's specific preferences."

[0256] The generated music game is transmitted to the device via a communication method. The user can play the game on this device, and the interactive experience stimulates cognitive function.

[0257] After the game ends, the device uses a feedback mechanism to recollect user interaction data and send it to the server. The server analyzes this data using a learning mechanism to improve the performance of the generation mechanism for future game offerings. This continuous feedback loop is expected to continuously improve the quality of the user experience.

[0258] As a concrete example, suppose a user selects classical music as their favorite genre and wants a rhythm game set to classical music. In this case, the generative AI model would design and provide game elements specifically tailored to classical music via prompt messages.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The user launches a music game app using their device and enters information about their preferred characters and music genres. This information is entered through the user interface in text and selection formats. This input forms the basis for generating a music game that reflects the user's specific preferences. The device then converts this input data into a digital format for preparation.

[0262] Step 2:

[0263] The terminal transmits the collected user input data to the server. A communication module is used during this process, ensuring data security during transmission. The transmitted data is then used as input for analysis processing on the server.

[0264] Step 3:

[0265] The server analyzes the received user data using an information processing device. First, it verifies the data content and converts it into a format suitable for the generation AI model. Next, it uses the generation AI model to design a customized music game based on the prompt "Generate a music game that reflects the user's specific preferences." The analyzed data is used to generate individual music activities, and as a result, game data is output.

[0266] Step 4:

[0267] The generated music game is sent from the server to the terminal. The data is delivered to the terminal quickly and securely via communication means. The received game data is converted into an executable format on the terminal and displayed to the user through the user interface.

[0268] Step 5:

[0269] The user plays a music game received on their device. Here, the device collects user input data in real time to provide an interactive experience. User input in the game (e.g., taps and swipes) is processed immediately within the system and reflected on the screen. This input information is stored on the device for use in the next step.

[0270] Step 6:

[0271] After the game ends, the device uses a feedback mechanism to send data and feedback about the user's gameplay to the server. This is done via a communication module, ensuring the accuracy and security of the data. This data is then used as analysis material for the server during the learning process.

[0272] Step 7:

[0273] The server analyzes the received interaction data using a learning mechanism. Based on previous experience, it identifies areas for improvement in the game and adjusts the parameters of the generative AI model. This improved data allows subsequent music games to more accurately meet user needs.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] The challenge lies in providing interactive experiences that effectively utilize the time spent traveling, a period often associated with boredom for the elderly and long-distance travelers, and that help maintain or improve cognitive function. In particular, it is essential to provide content tailored to individual hobbies and preferences to foster sustained interest and enjoyment.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes an information processing system means that performs analysis based on user information received from an information processing device, a generation means that creates individual musical activities based on the user information on the information processing system, and an automated mobile means equipped with a display device that provides activities to help the user maintain cognitive function during long-distance travel. This enables the provision of content based on the user's hobbies and preferences, and allows for meaningful use of time during travel.

[0279] An "information processing device" is a device that allows users to input information about their preferred icons and music genres.

[0280] An "information processing system" is a system that performs analysis based on user information received from an information processing device.

[0281] The "generation means" is something that has a function for creating individual music activities based on the user's information on an information processing system.

[0282] The "communication means" is something that has a function for transmitting the generated music activity to an information processing device and making it operable by the user.

[0283] The "feedback means" is something that has a function for collecting the user's operation data and transmitting it again to the information processing system.

[0284] The "learning means" is something that has a function for updating the generation means based on the collected data.

[0285] The "autonomous mobile body" is a moving means equipped with a display device that provides activities for promoting the maintenance of the user's cognitive function during long-distance movement.

[0286] The program of the system that realizes this application example is designed so that the user can enjoy their favorite music activities interactively. The system mainly includes an information processing device, an information processing system, a generation means, a communication means, a feedback means, and a learning means.

[0287] The server performs analysis in the information processing system based on the information about the user's icon and music genre received from the information processing device. This analysis result is used by the generation means to generate individual music activities that combine visual and acoustic elements according to the user's preferences. After generation, this music activity is transmitted to the information processing device through the communication means and becomes immediately operable by the user.

[0288] When the user experiences the generated music activity, the feedback means collects operation data in real time and transmits it to the server. At the server, the collected data is analyzed using the learning means and utilized for improving the capabilities of the generation means. As a result, more personalized content will be provided in future sessions.

[0289] For example, imagine an elderly person enjoying specific music while in an autonomous vehicle. In this scenario, the user selects their favorite music genre and associated icons, and the server uses this information to generate a suitable music game, providing the user with entertainment during long-distance travel.

[0290] An example of a prompt message would be, "Generate a scenario in which a user in their 60s experiences an interactive musical activity with their favorite music playing in the background while automatically traveling." In this way, users can utilize their travel time to maintain their cognitive function through fun and stimulating activities.

[0291] Thus, this invention can provide personalized activities that go beyond mere musical experiences, transforming long journeys into meaningful experiences. The technologies used include app development using Unity, server integration using AWS Lambda, and AI technology using TensorFlow.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The device receives information from the user, such as preferred icons and music genres. This input information is sent to the server. The input data includes the user's icon selection and music genre information, and serves as initial data for providing a personalized experience based on this information.

[0295] Step 2:

[0296] The server analyzes user information received from the terminal and generates individual musical activities using a generation mechanism. In this process, an AI model combines visual and acoustic elements based on the user's preferences. The input information consists of the user's icon and music genre, and the design of the generated musical activity is output based on this information.

[0297] Step 3:

[0298] The generated musical activity is transmitted to the terminal via communication, allowing the user to control the game within the automated mobile vehicle. The input data is the digital data of the generated musical activity, which is displayed in a user-operable format, enabling real-time interaction.

[0299] Step 4:

[0300] Users experience music activities on their devices and interact according to the given instructions. During this process, user interaction data is collected in real time and sent to the server via a feedback mechanism. The input is user interaction data, which is transmitted instantly, enabling real-time responses.

[0301] Step 5:

[0302] The server analyzes user interaction data collected by the server using a learning mechanism and provides feedback to improve the generation mechanism. This analysis adjusts the next musical activity based on the interaction data. The input is the interaction data, and the output is the improved musical activity design data. This allows for the provision of content that better meets the individual needs of each user.

[0303] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0304] The system of the present invention consists of a terminal, a server, a generation module, a communication module, a feedback module, a learning module, and an emotion engine. This enables not only the provision of individually optimized music games, but also dynamic game adjustments based on the user's emotions.

[0305] In the implementation of the system, first, the user uses the user interface installed on the terminal to input their favorite icons and music genres. This information is transmitted from the terminal to the server. The server utilizes the generation module to generate a personalized music game for each user. In this generation process, the visual and acoustic elements of the game are optimized based on the user's input.

[0306] The emotion engine analyzes the user's expressions and movements in real time while the user is playing the music game. This is done via a camera or sensors to obtain data corresponding to the user's emotional state. The terminal transmits this emotional data to the server and utilizes it to dynamically adjust the difficulty level and scenario of the game.

[0307] When the user ends the game, the terminal transmits the operation data and emotional data to the server through the feedback module. The server analyzes these data using the learning module and uses it to assist in the adjustment of the generation module for subsequent times. This enables the provision of a more accurate game in line with the user's emotions and further enhances the experiential value.

[0308] As a specific example, assume that an elderly user selects their favorite classical music artist and gladly inputs it on the program. The emotion engine detects smiling or serious expressions during play. The system analyzes this and dynamically adjusts the progress of the game, such as emphasizing scenes where positive feedback is obtained and the smiling increases. This enables the user to maintain positive emotions and enjoy the game according to their individual pace.

[0309] The following explains the processing flow.

[0310] Step 1:

[0311] The user activates the device and enters information about their preferred icons and music genres. The device provides a user interface for this purpose, and once the information is entered, it is ready to send the data.

[0312] Step 2:

[0313] The terminal sends user input data to the server. The data is encrypted in JSON format and sent to the server using a secure communication protocol.

[0314] Step 3:

[0315] The server analyzes the received data and provides information to the generation module to create a personalized music game based on the user's tastes and preferences. The generation module determines the game specifications and creates game data by combining music and visual elements.

[0316] Step 4:

[0317] The server sends the generated music game data to the terminal. The communication module is responsible for this, optimizing the data so that the game can run on the terminal.

[0318] Step 5:

[0319] The user starts a game sent to their device and plays it interactively. The device utilizes an emotion engine and cameras and sensors to monitor the user's facial expressions and movements, collecting emotion data in real time.

[0320] Step 6:

[0321] The device sends emotional data collected by the user during gameplay to the server. Based on this data, the server dynamically adjusts the game's difficulty and progression, providing feedback to optimize the user's experience.

[0322] Step 7:

[0323] When the game ends, the device sends operation data, including the user's play data, to the server via a feedback module. The server analyzes this data and uses a learning module to improve the algorithm for future game generation.

[0324] (Example 2)

[0325] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0326] Traditional music game systems lack individual optimization for each user and dynamic adjustments based on emotions, making it difficult to provide a personalized experience that reflects individual preferences and emotional states. Furthermore, the lack of means to reflect user emotions in game progression limits the quality of entertainment.

[0327] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0328] In this invention, the server includes an information processing device for analyzing information acquired from the user, a generation program for generating personalized musical activities based on the analysis results, and an emotion analysis device for acquiring the user's facial expressions and movements in real time and making individual adjustments. This makes it possible to generate a music game that is individually optimized according to the user's preferences and emotional state, and to dynamically adjust it in real time.

[0329] An "information terminal" is a device used by users to input or receive information, and primarily refers to a terminal device.

[0330] An "information processing device" is a computer system that analyzes received information and performs appropriate data processing.

[0331] A "generation program" is an algorithm and software for generating personalized music games on an information processing device.

[0332] "Communication means" refers to the technologies that enable communication, including protocols and physical infrastructure for sending and receiving information.

[0333] A "feedback mechanism" is a device for collecting data related to user operations and sending it back to an information processing device.

[0334] "Learning methods" refer to machine learning and data analysis techniques used to analyze collected data and improve generation programs.

[0335] "Emotional analysis means" refers to a system that includes sensors and analytical technologies to analyze the user's emotions in real time and adjust the game's progress accordingly.

[0336] The system of this invention is centered around a user, a terminal, and a server. In this system, the terminal is equipped with a user interface for receiving input information from the user. Through this interface, the user selects their preferred music genre and icons. This information is sent from the terminal to the server, which then analyzes it.

[0337] The server is equipped with an information processing device for information analysis and generates personalized music games based on user input. The generation program utilizes game engines such as Unity or Unreal Engine. This optimizes the game's visual and auditory elements to match the user's preferences.

[0338] While the user is playing the game, the device uses cameras and sensors to monitor the user's facial expressions and movements in real time. During this process, an emotion analysis system determines the user's emotional state and sends this data to a server. Based on this data, the server dynamically adjusts the game's difficulty and progression.

[0339] Once gameplay ends, the device sends operation and emotional data back to the server. The server analyzes this data through learning mechanisms and incorporates the findings into the next game generation. This learning process utilizes machine learning algorithms, and the generation program is improved based on the data analysis.

[0340] For example, if a user selects the classical music genre, the generation program designs a music game stage based on that genre. If the user's smile is detected through emotion analysis during gameplay, the game is adjusted to amplify this positive reaction. This allows the user to have a more fulfilling gaming experience.

[0341] An example of a prompt might be, "Analyze user emotion data and suggest strategies to optimize game progression." This prompt utilizes a generative AI model to provide ideas for personalizing the user experience.

[0342] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0343] Step 1:

[0344] Users input their preferred music genres and icons using the device's user interface. This data is entered into the device by the user tapping and selecting options displayed on the screen. The entered data reflects the user's personal preferences.

[0345] Step 2:

[0346] The terminal sends the information entered by the user to the server. The input here is user preference information, and the output is a data packet containing this information. The terminal transfers the data to the server, and as a result, the server's information processing device confirms receipt.

[0347] Step 3:

[0348] The server generates personalized music games using a generation program based on the received information. The input is user preference information, and the output is music game data configured specifically for the user. The server utilizes a generation AI model to integrate visual and auditory elements and design game content tailored to the user.

[0349] Step 4:

[0350] The generated music game is sent back to the terminal via a communication method. The server sends the generated game data as output, and the terminal receives and displays that data. The user can start the music game at this point.

[0351] Step 5:

[0352] While the user is playing the game, the device's camera and sensors monitor the user's facial expressions and movements in real time. This information is used as input, and emotional state data is generated as output. The device analyzes this data using an emotion analysis system and sends the emotional data to a server.

[0353] Step 6:

[0354] The server adjusts the game's difficulty and progression in real time based on the emotional data it receives. The input is emotional data, and the output is data showing the adjusted game progression. The server modifies the game experience according to the user's emotions, promoting more positive feedback.

[0355] Step 7:

[0356] When a user finishes a game, the device sends final operation and emotion data back to the server through a feedback mechanism. The input consists of all data from the gameplay, and the output is stored on the server as a training dataset. Based on this data, the server's training mechanism analyzes the data for the next game generation and uses it to improve the program.

[0357] (Application Example 2)

[0358] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0359] In commercial facilities and public spaces, there is a growing need to provide an optimal sound environment tailored to the psychological state of visitors. However, conventional sound systems simply play fixed music playlists and do not respond to the emotions or circumstances of visitors. As a result, there have been limitations in improving customer satisfaction and stimulating purchasing intent.

[0360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0361] In this invention, the server includes an information processing device means that performs analysis based on visitor information received from an information device, a generation means that creates an acoustic simulation based on the visitor information on the information processing device, and an emotion analysis engine means that estimates the visitor's psychological state and dynamically adjusts the acoustic elements. This makes it possible to dynamically provide an optimal acoustic environment according to the visitor's psychological state.

[0362] "Information equipment" refers to electronic devices used by users to input or manipulate information.

[0363] An "information processing device" is a computer system used to analyze and process data transmitted from information devices.

[0364] "Generation means" refers to a function in an information processing device that creates individual acoustic simulations based on user data.

[0365] "Communication means" refers to a means of transmitting the generated acoustic simulation to an information device, thereby enabling interaction with the user.

[0366] A "feedback mechanism" is a device for collecting user operation information and transmitting it back to the information processing device.

[0367] A "learning mechanism" is a system that updates the generation mechanism and improves its accuracy based on information collected by the feedback mechanism.

[0368] The "emotion analysis engine" is analysis software that estimates the psychological state of visitors and dynamically adjusts acoustic elements based on that estimation.

[0369] The system for carrying out this invention is an acoustic system that dynamically adjusts the acoustic environment based on the emotions of visitors. This system includes an information device, an information processing device, a generation means, a communication means, a feedback means, a learning means, and an emotion analysis engine.

[0370] The server receives visitor facial expressions and movements from information devices and performs real-time analysis using an information processing device. The analysis utilizes cameras and sensors, along with facial recognition libraries such as the Emotion SDK. This allows for the estimation of the visitor's psychological state.

[0371] The information processing device generates an acoustic simulation based on visitor information. This acoustic simulation dynamically selects music using the Spotify API and other means, providing acoustic elements tailored to the visitor's psychological state.

[0372] Using communication methods, the generated acoustic simulation is transmitted to an information device and played back in the physical space where the visitor is located. The impact of the played-back acoustic environment on the visitor is collected as operational information through a feedback mechanism.

[0373] The server adjusts its generation methods through learning mechanisms based on feedback, improving the accuracy of subsequent acoustic simulations. This process enables the continuous provision of a dynamic and optimal acoustic environment.

[0374] For example, if a visitor is looking at products with interest in a store, the emotion analysis engine can detect the visitor's joy and excitement and select and play upbeat music. As a result, the visitor's willingness to purchase can be increased.

[0375] An example of a prompt when using a generative AI model is an instruction such as, "Select background music that emphasizes items X and Y when the customer is smiling."

[0376] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0377] Step 1:

[0378] The server receives data on visitors' facial expressions and movements from information devices. This data is collected from cameras and sensors. Next, the server uses the Emotion SDK to perform facial recognition and analyze the visitor's psychological state in real time. This analysis provides an output that estimates the visitor's emotional state (e.g., joy, interest, concentration).

[0379] Step 2:

[0380] The server uses the emotional state obtained in Step 1 as input data to generate an acoustic simulation using a generative AI model. This model selects acoustic elements according to the user's psychological state and outputs the optimal combination of music and sound effects. Specifically, it utilizes music libraries such as the Spotify API to select songs that are appropriate for the visitor's psychological state.

[0381] Step 3:

[0382] The server transmits the acoustic simulation to the information device using a communication method. The information device then transfers the received acoustic data to a playback device (speaker, etc.) to provide an acoustic environment in the actual space. At this time, the music is set to play at the specified volume and tone.

[0383] Step 4:

[0384] As users (visitors) experience the acoustic environment, information devices use sensors to monitor their reactions and acquire operational information. The Emotion SDK is used again for emotional data analysis. The feedback collected along with the emotional data is sent to the server.

[0385] Step 5:

[0386] The server analyzes feedback data through feedback mechanisms and updates the generated AI model using learning mechanisms. This update optimizes the model based on previously collected feedback, improving the accuracy of subsequent acoustic simulations. This continuous improvement process makes it possible to provide visitors with a better acoustic experience.

[0387] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0398] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0399] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] The present invention's system supports dementia prevention by providing individually customized music games to each user through the coordinated operation of a terminal and a server. This system mainly includes a terminal, a server, a generation module, a communication module, a feedback module, and a learning module.

[0404] First, the user uses their device to input information about their preferred icons (e.g., favorite idols or characters) and their favorite music genres. The device then sends this user information to the server.

[0405] Based on the received user information, the server uses a generation module to create individual music games. This generation process utilizes AI technology to design games that combine game elements (e.g., visual and auditory elements) according to the user's preferences.

[0406] The generated music game is transmitted to the terminal via a communication module, allowing the user to play the provided game. The game progresses interactively based on the user's actions, and scoring is performed in real time.

[0407] Once a user finishes playing, the device uses a feedback module to send operation data and user feedback back to the server. The server analyzes this data using a learning module and uses it to improve the generation module. This ensures that future game generation will provide more appropriate and effective content.

[0408] As a concrete example, consider a scenario where an elderly user participates. Suppose the user selects a specific singer as their "favorite" icon on their device and specifies a particular genre as their favorite song. The server then uses this information to generate a game that includes visuals representing the singer's image and requires the user to perform tap and swipe actions in time with the selected music's rhythm. In this way, users can engage in activities based on their interests, naturally leading to the maintenance and improvement of their cognitive function.

[0409] The following describes the processing flow.

[0410] Step 1:

[0411] The user activates the device and enters information about their favorite "idol" icon and preferred music genre. The device provides a user interface to accept input, and the submit button becomes active once the user has entered the data.

[0412] Step 2:

[0413] When the user clicks the submit button, the device converts the entered data into JSON format and sends it to the server. The data is encrypted and communicated via a secure protocol.

[0414] Step 3:

[0415] The server analyzes the received data and determines the specifications of the music game to be generated. Using the generation module, the server compiles the game scenario, music, and visual elements best suited to the user based on the input data.

[0416] Step 4:

[0417] The server uses a generation module to create individually optimized music games. At this stage, AI technology is used to generate games that include the specified icons and musical elements.

[0418] Step 5:

[0419] The server sends the generated game data to the terminal via a communication module. The data is compressed into a lightweight format suitable for real-time processing on the terminal.

[0420] Step 6:

[0421] The device presents the received game to the user and initiates gameplay. The user can progress through the game by following visual and auditory instructions for interactive operation.

[0422] Step 7:

[0423] After gameplay ends, the device collects user operation data and feedback, and sends it to the server using a feedback module.

[0424] Step 8:

[0425] The server analyzes the collected data using a learning module and incorporates the findings into the improvement process for future game generation. This ensures that future music games better meet user needs.

[0426] (Example 1)

[0427] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0428] While music games are expected to promote cognitive function, there are challenges in customizing content based on the specific preferences of individual users, and general content often fails to attract user interest. Furthermore, there is a need for mechanisms that effectively incorporate user feedback to improve the game experience.

[0429] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0430] In this invention, the server includes an information processing device means that performs analysis based on user data received from a terminal, a generation mechanism means that designs individual musical activities generated on the information processing device based on the user data, and a learning mechanism means that updates the generation mechanism based on the collected data. This makes it possible to provide a customized music game that suits the specific preferences of each individual user and to improve the game content based on interaction data from the user.

[0431] A "terminal" is a device used by users to input and transmit information related to specific preferences.

[0432] An "information processing device" is a computer system that analyzes data received from a terminal and performs subsequent processing based on that data.

[0433] A "generation mechanism" is a module used to design individual musical activities based on data obtained by an information processing device.

[0434] "Communication means" refers to a communication method that transmits generated musical activity to a terminal, enabling the user to control that musical activity.

[0435] A "feedback mechanism" is a method for collecting user interaction data and sending that data back to an information processing device.

[0436] A "learning mechanism" is a computational means used to improve the generation mechanism based on collected data.

[0437] The embodiments for carrying out the present invention will be described in detail below.

[0438] This invention provides a system in which terminals and servers work together to offer customized music games to individual users. This helps to improve and maintain cognitive function.

[0439] First, the user uses a terminal to input information about their preferred characters and music genres. This terminal has a user-friendly interface. The information entered by the user is sent from the terminal to the server.

[0440] The server analyzes the received data using an information processing device and designs personalized musical activities tailored to the user's preferences. This analysis uses a generative AI model, with the specific prompt being "Generate a music game that reflects the user's specific preferences."

[0441] The generated music game is transmitted to the device via a communication method. The user can play the game on this device, and the interactive experience stimulates cognitive function.

[0442] After the game ends, the device uses a feedback mechanism to recollect user interaction data and send it to the server. The server analyzes this data using a learning mechanism to improve the performance of the generation mechanism for future game offerings. This continuous feedback loop is expected to continuously improve the quality of the user experience.

[0443] As a concrete example, suppose a user selects classical music as their favorite genre and wants a rhythm game set to classical music. In this case, the generative AI model would design and provide game elements specifically tailored to classical music via prompt messages.

[0444] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0445] Step 1:

[0446] The user launches a music game app using their device and enters information about their preferred characters and music genres. This information is entered through the user interface in text and selection formats. This input forms the basis for generating a music game that reflects the user's specific preferences. The device then converts this input data into a digital format for preparation.

[0447] Step 2:

[0448] The terminal transmits the collected user input data to the server. A communication module is used during this process, ensuring data security during transmission. The transmitted data is then used as input for analysis processing on the server.

[0449] Step 3:

[0450] The server analyzes the received user data using an information processing device. First, it verifies the data content and converts it into a format suitable for the generation AI model. Next, it uses the generation AI model to design a customized music game based on the prompt "Generate a music game that reflects the user's specific preferences." The analyzed data is used to generate individual music activities, and as a result, game data is output.

[0451] Step 4:

[0452] The generated music game is sent from the server to the terminal. The data is delivered to the terminal quickly and securely via communication means. The received game data is converted into an executable format on the terminal and displayed to the user through the user interface.

[0453] Step 5:

[0454] The user plays a music game received on their device. Here, the device collects user input data in real time to provide an interactive experience. User input in the game (e.g., taps and swipes) is processed immediately within the system and reflected on the screen. This input information is stored on the device for use in the next step.

[0455] Step 6:

[0456] After the game ends, the device uses a feedback mechanism to send data and feedback about the user's gameplay to the server. This is done via a communication module, ensuring the accuracy and security of the data. This data is then used as analysis material for the server during the learning process.

[0457] Step 7:

[0458] The server analyzes the received interaction data using a learning mechanism. Based on previous experience, it identifies areas for improvement in the game and adjusts the parameters of the generative AI model. This improved data allows subsequent music games to more accurately meet user needs.

[0459] (Application Example 1)

[0460] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0461] The challenge lies in providing interactive experiences that effectively utilize the time spent traveling, a period often associated with boredom for the elderly and long-distance travelers, and that help maintain or improve cognitive function. In particular, it is essential to provide content tailored to individual hobbies and preferences to foster sustained interest and enjoyment.

[0462] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0463] In this invention, the server includes an information processing system means that performs analysis based on user information received from an information processing device, a generation means that creates individual musical activities based on the user information on the information processing system, and an automated mobile means equipped with a display device that provides activities to help the user maintain cognitive function during long-distance travel. This enables the provision of content based on the user's hobbies and preferences, and allows for meaningful use of time during travel.

[0464] An "information processing device" is a device that allows users to input information about their preferred icons and music genres.

[0465] An "information processing system" is a system that performs analysis based on user information received from an information processing device.

[0466] "Generation means" refers to a system that has the function of creating individual musical activities based on user information on an information processing system.

[0467] "Communication means" refers to a device that has the function of transmitting generated musical activity to an information processing device and making it available for user operation.

[0468] A "feedback mechanism" is a device that has the function of collecting user operation data and sending it back to the information processing system.

[0469] A "learning tool" is a tool that has the function of updating the generation tool based on the collected data.

[0470] An "autonomous mobile vehicle" is a means of transportation equipped with a display device that provides activities to help users maintain cognitive function during long-distance travel.

[0471] The system program that implements this application is designed to allow users to interactively enjoy their preferred musical activities. The system mainly includes an information processing device, an information processing system, generation means, communication means, feedback means, and learning means.

[0472] The server analyzes information received from the information processing device, including the user's icon and music genre, using its information processing system. This analysis result is then used by a generation mechanism to generate personalized musical experiences that combine visual and auditory elements according to the user's preferences. After generation, these musical experiences are transmitted to the information processing device via communication, making them immediately available for user interaction.

[0473] When users experience generated musical activities, the feedback system collects operation data in real time and sends it to the server. The server analyzes the collected data using a learning system and uses it to improve the capabilities of the generation system. This allows for more personalized content to be provided in future sessions.

[0474] For example, imagine an elderly person enjoying specific music while in an autonomous vehicle. In this scenario, the user selects their favorite music genre and associated icons, and the server uses this information to generate a suitable music game, providing the user with entertainment during long-distance travel.

[0475] An example of a prompt message would be, "Generate a scenario in which a user in their 60s experiences an interactive musical activity with their favorite music playing in the background while automatically traveling." In this way, users can utilize their travel time to maintain their cognitive function through fun and stimulating activities.

[0476] Thus, this invention can provide personalized activities that go beyond mere musical experiences, transforming long journeys into meaningful experiences. The technologies used include app development using Unity, server integration using AWS Lambda, and AI technology using TensorFlow.

[0477] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0478] Step 1:

[0479] The device receives information from the user, such as preferred icons and music genres. This input information is sent to the server. The input data includes the user's icon selection and music genre information, and serves as initial data for providing a personalized experience based on this information.

[0480] Step 2:

[0481] The server analyzes user information received from the terminal and generates individual musical activities using a generation mechanism. In this process, an AI model combines visual and acoustic elements based on the user's preferences. The input information consists of the user's icon and music genre, and the design of the generated musical activity is output based on this information.

[0482] Step 3:

[0483] The generated musical activity is transmitted to the terminal via communication, allowing the user to control the game within the automated mobile vehicle. The input data is the digital data of the generated musical activity, which is displayed in a user-operable format, enabling real-time interaction.

[0484] Step 4:

[0485] Users experience music activities on their devices and interact according to the given instructions. During this process, user interaction data is collected in real time and sent to the server via a feedback mechanism. The input is user interaction data, which is transmitted instantly, enabling real-time responses.

[0486] Step 5:

[0487] The server analyzes user interaction data collected by the server using a learning mechanism and provides feedback to improve the generation mechanism. This analysis adjusts the next musical activity based on the interaction data. The input is the interaction data, and the output is the improved musical activity design data. This allows for the provision of content that better meets the individual needs of each user.

[0488] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0489] The system of the present invention consists of a terminal, a server, a generation module, a communication module, a feedback module, a learning module, and an emotion engine. This enables not only the provision of individually optimized music games, but also dynamic game adjustments based on the user's emotions.

[0490] In the system's operation, the user first inputs their preferred icons and music genres using the user interface on their device. This information is then sent from the device to the server. The server utilizes a generation module to create a personalized music game for each user. During this generation process, the game's visual and auditory elements are optimized based on the user's input.

[0491] The emotion engine analyzes the user's facial expressions and movements in real time while they play the music game. This is done via cameras and sensors, acquiring data according to the user's emotional state. The device sends this emotional data to a server, which is used to dynamically adjust the game's difficulty and scenarios.

[0492] When a user exits a game, the device sends operational and emotional data to the server via a feedback module. The server uses a learning module to analyze this data and use it to adjust the generation module for future games. This allows for a more accurate game experience tailored to the user's emotions, further enhancing the overall user experience.

[0493] As a concrete example, suppose an elderly user selects their favorite classical music artist and enters it into the program. The emotion engine detects smiles, serious expressions, and other reactions during gameplay. The system analyzes this data and dynamically adjusts the game's progression, for example, by highlighting moments where positive feedback is received to increase smiles. This allows the user to maintain positive emotions while enjoying the game at their own pace.

[0494] The following describes the processing flow.

[0495] Step 1:

[0496] The user activates the device and enters information about their preferred icons and music genres. The device provides a user interface for this purpose, and once the information is entered, it is ready to send the data.

[0497] Step 2:

[0498] The terminal sends user input data to the server. The data is encrypted in JSON format and sent to the server using a secure communication protocol.

[0499] Step 3:

[0500] The server analyzes the received data and provides information to the generation module to create a personalized music game based on the user's tastes and preferences. The generation module determines the game specifications and creates game data by combining music and visual elements.

[0501] Step 4:

[0502] The server sends the generated music game data to the terminal. The communication module is responsible for this, optimizing the data so that the game can run on the terminal.

[0503] Step 5:

[0504] The user starts a game sent to their device and plays it interactively. The device utilizes an emotion engine to monitor the user's facial expressions and movements using cameras and sensors, collecting emotion data in real time.

[0505] Step 6:

[0506] The device sends emotional data collected by the user during gameplay to the server. Based on this data, the server dynamically adjusts the game's difficulty and progression, providing feedback to optimize the user's experience.

[0507] Step 7:

[0508] When the game ends, the device sends operation data, including the user's play data, to the server via a feedback module. The server analyzes this data and uses a learning module to improve the algorithm for future game generation.

[0509] (Example 2)

[0510] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0511] Traditional music game systems lack individual optimization for each user and dynamic adjustments based on emotions, making it difficult to provide a personalized experience that reflects individual preferences and emotional states. Furthermore, the lack of means to reflect user emotions in game progression limits the quality of entertainment.

[0512] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0513] In this invention, the server includes an information processing device for analyzing information acquired from the user, a generation program for generating personalized musical activities based on the analysis results, and an emotion analysis device for acquiring the user's facial expressions and movements in real time and making individual adjustments. This makes it possible to generate a music game that is individually optimized according to the user's preferences and emotional state, and to dynamically adjust it in real time.

[0514] An "information terminal" is a device used by users to input or receive information, and primarily refers to a terminal device.

[0515] An "information processing device" is a computer system that analyzes received information and performs appropriate data processing.

[0516] A "generation program" is an algorithm and software for generating personalized music games on an information processing device.

[0517] "Communication means" refers to the technologies that enable communication, including protocols and physical infrastructure for sending and receiving information.

[0518] A "feedback mechanism" is a device for collecting data related to user operations and sending it back to an information processing device.

[0519] "Learning methods" refer to machine learning and data analysis techniques used to analyze collected data and improve generation programs.

[0520] "Emotional analysis means" refers to a system that includes sensors and analytical technologies to analyze the user's emotions in real time and adjust the game's progress accordingly.

[0521] The system of this invention is centered around a user, a terminal, and a server. In this system, the terminal is equipped with a user interface for receiving input information from the user. Through this interface, the user selects their preferred music genre and icons. This information is sent from the terminal to the server, which then analyzes it.

[0522] The server is equipped with an information processing device for information analysis and generates personalized music games based on user input. The generation program utilizes game engines such as Unity or Unreal Engine. This optimizes the game's visual and auditory elements to match the user's preferences.

[0523] While the user is playing the game, the device uses cameras and sensors to monitor the user's facial expressions and movements in real time. During this process, an emotion analysis system determines the user's emotional state and sends this data to a server. Based on this data, the server dynamically adjusts the game's difficulty and progression.

[0524] Once gameplay ends, the device sends operation and emotional data back to the server. The server analyzes this data through learning mechanisms and incorporates the findings into the next game generation. This learning process utilizes machine learning algorithms, and the generation program is improved based on the data analysis.

[0525] For example, if a user selects the classical music genre, the generation program designs a music game stage based on that genre. If the user's smile is detected through emotion analysis during gameplay, the game is adjusted to amplify this positive reaction. This allows the user to have a more fulfilling gaming experience.

[0526] An example of a prompt might be, "Analyze user emotion data and suggest strategies to optimize game progression." This prompt utilizes a generative AI model to provide ideas for personalizing the user experience.

[0527] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0528] Step 1:

[0529] Users input their preferred music genres and icons using the device's user interface. This data is entered into the device by the user tapping and selecting options displayed on the screen. The entered data reflects the user's personal preferences.

[0530] Step 2:

[0531] The terminal sends the information entered by the user to the server. The input here is user preference information, and the output is a data packet containing this information. The terminal transfers the data to the server, and as a result, the server's information processing device confirms receipt.

[0532] Step 3:

[0533] The server generates personalized music games using a generation program based on the received information. The input is user preference information, and the output is music game data configured specifically for the user. The server utilizes a generation AI model to integrate visual and auditory elements and design game content tailored to the user.

[0534] Step 4:

[0535] The generated music game is sent back to the terminal via a communication method. The server sends the generated game data as output, and the terminal receives and displays that data. The user can start the music game at this point.

[0536] Step 5:

[0537] While the user is playing the game, the device's camera and sensors monitor the user's facial expressions and movements in real time. This information is used as input, and emotional state data is generated as output. The device analyzes this data using an emotion analysis system and sends the emotional data to a server.

[0538] Step 6:

[0539] The server adjusts the game's difficulty and progression in real time based on the emotional data it receives. The input is emotional data, and the output is data showing the adjusted game progression. The server modifies the game experience according to the user's emotions, promoting more positive feedback.

[0540] Step 7:

[0541] When a user finishes a game, the device sends final operation and emotion data back to the server through a feedback mechanism. The input consists of all data from the gameplay, and the output is stored on the server as a training dataset. Based on this data, the server's training mechanism analyzes the data for the next game generation and uses it to improve the program.

[0542] (Application Example 2)

[0543] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In commercial facilities and public spaces, there is a growing need to provide an optimal sound environment tailored to the psychological state of visitors. However, conventional sound systems simply play fixed music playlists and do not respond to the emotions or circumstances of visitors. As a result, there have been limitations in improving customer satisfaction and stimulating purchasing intent.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0546] In this invention, the server includes an information processing device means that performs analysis based on visitor information received from an information device, a generation means that creates an acoustic simulation based on the visitor information on the information processing device, and an emotion analysis engine means that estimates the visitor's psychological state and dynamically adjusts the acoustic elements. This makes it possible to dynamically provide an optimal acoustic environment according to the visitor's psychological state.

[0547] "Information equipment" refers to electronic devices used by users to input or manipulate information.

[0548] An "information processing device" is a computer system used to analyze and process data transmitted from information devices.

[0549] "Generation means" refers to a function in an information processing device that creates individual acoustic simulations based on user data.

[0550] "Communication means" refers to a means of transmitting the generated acoustic simulation to an information device, thereby enabling interaction with the user.

[0551] A "feedback mechanism" is a device for collecting user operation information and transmitting it back to the information processing device.

[0552] A "learning mechanism" is a system that updates the generation mechanism and improves its accuracy based on information collected by the feedback mechanism.

[0553] The "emotion analysis engine" is analysis software that estimates the psychological state of visitors and dynamically adjusts acoustic elements based on that estimation.

[0554] The system for carrying out this invention is an acoustic system that dynamically adjusts the acoustic environment based on the emotions of visitors. This system includes an information device, an information processing device, a generation means, a communication means, a feedback means, a learning means, and an emotion analysis engine.

[0555] The server receives visitor facial expressions and movements from information devices and performs real-time analysis using an information processing device. The analysis utilizes cameras and sensors, and employs facial recognition libraries such as the Emotion SDK. This allows for the estimation of the visitor's psychological state.

[0556] The information processing device generates an acoustic simulation based on visitor information. This acoustic simulation dynamically selects music using the Spotify API and other means, providing acoustic elements tailored to the visitor's psychological state.

[0557] Using communication methods, the generated acoustic simulation is transmitted to an information device and played back in the physical space where the visitor is located. The impact of the played-back acoustic environment on the visitor is collected as operational information through a feedback mechanism.

[0558] The server adjusts its generation methods through learning mechanisms based on feedback, improving the accuracy of subsequent acoustic simulations. This process enables the continuous provision of a dynamic and optimal acoustic environment.

[0559] For example, if a visitor is looking at products with interest in a store, the emotion analysis engine can detect the visitor's joy and excitement and select and play upbeat music. As a result, the visitor's willingness to purchase can be increased.

[0560] An example of a prompt when using a generative AI model is an instruction such as, "Select background music that emphasizes items X and Y when the customer is smiling."

[0561] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0562] Step 1:

[0563] The server receives data on visitors' facial expressions and movements from information devices. This data is collected from cameras and sensors. Next, the server uses the Emotion SDK to perform facial recognition and analyze the visitor's psychological state in real time. This analysis provides an output that estimates the visitor's emotional state (e.g., joy, interest, concentration).

[0564] Step 2:

[0565] The server uses the emotional state obtained in Step 1 as input data to generate an acoustic simulation using a generative AI model. This model selects acoustic elements according to the user's psychological state and outputs the optimal combination of music and sound effects. Specifically, it utilizes music libraries such as the Spotify API to select songs that are appropriate for the visitor's psychological state.

[0566] Step 3:

[0567] The server transmits the acoustic simulation to the information device using a communication method. The information device then transfers the received acoustic data to a playback device (speaker, etc.) to provide an acoustic environment in the actual space. At this time, the music is set to play at the specified volume and tone.

[0568] Step 4:

[0569] As users (visitors) experience the acoustic environment, information devices use sensors to monitor their reactions and acquire operational information. The Emotion SDK is used again for emotional data analysis. The feedback collected along with the emotional data is sent to the server.

[0570] Step 5:

[0571] The server analyzes feedback data through feedback mechanisms and updates the generated AI model using learning mechanisms. This update optimizes the model based on previously collected feedback, improving the accuracy of subsequent acoustic simulations. This continuous improvement process makes it possible to provide visitors with a better acoustic experience.

[0572] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0573] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0574] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0578] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0579] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0580] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0581] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0582] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0583] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0584] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0585] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0586] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0587] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0588] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0589] The present invention's system supports dementia prevention by providing individually customized music games to each user through the coordinated operation of a terminal and a server. This system mainly includes a terminal, a server, a generation module, a communication module, a feedback module, and a learning module.

[0590] First, the user uses their device to input information about their preferred icons (e.g., favorite idols or characters) and their favorite music genres. The device then sends this user information to the server.

[0591] Based on the received user information, the server uses a generation module to create individual music games. This generation process utilizes AI technology to design games that combine game elements (e.g., visual and auditory elements) according to the user's preferences.

[0592] The generated music game is transmitted to the terminal via a communication module, allowing the user to play the provided game. The game progresses interactively based on the user's actions, and scoring is performed in real time.

[0593] Once a user finishes playing, the device uses a feedback module to send operation data and user feedback back to the server. The server analyzes this data using a learning module and uses it to improve the generation module. This ensures that future game generation will provide more appropriate and effective content.

[0594] As a concrete example, consider a scenario where an elderly user participates. Suppose the user selects a specific singer as their "favorite" icon on their device and specifies a particular genre as their favorite song. The server then uses this information to generate a game that includes visuals representing the singer's image and requires the user to perform tap and swipe actions in time with the selected music's rhythm. In this way, users can engage in activities based on their interests, naturally leading to the maintenance and improvement of their cognitive function.

[0595] The following describes the processing flow.

[0596] Step 1:

[0597] The user activates the device and enters information about their favorite "idol" icon and preferred music genre. The device provides a user interface to accept input, and the submit button becomes active once the user has entered the data.

[0598] Step 2:

[0599] When the user clicks the submit button, the device converts the entered data into JSON format and sends it to the server. The data is encrypted and communicated via a secure protocol.

[0600] Step 3:

[0601] The server analyzes the received data and determines the specifications of the music game to be generated. Using the generation module, the server compiles the game scenario, music, and visual elements best suited to the user based on the input data.

[0602] Step 4:

[0603] The server uses a generation module to create individually optimized music games. At this stage, AI technology is used to generate games that include the specified icons and musical elements.

[0604] Step 5:

[0605] The server sends the generated game data to the terminal via a communication module. The data is compressed into a lightweight format suitable for real-time processing on the terminal.

[0606] Step 6:

[0607] The device presents the received game to the user and initiates gameplay. The user can progress through the game by following visual and auditory instructions for interactive operation.

[0608] Step 7:

[0609] After gameplay ends, the device collects user operation data and feedback, and sends it to the server using a feedback module.

[0610] Step 8:

[0611] The server analyzes the collected data using a learning module and incorporates the findings into the improvement process for future game generation. This ensures that future music games better meet user needs.

[0612] (Example 1)

[0613] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0614] While music games are expected to promote cognitive function, there are challenges in customizing content based on the specific preferences of individual users, and general content often fails to attract user interest. Furthermore, there is a need for mechanisms that effectively incorporate user feedback to improve the game experience.

[0615] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0616] In this invention, the server includes an information processing device means that performs analysis based on user data received from a terminal, a generation mechanism means that designs individual musical activities generated on the information processing device based on the user data, and a learning mechanism means that updates the generation mechanism based on the collected data. This makes it possible to provide a customized music game that suits the specific preferences of each individual user and to improve the game content based on interaction data from the user.

[0617] A "terminal" is a device used by users to input and transmit information related to specific preferences.

[0618] An "information processing device" is a computer system that analyzes data received from a terminal and performs subsequent processing based on that data.

[0619] A "generation mechanism" is a module used to design individual musical activities based on data obtained by an information processing device.

[0620] "Communication means" refers to a communication method that transmits generated musical activity to a terminal, enabling the user to control that musical activity.

[0621] A "feedback mechanism" is a method for collecting user interaction data and sending that data back to an information processing device.

[0622] A "learning mechanism" is a computational means used to improve the generation mechanism based on collected data.

[0623] The embodiments for carrying out the present invention will be described in detail below.

[0624] This invention provides a system in which terminals and servers work together to offer customized music games to individual users. This helps to improve and maintain cognitive function.

[0625] First, the user uses a terminal to input information about their preferred characters and music genres. This terminal has a user-friendly interface. The information entered by the user is sent from the terminal to the server.

[0626] The server analyzes the received data using an information processing device and designs personalized musical activities tailored to the user's preferences. This analysis uses a generative AI model, with the specific prompt being "Generate a music game that reflects the user's specific preferences."

[0627] The generated music game is transmitted to the device via a communication method. The user can play the game on this device, and the interactive experience stimulates cognitive function.

[0628] After the game ends, the device uses a feedback mechanism to recollect user interaction data and send it to the server. The server analyzes this data using a learning mechanism to improve the performance of the generation mechanism for future game offerings. This continuous feedback loop is expected to continuously improve the quality of the user experience.

[0629] As a concrete example, suppose a user selects classical music as their favorite genre and wants a rhythm game set to classical music. In this case, the generative AI model would design and provide game elements specifically tailored to classical music via prompt messages.

[0630] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0631] Step 1:

[0632] The user launches a music game app using their device and enters information about their preferred characters and music genres. This information is entered through the user interface in text and selection formats. This input forms the basis for generating a music game that reflects the user's specific preferences. The device then converts this input data into a digital format for preparation.

[0633] Step 2:

[0634] The terminal transmits the collected user input data to the server. A communication module is used during this process, ensuring data security during transmission. The transmitted data is then used as input for analysis processing on the server.

[0635] Step 3:

[0636] The server analyzes the received user data using an information processing device. First, it verifies the data content and converts it into a format suitable for the generation AI model. Next, it uses the generation AI model to design a customized music game based on the prompt "Generate a music game that reflects the user's specific preferences." The analyzed data is used to generate individual music activities, and as a result, game data is output.

[0637] Step 4:

[0638] The generated music game is sent from the server to the terminal. The data is delivered to the terminal quickly and securely via communication means. The received game data is converted into an executable format on the terminal and displayed to the user through the user interface.

[0639] Step 5:

[0640] The user plays a music game received on their device. Here, the device collects user input data in real time to provide an interactive experience. User input in the game (e.g., taps and swipes) is processed immediately within the system and reflected on the screen. This input information is stored on the device for use in the next step.

[0641] Step 6:

[0642] After the game ends, the device uses a feedback mechanism to send data and feedback about the user's gameplay to the server. This is done via a communication module, ensuring the accuracy and security of the data. This data is then used as analysis material for the server during the learning process.

[0643] Step 7:

[0644] The server analyzes the received interaction data using a learning mechanism. Based on previous experience, it identifies areas for improvement in the game and adjusts the parameters of the generative AI model. This improved data allows subsequent music games to more accurately meet user needs.

[0645] (Application Example 1)

[0646] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0647] The challenge lies in providing interactive experiences that effectively utilize the time spent traveling, a period often associated with boredom for the elderly and long-distance travelers, and that help maintain or improve cognitive function. In particular, it is essential to provide content tailored to individual hobbies and preferences to foster sustained interest and enjoyment.

[0648] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0649] In this invention, the server includes an information processing system means that performs analysis based on user information received from an information processing device, a generation means that creates individual musical activities based on the user information on the information processing system, and an automated mobile means equipped with a display device that provides activities to help the user maintain cognitive function during long-distance travel. This enables the provision of content based on the user's hobbies and preferences, and allows for meaningful use of time during travel.

[0650] An "information processing device" is a device that allows users to input information about their preferred icons and music genres.

[0651] An "information processing system" is a system that performs analysis based on user information received from an information processing device.

[0652] "Generation means" refers to a system that has the function of creating individual musical activities based on user information on an information processing system.

[0653] "Communication means" refers to a device that has the function of transmitting generated musical activity to an information processing device and making it available for user operation.

[0654] A "feedback mechanism" is a device that has the function of collecting user operation data and sending it back to the information processing system.

[0655] A "learning tool" is a tool that has the function of updating the generation tool based on the collected data.

[0656] An "autonomous mobile vehicle" is a means of transportation equipped with a display device that provides activities to help users maintain cognitive function during long-distance travel.

[0657] The system program that implements this application is designed to allow users to interactively enjoy their preferred musical activities. The system mainly includes an information processing device, an information processing system, generation means, communication means, feedback means, and learning means.

[0658] The server analyzes information received from the information processing device, including the user's icon and music genre, using its information processing system. This analysis result is then used by a generation mechanism to generate personalized musical experiences that combine visual and auditory elements according to the user's preferences. After generation, these musical experiences are transmitted to the information processing device via communication, making them immediately available for user interaction.

[0659] When users experience generated musical activities, the feedback system collects operation data in real time and sends it to the server. The server analyzes the collected data using a learning system and uses it to improve the capabilities of the generation system. This allows for more personalized content to be provided in future sessions.

[0660] For example, imagine an elderly person enjoying specific music while in an autonomous vehicle. In this scenario, the user selects their favorite music genre and associated icons, and the server uses this information to generate a suitable music game, providing the user with entertainment during long-distance travel.

[0661] An example of a prompt message would be, "Generate a scenario in which a user in their 60s experiences an interactive musical activity with their favorite music playing in the background while automatically traveling." In this way, users can utilize their travel time to maintain their cognitive function through fun and stimulating activities.

[0662] Thus, this invention can provide personalized activities that go beyond mere musical experiences, transforming long journeys into meaningful experiences. The technologies used include app development using Unity, server integration using AWS Lambda, and AI technology using TensorFlow.

[0663] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0664] Step 1:

[0665] The device receives information from the user, such as preferred icons and music genres. This input information is sent to the server. The input data includes the user's icon selection and music genre information, and serves as initial data for providing a personalized experience based on this information.

[0666] Step 2:

[0667] The server analyzes user information received from the terminal and generates individual musical activities using a generation mechanism. In this process, an AI model combines visual and acoustic elements based on the user's preferences. The input information consists of the user's icon and music genre, and the design of the generated musical activity is output based on this information.

[0668] Step 3:

[0669] The generated musical activity is transmitted to the terminal via communication, allowing the user to control the game within the automated mobile vehicle. The input data is the digital data of the generated musical activity, which is displayed in a user-operable format, enabling real-time interaction.

[0670] Step 4:

[0671] Users experience music activities on their devices and interact according to the given instructions. During this process, user interaction data is collected in real time and sent to the server via a feedback mechanism. The input is user interaction data, which is transmitted instantly, enabling real-time responses.

[0672] Step 5:

[0673] The server analyzes user interaction data collected by the server using a learning mechanism and provides feedback to improve the generation mechanism. This analysis adjusts the next musical activity based on the interaction data. The input is the interaction data, and the output is the improved musical activity design data. This allows for the provision of content that better meets the individual needs of each user.

[0674] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0675] The system of the present invention consists of a terminal, a server, a generation module, a communication module, a feedback module, a learning module, and an emotion engine. This enables not only the provision of individually optimized music games, but also dynamic game adjustments based on the user's emotions.

[0676] In the system's operation, the user first inputs their preferred icons and music genres using the user interface on their device. This information is then sent from the device to the server. The server utilizes a generation module to create a personalized music game for each user. During this generation process, the game's visual and auditory elements are optimized based on the user's input.

[0677] The emotion engine analyzes the user's facial expressions and movements in real time while they play the music game. This is done via cameras and sensors, acquiring data according to the user's emotional state. The device sends this emotional data to a server, which is used to dynamically adjust the game's difficulty and scenarios.

[0678] When a user exits a game, the device sends operational and emotional data to the server via a feedback module. The server uses a learning module to analyze this data and use it to adjust the generation module for future games. This allows for a more accurate game experience tailored to the user's emotions, further enhancing the overall user experience.

[0679] As a concrete example, suppose an elderly user selects their favorite classical music artist and enters it into the program. The emotion engine detects smiles, serious expressions, and other reactions during gameplay. The system analyzes this data and dynamically adjusts the game's progression, for example, by highlighting moments where positive feedback is received to increase smiles. This allows the user to maintain positive emotions while enjoying the game at their own pace.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The user activates the device and enters information about their preferred icons and music genres. The device provides a user interface for this purpose, and once the information is entered, it is ready to send the data.

[0683] Step 2:

[0684] The terminal sends user input data to the server. The data is encrypted in JSON format and sent to the server using a secure communication protocol.

[0685] Step 3:

[0686] The server analyzes the received data and provides information to the generation module to create a personalized music game based on the user's tastes and preferences. The generation module determines the game specifications and creates game data by combining music and visual elements.

[0687] Step 4:

[0688] The server sends the generated music game data to the terminal. The communication module is responsible for this, optimizing the data so that the game can run on the terminal.

[0689] Step 5:

[0690] The user starts a game sent to their device and plays it interactively. The device utilizes an emotion engine to monitor the user's facial expressions and movements using cameras and sensors, collecting emotion data in real time.

[0691] Step 6:

[0692] The device sends emotional data collected by the user during gameplay to the server. Based on this data, the server dynamically adjusts the game's difficulty and progression, providing feedback to optimize the user's experience.

[0693] Step 7:

[0694] When the game ends, the device sends operation data, including the user's play data, to the server via a feedback module. The server analyzes this data and uses a learning module to improve the algorithm for future game generation.

[0695] (Example 2)

[0696] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0697] Traditional music game systems lack individual optimization for each user and dynamic adjustments based on emotions, making it difficult to provide a personalized experience that reflects individual preferences and emotional states. Furthermore, the lack of means to reflect user emotions in game progression limits the quality of entertainment.

[0698] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0699] In this invention, the server includes an information processing device for analyzing information acquired from the user, a generation program for generating personalized musical activities based on the analysis results, and an emotion analysis device for acquiring the user's facial expressions and movements in real time and making individual adjustments. This makes it possible to generate a music game that is individually optimized according to the user's preferences and emotional state, and to dynamically adjust it in real time.

[0700] An "information terminal" is a device used by users to input or receive information, and primarily refers to a terminal device.

[0701] An "information processing device" is a computer system that analyzes received information and performs appropriate data processing.

[0702] A "generation program" is an algorithm and software for generating personalized music games on an information processing device.

[0703] "Communication means" refers to the technologies that enable communication, including protocols and physical infrastructure for sending and receiving information.

[0704] A "feedback mechanism" is a device for collecting data related to user operations and sending it back to an information processing device.

[0705] "Learning methods" refer to machine learning and data analysis techniques used to analyze collected data and improve generation programs.

[0706] "Emotional analysis means" refers to a system that includes sensors and analytical technologies to analyze the user's emotions in real time and adjust the game's progress accordingly.

[0707] The system of this invention is centered around a user, a terminal, and a server. In this system, the terminal is equipped with a user interface for receiving input information from the user. Through this interface, the user selects their preferred music genre and icons. This information is sent from the terminal to the server, which then analyzes it.

[0708] The server is equipped with an information processing device for information analysis and generates personalized music games based on user input. The generation program utilizes game engines such as Unity or Unreal Engine. This optimizes the game's visual and auditory elements to match the user's preferences.

[0709] While the user is playing the game, the device uses cameras and sensors to monitor the user's facial expressions and movements in real time. During this process, an emotion analysis system determines the user's emotional state and sends this data to a server. Based on this data, the server dynamically adjusts the game's difficulty and progression.

[0710] Once gameplay ends, the device sends operation and emotional data back to the server. The server analyzes this data through learning mechanisms and incorporates the findings into the next game generation. This learning process utilizes machine learning algorithms, and the generation program is improved based on the data analysis.

[0711] For example, if a user selects the classical music genre, the generation program designs a music game stage based on that genre. If the user's smile is detected through emotion analysis during gameplay, the game is adjusted to amplify this positive reaction. This allows the user to have a more fulfilling gaming experience.

[0712] An example of a prompt might be, "Analyze user emotion data and suggest strategies to optimize game progression." This prompt utilizes a generative AI model to provide ideas for personalizing the user experience.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] Users input their preferred music genres and icons using the device's user interface. This data is entered into the device by the user tapping and selecting options displayed on the screen. The entered data reflects the user's personal preferences.

[0716] Step 2:

[0717] The terminal sends the information entered by the user to the server. The input here is user preference information, and the output is a data packet containing this information. The terminal transfers the data to the server, and as a result, the server's information processing device confirms receipt.

[0718] Step 3:

[0719] The server generates personalized music games using a generation program based on the received information. The input is user preference information, and the output is music game data configured specifically for the user. The server utilizes a generation AI model to integrate visual and auditory elements and design game content tailored to the user.

[0720] Step 4:

[0721] The generated music game is sent back to the terminal via a communication method. The server sends the generated game data as output, and the terminal receives and displays that data. The user can start the music game at this point.

[0722] Step 5:

[0723] While the user is playing the game, the device's camera and sensors monitor the user's facial expressions and movements in real time. This information is used as input, and emotional state data is generated as output. The device analyzes this data using an emotion analysis system and sends the emotional data to a server.

[0724] Step 6:

[0725] The server adjusts the game's difficulty and progression in real time based on the emotional data it receives. The input is emotional data, and the output is data showing the adjusted game progression. The server modifies the game experience according to the user's emotions, promoting more positive feedback.

[0726] Step 7:

[0727] When a user finishes a game, the device sends final operation and emotion data back to the server through a feedback mechanism. The input consists of all data from the gameplay, and the output is stored on the server as a training dataset. Based on this data, the server's training mechanism analyzes the data for the next game generation and uses it to improve the program.

[0728] (Application Example 2)

[0729] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0730] In commercial facilities and public spaces, there is a growing need to provide an optimal sound environment tailored to the psychological state of visitors. However, conventional sound systems simply play fixed music playlists and do not respond to the emotions or circumstances of visitors. As a result, there have been limitations in improving customer satisfaction and stimulating purchasing intent.

[0731] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0732] In this invention, the server includes an information processing device means that performs analysis based on visitor information received from an information device, a generation means that creates an acoustic simulation based on the visitor information on the information processing device, and an emotion analysis engine means that estimates the visitor's psychological state and dynamically adjusts the acoustic elements. This makes it possible to dynamically provide an optimal acoustic environment according to the visitor's psychological state.

[0733] "Information equipment" refers to electronic devices used by users to input or manipulate information.

[0734] An "information processing device" is a computer system used to analyze and process data transmitted from information devices.

[0735] "Generation means" refers to a function in an information processing device that creates individual acoustic simulations based on user data.

[0736] "Communication means" refers to a means of transmitting the generated acoustic simulation to an information device, thereby enabling interaction with the user.

[0737] A "feedback mechanism" is a device for collecting user operation information and transmitting it back to the information processing device.

[0738] A "learning mechanism" is a system that updates the generation mechanism and improves its accuracy based on information collected by the feedback mechanism.

[0739] The "emotion analysis engine" is analysis software that estimates the psychological state of visitors and dynamically adjusts acoustic elements based on that estimation.

[0740] The system for carrying out this invention is an acoustic system that dynamically adjusts the acoustic environment based on the emotions of visitors. This system includes an information device, an information processing device, a generation means, a communication means, a feedback means, a learning means, and an emotion analysis engine.

[0741] The server receives visitor facial expressions and movements from information devices and performs real-time analysis using an information processing device. The analysis utilizes cameras and sensors, and employs facial recognition libraries such as the Emotion SDK. This allows for the estimation of the visitor's psychological state.

[0742] The information processing device generates an acoustic simulation based on visitor information. This acoustic simulation dynamically selects music using the Spotify API and other means, providing acoustic elements tailored to the visitor's psychological state.

[0743] Using communication methods, the generated acoustic simulation is transmitted to an information device and played back in the physical space where the visitor is located. The impact of the played-back acoustic environment on the visitor is collected as operational information through a feedback mechanism.

[0744] The server adjusts its generation methods through learning mechanisms based on feedback, improving the accuracy of subsequent acoustic simulations. This process enables the continuous provision of a dynamic and optimal acoustic environment.

[0745] For example, if a visitor is looking at products with interest in a store, the emotion analysis engine can detect the visitor's joy and excitement and select and play upbeat music. As a result, the visitor's willingness to purchase can be increased.

[0746] An example of a prompt when using a generative AI model is an instruction such as, "Select background music that emphasizes items X and Y when the customer is smiling."

[0747] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0748] Step 1:

[0749] The server receives data on visitors' facial expressions and movements from information devices. This data is collected from cameras and sensors. Next, the server uses the Emotion SDK to perform facial recognition and analyze the visitor's psychological state in real time. This analysis provides an output that estimates the visitor's emotional state (e.g., joy, interest, concentration).

[0750] Step 2:

[0751] The server uses the emotional state obtained in Step 1 as input data to generate an acoustic simulation using a generative AI model. This model selects acoustic elements according to the user's psychological state and outputs the optimal combination of music and sound effects. Specifically, it utilizes music libraries such as the Spotify API to select songs that are appropriate for the visitor's psychological state.

[0752] Step 3:

[0753] The server transmits the acoustic simulation to the information device using a communication method. The information device then transfers the received acoustic data to a playback device (speaker, etc.) to provide an acoustic environment in the actual space. At this time, the music is set to play at the specified volume and tone.

[0754] Step 4:

[0755] As users (visitors) experience the acoustic environment, information devices use sensors to monitor their reactions and acquire operational information. The Emotion SDK is used again for emotional data analysis. The feedback collected along with the emotional data is sent to the server.

[0756] Step 5:

[0757] The server analyzes feedback data through feedback mechanisms and updates the generated AI model using learning mechanisms. This update optimizes the model based on previously collected feedback, improving the accuracy of subsequent acoustic simulations. This continuous improvement process makes it possible to provide visitors with a better acoustic experience.

[0758] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0759] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0760] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0761] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0762] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0763] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0764] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0765] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0766] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0767] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0768] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0769] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0770] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0771] 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.

[0772] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0773] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0774] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0775] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0776] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0777] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0778] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0779] The following is further disclosed regarding the embodiments described above.

[0780] (Claim 1)

[0781] A terminal device for inputting information about the user's preferred icons,

[0782] A server means that performs analysis based on user information received from the terminal,

[0783] A generation module means for creating individual music games that are generated on a server based on user information,

[0784] A communication module means for sending the generated music game to a terminal and making it playable by the user,

[0785] A feedback module means that collects user operation data and sends it back to the server,

[0786] A learning module means for updating the generation module based on collected data,

[0787] A system that includes this.

[0788] (Claim 2)

[0789] The system according to claim 1, wherein the terminal has a user interface for inputting the user's preferred icons and music genres.

[0790] (Claim 3)

[0791] The system according to claim 1, wherein the generation module has the function of combining visual and acoustic elements for the user's individual music game.

[0792] "Example 1"

[0793] (Claim 1)

[0794] A terminal device for inputting information related to the user's specific preferences,

[0795] An information processing device means that performs analysis based on user data received from a terminal,

[0796] A generation mechanism means for designing individual musical activities generated on an information processing device based on user data,

[0797] A communication means for transmitting the generated music activity to a terminal and making it operable by the user,

[0798] A feedback mechanism means that collects user interaction data and transmits it back to the information processing device,

[0799] A learning mechanism means for updating the generation mechanism based on collected data,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the terminal is equipped with a user input device for inputting the user's specific preferences and musical style.

[0803] (Claim 3)

[0804] The generation mechanism has the function of combining visual and acoustic expressions for the user's individual musical activities, according to claim 1.

[0805] "Application Example 1"

[0806] (Claim 1)

[0807] Information processing device means for inputting information about the user's preferred icons,

[0808] An information processing system means that performs analysis based on user information received from an information processing device,

[0809] A generation means for creating individual musical activities based on user information on an information processing system,

[0810] A communication means for transmitting the generated musical activity to an information processing device and making it operable by the user,

[0811] A feedback mechanism that collects user operation data and transmits it back to the information processing system,

[0812] A learning means for updating the generation means based on the collected data,

[0813] An automated mobile means equipped with a display device that provides activities to promote the maintenance of cognitive function in the user during long-distance travel,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The information processing device is equipped with a user operation unit for inputting the user's preferred icons and music genres, according to claim 1.

[0817] (Claim 3)

[0818] The system according to claim 1, wherein the generation means has the function of combining visual and acoustic components for the user's individual musical activity.

[0819] "Example 2 of combining an emotion engine"

[0820] (Claim 1)

[0821] Information terminal means for receiving information based on user preferences,

[0822] Information processing device means for analyzing information obtained from users,

[0823] A generation program means that generates individualized musical activities based on the analysis results,

[0824] A communication means for transmitting generated musical activity to an information terminal and preparing it for user operation,

[0825] A feedback means for collecting data on user operations and transmitting it to an information processing device,

[0826] A learning method for improving the generation program using collected data,

[0827] An emotional analysis system that acquires the user's facial expressions and movements in real time and makes individual adjustments,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the information terminal is equipped with a user interface for inputting user preference information.

[0831] (Claim 3)

[0832] The system according to claim 1, wherein the generation program has the function of incorporating visual and auditory elements into the user's individual musical activities.

[0833] "Application example 2 when combining with an emotional engine"

[0834] (Claim 1)

[0835] Information device means for inputting information regarding the representation of user preferences,

[0836] An information processing device means that performs analysis based on user information received from an information device,

[0837] A generation means for creating individual music simulations based on user information on an information processing device,

[0838] A communication means for transmitting the generated music simulation to an information device and making it operable by the user,

[0839] A feedback means that collects user operation information and transmits it back to the information processing device,

[0840] A learning means for updating the generation means based on the collected information,

[0841] An emotion analysis engine means for estimating the psychological state of visitors and dynamically adjusting acoustic elements,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The information device comprises an input device for inputting the user's preferred representations and sound ranges, according to claim 1.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the generation means has the function of combining visual and acoustic elements for the user's individual music simulation, and also has the function of providing an acoustic environment that corresponds to the visitor's psychological state. [Explanation of Symbols]

[0847] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal device for inputting information about the user's preferred icons, A server means that performs analysis based on user information received from the terminal, A generation module means for creating individual music games that are generated on a server based on user information, A communication module means for sending the generated music game to a terminal and making it playable by the user, A feedback module means that collects user operation data and sends it back to the server, A learning module means for updating the generation module based on collected data, A system that includes this.

2. The system according to claim 1, wherein the terminal has a user interface for inputting the user's preferred icons and music genres.

3. The system according to claim 1, wherein the generation module has the function of combining visual and acoustic elements for the user's individual music game.

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