System

Abstract

A system is provided.SOLUTION: A system includes a means for collecting game play data of a user in real time, a means for transmitting the collected data to a server, a means including an AI model for comparing and analyzing the transmitted data with play data of a professional player, a means for generating feedback to the user based on a result of the comparison and analysis, a means for transmitting the generated feedback to a terminal of the user, and a means for providing the feedback displayed on the terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, the popularity of online games and e-sports has skyrocketed, and the number of players seeking higher skills is also increasing. However, currently, there are limited ways for players to efficiently improve their skills. Many players learn through instructional videos and streaming, but these methods do not provide specific advice tailored to individual players. This situation has led to the issue of players not being able to sufficiently improve their skills. [Means for solving the problem]

[0005] This invention provides a means for collecting a user's gameplay data in real time and transmitting that data to a server. The transmitted data is input into an AI model for comparative analysis with the play data of professional gamers. The AI ​​model generates feedback for the user based on the results of the comparison and analysis and transmits the generated feedback to the user's device. Finally, by providing feedback displayed on the user's device, the player can easily grasp specific areas for improvement and practice methods, thereby achieving efficient skill improvement. This system allows players to receive individualized training measures, enabling more specific and effective training.

[0006] "User" refers to an individual who uses the Service to play games and receive feedback.

[0007] "Gameplay data" refers to detailed data captured in real time about a user's in-game movements, reaction times, scores, behavior patterns, characters and weapons used, and the like.

[0008] "Terminal" refers to a device such as a computer, game console, or smartphone that a user uses to play a game.

[0009] "Server" refers to a centralized computing system that receives, stores, and analyzes user gameplay data through AI models.

[0010] An "AI model" refers to an algorithm or machine learning model that learns based on the play data of professional gamers, analyzes the play data of users, and generates feedback.

[0011] "Feedback" refers to information about specific areas for improvement and practice methods that are provided based on the results of analyzing the user's gameplay data.

[0012] A "professional gamer" is a game player who has high skills and strategies in a particular game and works professionally.

[0013] "Comparative analysis" refers to the process of comparing a user's gameplay data with that of a professional gamer and evaluating differences in play style, reaction time, shooting accuracy, etc. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] MODE FOR CARRYING OUT THE INVENTION

[0036] This invention is a system that efficiently collects and analyzes user gameplay data and provides feedback. This system is mainly composed of the user's device, a server, and an AI model.

[0037] 1. Collection of Gameplay Data

[0038] The device collects gameplay data in real time while the user is playing the game. This data includes all actions performed by the user (movement, attacks, abilities used, etc.), specifically character position information, aiming movement patterns, button press timing, types of items and weapons used, etc.

[0039] example:

[0040] When a user is playing an online FPS game, the device records detailed data such as from which position the user's character shot the enemy and whether the shot was successful.

[0041] 2. Data transmission

[0042] The devices transmit the collected data to the server in real time, using a secure communication protocol (e.g., HTTPS) to ensure data integrity and confidentiality.

[0043] 3. Data storage and analysis

[0044] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[0045] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. Based on specific parameters (reaction time, shooting accuracy, movement patterns, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[0046] example:

[0047] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0048] 4. Generate feedback

[0049] The server generates feedback for the user based on the results of the AI ​​model's analysis, including specific areas for improvement, recommended practice methods, and helpful video links.

[0050] example:

[0051] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend you play a specific reaction training mini-game every day."

[0052] 5. Sending and Viewing Feedback

[0053] The server transmits the generated feedback to the user's terminal.

[0054] The device displays the received feedback to the user, and through a user-friendly interface, the user can easily understand specific areas for improvement and practice methods.

[0055] example:

[0056] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published in the link below every day."

[0057] In this way, the present invention provides a concrete means for users to efficiently improve their gaming skills. Users can receive individual feedback based on their own play data in real time, allowing them to continuously and effectively improve their skills.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The user plays a game. The user launches an online FPS game or other competitive game and begins playing as normal.

[0061] Step 2:

[0062] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted with metadata (user ID, game session ID, date and time, etc.).

[0063] Step 3:

[0064] The device compresses the collected data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[0065] Step 4:

[0066] The server receives the data sent from the device and stores it in a database, which structures each user's play history and stores it together with other data.

[0067] Step 5:

[0068] The server (AI model) retrieves the user's play data stored in the database and compares it with data from professional gamers. The AI ​​model learns using past professional data and compares it with the user's play data to detect different movements.

[0069] Step 6:

[0070] When the AI ​​model analyzes the play data of users and professional gamers, it evaluates certain parameters (e.g., reaction time, aiming accuracy, movement patterns, and success rate of shots). As a result of the evaluation, it detects delays or inaccuracies in certain user actions.

[0071] Step 7:

[0072] The server generates useful feedback for users based on the results of the comparative analysis of the AI ​​models, including areas for improvement, recommended practice, and specific action plans.

[0073] Step 8:

[0074] The server transmits the generated feedback to the user's device, ensuring the accuracy and timeliness of the feedback during transmission.

[0075] Step 9:

[0076] The device displays feedback to the user, which is presented in a user-friendly interface designed to allow the user to easily understand and act on the content.

[0077] Step 10:

[0078] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. The user then performs specific practice exercises and changes to the settings based on the feedback.

[0079] By repeating these steps, users can effectively improve their gameplay, promoting continuous growth.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Conventional gameplay data analysis systems have difficulty collecting detailed gameplay data from users, comparing it with data from professional gamers, and providing effective and specific feedback to users. Ensuring real-time performance, data integrity, and security are also issues. Therefore, a means has been sought that allows users to quickly and accurately identify their weaknesses and effectively improve their skills.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes means for collecting user gameplay data in real time, means for transmitting the collected data to the server, means for storing the transmitted data in a database and appropriately formatting it for analysis, means including an AI model for comparing and analyzing the formatted data with play data of professional gamers, means for generating feedback to the user based on the results of the comparison and analysis obtained by the AI ​​model, means for transmitting the generated feedback to the user's device, and means for displaying the transmitted feedback to the user. This makes it possible to provide specific and practical feedback by collecting detailed user play data in real time and comparing it with data from professional gamers.

[0085] "User gameplay data" refers to data that includes detailed information such as location information, button press timing, aiming movement patterns, and types of items and weapons used, which are generated when a user plays a game.

[0086] "Real-time collection means" refers to a method or device for acquiring a user's gameplay data in real time while they are playing, and includes the use of high-precision sensors and software modules.

[0087] "Means for sending to a server" refers to a method or device for sending collected data to a server using a secure communication protocol (e.g., HTTPS).

[0088] "Means for storing in a database and appropriate formatting" means a method or device for storing the data sent to the server in a database and formatting it for input to an AI model, including removing noise from the data and standardizing timestamps.

[0089] An "AI model for comparative analysis of professional gamer play data" is an artificial intelligence model trained based on the play data of professional gamers, and is a means for conducting comparative analysis of user play data and professional gamer data.

[0090] "Means for generating feedback" means a method or device for automatically generating feedback based on the analysis results obtained from the AI ​​model, including areas for improvement in the user's play and recommended practice methods.

[0091] The "means for transmitting feedback to the user's terminal" refers to a method or device for transmitting the generated feedback to the user's terminal, and it is recommended that a secure communication protocol be used.

[0092] "Means for displaying feedback" refers to a method or device for providing the feedback received at the user's terminal to the user in the form of visual, audio, etc. This includes a user-friendly interface.

[0093] MODE FOR CARRYING OUT THE INVENTION

[0094] This invention is a system that collects gameplay data in real time when a user plays an online game, analyzes the data, and provides specific feedback. The system mainly consists of a user's device, a server, and an AI model. Each step and the hardware and software used are described in detail below.

[0095] 1. Collection of Gameplay Data

[0096] The device collects real-time gameplay data as the user plays the game. This data includes the character's position, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, high-precision data acquisition is achieved by using dedicated sensor modules and software (e.g., the logging function of Unity or Unreal Engine).

[0097] example:

[0098] When a user plays an online FPS game, the device records detailed data such as where the character shot the enemy from and whether the shot was successful. This data includes positional data and button input events for each frame of play.

[0099] 2. Data transmission

[0100] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS to ensure data integrity and confidentiality, along with metadata such as user ID and session ID.

[0101] 3. Data storage and preprocessing

[0102] The server receives the data sent from the device and first stores it in a database. This database uses SQL or NoSQL to store data quickly and reliably. The stored data includes the user ID, session ID, date and time, and various play data. The stored data is then formatted and converted into a format suitable for input into the AI ​​model. This process includes removing noise from the data and standardizing timestamps.

[0103] example:

[0104] The server analyzes the received data and inserts it into a database using, for example, a user ID or session ID as a key. In the formatting stage, the data is de-noised and any necessary transformations (e.g., standardization of timestamps) are performed.

[0105] 4. Data Analysis

[0106] The server inputs the formatted data into a generative AI model, which has been pre-trained based on the play data of professional gamers. The AI ​​model compares the user's play data with the professional gamers' data and performs analysis based on specific evaluation metrics (e.g., reaction time, shooting accuracy, movement patterns, etc.). The analysis results identify which aspects of the user's play differ from the professional gamers'.

[0107] example:

[0108] The AI ​​model analyzes the received gameplay data and identifies that the user's reaction time is 0.3 seconds slower than that of a professional gamer. This process uses deep learning frameworks (e.g., TensorFlow, PyTorch).

[0109] 5. Generate feedback

[0110] The server generates feedback for the user based on the analysis results obtained from the AI ​​model. This feedback includes specific areas for improvement, recommended practice methods, reference video links, etc. The generated feedback is output in a format that is easy for the user to understand.

[0111] example:

[0112] Feedback is generated in the form of, "Your reaction time is delayed by an average of 0.3 seconds. To improve this time lag, we recommend that you play a specific reaction training mini-game every day." The reference video link includes a tutorial video by a professional gamer.

[0113] 6. Sending and Viewing Feedback

[0114] The server sends the generated feedback to the user's device, which then displays it to the user, allowing them to intuitively understand specific areas for improvement and practice methods. The feedback is displayed on the device through a pop-up notification or a dedicated app interface.

[0115] example:

[0116] A message such as "Your reaction time is 0.3 seconds slower. You can improve your reaction speed by playing the mini-game linked below every day" will be displayed, and clicking the link will launch the corresponding mini-game.

[0117] Specific examples of input prompts for generative AI models

[0118] "Analyze this person's gameplay data to see how they compare to professional gamers and identify areas for improvement. Pay particular attention to reaction time, shooting accuracy, and movement patterns."

[0119] The system of the present invention allows users to receive personalized feedback based on their own play data in real time, enabling them to continuously improve their skills. An efficient feedback system can be provided by integrating the server, device, and AI model. By implementing this system, users can quickly and accurately identify their weaknesses and improve their skills in specific ways.

[0120] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0121] Specific processing flow of the program

[0122] Step 1: Collecting gameplay data

[0123] The device collects gameplay data in real time as the user plays the game. This data includes character position information, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, the device uses dedicated sensor modules and software to collect this data with high precision.

[0124] Input: User actions during gameplay, location information, button press timing, etc.

[0125] Output: Collected gameplay data

[0126] Step 2: Sending data

[0127] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS, along with metadata such as user ID and session ID.

[0128] Input: Collected gameplay data, user ID, session ID

[0129] Output: Data in the format sent to the server

[0130] Step 3: Storing and Preprocessing Data

[0131] The server receives the data sent from the device and stores it in a database. The stored data includes the user ID, session ID, date and time, and various play data. The data is then appropriately formatted for input into the AI ​​model. This formatting includes removing noise from the data and standardizing timestamps.

[0132] Input: Submitted gameplay data, user ID, session ID

[0133] Output: Data in a formatted form

[0134] Specific behavior:

[0135] 1. The server receives the collected data.

[0136] 2. Save it to the database.

[0137] 3. Format the stored data to input into the AI ​​model.

[0138] Step 4: Analyze the data

[0139] The server inputs the formatted data into a generative AI model. The AI ​​model is pre-trained based on the play data of professional gamers and compares the user's play data with the professional gamer's data. It performs analysis based on specific evaluation indicators (e.g., reaction time, shooting accuracy, movement patterns, etc.).

[0140] Input: Data in a formatted form

[0141] Output: Analysis results

[0142] Specific behavior:

[0143] 1. The server inputs the formatted data into the AI ​​model.

[0144] 2. The AI ​​model analyzes the data and identifies the differences between users and professional gamers.

[0145] Step 5: Generate feedback

[0146] The server generates feedback for the user based on the analysis results obtained from the AI ​​model, including specific areas for improvement, recommended practice methods, and helpful video links.

[0147] Input: Analysis results

[0148] Output: Generated feedback

[0149] Specific behavior:

[0150] 1. The server receives the analysis results.

[0151] 2. Automatically generate feedback based on the analysis results.

[0152] 3. Feedback will include areas for improvement, practice methods, and helpful video links.

[0153] Step 6: Submit and view feedback

[0154] The server sends the generated feedback to the user's device, which then displays the received feedback to the user. The device provides a user-friendly interface, allowing the user to intuitively understand specific points for improvement and practice methods.

[0155] Input: Generated feedback

[0156] Output: Feedback that is displayed to the user

[0157] Specific behavior:

[0158] 1. The server sends the generated feedback to the device.

[0159] 2. The device receives and displays the feedback.

[0160] 3. Users review the feedback and understand areas for improvement and how to practice.

[0161] In this way, the system of the present invention efficiently supports users in improving their gaming skills.

[0162] (Application example 1)

[0163] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0164] Currently, robotic work in factories is often not sufficiently focused on efficiency and precision. This creates a need for work optimization and efficiency, but on-site data collection and analysis is not easy. Furthermore, there are limited means of comparing results with professional work, creating a lack of concrete feedback for improving robotic work efficiency and technology.

[0165] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0166] In this invention, the server includes means for collecting user work data in real time, means for transmitting the collected data to the server, means including a generative AI model for comparing and analyzing the transmitted data with professional work data, means for generating feedback to the user based on the results of the comparison and analysis, means for transmitting the generated feedback to the user's terminal, and means for providing feedback displayed on the user's terminal, thereby enabling improvement of the work efficiency of factory robots and provision of specific feedback.

[0167] "User work data" refers to data about all actions performed by robots in a factory, including moving, lifting, placing, etc.

[0168] A "server" is a computing device that has the ability to receive, store, analyze, and transmit data over the Internet or a local network.

[0169] A "generative AI model" is an artificial intelligence model trained based on professional work data, and is used to compare and analyze user work data.

[0170] "Professional work data" refers to the movement data of experts who perform work efficiently and with high precision.

[0171] "Feedback" refers to advice and suggestions for improvement provided to users based on the analysis results.

[0172] A "user terminal" is an information processing device that displays feedback and can be operated by the user, and includes a smartphone, tablet, etc.

[0173] This invention aims to improve the work efficiency of factory robots by building a system that collects user work data in real time, analyzes it using a generative AI model, and provides appropriate feedback.

[0174] System Program

[0175] 1. Collecting user work data

[0176] This system collects all actions (moving, lifting, placing, etc.) performed by factory robots in real time. Specifically, motion and position sensors are attached to the robots to collect the data. The collected data records the robot's actions in detail and serves as the basis for future analysis.

[0177] 2. Data transmission

[0178] The collected data is sent to the server using a secure communication protocol (e.g. HTTPS), which ensures the integrity and confidentiality of the data.

[0179] 3. Data storage and analysis

[0180] The server receives the submitted data and automatically stores it in a database, including metadata such as the robot ID, session ID, date and time, etc. The server then formats the data appropriately for input into the generative AI model.

[0181] The generative AI model is pre-trained based on the work data of professionals and performs comparative analysis with the user's work data. Based on specific parameters (processing time, movement patterns, work accuracy, etc.), the AI ​​model detects which aspects of the user's work differ from those of professionals.

[0182] 4. Generate feedback

[0183] The server generates feedback for users based on the analysis results of the generative AI model, including specific points for improvement, recommended work methods, and reference video links. Users can quickly implement appropriate improvement measures to improve their work efficiency.

[0184] 5. Sending and Viewing Feedback

[0185] The server sends the generated feedback to the user's device, and the device displays the received feedback to the user. Through a user-friendly interface, the user can easily understand specific improvements and work methods.

[0186] Examples and prompts

[0187] For example, if a robot used in a factory deviates from the optimal movement pattern when lifting an object, the generative AI model will determine that "movement efficiency could be improved" and provide feedback on the appropriate way to lift the object.

[0188] Example prompt sentence:

[0189] "Based on the operation data of factory robots, identify the difference between the current operation performance and the optimal operation pattern and provide feedback on areas for improvement."

[0190] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0191] Step 1:

[0192] Collecting user work data

[0193] The terminal collects data in real time from motion and position sensors attached to the factory robots. This data includes information on the robot's movements, lifting, and placement. The terminal's input is real-time data from the sensors, and its output is the collected motion data.

[0194] Step 2:

[0195] Sending data

[0196] The terminal sends the collected data to the server using the HTTPS communication protocol. Using this communication protocol ensures the integrity and confidentiality of the data. The input is the operational data collected by the terminal, and the output is the data sent to the server.

[0197] Step 3:

[0198] Data storage

[0199] The server receives the transmitted data and automatically stores it in a database. The stored data also includes metadata such as the robot ID, session ID, date and time. The input is the data transmitted to the server, and the output is the data stored in the database.

[0200] Step 4:

[0201] Data formatting and analysis

[0202] The server formats the stored data appropriately for input into the generative AI model. The formatted data is passed to the generative AI model and compared with the professional's work data for analysis. The input is the data stored in the database, and the output is the formatted data input into the generative AI model.

[0203] Step 5:

[0204] Generate feedback

[0205] The server generates feedback for the user based on the analysis results of the generative AI model, including specific areas for improvement, recommended work methods, reference video links, etc. The input is the analysis results of the generative AI model, and the output is the generated feedback.

[0206] Step 6:

[0207] Send Feedback

[0208] The server sends the generated feedback to the user's terminal, where the input is the generated feedback and the output is the feedback sent to the user's terminal.

[0209] Step 7:

[0210] View Feedback

[0211] The terminal displays the received feedback to the user, allowing the user to easily understand specific improvements and work methods based on the feedback. The input is the feedback sent from the server, and the output is the feedback displayed to the user.

[0212] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0213] MODE FOR CARRYING OUT THE INVENTION

[0214] The present invention is a system that includes an emotion engine that collects, analyzes, and provides feedback on a user's gameplay data, as well as recognizes the user's emotional state and adjusts the feedback accordingly. The system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[0215] 1. Collection of Gameplay Data

[0216] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[0217] 2. Collecting Emotional Data

[0218] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[0219] example:

[0220] If the user is nervous, changes in facial expression, an increased heart rate, and a change in tone of voice are detected.

[0221] 3. Data transmission

[0222] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[0223] 4. Data storage and analysis

[0224] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[0225] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. The AI ​​model detects which aspects of the user's play differ from those of professional gamers based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.).

[0226] example:

[0227] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0228] 5. Emotion Data Analysis

[0229] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[0230] example:

[0231] The analysis results show that users are tense while playing.

[0232] 6. Generate feedback

[0233] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[0234] example:

[0235] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game daily. We also recommend that you incorporate relaxation techniques into your play, as you are feeling tense."

[0236] 7. Sending and Viewing Feedback

[0237] The server transmits the generated feedback to the user's terminal.

[0238] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the content.

[0239] example:

[0240] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published at the link below every day. Also, it has been analyzed that you are feeling tense. Please try a 5-minute relaxation exercise before playing the game."

[0241] 8. User Practices

[0242] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. Based on the feedback, the user makes specific practice exercises and changes settings, and also manages their emotional state.

[0243] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[0244] The processing flow will be explained below.

[0245] Step 1:

[0246] The user starts a competitive game, such as an online FPS game, and begins playing as usual.

[0247] Step 2:

[0248] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The data is captured every second and converted into a format (e.g., JSON).

[0249] Step 3:

[0250] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. Emotional data is also processed in the same way.

[0251] Step 4:

[0252] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the accuracy and safety of data transmission.

[0253] Step 5:

[0254] The server receives the gameplay data and emotion data sent from the device and stores them in a database, including metadata such as the user ID, session ID, and date and time.

[0255] Step 6:

[0256] The server (AI model) retrieves the user's play data stored in the database and compares and analyzes it with the data of professional gamers. The AI ​​model learns using past professional data and evaluates it based on specific parameters (reaction time, aim position, movement pattern, success rate of gunfights).

[0257] example:

[0258] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0259] Step 7:

[0260] The emotion engine analyzes the user's current emotional state (e.g., excited, nervous, relaxed) based on collected emotional data. The algorithm evaluates the user's emotional state based on facial expressions, heart rate, tone of voice, etc.

[0261] example:

[0262] The emotion engine detects when the user is in a tense state.

[0263] Step 8:

[0264] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[0265] example:

[0266] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game every day. Also, your emotional data suggests that you are feeling tense, so we recommend that you also incorporate some simple exercises to help you relax."

[0267] Step 9:

[0268] The server sends the generated feedback to the user's device, ensuring the accuracy and timeliness of the information.

[0269] Step 10:

[0270] The device displays the received feedback to the user through a user-friendly interface designed to help users easily understand and act on the content.

[0271] example:

[0272] "Spend 10 minutes each day playing the reaction speed mini-game linked below. Also, try the relaxation exercises below to help relieve tension."

[0273] Step 11:

[0274] The user checks the provided feedback and plays the game again, taking into consideration the areas for improvement and recommended practice methods. Based on the feedback, the user implements specific practice and setting changes, and also manages their emotional state.

[0275] By repeating these steps, users can effectively improve their play, promoting continuous growth and management of their emotional state.

[0276] Example 2

[0277] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0278] In modern gaming environments, the importance of player skill improvement and emotional management is increasing, but current systems lack comprehensive support for these. Therefore, a comprehensive system is needed to help players efficiently improve their skills and simultaneously manage their emotional state during gameplay. This system must also be capable of collecting user data in real time and providing appropriate feedback.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0280] In this invention, the server includes: means for collecting gameplay data of users in real time; means for collecting facial expressions, voice, and biometric information to recognize the user's emotional state; means for transmitting the collected gameplay data and emotional data to the server; means for storing and shaping the transmitted data in a database; means including an AI model for comparing and analyzing the shaped data with play data of professional gamers; means including an emotion engine for analyzing the emotional data and identifying the user's emotional state; means for generating feedback to the user based on the analysis results of the AI ​​model and the emotion engine; means for transmitting the generated feedback to the user's device; and means for providing feedback displayed on the user's device. This allows the user to receive appropriate feedback in real time according to their skill level and emotional state.

[0281] "User" refers to an individual who uses the system to play games.

[0282] "Gameplay data" refers to data that includes information such as movement trajectory, aim position, firing timing, characters used, weapons used, and enemy detection time that is generated in the course of a user playing a game.

[0283] "Emotional state" refers to the user's psychological and physiological state while playing a game, and specifically includes emotions such as stress, tension, and joy.

[0284] "Terminal" refers to a device that allows a user to play a game and has the function of collecting game play data and emotional data and transmitting them to a server.

[0285] A "server" is a computer system that receives and stores data sent from a device, and then analyzes it using an AI model and emotion engine.

[0286] A "database" is a system for systematically storing and managing data stored on a server.

[0287] An "AI model" is an artificial intelligence model that is trained based on the play data of professional gamers and used to compare and analyze user gameplay data.

[0288] An "emotion engine" is software with analytical functions that analyzes a user's emotional data and identifies their current emotional state.

[0289] "Feedback" is information that includes specific improvements and advice for users, generated based on the analysis results of the AI ​​model and emotion engine.

[0290] "Real-time" refers to gameplay and data collection occurring instantly, without delay.

[0291] A "professional gamer" is a game player with specialized skills and experience, and their data is used to train the AI ​​model.

[0292] This system collects, analyzes, and provides feedback on a user's gameplay data, and also recognizes the user's emotional state and adjusts the feedback accordingly. This system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[0293] Gameplay data collection

[0294] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[0295] Example: While a user is playing a shooting game, the device records the user's movement trajectory and firing timing in milliseconds.

[0296] Collecting Emotional Data

[0297] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[0298] For example, if the user is nervous, the increase in heart rate and changes in facial expression are collected and sent to the emotion engine.

[0299] Sending data

[0300] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), where the data integrity and security are verified.

[0301] Example: Collected data is encrypted and sent securely to a server.

[0302] Data storage and analysis

[0303] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data for input into the AI ​​model.

[0304] The AI ​​model is pre-trained based on the play data of professional gamers and then performs a comparative analysis with the user's play data. Based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[0305] Example: The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0306] Emotional Data Analysis

[0307] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[0308] Example: The analysis results show that the user is playing in a tense state.

[0309] Generate feedback

[0310] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[0311] For example: "Your reaction time is off by an average of 0.3 seconds. To improve this lag, we recommend that you play a specific mini-game every day. We also recommend some relaxation exercises to help relieve tension."

[0312] Sending and Viewing Feedback

[0313] The server transmits the generated feedback to the user's terminal.

[0314] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the feedback.

[0315] For example: "Your reaction time is off by 0.3 seconds. Please click the link below to play a mini-game. Also, try 5 minutes of relaxation before playing."

[0316] User Practice

[0317] The user reviews the provided feedback and plays the game again, focusing on areas for improvement in actual gameplay. They then carry out specific exercises, change settings, and manage their emotional state.

[0318] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Specific processing explanation of the system program divided into processing steps

[0321] Step 1: Collecting gameplay data

[0322] The terminal collects the user's gameplay data in real time.

[0323] Input: User's gameplay status (movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time).

[0324] Processing: The device monitors and records this data using sensors and software modules.

[0325] Output: Formatted gameplay data (including user ID, game session ID, date and time).

[0326] Specific operation: While a user is playing a shooting game, the device collects the user's movement trajectory and firing timing in milliseconds and temporarily stores them in a database.

[0327] Step 2: Collecting emotion data

[0328] The device collects data to recognize the user's emotional state in real time.

[0329] Input: User's facial expressions, voice, and biometric information (heart rate, skin potential, etc.).

[0330] Processing: The device collects data from cameras, microphones, biosensors, etc. and pre-processes it to identify emotional states.

[0331] Output: Formatted emotion data (including user ID and timestamp).

[0332] What it does: If a user becomes tense while playing a game, the device will use the camera to detect the movement of the user's facial muscles and the heart rate sensor to record an increase in heart rate.

[0333] Step 3: Sending data

[0334] The terminal transmits the collected game play data and emotion data to the server.

[0335] Input: Collected gameplay and emotion data.

[0336] Processing: The device compresses this data, transmits it using a secure communication protocol (e.g. HTTPS), and checks the data for integrity and security.

[0337] Output: Data securely sent to the server.

[0338] What it does: Encrypts collected data, establishes a secure communication channel and sends it to the server.

[0339] Step 4: Saving and formatting the data

[0340] The server receives and stores the data.

[0341] Input: Gameplay and emotion data sent from the device.

[0342] Processing: The server saves these data in a database, adding necessary metadata (user ID, session ID, date and time).

[0343] Output: Organized data stored in a database.

[0344] What happens: The server creates a new session in the database and saves each data entry.

[0345] The server formats the stored data for input into the AI ​​model.

[0346] Input: Saved gameplay and emotion data.

[0347] Processing: Converting data into an input format for the AI ​​model.

[0348] Output: Data in a format that can be input into an AI model.

[0349] What it does: The server formats the data and converts it into a form that is easy for the AI ​​model to handle.

[0350] Step 5: Analyze the data

[0351] The AI ​​model analyzes the user's play data.

[0352] Input: Formatted gameplay data.

[0353] Processing: The AI ​​model compares the gameplay data of professional gamers and analyzes it based on specific parameters (reaction time, aim position, movement patterns, and success rate of gunfights).

[0354] Output: Comparative analysis results of user gameplay.

[0355] How it works: The AI ​​model analyzes the user's reaction time and compares the time lag with that of professional gamers.

[0356] The emotion engine analyzes the emotion data.

[0357] Input: Formatted emotion data.

[0358] Processing: The emotion engine analyzes the user's emotional state (stress, tension, joy, etc.) based on the collected emotion data.

[0359] Output: Analysis results about the user's emotional state.

[0360] Specific operation: The emotion engine analyzes the user's heart rate and facial expression changes to detect tension.

[0361] Step 6: Generate feedback

[0362] The server generates feedback based on the analysis results of the AI ​​model and emotion engine.

[0363] Input: Comparative analysis results of AI models and emotion engine analysis results.

[0364] Processing: Comprehensively evaluate the analysis results and generate advice on areas for improvement, recommended practice, and emotional state.

[0365] Output: The generated feedback.

[0366] Specific action: "Your reaction time is delayed by an average of 0.3 seconds. Play specific mini-games every day and try relaxation exercises to reduce tension."

[0367] Step 7: Submit and view feedback

[0368] The server transmits the generated feedback to the user's terminal.

[0369] Input: The generated feedback.

[0370] Action: Send feedback to the device.

[0371] Output: Feedback sent to the device.

[0372] Specific behavior: The generated feedback is immediately sent to the user's device.

[0373] The device displays the feedback to the user.

[0374] Input: Feedback sent by the server.

[0375] Processing: The feedback is displayed in a user-friendly interface.

[0376] Output: The feedback displayed to the user.

[0377] Specific action: "Your reaction time is off by 0.3 seconds. Please click the link below to play the mini-game. Also, try 5 minutes of relaxation before playing."

[0378] Step 8: User Practice

[0379] Enhance gameplay based on user-provided feedback.

[0380] Input: Feedback displayed on the terminal.

[0381] Treatment: Based on the feedback, specific exercises and setting changes are implemented, as well as emotional state management.

[0382] Output: Improved gameplay and emotional state management.

[0383] Specific actions: Users practice the presented mini-games daily and incorporate relaxation exercises before playing.

[0384] (Application example 2)

[0385] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0386] Conventional virtual stores offer product recommendations and customer support based solely on user behavior data, meaning they are unable to provide feedback or support that takes into account the user's emotional state. This reduces the psychological satisfaction and convenience of users when making purchasing decisions, and undermines the appeal of virtual stores.

[0387] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data in real time, means for collecting user emotional data in real time, means for transmitting the collected behavioral data and emotional data to the server, means including an AI model for comparing and analyzing the transmitted data with professional data, means including an emotion engine for analyzing the user's emotional state, means for generating feedback to the user based on the results of the comparison and analysis and the emotion analysis, means for transmitting the generated feedback to the user terminal, and means for providing feedback displayed on the user terminal. This makes it possible to analyze both the user's behavioral data and emotional data and recommend products and provide customer support tailored to the user's psychological state.

[0388] "User behavior data" refers to information on specific operations and actions such as the user's line of sight, movement trajectory, selected products, and purchase history within the virtual store.

[0389] "User emotional data" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, voice, and biometric information (such as heart rate and skin potential).

[0390] "Server" is a central computing system for receiving, storing, analyzing user behavioral and emotional data and generating feedback.

[0391] "Professional data" is reference data relating to the actions and operations performed by skilled professionals, and is used for comparative analysis with user behavior data.

[0392] An "AI model" is an algorithm or program that utilizes artificial intelligence to compare and analyze user data and professional data to evaluate user behavior patterns and reactions.

[0393] The "emotion engine" is a dedicated analysis system that analyzes a user's emotional data and identifies the user's current emotional state.

[0394] "Feedback" refers to information such as improvements, recommended products, and customer support provided to users based on analyzed behavioral and emotional data.

[0395] "User terminal" refers to a device operated by a user, and is a device for collecting and displaying behavioral data and emotional data through the virtual store interface.

[0396] A system embodying the present invention collects user behavioral data and emotional data in real time, transmits this data to a server for analysis, and provides appropriate feedback to the user. Detailed embodiments of the system are described below.

[0397] First, the terminal is a device that the user operates in the virtual store, and collects behavioral data such as the user's gaze, movement trajectory, selected products, purchase history, etc. Furthermore, the terminal is equipped with sensors that collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires the user's emotional data.

[0398] The device then compresses the collected behavioral and emotional data and sends it to a server using a secure communication protocol (e.g., HTTPS). The server receives the data and stores it in a database.

[0399] The server formats the received data and inputs it into a pre-trained AI model and emotion engine to analyze the user's behavioral and emotional data. The AI ​​model compares the user data with professional data to evaluate the user's behavioral patterns, reaction time, and types of products they are interested in. The emotion engine also analyzes the collected emotional data to identify the user's current emotional state.

[0400] The server integrates these analysis results and generates optimal feedback for the user. This feedback includes specific improvements, recommended products, and customer support tailored to the user's emotional state. The generated feedback is sent to the device and displayed on the user's interface. The user can then make purchase decisions or change settings based on the presented feedback.

[0401] This section describes specific hardware and software for implementing this system. The hardware includes a VR head-mounted display (HMD) for collecting user behavior data, and a webcam and biometric sensors for collecting user emotion data. The software includes an AI model using TensorFlow / Keras for analyzing the data, an emotion engine for analyzing the emotion data, and the Requests library for data communication.

[0402] For example, when a user looks at a product in a virtual store, the HMD collects gaze data and information about the selected product, while the webcam collects facial expression data and sensors collect heart rate and skin potential. This data is sent to a server and analyzed by an AI model and emotion engine. For example, if a user looks at a particular product with excitement, the system will determine that the user is highly interested in that product and generate feedback recommending related products.

[0403] Below is an example of a prompt sentence using a generative AI model.

[0404] "Generate application code that collects user behavioral and sentiment data and provides optimal product recommendations and customer support within a virtual store."

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] The device collects user behavior data (eye gaze, movement trajectory, selected products, purchase history, etc.) in real time. The device is also equipped with sensors to collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires emotional data. This data is input from the collection sensors and stored in a database in real time.

[0408] Step 2:

[0409] The device compresses the collected behavioral and emotional data and transmits it to the server using a secure communication protocol (e.g., HTTPS). The transmitted data also includes metadata such as the user ID, session ID, and date and time. The compressed data is input to and received by the server via the transmission protocol.

[0410] Step 3:

[0411] The server stores the received data in a database. The stored data includes user behavior data, emotion data, and metadata. The data is stored appropriately in the database using the user ID as a key. This allows the required data to be quickly searched and analyzed.

[0412] Step 4:

[0413] The server formats the stored data and inputs it into the AI ​​model. The user's behavioral and emotional data is converted into a format that is easy for the AI ​​model to analyze. The formatted data is then input into the AI ​​model as user data. During this process, data cleaning and preprocessing are performed.

[0414] Step 5:

[0415] The AI ​​model installed on the server compares and analyzes user data with professional data. The AI ​​model evaluates the user's behavioral patterns, reaction time, and types of products they are interested in. Specifically, the AI ​​model analyzes the user's eye movements and product selection patterns and compares them with the professional data. The results of this analysis become input for the feedback generation process.

[0416] Step 6:

[0417] The emotion engine analyzes the user's emotion data to identify the user's current emotional state. For example, if the user is excited, the emotion engine detects that state based on changes in facial expressions and biometric data. The emotion analysis results are also used as input for the feedback generation process.

[0418] Step 7:

[0419] The server combines the analysis results of the AI ​​model and the emotion engine to generate optimal feedback for the user. This feedback can include specific improvements, recommended products, and customer support tailored to the user's emotional state. For example, if the user is excited, product recommendations will reflect that excitement.

[0420] Step 8:

[0421] The server transmits the generated feedback to the terminal, and the feedback is displayed in real time on the user's terminal, and the transmitted data includes a specific feedback message to be displayed to the user.

[0422] Step 9:

[0423] The user checks the feedback displayed on the device and reflects it in their actual actions. For example, they check the recommended product and purchase it, or decide on their next action based on the suggested improvements. They then take specific actions or make purchases based on the displayed feedback message.

[0424] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0425] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0426] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0430] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0431] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0432] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0433] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0435] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0436] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0437] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0438] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0439] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0440] MODE FOR CARRYING OUT THE INVENTION

[0441] This invention is a system that efficiently collects and analyzes user gameplay data and provides feedback. This system is mainly composed of the user's device, a server, and an AI model.

[0442] 1. Collection of Gameplay Data

[0443] The device collects gameplay data in real time while the user is playing the game. This data includes all actions performed by the user (movement, attacks, abilities used, etc.), specifically character position information, aiming movement patterns, button press timing, types of items and weapons used, etc.

[0444] example:

[0445] When a user is playing an online FPS game, the device records detailed data such as from which position the user's character shot the enemy and whether the shot was successful.

[0446] 2. Data transmission

[0447] The devices transmit the collected data to the server in real time, using a secure communication protocol (e.g., HTTPS) to ensure data integrity and confidentiality.

[0448] 3. Data storage and analysis

[0449] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[0450] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. Based on specific parameters (reaction time, shooting accuracy, movement patterns, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[0451] example:

[0452] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0453] 4. Generate feedback

[0454] The server generates feedback for the user based on the results of the AI ​​model's analysis, including specific areas for improvement, recommended practice methods, and helpful video links.

[0455] example:

[0456] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend you play a specific reaction training mini-game every day."

[0457] 5. Sending and Viewing Feedback

[0458] The server transmits the generated feedback to the user's terminal.

[0459] The device displays the received feedback to the user, and through a user-friendly interface, the user can easily understand specific areas for improvement and practice methods.

[0460] example:

[0461] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published in the link below every day."

[0462] In this way, the present invention provides a concrete means for users to efficiently improve their gaming skills. Users can receive individual feedback based on their own play data in real time, allowing them to continuously and effectively improve their skills.

[0463] The processing flow will be explained below.

[0464] Step 1:

[0465] The user plays a game. The user launches an online FPS game or other competitive game and begins playing as normal.

[0466] Step 2:

[0467] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted with metadata (user ID, game session ID, date and time, etc.).

[0468] Step 3:

[0469] The device compresses the collected data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[0470] Step 4:

[0471] The server receives the data sent from the device and stores it in a database, which structures each user's play history and stores it together with other data.

[0472] Step 5:

[0473] The server (AI model) retrieves the user's play data stored in the database and compares it with data from professional gamers. The AI ​​model learns using past professional data and compares it with the user's play data to detect different movements.

[0474] Step 6:

[0475] When the AI ​​model analyzes the play data of users and professional gamers, it evaluates certain parameters (e.g., reaction time, aiming accuracy, movement patterns, and success rate of shots). As a result of the evaluation, it detects delays or inaccuracies in certain user actions.

[0476] Step 7:

[0477] The server generates useful feedback for users based on the results of the comparative analysis of the AI ​​models, including areas for improvement, recommended practice, and specific action plans.

[0478] Step 8:

[0479] The server transmits the generated feedback to the user's device, ensuring the accuracy and timeliness of the feedback during transmission.

[0480] Step 9:

[0481] The device displays feedback to the user, which is presented in a user-friendly interface designed to allow the user to easily understand and act on the content.

[0482] Step 10:

[0483] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. The user then performs specific practice exercises and changes to the settings based on the feedback.

[0484] By repeating these steps, users can effectively improve their gameplay, promoting continuous growth.

[0485] Example 1

[0486] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0487] Conventional gameplay data analysis systems have difficulty collecting detailed gameplay data from users, comparing it with data from professional gamers, and providing effective and specific feedback to users. Ensuring real-time performance, data integrity, and security are also issues. Therefore, a means has been sought that allows users to quickly and accurately identify their weaknesses and effectively improve their skills.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0489] In this invention, the server includes means for collecting user gameplay data in real time, means for transmitting the collected data to the server, means for storing the transmitted data in a database and appropriately formatting it for analysis, means including an AI model for comparing and analyzing the formatted data with play data of professional gamers, means for generating feedback to the user based on the results of the comparison and analysis obtained by the AI ​​model, means for transmitting the generated feedback to the user's device, and means for displaying the transmitted feedback to the user. This makes it possible to provide specific and practical feedback by collecting detailed user play data in real time and comparing it with data from professional gamers.

[0490] "User gameplay data" refers to data that includes detailed information such as location information, button press timing, aiming movement patterns, and types of items and weapons used, which are generated when a user plays a game.

[0491] "Real-time collection means" refers to a method or device for acquiring a user's gameplay data in real time while they are playing, and includes the use of high-precision sensors and software modules.

[0492] "Means for sending to a server" refers to a method or device for sending collected data to a server using a secure communication protocol (e.g., HTTPS).

[0493] "Means for storing in a database and appropriate formatting" means a method or device for storing the data sent to the server in a database and formatting it for input to an AI model, including removing noise from the data and standardizing timestamps.

[0494] An "AI model for comparative analysis of professional gamer play data" is an artificial intelligence model trained based on the play data of professional gamers, and is a means for conducting comparative analysis of user play data and professional gamer data.

[0495] "Means for generating feedback" means a method or device for automatically generating feedback based on the analysis results obtained from the AI ​​model, including areas for improvement in the user's play and recommended practice methods.

[0496] The "means for transmitting feedback to the user's terminal" refers to a method or device for transmitting the generated feedback to the user's terminal, and it is recommended that a secure communication protocol be used.

[0497] "Means for displaying feedback" refers to a method or device for providing the feedback received at the user's terminal to the user in the form of visual, audio, etc. This includes a user-friendly interface.

[0498] MODE FOR CARRYING OUT THE INVENTION

[0499] This invention is a system that collects gameplay data in real time when a user plays an online game, analyzes the data, and provides specific feedback. The system mainly consists of a user's device, a server, and an AI model. Each step and the hardware and software used are described in detail below.

[0500] 1. Collection of Gameplay Data

[0501] The device collects real-time gameplay data as the user plays the game. This data includes the character's position, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, high-precision data acquisition is achieved by using dedicated sensor modules and software (e.g., the logging function of Unity or Unreal Engine).

[0502] example:

[0503] When a user plays an online FPS game, the device records detailed data such as where the character shot the enemy from and whether the shot was successful. This data includes positional data and button input events for each frame of play.

[0504] 2. Data transmission

[0505] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS to ensure data integrity and confidentiality, along with metadata such as user ID and session ID.

[0506] 3. Data storage and preprocessing

[0507] The server receives the data sent from the device and first stores it in a database. This database uses SQL or NoSQL to store data quickly and reliably. The stored data includes the user ID, session ID, date and time, and various play data. The stored data is then formatted and converted into a format suitable for input into the AI ​​model. This process includes removing noise from the data and standardizing timestamps.

[0508] example:

[0509] The server analyzes the received data and inserts it into a database using, for example, a user ID or session ID as a key. In the formatting stage, the data is de-noised and any necessary transformations (e.g., standardization of timestamps) are performed.

[0510] 4. Data Analysis

[0511] The server inputs the formatted data into a generative AI model, which has been pre-trained based on the play data of professional gamers. The AI ​​model compares the user's play data with the professional gamers' data and performs analysis based on specific evaluation metrics (e.g., reaction time, shooting accuracy, movement patterns, etc.). The analysis results identify which aspects of the user's play differ from the professional gamers'.

[0512] example:

[0513] The AI ​​model analyzes the received gameplay data and identifies that the user's reaction time is 0.3 seconds slower than that of a professional gamer. This process uses deep learning frameworks (e.g., TensorFlow, PyTorch).

[0514] 5. Generate feedback

[0515] The server generates feedback for the user based on the analysis results obtained from the AI ​​model. This feedback includes specific areas for improvement, recommended practice methods, reference video links, etc. The generated feedback is output in a format that is easy for the user to understand.

[0516] example:

[0517] Feedback is generated in the form of, "Your reaction time is delayed by an average of 0.3 seconds. To improve this time lag, we recommend that you play a specific reaction training mini-game every day." The reference video link includes a tutorial video by a professional gamer.

[0518] 6. Sending and Viewing Feedback

[0519] The server sends the generated feedback to the user's device, which then displays it to the user, allowing them to intuitively understand specific areas for improvement and practice methods. The feedback is displayed on the device through a pop-up notification or a dedicated app interface.

[0520] example:

[0521] A message such as "Your reaction time is 0.3 seconds slower. You can improve your reaction speed by playing the mini-game linked below every day" will be displayed, and clicking the link will launch the corresponding mini-game.

[0522] Specific examples of input prompts for generative AI models

[0523] "Analyze this person's gameplay data to see how they compare to professional gamers and identify areas for improvement. Pay particular attention to reaction time, shooting accuracy, and movement patterns."

[0524] The system of the present invention allows users to receive personalized feedback based on their own play data in real time, enabling them to continuously improve their skills. An efficient feedback system can be provided by integrating the server, device, and AI model. By implementing this system, users can quickly and accurately identify their weaknesses and improve their skills in specific ways.

[0525] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0526] Specific processing flow of the program

[0527] Step 1: Collecting gameplay data

[0528] The device collects gameplay data in real time as the user plays the game. This data includes character position information, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, the device uses dedicated sensor modules and software to collect this data with high precision.

[0529] Input: User actions during gameplay, location information, button press timing, etc.

[0530] Output: Collected gameplay data

[0531] Step 2: Sending data

[0532] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS, along with metadata such as user ID and session ID.

[0533] Input: Collected gameplay data, user ID, session ID

[0534] Output: Data in the format sent to the server

[0535] Step 3: Storing and Preprocessing Data

[0536] The server receives the data sent from the device and stores it in a database. The stored data includes the user ID, session ID, date and time, and various play data. The data is then appropriately formatted for input into the AI ​​model. This formatting includes removing noise from the data and standardizing timestamps.

[0537] Input: Submitted gameplay data, user ID, session ID

[0538] Output: Data in a formatted form

[0539] Specific behavior:

[0540] 1. The server receives the collected data.

[0541] 2. Save it to the database.

[0542] 3. Format the stored data to input into the AI ​​model.

[0543] Step 4: Analyze the data

[0544] The server inputs the formatted data into a generative AI model. The AI ​​model is pre-trained based on the play data of professional gamers and compares the user's play data with the professional gamer's data. It performs analysis based on specific evaluation indicators (e.g., reaction time, shooting accuracy, movement patterns, etc.).

[0545] Input: Data in a formatted form

[0546] Output: Analysis results

[0547] Specific behavior:

[0548] 1. The server inputs the formatted data into the AI ​​model.

[0549] 2. The AI ​​model analyzes the data and identifies the differences between users and professional gamers.

[0550] Step 5: Generate feedback

[0551] The server generates feedback for the user based on the analysis results obtained from the AI ​​model, including specific areas for improvement, recommended practice methods, and helpful video links.

[0552] Input: Analysis results

[0553] Output: Generated feedback

[0554] Specific behavior:

[0555] 1. The server receives the analysis results.

[0556] 2. Automatically generate feedback based on the analysis results.

[0557] 3. Feedback will include areas for improvement, practice methods, and helpful video links.

[0558] Step 6: Submit and view feedback

[0559] The server sends the generated feedback to the user's device, which then displays the received feedback to the user. The device provides a user-friendly interface, allowing the user to intuitively understand specific points for improvement and practice methods.

[0560] Input: Generated feedback

[0561] Output: Feedback that is displayed to the user

[0562] Specific behavior:

[0563] 1. The server sends the generated feedback to the device.

[0564] 2. The device receives and displays the feedback.

[0565] 3. Users review the feedback and understand areas for improvement and how to practice.

[0566] In this way, the system of the present invention efficiently supports users in improving their gaming skills.

[0567] (Application example 1)

[0568] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0569] Currently, robotic work in factories is often not sufficiently focused on efficiency and precision. This creates a need for work optimization and efficiency, but on-site data collection and analysis is not easy. Furthermore, there are limited means of comparing results with professional work, creating a lack of concrete feedback for improving robotic work efficiency and technology.

[0570] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0571] In this invention, the server includes means for collecting user work data in real time, means for transmitting the collected data to the server, means including a generative AI model for comparing and analyzing the transmitted data with professional work data, means for generating feedback to the user based on the results of the comparison and analysis, means for transmitting the generated feedback to the user's terminal, and means for providing feedback displayed on the user's terminal, thereby enabling improvement of the work efficiency of factory robots and provision of specific feedback.

[0572] "User work data" refers to data about all actions performed by robots in a factory, including moving, lifting, placing, etc.

[0573] A "server" is a computing device that has the ability to receive, store, analyze, and transmit data over the Internet or a local network.

[0574] A "generative AI model" is an artificial intelligence model trained based on professional work data, and is used to compare and analyze user work data.

[0575] "Professional work data" refers to the movement data of experts who perform work efficiently and with high precision.

[0576] "Feedback" refers to advice and suggestions for improvement provided to users based on the analysis results.

[0577] A "user terminal" is an information processing device that displays feedback and can be operated by the user, and includes a smartphone, tablet, etc.

[0578] This invention aims to improve the work efficiency of factory robots by building a system that collects user work data in real time, analyzes it using a generative AI model, and provides appropriate feedback.

[0579] System Program

[0580] 1. Collecting user work data

[0581] This system collects all actions (moving, lifting, placing, etc.) performed by factory robots in real time. Specifically, motion and position sensors are attached to the robots to collect the data. The collected data records the robot's actions in detail and serves as the basis for future analysis.

[0582] 2. Data transmission

[0583] The collected data is sent to the server using a secure communication protocol (e.g. HTTPS), which ensures the integrity and confidentiality of the data.

[0584] 3. Data storage and analysis

[0585] The server receives the submitted data and automatically stores it in a database, including metadata such as the robot ID, session ID, date and time, etc. The server then formats the data appropriately for input into the generative AI model.

[0586] The generative AI model is pre-trained based on the work data of professionals and performs comparative analysis with the user's work data. Based on specific parameters (processing time, movement patterns, work accuracy, etc.), the AI ​​model detects which aspects of the user's work differ from those of professionals.

[0587] 4. Generate feedback

[0588] The server generates feedback for users based on the analysis results of the generative AI model, including specific points for improvement, recommended work methods, and reference video links. Users can quickly implement appropriate improvement measures to improve their work efficiency.

[0589] 5. Sending and Viewing Feedback

[0590] The server sends the generated feedback to the user's device, and the device displays the received feedback to the user. Through a user-friendly interface, the user can easily understand specific improvements and work methods.

[0591] Examples and prompts

[0592] For example, if a robot used in a factory deviates from the optimal movement pattern when lifting an object, the generative AI model will determine that "movement efficiency could be improved" and provide feedback on the appropriate way to lift the object.

[0593] Example prompt sentence:

[0594] "Based on the operation data of factory robots, identify the difference between the current operation performance and the optimal operation pattern and provide feedback on areas for improvement."

[0595] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0596] Step 1:

[0597] Collecting user work data

[0598] The terminal collects data in real time from motion and position sensors attached to the factory robots. This data includes information on the robot's movements, lifting, and placement. The terminal's input is real-time data from the sensors, and its output is the collected motion data.

[0599] Step 2:

[0600] Sending data

[0601] The terminal sends the collected data to the server using the HTTPS communication protocol. Using this communication protocol ensures the integrity and confidentiality of the data. The input is the operational data collected by the terminal, and the output is the data sent to the server.

[0602] Step 3:

[0603] Data storage

[0604] The server receives the transmitted data and automatically stores it in a database. The stored data also includes metadata such as the robot ID, session ID, date and time. The input is the data transmitted to the server, and the output is the data stored in the database.

[0605] Step 4:

[0606] Data formatting and analysis

[0607] The server formats the stored data appropriately for input into the generative AI model. The formatted data is passed to the generative AI model and compared with the professional's work data for analysis. The input is the data stored in the database, and the output is the formatted data input into the generative AI model.

[0608] Step 5:

[0609] Generate feedback

[0610] The server generates feedback for the user based on the analysis results of the generative AI model, including specific areas for improvement, recommended work methods, reference video links, etc. The input is the analysis results of the generative AI model, and the output is the generated feedback.

[0611] Step 6:

[0612] Send Feedback

[0613] The server sends the generated feedback to the user's terminal, where the input is the generated feedback and the output is the feedback sent to the user's terminal.

[0614] Step 7:

[0615] View Feedback

[0616] The terminal displays the received feedback to the user, allowing the user to easily understand specific improvements and work methods based on the feedback. The input is the feedback sent from the server, and the output is the feedback displayed to the user.

[0617] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0618] MODE FOR CARRYING OUT THE INVENTION

[0619] The present invention is a system that includes an emotion engine that collects, analyzes, and provides feedback on a user's gameplay data, as well as recognizes the user's emotional state and adjusts the feedback accordingly. The system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[0620] 1. Collection of Gameplay Data

[0621] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[0622] 2. Collecting Emotional Data

[0623] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[0624] example:

[0625] If the user is nervous, changes in facial expression, an increased heart rate, and a change in tone of voice are detected.

[0626] 3. Data transmission

[0627] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[0628] 4. Data storage and analysis

[0629] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[0630] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. The AI ​​model detects which aspects of the user's play differ from those of professional gamers based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.).

[0631] example:

[0632] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0633] 5. Emotion Data Analysis

[0634] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[0635] example:

[0636] The analysis results show that users are tense while playing.

[0637] 6. Generate feedback

[0638] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[0639] example:

[0640] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game daily. We also recommend that you incorporate relaxation techniques into your play, as you are feeling tense."

[0641] 7. Sending and Viewing Feedback

[0642] The server transmits the generated feedback to the user's terminal.

[0643] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the content.

[0644] example:

[0645] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published at the link below every day. Also, it has been analyzed that you are feeling tense. Please try a 5-minute relaxation exercise before playing the game."

[0646] 8. User Practices

[0647] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. Based on the feedback, the user makes specific practice exercises and changes settings, and also manages their emotional state.

[0648] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[0649] The processing flow will be explained below.

[0650] Step 1:

[0651] The user starts a competitive game, such as an online FPS game, and begins playing as usual.

[0652] Step 2:

[0653] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The data is captured every second and converted into a format (e.g., JSON).

[0654] Step 3:

[0655] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. Emotional data is also processed in the same way.

[0656] Step 4:

[0657] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the accuracy and safety of data transmission.

[0658] Step 5:

[0659] The server receives the gameplay data and emotion data sent from the device and stores them in a database, including metadata such as the user ID, session ID, and date and time.

[0660] Step 6:

[0661] The server (AI model) retrieves the user's play data stored in the database and compares and analyzes it with the data of professional gamers. The AI ​​model learns using past professional data and evaluates it based on specific parameters (reaction time, aim position, movement pattern, success rate of gunfights).

[0662] example:

[0663] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0664] Step 7:

[0665] The emotion engine analyzes the user's current emotional state (e.g., excited, nervous, relaxed) based on collected emotional data. The algorithm evaluates the user's emotional state based on facial expressions, heart rate, tone of voice, etc.

[0666] example:

[0667] The emotion engine detects when the user is in a tense state.

[0668] Step 8:

[0669] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[0670] example:

[0671] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game every day. Also, your emotional data suggests that you are feeling tense, so we recommend that you also incorporate some simple exercises to help you relax."

[0672] Step 9:

[0673] The server sends the generated feedback to the user's device, ensuring the accuracy and timeliness of the information.

[0674] Step 10:

[0675] The device displays the received feedback to the user through a user-friendly interface designed to help users easily understand and act on the content.

[0676] example:

[0677] "Spend 10 minutes each day playing the reaction speed mini-game linked below. Also, try the relaxation exercises below to help relieve tension."

[0678] Step 11:

[0679] The user checks the provided feedback and plays the game again, taking into consideration the areas for improvement and recommended practice methods. Based on the feedback, the user implements specific practice and setting changes, and also manages their emotional state.

[0680] By repeating these steps, users can effectively improve their play, promoting continuous growth and management of their emotional state.

[0681] Example 2

[0682] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0683] In modern gaming environments, the importance of player skill improvement and emotional management is increasing, but current systems lack comprehensive support for these. Therefore, a comprehensive system is needed to help players efficiently improve their skills and simultaneously manage their emotional state during gameplay. This system must also be capable of collecting user data in real time and providing appropriate feedback.

[0684] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0685] In this invention, the server includes: means for collecting gameplay data of users in real time; means for collecting facial expressions, voice, and biometric information to recognize the user's emotional state; means for transmitting the collected gameplay data and emotional data to the server; means for storing and shaping the transmitted data in a database; means including an AI model for comparing and analyzing the shaped data with play data of professional gamers; means including an emotion engine for analyzing the emotional data and identifying the user's emotional state; means for generating feedback to the user based on the analysis results of the AI ​​model and the emotion engine; means for transmitting the generated feedback to the user's device; and means for providing feedback displayed on the user's device. This allows the user to receive appropriate feedback in real time according to their skill level and emotional state.

[0686] "User" refers to an individual who uses the system to play games.

[0687] "Gameplay data" refers to data that includes information such as movement trajectory, aim position, firing timing, characters used, weapons used, and enemy detection time that is generated in the course of a user playing a game.

[0688] "Emotional state" refers to the user's psychological and physiological state while playing a game, and specifically includes emotions such as stress, tension, and joy.

[0689] "Terminal" refers to a device that allows a user to play a game and has the function of collecting game play data and emotional data and transmitting them to a server.

[0690] A "server" is a computer system that receives and stores data sent from a device, and then analyzes it using an AI model and emotion engine.

[0691] A "database" is a system for systematically storing and managing data stored on a server.

[0692] An "AI model" is an artificial intelligence model that is trained based on the play data of professional gamers and used to compare and analyze user gameplay data.

[0693] An "emotion engine" is software with analytical functions that analyzes a user's emotional data and identifies their current emotional state.

[0694] "Feedback" is information that includes specific improvements and advice for users, generated based on the analysis results of the AI ​​model and emotion engine.

[0695] "Real-time" refers to gameplay and data collection occurring instantly, without delay.

[0696] A "professional gamer" is a game player with specialized skills and experience, and their data is used to train the AI ​​model.

[0697] This system collects, analyzes, and provides feedback on a user's gameplay data, and also recognizes the user's emotional state and adjusts the feedback accordingly. This system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[0698] Gameplay data collection

[0699] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[0700] Example: While a user is playing a shooting game, the device records the user's movement trajectory and firing timing in milliseconds.

[0701] Collecting Emotional Data

[0702] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[0703] For example, if the user is nervous, the increase in heart rate and changes in facial expression are collected and sent to the emotion engine.

[0704] Sending data

[0705] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), where the data integrity and security are verified.

[0706] Example: Collected data is encrypted and sent securely to a server.

[0707] Data storage and analysis

[0708] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data for input into the AI ​​model.

[0709] The AI ​​model is pre-trained based on the play data of professional gamers and then performs a comparative analysis with the user's play data. Based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[0710] Example: The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0711] Emotional Data Analysis

[0712] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[0713] Example: The analysis results show that the user is playing in a tense state.

[0714] Generate feedback

[0715] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[0716] For example: "Your reaction time is off by an average of 0.3 seconds. To improve this lag, we recommend that you play a specific mini-game every day. We also recommend some relaxation exercises to help relieve tension."

[0717] Sending and Viewing Feedback

[0718] The server transmits the generated feedback to the user's terminal.

[0719] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the feedback.

[0720] For example: "Your reaction time is off by 0.3 seconds. Please click the link below to play a mini-game. Also, try 5 minutes of relaxation before playing."

[0721] User Practice

[0722] The user reviews the provided feedback and plays the game again, focusing on areas for improvement in actual gameplay. They then carry out specific exercises, change settings, and manage their emotional state.

[0723] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[0724] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0725] Specific processing explanation of the system program divided into processing steps

[0726] Step 1: Collecting gameplay data

[0727] The terminal collects the user's gameplay data in real time.

[0728] Input: User's gameplay status (movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time).

[0729] Processing: The device monitors and records this data using sensors and software modules.

[0730] Output: Formatted gameplay data (including user ID, game session ID, date and time).

[0731] Specific operation: While a user is playing a shooting game, the device collects the user's movement trajectory and firing timing in milliseconds and temporarily stores them in a database.

[0732] Step 2: Collecting emotion data

[0733] The device collects data to recognize the user's emotional state in real time.

[0734] Input: User's facial expressions, voice, and biometric information (heart rate, skin potential, etc.).

[0735] Processing: The device collects data from cameras, microphones, biosensors, etc. and pre-processes it to identify emotional states.

[0736] Output: Formatted emotion data (including user ID and timestamp).

[0737] What it does: If a user becomes tense while playing a game, the device will use the camera to detect the movement of the user's facial muscles and the heart rate sensor to record an increase in heart rate.

[0738] Step 3: Sending data

[0739] The terminal transmits the collected game play data and emotion data to the server.

[0740] Input: Collected gameplay and emotion data.

[0741] Processing: The device compresses this data, transmits it using a secure communication protocol (e.g. HTTPS), and checks the data for integrity and security.

[0742] Output: Data securely sent to the server.

[0743] What it does: Encrypts collected data, establishes a secure communication channel and sends it to the server.

[0744] Step 4: Saving and formatting the data

[0745] The server receives and stores the data.

[0746] Input: Gameplay and emotion data sent from the device.

[0747] Processing: The server saves these data in a database, adding necessary metadata (user ID, session ID, date and time).

[0748] Output: Organized data stored in a database.

[0749] What happens: The server creates a new session in the database and saves each data entry.

[0750] The server formats the stored data for input into the AI ​​model.

[0751] Input: Saved gameplay and emotion data.

[0752] Processing: Converting data into an input format for the AI ​​model.

[0753] Output: Data in a format that can be input into an AI model.

[0754] What it does: The server formats the data and converts it into a form that is easy for the AI ​​model to handle.

[0755] Step 5: Analyze the data

[0756] The AI ​​model analyzes the user's play data.

[0757] Input: Formatted gameplay data.

[0758] Processing: The AI ​​model compares the gameplay data of professional gamers and analyzes it based on specific parameters (reaction time, aim position, movement patterns, and success rate of gunfights).

[0759] Output: Comparative analysis results of user gameplay.

[0760] How it works: The AI ​​model analyzes the user's reaction time and compares the time lag with that of professional gamers.

[0761] The emotion engine analyzes the emotion data.

[0762] Input: Formatted emotion data.

[0763] Processing: The emotion engine analyzes the user's emotional state (stress, tension, joy, etc.) based on the collected emotion data.

[0764] Output: Analysis results about the user's emotional state.

[0765] Specific operation: The emotion engine analyzes the user's heart rate and facial expression changes to detect tension.

[0766] Step 6: Generate feedback

[0767] The server generates feedback based on the analysis results of the AI ​​model and emotion engine.

[0768] Input: Comparative analysis results of AI models and emotion engine analysis results.

[0769] Processing: Comprehensively evaluate the analysis results and generate advice on areas for improvement, recommended practice, and emotional state.

[0770] Output: The generated feedback.

[0771] Specific action: "Your reaction time is delayed by an average of 0.3 seconds. Play specific mini-games every day and try relaxation exercises to reduce tension."

[0772] Step 7: Submit and view feedback

[0773] The server transmits the generated feedback to the user's terminal.

[0774] Input: The generated feedback.

[0775] Action: Send feedback to the device.

[0776] Output: Feedback sent to the device.

[0777] Specific behavior: The generated feedback is immediately sent to the user's device.

[0778] The device displays the feedback to the user.

[0779] Input: Feedback sent by the server.

[0780] Processing: The feedback is displayed in a user-friendly interface.

[0781] Output: The feedback displayed to the user.

[0782] Specific action: "Your reaction time is off by 0.3 seconds. Please click the link below to play the mini-game. Also, try 5 minutes of relaxation before playing."

[0783] Step 8: User Practice

[0784] Enhance gameplay based on user-provided feedback.

[0785] Input: Feedback displayed on the terminal.

[0786] Treatment: Based on the feedback, specific exercises and setting changes are implemented, as well as emotional state management.

[0787] Output: Improved gameplay and emotional state management.

[0788] Specific actions: Users practice the presented mini-games daily and incorporate relaxation exercises before playing.

[0789] (Application example 2)

[0790] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0791] Conventional virtual stores offer product recommendations and customer support based solely on user behavior data, meaning they are unable to provide feedback or support that takes into account the user's emotional state. This reduces the psychological satisfaction and convenience of users when making purchasing decisions, and undermines the appeal of virtual stores.

[0792] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data in real time, means for collecting user emotional data in real time, means for transmitting the collected behavioral data and emotional data to the server, means including an AI model for comparing and analyzing the transmitted data with professional data, means including an emotion engine for analyzing the user's emotional state, means for generating feedback to the user based on the results of the comparison and analysis and the emotion analysis, means for transmitting the generated feedback to the user terminal, and means for providing feedback displayed on the user terminal. This makes it possible to analyze both the user's behavioral data and emotional data and recommend products and provide customer support tailored to the user's psychological state.

[0793] "User behavior data" refers to information on specific operations and actions such as the user's line of sight, movement trajectory, selected products, and purchase history within the virtual store.

[0794] "User emotional data" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, voice, and biometric information (such as heart rate and skin potential).

[0795] "Server" is a central computing system for receiving, storing, analyzing user behavioral and emotional data and generating feedback.

[0796] "Professional data" is reference data relating to the actions and operations performed by skilled professionals, and is used for comparative analysis with user behavior data.

[0797] An "AI model" is an algorithm or program that utilizes artificial intelligence to compare and analyze user data and professional data to evaluate user behavior patterns and reactions.

[0798] The "emotion engine" is a dedicated analysis system that analyzes a user's emotional data and identifies the user's current emotional state.

[0799] "Feedback" refers to information such as improvements, recommended products, and customer support provided to users based on analyzed behavioral and emotional data.

[0800] "User terminal" refers to a device operated by a user, and is a device for collecting and displaying behavioral data and emotional data through the virtual store interface.

[0801] A system embodying the present invention collects user behavioral data and emotional data in real time, transmits this data to a server for analysis, and provides appropriate feedback to the user. Detailed embodiments of the system are described below.

[0802] First, the terminal is a device that the user operates in the virtual store, and collects behavioral data such as the user's gaze, movement trajectory, selected products, purchase history, etc. Furthermore, the terminal is equipped with sensors that collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires the user's emotional data.

[0803] The device then compresses the collected behavioral and emotional data and sends it to a server using a secure communication protocol (e.g., HTTPS). The server receives the data and stores it in a database.

[0804] The server formats the received data and inputs it into a pre-trained AI model and emotion engine to analyze the user's behavioral and emotional data. The AI ​​model compares the user data with professional data to evaluate the user's behavioral patterns, reaction time, and types of products they are interested in. The emotion engine also analyzes the collected emotional data to identify the user's current emotional state.

[0805] The server integrates these analysis results and generates optimal feedback for the user. This feedback includes specific improvements, recommended products, and customer support tailored to the user's emotional state. The generated feedback is sent to the device and displayed on the user's interface. The user can then make purchase decisions or change settings based on the presented feedback.

[0806] This section describes specific hardware and software for implementing this system. The hardware includes a VR head-mounted display (HMD) for collecting user behavior data, and a webcam and biometric sensors for collecting user emotion data. The software includes an AI model using TensorFlow / Keras for analyzing the data, an emotion engine for analyzing the emotion data, and the Requests library for data communication.

[0807] For example, when a user looks at a product in a virtual store, the HMD collects gaze data and information about the selected product, while the webcam collects facial expression data and sensors collect heart rate and skin potential. This data is sent to a server and analyzed by an AI model and emotion engine. For example, if a user looks at a particular product with excitement, the system will determine that the user is highly interested in that product and generate feedback recommending related products.

[0808] Below is an example of a prompt sentence using a generative AI model.

[0809] "Generate application code that collects user behavioral and sentiment data and provides optimal product recommendations and customer support within a virtual store."

[0810] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0811] Step 1:

[0812] The device collects user behavior data (eye gaze, movement trajectory, selected products, purchase history, etc.) in real time. The device is also equipped with sensors to collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires emotional data. This data is input from the collection sensors and stored in a database in real time.

[0813] Step 2:

[0814] The device compresses the collected behavioral and emotional data and transmits it to the server using a secure communication protocol (e.g., HTTPS). The transmitted data also includes metadata such as the user ID, session ID, and date and time. The compressed data is input to and received by the server via the transmission protocol.

[0815] Step 3:

[0816] The server stores the received data in a database. The stored data includes user behavior data, emotion data, and metadata. The data is stored appropriately in the database using the user ID as a key. This allows the required data to be quickly searched and analyzed.

[0817] Step 4:

[0818] The server formats the stored data and inputs it into the AI ​​model. The user's behavioral and emotional data is converted into a format that is easy for the AI ​​model to analyze. The formatted data is then input into the AI ​​model as user data. During this process, data cleaning and preprocessing are performed.

[0819] Step 5:

[0820] The AI ​​model installed on the server compares and analyzes user data with professional data. The AI ​​model evaluates the user's behavioral patterns, reaction time, and types of products they are interested in. Specifically, the AI ​​model analyzes the user's eye movements and product selection patterns and compares them with the professional data. The results of this analysis become input for the feedback generation process.

[0821] Step 6:

[0822] The emotion engine analyzes the user's emotion data to identify the user's current emotional state. For example, if the user is excited, the emotion engine detects that state based on changes in facial expressions and biometric data. The emotion analysis results are also used as input for the feedback generation process.

[0823] Step 7:

[0824] The server combines the analysis results of the AI ​​model and the emotion engine to generate optimal feedback for the user. This feedback can include specific improvements, recommended products, and customer support tailored to the user's emotional state. For example, if the user is excited, product recommendations will reflect that excitement.

[0825] Step 8:

[0826] The server transmits the generated feedback to the terminal, and the feedback is displayed in real time on the user's terminal, and the transmitted data includes a specific feedback message to be displayed to the user.

[0827] Step 9:

[0828] The user checks the feedback displayed on the device and reflects it in their actual actions. For example, they check the recommended product and purchase it, or decide on their next action based on the suggested improvements. They then take specific actions or make purchases based on the displayed feedback message.

[0829] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0832] [Third embodiment]

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

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

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

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

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

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

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

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

[0841] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

[0844] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0845] MODE FOR CARRYING OUT THE INVENTION

[0846] This invention is a system that efficiently collects and analyzes user gameplay data and provides feedback. This system is mainly composed of the user's device, a server, and an AI model.

[0847] 1. Collection of Gameplay Data

[0848] The device collects gameplay data in real time while the user is playing the game. This data includes all actions performed by the user (movement, attacks, abilities used, etc.), specifically character position information, aiming movement patterns, button press timing, types of items and weapons used, etc.

[0849] example:

[0850] When a user is playing an online FPS game, the device records detailed data such as from which position the user's character shot the enemy and whether the shot was successful.

[0851] 2. Data transmission

[0852] The devices transmit the collected data to the server in real time, using a secure communication protocol (e.g., HTTPS) to ensure data integrity and confidentiality.

[0853] 3. Data storage and analysis

[0854] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[0855] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. Based on specific parameters (reaction time, shooting accuracy, movement patterns, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[0856] example:

[0857] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[0858] 4. Generate feedback

[0859] The server generates feedback for the user based on the results of the AI ​​model's analysis, including specific areas for improvement, recommended practice methods, and helpful video links.

[0860] example:

[0861] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend you play a specific reaction training mini-game every day."

[0862] 5. Sending and Viewing Feedback

[0863] The server transmits the generated feedback to the user's terminal.

[0864] The device displays the received feedback to the user, and through a user-friendly interface, the user can easily understand specific areas for improvement and practice methods.

[0865] example:

[0866] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published in the link below every day."

[0867] In this way, the present invention provides a concrete means for users to efficiently improve their gaming skills. Users can receive individual feedback based on their own play data in real time, allowing them to continuously and effectively improve their skills.

[0868] The processing flow will be explained below.

[0869] Step 1:

[0870] The user plays a game. The user launches an online FPS game or other competitive game and begins playing as normal.

[0871] Step 2:

[0872] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted with metadata (user ID, game session ID, date and time, etc.).

[0873] Step 3:

[0874] The device compresses the collected data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[0875] Step 4:

[0876] The server receives the data sent from the device and stores it in a database, which structures each user's play history and stores it together with other data.

[0877] Step 5:

[0878] The server (AI model) retrieves the user's play data stored in the database and compares it with data from professional gamers. The AI ​​model learns using past professional data and compares it with the user's play data to detect different movements.

[0879] Step 6:

[0880] When the AI ​​model analyzes the play data of users and professional gamers, it evaluates certain parameters (e.g., reaction time, aiming accuracy, movement patterns, and success rate of shots). As a result of the evaluation, it detects delays or inaccuracies in certain user actions.

[0881] Step 7:

[0882] The server generates useful feedback for users based on the results of the comparative analysis of the AI ​​models, including areas for improvement, recommended practice, and specific action plans.

[0883] Step 8:

[0884] The server transmits the generated feedback to the user's device, ensuring the accuracy and timeliness of the feedback during transmission.

[0885] Step 9:

[0886] The device displays feedback to the user, which is presented in a user-friendly interface designed to allow the user to easily understand and act on the content.

[0887] Step 10:

[0888] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. The user then performs specific practice exercises and changes to the settings based on the feedback.

[0889] By repeating these steps, users can effectively improve their gameplay, promoting continuous growth.

[0890] Example 1

[0891] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0892] Conventional gameplay data analysis systems have difficulty collecting detailed gameplay data from users, comparing it with data from professional gamers, and providing effective and specific feedback to users. Ensuring real-time performance, data integrity, and security are also issues. Therefore, a means has been sought that allows users to quickly and accurately identify their weaknesses and effectively improve their skills.

[0893] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0894] In this invention, the server includes means for collecting user gameplay data in real time, means for transmitting the collected data to the server, means for storing the transmitted data in a database and appropriately formatting it for analysis, means including an AI model for comparing and analyzing the formatted data with play data of professional gamers, means for generating feedback to the user based on the results of the comparison and analysis obtained by the AI ​​model, means for transmitting the generated feedback to the user's device, and means for displaying the transmitted feedback to the user. This makes it possible to provide specific and practical feedback by collecting detailed user play data in real time and comparing it with data from professional gamers.

[0895] "User gameplay data" refers to data that includes detailed information such as location information, button press timing, aiming movement patterns, and types of items and weapons used, which are generated when a user plays a game.

[0896] "Real-time collection means" refers to a method or device for acquiring a user's gameplay data in real time while they are playing, and includes the use of high-precision sensors and software modules.

[0897] "Means for sending to a server" refers to a method or device for sending collected data to a server using a secure communication protocol (e.g., HTTPS).

[0898] "Means for storing in a database and appropriate formatting" means a method or device for storing the data sent to the server in a database and formatting it for input to an AI model, including removing noise from the data and standardizing timestamps.

[0899] An "AI model for comparative analysis of professional gamer play data" is an artificial intelligence model trained based on the play data of professional gamers, and is a means for conducting comparative analysis of user play data and professional gamer data.

[0900] "Means for generating feedback" means a method or device for automatically generating feedback based on the analysis results obtained from the AI ​​model, including areas for improvement in the user's play and recommended practice methods.

[0901] The "means for transmitting feedback to the user's terminal" refers to a method or device for transmitting the generated feedback to the user's terminal, and it is recommended that a secure communication protocol be used.

[0902] "Means for displaying feedback" refers to a method or device for providing the feedback received at the user's terminal to the user in the form of visual, audio, etc. This includes a user-friendly interface.

[0903] MODE FOR CARRYING OUT THE INVENTION

[0904] This invention is a system that collects gameplay data in real time when a user plays an online game, analyzes the data, and provides specific feedback. The system mainly consists of a user's device, a server, and an AI model. Each step and the hardware and software used are described in detail below.

[0905] 1. Collection of Gameplay Data

[0906] The device collects real-time gameplay data as the user plays the game. This data includes the character's position, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, high-precision data acquisition is achieved by using dedicated sensor modules and software (e.g., the logging function of Unity or Unreal Engine).

[0907] example:

[0908] When a user plays an online FPS game, the device records detailed data such as where the character shot the enemy from and whether the shot was successful. This data includes positional data and button input events for each frame of play.

[0909] 2. Data transmission

[0910] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS to ensure data integrity and confidentiality, along with metadata such as user ID and session ID.

[0911] 3. Data storage and preprocessing

[0912] The server receives the data sent from the device and first stores it in a database. This database uses SQL or NoSQL to store data quickly and reliably. The stored data includes the user ID, session ID, date and time, and various play data. The stored data is then formatted and converted into a format suitable for input into the AI ​​model. This process includes removing noise from the data and standardizing timestamps.

[0913] example:

[0914] The server analyzes the received data and inserts it into a database using, for example, a user ID or session ID as a key. In the formatting stage, the data is de-noised and any necessary transformations (e.g., standardization of timestamps) are performed.

[0915] 4. Data Analysis

[0916] The server inputs the formatted data into a generative AI model, which has been pre-trained based on the play data of professional gamers. The AI ​​model compares the user's play data with the professional gamers' data and performs analysis based on specific evaluation metrics (e.g., reaction time, shooting accuracy, movement patterns, etc.). The analysis results identify which aspects of the user's play differ from the professional gamers'.

[0917] example:

[0918] The AI ​​model analyzes the received gameplay data and identifies that the user's reaction time is 0.3 seconds slower than that of a professional gamer. This process uses deep learning frameworks (e.g., TensorFlow, PyTorch).

[0919] 5. Generate feedback

[0920] The server generates feedback for the user based on the analysis results obtained from the AI ​​model. This feedback includes specific areas for improvement, recommended practice methods, reference video links, etc. The generated feedback is output in a format that is easy for the user to understand.

[0921] example:

[0922] Feedback is generated in the form of, "Your reaction time is delayed by an average of 0.3 seconds. To improve this time lag, we recommend that you play a specific reaction training mini-game every day." The reference video link includes a tutorial video by a professional gamer.

[0923] 6. Sending and Viewing Feedback

[0924] The server sends the generated feedback to the user's device, which then displays it to the user, allowing them to intuitively understand specific areas for improvement and practice methods. The feedback is displayed on the device through a pop-up notification or a dedicated app interface.

[0925] example:

[0926] A message such as "Your reaction time is 0.3 seconds slower. You can improve your reaction speed by playing the mini-game linked below every day" will be displayed, and clicking the link will launch the corresponding mini-game.

[0927] Specific examples of input prompts for generative AI models

[0928] "Analyze this person's gameplay data to see how they compare to professional gamers and identify areas for improvement. Pay particular attention to reaction time, shooting accuracy, and movement patterns."

[0929] The system of the present invention allows users to receive personalized feedback based on their own play data in real time, enabling them to continuously improve their skills. An efficient feedback system can be provided by integrating the server, device, and AI model. By implementing this system, users can quickly and accurately identify their weaknesses and improve their skills in specific ways.

[0930] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0931] Specific processing flow of the program

[0932] Step 1: Collecting gameplay data

[0933] The device collects gameplay data in real time as the user plays the game. This data includes character position information, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, the device uses dedicated sensor modules and software to collect this data with high precision.

[0934] Input: User actions during gameplay, location information, button press timing, etc.

[0935] Output: Collected gameplay data

[0936] Step 2: Sending data

[0937] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS, along with metadata such as user ID and session ID.

[0938] Input: Collected gameplay data, user ID, session ID

[0939] Output: Data in the format sent to the server

[0940] Step 3: Storing and Preprocessing Data

[0941] The server receives the data sent from the device and stores it in a database. The stored data includes the user ID, session ID, date and time, and various play data. The data is then appropriately formatted for input into the AI ​​model. This formatting includes removing noise from the data and standardizing timestamps.

[0942] Input: Submitted gameplay data, user ID, session ID

[0943] Output: Data in a formatted form

[0944] Specific behavior:

[0945] 1. The server receives the collected data.

[0946] 2. Save it to the database.

[0947] 3. Format the stored data to input into the AI ​​model.

[0948] Step 4: Analyze the data

[0949] The server inputs the formatted data into a generative AI model. The AI ​​model is pre-trained based on the play data of professional gamers and compares the user's play data with the professional gamer's data. It performs analysis based on specific evaluation indicators (e.g., reaction time, shooting accuracy, movement patterns, etc.).

[0950] Input: Data in a formatted form

[0951] Output: Analysis results

[0952] Specific behavior:

[0953] 1. The server inputs the formatted data into the AI ​​model.

[0954] 2. The AI ​​model analyzes the data and identifies the differences between users and professional gamers.

[0955] Step 5: Generate feedback

[0956] The server generates feedback for the user based on the analysis results obtained from the AI ​​model, including specific areas for improvement, recommended practice methods, and helpful video links.

[0957] Input: Analysis results

[0958] Output: Generated feedback

[0959] Specific behavior:

[0960] 1. The server receives the analysis results.

[0961] 2. Automatically generate feedback based on the analysis results.

[0962] 3. Feedback will include areas for improvement, practice methods, and helpful video links.

[0963] Step 6: Submit and view feedback

[0964] The server sends the generated feedback to the user's device, which then displays the received feedback to the user. The device provides a user-friendly interface, allowing the user to intuitively understand specific points for improvement and practice methods.

[0965] Input: Generated feedback

[0966] Output: Feedback that is displayed to the user

[0967] Specific behavior:

[0968] 1. The server sends the generated feedback to the device.

[0969] 2. The device receives and displays the feedback.

[0970] 3. Users review the feedback and understand areas for improvement and how to practice.

[0971] In this way, the system of the present invention efficiently supports users in improving their gaming skills.

[0972] (Application example 1)

[0973] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0974] Currently, robotic work in factories is often not sufficiently focused on efficiency and precision. This creates a need for work optimization and efficiency, but on-site data collection and analysis is not easy. Furthermore, there are limited means of comparing results with professional work, creating a lack of concrete feedback for improving robotic work efficiency and technology.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0976] In this invention, the server includes means for collecting user work data in real time, means for transmitting the collected data to the server, means including a generative AI model for comparing and analyzing the transmitted data with professional work data, means for generating feedback to the user based on the results of the comparison and analysis, means for transmitting the generated feedback to the user's terminal, and means for providing feedback displayed on the user's terminal, thereby enabling improvement of the work efficiency of factory robots and provision of specific feedback.

[0977] "User work data" refers to data about all actions performed by robots in a factory, including moving, lifting, placing, etc.

[0978] A "server" is a computing device that has the ability to receive, store, analyze, and transmit data over the Internet or a local network.

[0979] A "generative AI model" is an artificial intelligence model trained based on professional work data, and is used to compare and analyze user work data.

[0980] "Professional work data" refers to the movement data of experts who perform work efficiently and with high precision.

[0981] "Feedback" refers to advice and suggestions for improvement provided to users based on the analysis results.

[0982] A "user terminal" is an information processing device that displays feedback and can be operated by the user, and includes a smartphone, tablet, etc.

[0983] This invention aims to improve the work efficiency of factory robots by building a system that collects user work data in real time, analyzes it using a generative AI model, and provides appropriate feedback.

[0984] System Program

[0985] 1. Collecting user work data

[0986] This system collects all actions (moving, lifting, placing, etc.) performed by factory robots in real time. Specifically, motion and position sensors are attached to the robots to collect the data. The collected data records the robot's actions in detail and serves as the basis for future analysis.

[0987] 2. Data transmission

[0988] The collected data is sent to the server using a secure communication protocol (e.g. HTTPS), which ensures the integrity and confidentiality of the data.

[0989] 3. Data storage and analysis

[0990] The server receives the submitted data and automatically stores it in a database, including metadata such as the robot ID, session ID, date and time, etc. The server then formats the data appropriately for input into the generative AI model.

[0991] The generative AI model is pre-trained based on the work data of professionals and performs comparative analysis with the user's work data. Based on specific parameters (processing time, movement patterns, work accuracy, etc.), the AI ​​model detects which aspects of the user's work differ from those of professionals.

[0992] 4. Generate feedback

[0993] The server generates feedback for users based on the analysis results of the generative AI model, including specific points for improvement, recommended work methods, and reference video links. Users can quickly implement appropriate improvement measures to improve their work efficiency.

[0994] 5. Sending and Viewing Feedback

[0995] The server sends the generated feedback to the user's device, and the device displays the received feedback to the user. Through a user-friendly interface, the user can easily understand specific improvements and work methods.

[0996] Examples and prompts

[0997] For example, if a robot used in a factory deviates from the optimal movement pattern when lifting an object, the generative AI model will determine that "movement efficiency could be improved" and provide feedback on the appropriate way to lift the object.

[0998] Example prompt sentence:

[0999] "Based on the operation data of factory robots, identify the difference between the current operation performance and the optimal operation pattern and provide feedback on areas for improvement."

[1000] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1001] Step 1:

[1002] Collecting user work data

[1003] The terminal collects data in real time from motion and position sensors attached to the factory robots. This data includes information on the robot's movements, lifting, and placement. The terminal's input is real-time data from the sensors, and its output is the collected motion data.

[1004] Step 2:

[1005] Sending data

[1006] The terminal sends the collected data to the server using the HTTPS communication protocol. Using this communication protocol ensures the integrity and confidentiality of the data. The input is the operational data collected by the terminal, and the output is the data sent to the server.

[1007] Step 3:

[1008] Data storage

[1009] The server receives the transmitted data and automatically stores it in a database. The stored data also includes metadata such as the robot ID, session ID, date and time. The input is the data transmitted to the server, and the output is the data stored in the database.

[1010] Step 4:

[1011] Data formatting and analysis

[1012] The server formats the stored data appropriately for input into the generative AI model. The formatted data is passed to the generative AI model and compared with the professional's work data for analysis. The input is the data stored in the database, and the output is the formatted data input into the generative AI model.

[1013] Step 5:

[1014] Generate feedback

[1015] The server generates feedback for the user based on the analysis results of the generative AI model, including specific areas for improvement, recommended work methods, reference video links, etc. The input is the analysis results of the generative AI model, and the output is the generated feedback.

[1016] Step 6:

[1017] Send Feedback

[1018] The server sends the generated feedback to the user's terminal, where the input is the generated feedback and the output is the feedback sent to the user's terminal.

[1019] Step 7:

[1020] View Feedback

[1021] The terminal displays the received feedback to the user, allowing the user to easily understand specific improvements and work methods based on the feedback. The input is the feedback sent from the server, and the output is the feedback displayed to the user.

[1022] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1023] MODE FOR CARRYING OUT THE INVENTION

[1024] The present invention is a system that includes an emotion engine that collects, analyzes, and provides feedback on a user's gameplay data, as well as recognizes the user's emotional state and adjusts the feedback accordingly. The system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[1025] 1. Collection of Gameplay Data

[1026] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[1027] 2. Collecting Emotional Data

[1028] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[1029] example:

[1030] If the user is nervous, changes in facial expression, an increased heart rate, and a change in tone of voice are detected.

[1031] 3. Data transmission

[1032] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[1033] 4. Data storage and analysis

[1034] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[1035] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. The AI ​​model detects which aspects of the user's play differ from those of professional gamers based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.).

[1036] example:

[1037] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1038] 5. Emotion Data Analysis

[1039] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[1040] example:

[1041] The analysis results show that users are tense while playing.

[1042] 6. Generate feedback

[1043] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[1044] example:

[1045] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game daily. We also recommend that you incorporate relaxation techniques into your play, as you are feeling tense."

[1046] 7. Sending and Viewing Feedback

[1047] The server transmits the generated feedback to the user's terminal.

[1048] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the content.

[1049] example:

[1050] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published at the link below every day. Also, it has been analyzed that you are feeling tense. Please try a 5-minute relaxation exercise before playing the game."

[1051] 8. User Practices

[1052] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. Based on the feedback, the user makes specific practice exercises and changes settings, and also manages their emotional state.

[1053] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] The user starts a competitive game, such as an online FPS game, and begins playing as usual.

[1057] Step 2:

[1058] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The data is captured every second and converted into a format (e.g., JSON).

[1059] Step 3:

[1060] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. Emotional data is also processed in the same way.

[1061] Step 4:

[1062] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the accuracy and safety of data transmission.

[1063] Step 5:

[1064] The server receives the gameplay data and emotion data sent from the device and stores them in a database, including metadata such as the user ID, session ID, and date and time.

[1065] Step 6:

[1066] The server (AI model) retrieves the user's play data stored in the database and compares and analyzes it with the data of professional gamers. The AI ​​model learns using past professional data and evaluates it based on specific parameters (reaction time, aim position, movement pattern, success rate of gunfights).

[1067] example:

[1068] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1069] Step 7:

[1070] The emotion engine analyzes the user's current emotional state (e.g., excited, nervous, relaxed) based on collected emotional data. The algorithm evaluates the user's emotional state based on facial expressions, heart rate, tone of voice, etc.

[1071] example:

[1072] The emotion engine detects when the user is in a tense state.

[1073] Step 8:

[1074] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[1075] example:

[1076] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game every day. Also, your emotional data suggests that you are feeling tense, so we recommend that you also incorporate some simple exercises to help you relax."

[1077] Step 9:

[1078] The server sends the generated feedback to the user's device, ensuring the accuracy and timeliness of the information.

[1079] Step 10:

[1080] The device displays the received feedback to the user through a user-friendly interface designed to help users easily understand and act on the content.

[1081] example:

[1082] "Spend 10 minutes each day playing the reaction speed mini-game linked below. Also, try the relaxation exercises below to help relieve tension."

[1083] Step 11:

[1084] The user checks the provided feedback and plays the game again, taking into consideration the areas for improvement and recommended practice methods. Based on the feedback, the user implements specific practice and setting changes, and also manages their emotional state.

[1085] By repeating these steps, users can effectively improve their play, promoting continuous growth and management of their emotional state.

[1086] Example 2

[1087] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1088] In modern gaming environments, the importance of player skill improvement and emotional management is increasing, but current systems lack comprehensive support for these. Therefore, a comprehensive system is needed to help players efficiently improve their skills and simultaneously manage their emotional state during gameplay. This system must also be capable of collecting user data in real time and providing appropriate feedback.

[1089] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1090] In this invention, the server includes: means for collecting gameplay data of users in real time; means for collecting facial expressions, voice, and biometric information to recognize the user's emotional state; means for transmitting the collected gameplay data and emotional data to the server; means for storing and shaping the transmitted data in a database; means including an AI model for comparing and analyzing the shaped data with play data of professional gamers; means including an emotion engine for analyzing the emotional data and identifying the user's emotional state; means for generating feedback to the user based on the analysis results of the AI ​​model and the emotion engine; means for transmitting the generated feedback to the user's device; and means for providing feedback displayed on the user's device. This allows the user to receive appropriate feedback in real time according to their skill level and emotional state.

[1091] "User" refers to an individual who uses the system to play games.

[1092] "Gameplay data" refers to data that includes information such as movement trajectory, aim position, firing timing, characters used, weapons used, and enemy detection time that is generated in the course of a user playing a game.

[1093] "Emotional state" refers to the user's psychological and physiological state while playing a game, and specifically includes emotions such as stress, tension, and joy.

[1094] "Terminal" refers to a device that allows a user to play a game and has the function of collecting game play data and emotional data and transmitting them to a server.

[1095] A "server" is a computer system that receives and stores data sent from a device, and then analyzes it using an AI model and emotion engine.

[1096] A "database" is a system for systematically storing and managing data stored on a server.

[1097] An "AI model" is an artificial intelligence model that is trained based on the play data of professional gamers and used to compare and analyze user gameplay data.

[1098] An "emotion engine" is software with analytical functions that analyzes a user's emotional data and identifies their current emotional state.

[1099] "Feedback" is information that includes specific improvements and advice for users, generated based on the analysis results of the AI ​​model and emotion engine.

[1100] "Real-time" refers to gameplay and data collection occurring instantly, without delay.

[1101] A "professional gamer" is a game player with specialized skills and experience, and their data is used to train the AI ​​model.

[1102] This system collects, analyzes, and provides feedback on a user's gameplay data, and also recognizes the user's emotional state and adjusts the feedback accordingly. This system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[1103] Gameplay data collection

[1104] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[1105] Example: While a user is playing a shooting game, the device records the user's movement trajectory and firing timing in milliseconds.

[1106] Collecting Emotional Data

[1107] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[1108] For example, if the user is nervous, the increase in heart rate and changes in facial expression are collected and sent to the emotion engine.

[1109] Sending data

[1110] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), where the data integrity and security are verified.

[1111] Example: Collected data is encrypted and sent securely to a server.

[1112] Data storage and analysis

[1113] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data for input into the AI ​​model.

[1114] The AI ​​model is pre-trained based on the play data of professional gamers and then performs a comparative analysis with the user's play data. Based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[1115] Example: The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1116] Emotional Data Analysis

[1117] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[1118] Example: The analysis results show that the user is playing in a tense state.

[1119] Generate feedback

[1120] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[1121] For example: "Your reaction time is off by an average of 0.3 seconds. To improve this lag, we recommend that you play a specific mini-game every day. We also recommend some relaxation exercises to help relieve tension."

[1122] Sending and Viewing Feedback

[1123] The server transmits the generated feedback to the user's terminal.

[1124] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the feedback.

[1125] For example: "Your reaction time is off by 0.3 seconds. Please click the link below to play a mini-game. Also, try 5 minutes of relaxation before playing."

[1126] User Practice

[1127] The user reviews the provided feedback and plays the game again, focusing on areas for improvement in actual gameplay. They then carry out specific exercises, change settings, and manage their emotional state.

[1128] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[1129] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1130] Specific processing explanation of the system program divided into processing steps

[1131] Step 1: Collecting gameplay data

[1132] The terminal collects the user's gameplay data in real time.

[1133] Input: User's gameplay status (movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time).

[1134] Processing: The device monitors and records this data using sensors and software modules.

[1135] Output: Formatted gameplay data (including user ID, game session ID, date and time).

[1136] Specific operation: While a user is playing a shooting game, the device collects the user's movement trajectory and firing timing in milliseconds and temporarily stores them in a database.

[1137] Step 2: Collecting emotion data

[1138] The device collects data to recognize the user's emotional state in real time.

[1139] Input: User's facial expressions, voice, and biometric information (heart rate, skin potential, etc.).

[1140] Processing: The device collects data from cameras, microphones, biosensors, etc. and pre-processes it to identify emotional states.

[1141] Output: Formatted emotion data (including user ID and timestamp).

[1142] What it does: If a user becomes tense while playing a game, the device will use the camera to detect the movement of the user's facial muscles and the heart rate sensor to record an increase in heart rate.

[1143] Step 3: Sending data

[1144] The terminal transmits the collected game play data and emotion data to the server.

[1145] Input: Collected gameplay and emotion data.

[1146] Processing: The device compresses this data, transmits it using a secure communication protocol (e.g. HTTPS), and checks the data for integrity and security.

[1147] Output: Data securely sent to the server.

[1148] What it does: Encrypts collected data, establishes a secure communication channel and sends it to the server.

[1149] Step 4: Saving and formatting the data

[1150] The server receives and stores the data.

[1151] Input: Gameplay and emotion data sent from the device.

[1152] Processing: The server saves these data in a database, adding necessary metadata (user ID, session ID, date and time).

[1153] Output: Organized data stored in a database.

[1154] What happens: The server creates a new session in the database and saves each data entry.

[1155] The server formats the stored data for input into the AI ​​model.

[1156] Input: Saved gameplay and emotion data.

[1157] Processing: Converting data into an input format for the AI ​​model.

[1158] Output: Data in a format that can be input into an AI model.

[1159] What it does: The server formats the data and converts it into a form that is easy for the AI ​​model to handle.

[1160] Step 5: Analyze the data

[1161] The AI ​​model analyzes the user's play data.

[1162] Input: Formatted gameplay data.

[1163] Processing: The AI ​​model compares the gameplay data of professional gamers and analyzes it based on specific parameters (reaction time, aim position, movement patterns, and success rate of gunfights).

[1164] Output: Comparative analysis results of user gameplay.

[1165] How it works: The AI ​​model analyzes the user's reaction time and compares the time lag with that of professional gamers.

[1166] The emotion engine analyzes the emotion data.

[1167] Input: Formatted emotion data.

[1168] Processing: The emotion engine analyzes the user's emotional state (stress, tension, joy, etc.) based on the collected emotion data.

[1169] Output: Analysis results about the user's emotional state.

[1170] Specific operation: The emotion engine analyzes the user's heart rate and facial expression changes to detect tension.

[1171] Step 6: Generate feedback

[1172] The server generates feedback based on the analysis results of the AI ​​model and emotion engine.

[1173] Input: Comparative analysis results of AI models and emotion engine analysis results.

[1174] Processing: Comprehensively evaluate the analysis results and generate advice on areas for improvement, recommended practice, and emotional state.

[1175] Output: The generated feedback.

[1176] Specific action: "Your reaction time is delayed by an average of 0.3 seconds. Play specific mini-games every day and try relaxation exercises to reduce tension."

[1177] Step 7: Submit and view feedback

[1178] The server transmits the generated feedback to the user's terminal.

[1179] Input: The generated feedback.

[1180] Action: Send feedback to the device.

[1181] Output: Feedback sent to the device.

[1182] Specific behavior: The generated feedback is immediately sent to the user's device.

[1183] The device displays the feedback to the user.

[1184] Input: Feedback sent by the server.

[1185] Processing: The feedback is displayed in a user-friendly interface.

[1186] Output: The feedback displayed to the user.

[1187] Specific action: "Your reaction time is off by 0.3 seconds. Please click the link below to play the mini-game. Also, try 5 minutes of relaxation before playing."

[1188] Step 8: User Practice

[1189] Enhance gameplay based on user-provided feedback.

[1190] Input: Feedback displayed on the terminal.

[1191] Treatment: Based on the feedback, specific exercises and setting changes are implemented, as well as emotional state management.

[1192] Output: Improved gameplay and emotional state management.

[1193] Specific actions: Users practice the presented mini-games daily and incorporate relaxation exercises before playing.

[1194] (Application example 2)

[1195] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1196] Conventional virtual stores offer product recommendations and customer support based solely on user behavior data, meaning they are unable to provide feedback or support that takes into account the user's emotional state. This reduces the psychological satisfaction and convenience of users when making purchasing decisions, and undermines the appeal of virtual stores.

[1197] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data in real time, means for collecting user emotional data in real time, means for transmitting the collected behavioral data and emotional data to the server, means including an AI model for comparing and analyzing the transmitted data with professional data, means including an emotion engine for analyzing the user's emotional state, means for generating feedback to the user based on the results of the comparison and analysis and the emotion analysis, means for transmitting the generated feedback to the user terminal, and means for providing feedback displayed on the user terminal. This makes it possible to analyze both the user's behavioral data and emotional data and recommend products and provide customer support tailored to the user's psychological state.

[1198] "User behavior data" refers to information on specific operations and actions such as the user's line of sight, movement trajectory, selected products, and purchase history within the virtual store.

[1199] "User emotional data" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, voice, and biometric information (such as heart rate and skin potential).

[1200] "Server" is a central computing system for receiving, storing, analyzing user behavioral and emotional data and generating feedback.

[1201] "Professional data" is reference data relating to the actions and operations performed by skilled professionals, and is used for comparative analysis with user behavior data.

[1202] An "AI model" is an algorithm or program that utilizes artificial intelligence to compare and analyze user data and professional data to evaluate user behavior patterns and reactions.

[1203] The "emotion engine" is a dedicated analysis system that analyzes a user's emotional data and identifies the user's current emotional state.

[1204] "Feedback" refers to information such as improvements, recommended products, and customer support provided to users based on analyzed behavioral and emotional data.

[1205] "User terminal" refers to a device operated by a user, and is a device for collecting and displaying behavioral data and emotional data through the virtual store interface.

[1206] A system embodying the present invention collects user behavioral data and emotional data in real time, transmits this data to a server for analysis, and provides appropriate feedback to the user. Detailed embodiments of the system are described below.

[1207] First, the terminal is a device that the user operates in the virtual store, and collects behavioral data such as the user's gaze, movement trajectory, selected products, purchase history, etc. Furthermore, the terminal is equipped with sensors that collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires the user's emotional data.

[1208] The device then compresses the collected behavioral and emotional data and sends it to a server using a secure communication protocol (e.g., HTTPS). The server receives the data and stores it in a database.

[1209] The server formats the received data and inputs it into a pre-trained AI model and emotion engine to analyze the user's behavioral and emotional data. The AI ​​model compares the user data with professional data to evaluate the user's behavioral patterns, reaction time, and types of products they are interested in. The emotion engine also analyzes the collected emotional data to identify the user's current emotional state.

[1210] The server integrates these analysis results and generates optimal feedback for the user. This feedback includes specific improvements, recommended products, and customer support tailored to the user's emotional state. The generated feedback is sent to the device and displayed on the user's interface. The user can then make purchase decisions or change settings based on the presented feedback.

[1211] This section describes specific hardware and software for implementing this system. The hardware includes a VR head-mounted display (HMD) for collecting user behavior data, and a webcam and biometric sensors for collecting user emotion data. The software includes an AI model using TensorFlow / Keras for analyzing the data, an emotion engine for analyzing the emotion data, and the Requests library for data communication.

[1212] For example, when a user looks at a product in a virtual store, the HMD collects gaze data and information about the selected product, while the webcam collects facial expression data and sensors collect heart rate and skin potential. This data is sent to a server and analyzed by an AI model and emotion engine. For example, if a user looks at a particular product with excitement, the system will determine that the user is highly interested in that product and generate feedback recommending related products.

[1213] Below is an example of a prompt sentence using a generative AI model.

[1214] "Generate application code that collects user behavioral and sentiment data and provides optimal product recommendations and customer support within a virtual store."

[1215] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1216] Step 1:

[1217] The device collects user behavior data (eye gaze, movement trajectory, selected products, purchase history, etc.) in real time. The device is also equipped with sensors to collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires emotional data. This data is input from the collection sensors and stored in a database in real time.

[1218] Step 2:

[1219] The device compresses the collected behavioral and emotional data and transmits it to the server using a secure communication protocol (e.g., HTTPS). The transmitted data also includes metadata such as the user ID, session ID, and date and time. The compressed data is input to and received by the server via the transmission protocol.

[1220] Step 3:

[1221] The server stores the received data in a database. The stored data includes user behavior data, emotion data, and metadata. The data is stored appropriately in the database using the user ID as a key. This allows the required data to be quickly searched and analyzed.

[1222] Step 4:

[1223] The server formats the stored data and inputs it into the AI ​​model. The user's behavioral and emotional data is converted into a format that is easy for the AI ​​model to analyze. The formatted data is then input into the AI ​​model as user data. During this process, data cleaning and preprocessing are performed.

[1224] Step 5:

[1225] The AI ​​model installed on the server compares and analyzes user data with professional data. The AI ​​model evaluates the user's behavioral patterns, reaction time, and types of products they are interested in. Specifically, the AI ​​model analyzes the user's eye movements and product selection patterns and compares them with the professional data. The results of this analysis become input for the feedback generation process.

[1226] Step 6:

[1227] The emotion engine analyzes the user's emotion data to identify the user's current emotional state. For example, if the user is excited, the emotion engine detects that state based on changes in facial expressions and biometric data. The emotion analysis results are also used as input for the feedback generation process.

[1228] Step 7:

[1229] The server combines the analysis results of the AI ​​model and the emotion engine to generate optimal feedback for the user. This feedback can include specific improvements, recommended products, and customer support tailored to the user's emotional state. For example, if the user is excited, product recommendations will reflect that excitement.

[1230] Step 8:

[1231] The server transmits the generated feedback to the terminal, and the feedback is displayed in real time on the user's terminal, and the transmitted data includes a specific feedback message to be displayed to the user.

[1232] Step 9:

[1233] The user checks the feedback displayed on the device and reflects it in their actual actions. For example, they check the recommended product and purchase it, or decide on their next action based on the suggested improvements. They then take specific actions or make purchases based on the displayed feedback message.

[1234] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1235] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1236] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1237] [Fourth embodiment]

[1238] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1239] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1240] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1241] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1242] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1243] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1244] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1245] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1246] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1247] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1248] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1249] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1250] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1251] MODE FOR CARRYING OUT THE INVENTION

[1252] This invention is a system that efficiently collects and analyzes user gameplay data and provides feedback. This system is mainly composed of the user's device, a server, and an AI model.

[1253] 1. Collection of Gameplay Data

[1254] The device collects gameplay data in real time while the user is playing the game. This data includes all actions performed by the user (movement, attacks, abilities used, etc.), specifically character position information, aiming movement patterns, button press timing, types of items and weapons used, etc.

[1255] example:

[1256] When a user is playing an online FPS game, the device records detailed data such as from which position the user's character shot the enemy and whether the shot was successful.

[1257] 2. Data transmission

[1258] The devices transmit the collected data to the server in real time, using a secure communication protocol (e.g., HTTPS) to ensure data integrity and confidentiality.

[1259] 3. Data storage and analysis

[1260] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[1261] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. Based on specific parameters (reaction time, shooting accuracy, movement patterns, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[1262] example:

[1263] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1264] 4. Generate feedback

[1265] The server generates feedback for the user based on the results of the AI ​​model's analysis, including specific areas for improvement, recommended practice methods, and helpful video links.

[1266] example:

[1267] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend you play a specific reaction training mini-game every day."

[1268] 5. Sending and Viewing Feedback

[1269] The server transmits the generated feedback to the user's terminal.

[1270] The device displays the received feedback to the user, and through a user-friendly interface, the user can easily understand specific areas for improvement and practice methods.

[1271] example:

[1272] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published in the link below every day."

[1273] In this way, the present invention provides a concrete means for users to efficiently improve their gaming skills. Users can receive individual feedback based on their own play data in real time, allowing them to continuously and effectively improve their skills.

[1274] The processing flow will be explained below.

[1275] Step 1:

[1276] The user plays a game. The user launches an online FPS game or other competitive game and begins playing as normal.

[1277] Step 2:

[1278] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted with metadata (user ID, game session ID, date and time, etc.).

[1279] Step 3:

[1280] The device compresses the collected data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[1281] Step 4:

[1282] The server receives the data sent from the device and stores it in a database, which structures each user's play history and stores it together with other data.

[1283] Step 5:

[1284] The server (AI model) retrieves the user's play data stored in the database and compares it with data from professional gamers. The AI ​​model learns using past professional data and compares it with the user's play data to detect different movements.

[1285] Step 6:

[1286] When the AI ​​model analyzes the play data of users and professional gamers, it evaluates certain parameters (e.g., reaction time, aiming accuracy, movement patterns, and success rate of shots). As a result of the evaluation, it detects delays or inaccuracies in certain user actions.

[1287] Step 7:

[1288] The server generates useful feedback for users based on the results of the comparative analysis of the AI ​​models, including areas for improvement, recommended practice, and specific action plans.

[1289] Step 8:

[1290] The server transmits the generated feedback to the user's device, ensuring the accuracy and timeliness of the feedback during transmission.

[1291] Step 9:

[1292] The device displays feedback to the user, which is presented in a user-friendly interface designed to allow the user to easily understand and act on the content.

[1293] Step 10:

[1294] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. The user then performs specific practice exercises and changes to the settings based on the feedback.

[1295] By repeating these steps, users can effectively improve their gameplay, promoting continuous growth.

[1296] Example 1

[1297] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1298] Conventional gameplay data analysis systems have difficulty collecting detailed gameplay data from users, comparing it with data from professional gamers, and providing effective and specific feedback to users. Ensuring real-time performance, data integrity, and security are also issues. Therefore, a means has been sought that allows users to quickly and accurately identify their weaknesses and effectively improve their skills.

[1299] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1300] In this invention, the server includes means for collecting user gameplay data in real time, means for transmitting the collected data to the server, means for storing the transmitted data in a database and appropriately formatting it for analysis, means including an AI model for comparing and analyzing the formatted data with play data of professional gamers, means for generating feedback to the user based on the results of the comparison and analysis obtained by the AI ​​model, means for transmitting the generated feedback to the user's device, and means for displaying the transmitted feedback to the user. This makes it possible to provide specific and practical feedback by collecting detailed user play data in real time and comparing it with data from professional gamers.

[1301] "User gameplay data" refers to data that includes detailed information such as location information, button press timing, aiming movement patterns, and types of items and weapons used, which are generated when a user plays a game.

[1302] "Real-time collection means" refers to a method or device for acquiring a user's gameplay data in real time while they are playing, and includes the use of high-precision sensors and software modules.

[1303] "Means for sending to a server" refers to a method or device for sending collected data to a server using a secure communication protocol (e.g., HTTPS).

[1304] "Means for storing in a database and appropriate formatting" means a method or device for storing the data sent to the server in a database and formatting it for input to an AI model, including removing noise from the data and standardizing timestamps.

[1305] An "AI model for comparative analysis of professional gamer play data" is an artificial intelligence model trained based on the play data of professional gamers, and is a means for conducting comparative analysis of user play data and professional gamer data.

[1306] "Means for generating feedback" means a method or device for automatically generating feedback based on the analysis results obtained from the AI ​​model, including areas for improvement in the user's play and recommended practice methods.

[1307] The "means for transmitting feedback to the user's terminal" refers to a method or device for transmitting the generated feedback to the user's terminal, and it is recommended that a secure communication protocol be used.

[1308] "Means for displaying feedback" refers to a method or device for providing the feedback received at the user's terminal to the user in the form of visual, audio, etc. This includes a user-friendly interface.

[1309] MODE FOR CARRYING OUT THE INVENTION

[1310] This invention is a system that collects gameplay data in real time when a user plays an online game, analyzes the data, and provides specific feedback. The system mainly consists of a user's device, a server, and an AI model. Each step and the hardware and software used are described in detail below.

[1311] 1. Collection of Gameplay Data

[1312] The device collects real-time gameplay data as the user plays the game. This data includes the character's position, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, high-precision data acquisition is achieved by using dedicated sensor modules and software (e.g., the logging function of Unity or Unreal Engine).

[1313] example:

[1314] When a user plays an online FPS game, the device records detailed data such as where the character shot the enemy from and whether the shot was successful. This data includes positional data and button input events for each frame of play.

[1315] 2. Data transmission

[1316] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS to ensure data integrity and confidentiality, along with metadata such as user ID and session ID.

[1317] 3. Data storage and preprocessing

[1318] The server receives the data sent from the device and first stores it in a database. This database uses SQL or NoSQL to store data quickly and reliably. The stored data includes the user ID, session ID, date and time, and various play data. The stored data is then formatted and converted into a format suitable for input into the AI ​​model. This process includes removing noise from the data and standardizing timestamps.

[1319] example:

[1320] The server analyzes the received data and inserts it into a database using, for example, a user ID or session ID as a key. In the formatting stage, the data is de-noised and any necessary transformations (e.g., standardization of timestamps) are performed.

[1321] 4. Data Analysis

[1322] The server inputs the formatted data into a generative AI model, which has been pre-trained based on the play data of professional gamers. The AI ​​model compares the user's play data with the professional gamers' data and performs analysis based on specific evaluation metrics (e.g., reaction time, shooting accuracy, movement patterns, etc.). The analysis results identify which aspects of the user's play differ from the professional gamers'.

[1323] example:

[1324] The AI ​​model analyzes the received gameplay data and identifies that the user's reaction time is 0.3 seconds slower than that of a professional gamer. This process uses deep learning frameworks (e.g., TensorFlow, PyTorch).

[1325] 5. Generate feedback

[1326] The server generates feedback for the user based on the analysis results obtained from the AI ​​model. This feedback includes specific areas for improvement, recommended practice methods, reference video links, etc. The generated feedback is output in a format that is easy for the user to understand.

[1327] example:

[1328] Feedback is generated in the form of, "Your reaction time is delayed by an average of 0.3 seconds. To improve this time lag, we recommend that you play a specific reaction training mini-game every day." The reference video link includes a tutorial video by a professional gamer.

[1329] 6. Sending and Viewing Feedback

[1330] The server sends the generated feedback to the user's device, which then displays it to the user, allowing them to intuitively understand specific areas for improvement and practice methods. The feedback is displayed on the device through a pop-up notification or a dedicated app interface.

[1331] example:

[1332] A message such as "Your reaction time is 0.3 seconds slower. You can improve your reaction speed by playing the mini-game linked below every day" will be displayed, and clicking the link will launch the corresponding mini-game.

[1333] Specific examples of input prompts for generative AI models

[1334] "Analyze this person's gameplay data to see how they compare to professional gamers and identify areas for improvement. Pay particular attention to reaction time, shooting accuracy, and movement patterns."

[1335] The system of the present invention allows users to receive personalized feedback based on their own play data in real time, enabling them to continuously improve their skills. An efficient feedback system can be provided by integrating the server, device, and AI model. By implementing this system, users can quickly and accurately identify their weaknesses and improve their skills in specific ways.

[1336] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1337] Specific processing flow of the program

[1338] Step 1: Collecting gameplay data

[1339] The device collects gameplay data in real time as the user plays the game. This data includes character position information, aiming movement patterns, button press timing, and the types of items and weapons used. Specifically, the device uses dedicated sensor modules and software to collect this data with high precision.

[1340] Input: User actions during gameplay, location information, button press timing, etc.

[1341] Output: Collected gameplay data

[1342] Step 2: Sending data

[1343] The device transmits the collected gameplay data to the server in real time using a secure communication protocol such as HTTPS, along with metadata such as user ID and session ID.

[1344] Input: Collected gameplay data, user ID, session ID

[1345] Output: Data in the format sent to the server

[1346] Step 3: Storing and Preprocessing Data

[1347] The server receives the data sent from the device and stores it in a database. The stored data includes the user ID, session ID, date and time, and various play data. The data is then appropriately formatted for input into the AI ​​model. This formatting includes removing noise from the data and standardizing timestamps.

[1348] Input: Submitted gameplay data, user ID, session ID

[1349] Output: Data in a formatted form

[1350] Specific behavior:

[1351] 1. The server receives the collected data.

[1352] 2. Save it to the database.

[1353] 3. Format the stored data to input into the AI ​​model.

[1354] Step 4: Analyze the data

[1355] The server inputs the formatted data into a generative AI model. The AI ​​model is pre-trained based on the play data of professional gamers and compares the user's play data with the professional gamer's data. It performs analysis based on specific evaluation indicators (e.g., reaction time, shooting accuracy, movement patterns, etc.).

[1356] Input: Data in a formatted form

[1357] Output: Analysis results

[1358] Specific behavior:

[1359] 1. The server inputs the formatted data into the AI ​​model.

[1360] 2. The AI ​​model analyzes the data and identifies the differences between users and professional gamers.

[1361] Step 5: Generate feedback

[1362] The server generates feedback for the user based on the analysis results obtained from the AI ​​model, including specific areas for improvement, recommended practice methods, and helpful video links.

[1363] Input: Analysis results

[1364] Output: Generated feedback

[1365] Specific behavior:

[1366] 1. The server receives the analysis results.

[1367] 2. Automatically generate feedback based on the analysis results.

[1368] 3. Feedback will include areas for improvement, practice methods, and helpful video links.

[1369] Step 6: Submit and view feedback

[1370] The server sends the generated feedback to the user's device, which then displays the received feedback to the user. The device provides a user-friendly interface, allowing the user to intuitively understand specific points for improvement and practice methods.

[1371] Input: Generated feedback

[1372] Output: Feedback that is displayed to the user

[1373] Specific behavior:

[1374] 1. The server sends the generated feedback to the device.

[1375] 2. The device receives and displays the feedback.

[1376] 3. Users review the feedback and understand areas for improvement and how to practice.

[1377] In this way, the system of the present invention efficiently supports users in improving their gaming skills.

[1378] (Application example 1)

[1379] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1380] Currently, robotic work in factories is often not sufficiently focused on efficiency and precision. This creates a need for work optimization and efficiency, but on-site data collection and analysis is not easy. Furthermore, there are limited means of comparing results with professional work, creating a lack of concrete feedback for improving robotic work efficiency and technology.

[1381] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1382] In this invention, the server includes means for collecting user work data in real time, means for transmitting the collected data to the server, means including a generative AI model for comparing and analyzing the transmitted data with professional work data, means for generating feedback to the user based on the results of the comparison and analysis, means for transmitting the generated feedback to the user's terminal, and means for providing feedback displayed on the user's terminal, thereby enabling improvement of the work efficiency of factory robots and provision of specific feedback.

[1383] "User work data" refers to data about all actions performed by robots in a factory, including moving, lifting, placing, etc.

[1384] A "server" is a computing device that has the ability to receive, store, analyze, and transmit data over the Internet or a local network.

[1385] A "generative AI model" is an artificial intelligence model trained based on professional work data, and is used to compare and analyze user work data.

[1386] "Professional work data" refers to the movement data of experts who perform work efficiently and with high precision.

[1387] "Feedback" refers to advice and suggestions for improvement provided to users based on the analysis results.

[1388] A "user terminal" is an information processing device that displays feedback and can be operated by the user, and includes a smartphone, tablet, etc.

[1389] This invention aims to improve the work efficiency of factory robots by building a system that collects user work data in real time, analyzes it using a generative AI model, and provides appropriate feedback.

[1390] System Program

[1391] 1. Collecting user work data

[1392] This system collects all actions (moving, lifting, placing, etc.) performed by factory robots in real time. Specifically, motion and position sensors are attached to the robots to collect the data. The collected data records the robot's actions in detail and serves as the basis for future analysis.

[1393] 2. Data transmission

[1394] The collected data is sent to the server using a secure communication protocol (e.g. HTTPS), which ensures the integrity and confidentiality of the data.

[1395] 3. Data storage and analysis

[1396] The server receives the submitted data and automatically stores it in a database, including metadata such as the robot ID, session ID, date and time, etc. The server then formats the data appropriately for input into the generative AI model.

[1397] The generative AI model is pre-trained based on the work data of professionals and performs comparative analysis with the user's work data. Based on specific parameters (processing time, movement patterns, work accuracy, etc.), the AI ​​model detects which aspects of the user's work differ from those of professionals.

[1398] 4. Generate feedback

[1399] The server generates feedback for users based on the analysis results of the generative AI model, including specific points for improvement, recommended work methods, and reference video links. Users can quickly implement appropriate improvement measures to improve their work efficiency.

[1400] 5. Sending and Viewing Feedback

[1401] The server sends the generated feedback to the user's device, and the device displays the received feedback to the user. Through a user-friendly interface, the user can easily understand specific improvements and work methods.

[1402] Examples and prompts

[1403] For example, if a robot used in a factory deviates from the optimal movement pattern when lifting an object, the generative AI model will determine that "movement efficiency could be improved" and provide feedback on the appropriate way to lift the object.

[1404] Example prompt sentence:

[1405] "Based on the operation data of factory robots, identify the difference between the current operation performance and the optimal operation pattern and provide feedback on areas for improvement."

[1406] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1407] Step 1:

[1408] Collecting user work data

[1409] The terminal collects data in real time from motion and position sensors attached to the factory robots. This data includes information on the robot's movements, lifting, and placement. The terminal's input is real-time data from the sensors, and its output is the collected motion data.

[1410] Step 2:

[1411] Sending data

[1412] The terminal sends the collected data to the server using the HTTPS communication protocol. Using this communication protocol ensures the integrity and confidentiality of the data. The input is the operational data collected by the terminal, and the output is the data sent to the server.

[1413] Step 3:

[1414] Data storage

[1415] The server receives the transmitted data and automatically stores it in a database. The stored data also includes metadata such as the robot ID, session ID, date and time. The input is the data transmitted to the server, and the output is the data stored in the database.

[1416] Step 4:

[1417] Data formatting and analysis

[1418] The server formats the stored data appropriately for input into the generative AI model. The formatted data is passed to the generative AI model and compared with the professional's work data for analysis. The input is the data stored in the database, and the output is the formatted data input into the generative AI model.

[1419] Step 5:

[1420] Generate feedback

[1421] The server generates feedback for the user based on the analysis results of the generative AI model, including specific areas for improvement, recommended work methods, reference video links, etc. The input is the analysis results of the generative AI model, and the output is the generated feedback.

[1422] Step 6:

[1423] Send Feedback

[1424] The server sends the generated feedback to the user's terminal, where the input is the generated feedback and the output is the feedback sent to the user's terminal.

[1425] Step 7:

[1426] View Feedback

[1427] The terminal displays the received feedback to the user, allowing the user to easily understand specific improvements and work methods based on the feedback. The input is the feedback sent from the server, and the output is the feedback displayed to the user.

[1428] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1429] MODE FOR CARRYING OUT THE INVENTION

[1430] The present invention is a system that includes an emotion engine that collects, analyzes, and provides feedback on a user's gameplay data, as well as recognizes the user's emotional state and adjusts the feedback accordingly. The system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[1431] 1. Collection of Gameplay Data

[1432] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[1433] 2. Collecting Emotional Data

[1434] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[1435] example:

[1436] If the user is nervous, changes in facial expression, an increased heart rate, and a change in tone of voice are detected.

[1437] 3. Data transmission

[1438] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the integrity and security of the data during transmission.

[1439] 4. Data storage and analysis

[1440] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data appropriately for input into the AI ​​model.

[1441] The AI ​​model is pre-trained based on the play data of professional gamers and performs comparative analysis with the user's play data. The AI ​​model detects which aspects of the user's play differ from those of professional gamers based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.).

[1442] example:

[1443] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1444] 5. Emotion Data Analysis

[1445] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[1446] example:

[1447] The analysis results show that users are tense while playing.

[1448] 6. Generate feedback

[1449] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[1450] example:

[1451] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game daily. We also recommend that you incorporate relaxation techniques into your play, as you are feeling tense."

[1452] 7. Sending and Viewing Feedback

[1453] The server transmits the generated feedback to the user's terminal.

[1454] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the content.

[1455] example:

[1456] "Your reaction time is 0.3 seconds slower. You can improve your reaction time by playing the mini-game published at the link below every day. Also, it has been analyzed that you are feeling tense. Please try a 5-minute relaxation exercise before playing the game."

[1457] 8. User Practices

[1458] The user checks the provided feedback and plays the game again, keeping in mind the areas for improvement. Based on the feedback, the user makes specific practice exercises and changes settings, and also manages their emotional state.

[1459] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[1460] The processing flow will be explained below.

[1461] Step 1:

[1462] The user starts a competitive game, such as an online FPS game, and begins playing as usual.

[1463] Step 2:

[1464] The device collects the user's gameplay data in real time, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The data is captured every second and converted into a format (e.g., JSON).

[1465] Step 3:

[1466] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. Emotional data is also processed in the same way.

[1467] Step 4:

[1468] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), ensuring the accuracy and safety of data transmission.

[1469] Step 5:

[1470] The server receives the gameplay data and emotion data sent from the device and stores them in a database, including metadata such as the user ID, session ID, and date and time.

[1471] Step 6:

[1472] The server (AI model) retrieves the user's play data stored in the database and compares and analyzes it with the data of professional gamers. The AI ​​model learns using past professional data and evaluates it based on specific parameters (reaction time, aim position, movement pattern, success rate of gunfights).

[1473] example:

[1474] The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1475] Step 7:

[1476] The emotion engine analyzes the user's current emotional state (e.g., excited, nervous, relaxed) based on collected emotional data. The algorithm evaluates the user's emotional state based on facial expressions, heart rate, tone of voice, etc.

[1477] example:

[1478] The emotion engine detects when the user is in a tense state.

[1479] Step 8:

[1480] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[1481] example:

[1482] "Your reaction time is delayed by an average of 0.3 seconds. To improve this lag, we recommend that you play the reaction training mini-game every day. Also, your emotional data suggests that you are feeling tense, so we recommend that you also incorporate some simple exercises to help you relax."

[1483] Step 9:

[1484] The server sends the generated feedback to the user's device, ensuring the accuracy and timeliness of the information.

[1485] Step 10:

[1486] The device displays the received feedback to the user through a user-friendly interface designed to help users easily understand and act on the content.

[1487] example:

[1488] "Spend 10 minutes each day playing the reaction speed mini-game linked below. Also, try the relaxation exercises below to help relieve tension."

[1489] Step 11:

[1490] The user checks the provided feedback and plays the game again, taking into consideration the areas for improvement and recommended practice methods. Based on the feedback, the user implements specific practice and setting changes, and also manages their emotional state.

[1491] By repeating these steps, users can effectively improve their play, promoting continuous growth and management of their emotional state.

[1492] Example 2

[1493] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1494] In modern gaming environments, the importance of player skill improvement and emotional management is increasing, but current systems lack comprehensive support for these. Therefore, a comprehensive system is needed to help players efficiently improve their skills and simultaneously manage their emotional state during gameplay. This system must also be capable of collecting user data in real time and providing appropriate feedback.

[1495] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1496] In this invention, the server includes: means for collecting gameplay data of users in real time; means for collecting facial expressions, voice, and biometric information to recognize the user's emotional state; means for transmitting the collected gameplay data and emotional data to the server; means for storing and shaping the transmitted data in a database; means including an AI model for comparing and analyzing the shaped data with play data of professional gamers; means including an emotion engine for analyzing the emotional data and identifying the user's emotional state; means for generating feedback to the user based on the analysis results of the AI ​​model and the emotion engine; means for transmitting the generated feedback to the user's device; and means for providing feedback displayed on the user's device. This allows the user to receive appropriate feedback in real time according to their skill level and emotional state.

[1497] "User" refers to an individual who uses the system to play games.

[1498] "Gameplay data" refers to data that includes information such as movement trajectory, aim position, firing timing, characters used, weapons used, and enemy detection time that is generated in the course of a user playing a game.

[1499] "Emotional state" refers to the user's psychological and physiological state while playing a game, and specifically includes emotions such as stress, tension, and joy.

[1500] "Terminal" refers to a device that allows a user to play a game and has the function of collecting game play data and emotional data and transmitting them to a server.

[1501] A "server" is a computer system that receives and stores data sent from a device, and then analyzes it using an AI model and emotion engine.

[1502] A "database" is a system for systematically storing and managing data stored on a server.

[1503] An "AI model" is an artificial intelligence model that is trained based on the play data of professional gamers and used to compare and analyze user gameplay data.

[1504] An "emotion engine" is software with analytical functions that analyzes a user's emotional data and identifies their current emotional state.

[1505] "Feedback" is information that includes specific improvements and advice for users, generated based on the analysis results of the AI ​​model and emotion engine.

[1506] "Real-time" refers to gameplay and data collection occurring instantly, without delay.

[1507] A "professional gamer" is a game player with specialized skills and experience, and their data is used to train the AI ​​model.

[1508] This system collects, analyzes, and provides feedback on a user's gameplay data, and also recognizes the user's emotional state and adjusts the feedback accordingly. This system is primarily composed of a user's device, a server, an AI model, and an emotion engine.

[1509] Gameplay data collection

[1510] The device collects gameplay data in real time while the user is playing the game, including movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time, etc. The collected data is formatted along with metadata (user ID, game session ID, date and time, etc.).

[1511] Example: While a user is playing a shooting game, the device records the user's movement trajectory and firing timing in milliseconds.

[1512] Collecting Emotional Data

[1513] The device collects facial expressions, voice, and biometric information (e.g., heart rate, skin potential, etc.) in real time to recognize the user's emotional state. This data is sent to the emotion engine and analyzed to identify the user's current emotional state.

[1514] For example, if the user is nervous, the increase in heart rate and changes in facial expression are collected and sent to the emotion engine.

[1515] Sending data

[1516] The device compresses the collected gameplay data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS), where the data integrity and security are verified.

[1517] Example: Collected data is encrypted and sent securely to a server.

[1518] Data storage and analysis

[1519] The server receives the data sent from the device and stores it in a database, including metadata such as the user ID, session ID, date and time, and then formats the data for input into the AI ​​model.

[1520] The AI ​​model is pre-trained based on the play data of professional gamers and then performs a comparative analysis with the user's play data. Based on specific parameters (reaction time, aim position, movement patterns, success rate of shooting, etc.), the AI ​​model detects which aspects of the user's play differ from those of professional gamers.

[1521] Example: The server determines that the user's reaction time is 0.3 seconds slower than a professional gamer.

[1522] Emotional Data Analysis

[1523] The emotion engine analyzes the user's current emotional state based on the collected emotion data, and based on the analysis results, identifies emotions such as stress, tension, and joy that the user is feeling while playing the game.

[1524] Example: The analysis results show that the user is playing in a tense state.

[1525] Generate feedback

[1526] Based on the results of the comparative analysis of the AI ​​model and the analysis of the emotion engine, the server generates optimal feedback for the user, including specific areas for improvement, recommended practice methods, and advice corresponding to the user's emotional state.

[1527] For example: "Your reaction time is off by an average of 0.3 seconds. To improve this lag, we recommend that you play a specific mini-game every day. We also recommend some relaxation exercises to help relieve tension."

[1528] Sending and Viewing Feedback

[1529] The server transmits the generated feedback to the user's terminal.

[1530] The device displays the received feedback to the user through a user-friendly interface designed to allow the user to easily understand and act on the feedback.

[1531] For example: "Your reaction time is off by 0.3 seconds. Please click the link below to play a mini-game. Also, try 5 minutes of relaxation before playing."

[1532] User Practice

[1533] The user reviews the provided feedback and plays the game again, focusing on areas for improvement in actual gameplay. They then carry out specific exercises, change settings, and manage their emotional state.

[1534] In this way, the present invention efficiently improves a user's gaming skills and supports the player's continued development and stress management by providing feedback that takes into account the player's emotional state during gameplay.

[1535] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1536] Specific processing explanation of the system program divided into processing steps

[1537] Step 1: Collecting gameplay data

[1538] The terminal collects the user's gameplay data in real time.

[1539] Input: User's gameplay status (movement trajectory, aim position, firing timing, character used, weapon used, enemy detection time).

[1540] Processing: The device monitors and records this data using sensors and software modules.

[1541] Output: Formatted gameplay data (including user ID, game session ID, date and time).

[1542] Specific operation: While a user is playing a shooting game, the device collects the user's movement trajectory and firing timing in milliseconds and temporarily stores them in a database.

[1543] Step 2: Collecting emotion data

[1544] The device collects data to recognize the user's emotional state in real time.

[1545] Input: User's facial expressions, voice, and biometric information (heart rate, skin potential, etc.).

[1546] Processing: The device collects data from cameras, microphones, biosensors, etc. and pre-processes it to identify emotional states.

[1547] Output: Formatted emotion data (including user ID and timestamp).

[1548] What it does: If a user becomes tense while playing a game, the device will use the camera to detect the movement of the user's facial muscles and the heart rate sensor to record an increase in heart rate.

[1549] Step 3: Sending data

[1550] The terminal transmits the collected game play data and emotion data to the server.

[1551] Input: Collected gameplay and emotion data.

[1552] Processing: The device compresses this data, transmits it using a secure communication protocol (e.g. HTTPS), and checks the data for integrity and security.

[1553] Output: Data securely sent to the server.

[1554] What it does: Encrypts collected data, establishes a secure communication channel and sends it to the server.

[1555] Step 4: Saving and formatting the data

[1556] The server receives and stores the data.

[1557] Input: Gameplay and emotion data sent from the device.

[1558] Processing: The server saves these data in a database, adding necessary metadata (user ID, session ID, date and time).

[1559] Output: Organized data stored in a database.

[1560] What happens: The server creates a new session in the database and saves each data entry.

[1561] The server formats the stored data for input into the AI ​​model.

[1562] Input: Saved gameplay and emotion data.

[1563] Processing: Converting data into an input format for the AI ​​model.

[1564] Output: Data in a format that can be input into an AI model.

[1565] What it does: The server formats the data and converts it into a form that is easy for the AI ​​model to handle.

[1566] Step 5: Analyze the data

[1567] The AI ​​model analyzes the user's play data.

[1568] Input: Formatted gameplay data.

[1569] Processing: The AI ​​model compares the gameplay data of professional gamers and analyzes it based on specific parameters (reaction time, aim position, movement patterns, and success rate of gunfights).

[1570] Output: Comparative analysis results of user gameplay.

[1571] How it works: The AI ​​model analyzes the user's reaction time and compares the time lag with that of professional gamers.

[1572] The emotion engine analyzes the emotion data.

[1573] Input: Formatted emotion data.

[1574] Processing: The emotion engine analyzes the user's emotional state (stress, tension, joy, etc.) based on the collected emotion data.

[1575] Output: Analysis results about the user's emotional state.

[1576] Specific operation: The emotion engine analyzes the user's heart rate and facial expression changes to detect tension.

[1577] Step 6: Generate feedback

[1578] The server generates feedback based on the analysis results of the AI ​​model and emotion engine.

[1579] Input: Comparative analysis results of AI models and emotion engine analysis results.

[1580] Processing: Comprehensively evaluate the analysis results and generate advice on areas for improvement, recommended practice, and emotional state.

[1581] Output: The generated feedback.

[1582] Specific action: "Your reaction time is delayed by an average of 0.3 seconds. Play specific mini-games every day and try relaxation exercises to reduce tension."

[1583] Step 7: Submit and view feedback

[1584] The server transmits the generated feedback to the user's terminal.

[1585] Input: The generated feedback.

[1586] Action: Send feedback to the device.

[1587] Output: Feedback sent to the device.

[1588] Specific behavior: The generated feedback is immediately sent to the user's device.

[1589] The device displays the feedback to the user.

[1590] Input: Feedback sent by the server.

[1591] Processing: The feedback is displayed in a user-friendly interface.

[1592] Output: The feedback displayed to the user.

[1593] Specific action: "Your reaction time is off by 0.3 seconds. Please click the link below to play the mini-game. Also, try 5 minutes of relaxation before playing."

[1594] Step 8: User Practice

[1595] Enhance gameplay based on user-provided feedback.

[1596] Input: Feedback displayed on the terminal.

[1597] Treatment: Based on the feedback, specific exercises and setting changes are implemented, as well as emotional state management.

[1598] Output: Improved gameplay and emotional state management.

[1599] Specific actions: Users practice the presented mini-games daily and incorporate relaxation exercises before playing.

[1600] (Application example 2)

[1601] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1602] Conventional virtual stores offer product recommendations and customer support based solely on user behavior data, meaning they are unable to provide feedback or support that takes into account the user's emotional state. This reduces the psychological satisfaction and convenience of users when making purchasing decisions, and undermines the appeal of virtual stores.

[1603] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user behavioral data in real time, means for collecting user emotional data in real time, means for transmitting the collected behavioral data and emotional data to the server, means including an AI model for comparing and analyzing the transmitted data with professional data, means including an emotion engine for analyzing the user's emotional state, means for generating feedback to the user based on the results of the comparison and analysis and the emotion analysis, means for transmitting the generated feedback to the user terminal, and means for providing feedback displayed on the user terminal. This makes it possible to analyze both the user's behavioral data and emotional data and recommend products and provide customer support tailored to the user's psychological state.

[1604] "User behavior data" refers to information on specific operations and actions such as the user's line of sight, movement trajectory, selected products, and purchase history within the virtual store.

[1605] "User emotional data" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, voice, and biometric information (such as heart rate and skin potential).

[1606] "Server" is a central computing system for receiving, storing, analyzing user behavioral and emotional data and generating feedback.

[1607] "Professional data" is reference data relating to the actions and operations performed by skilled professionals, and is used for comparative analysis with user behavior data.

[1608] An "AI model" is an algorithm or program that utilizes artificial intelligence to compare and analyze user data and professional data to evaluate user behavior patterns and reactions.

[1609] The "emotion engine" is a dedicated analysis system that analyzes a user's emotional data and identifies the user's current emotional state.

[1610] "Feedback" refers to information such as improvements, recommended products, and customer support provided to users based on analyzed behavioral and emotional data.

[1611] "User terminal" refers to a device operated by a user, and is a device for collecting and displaying behavioral data and emotional data through the virtual store interface.

[1612] A system embodying the present invention collects user behavioral data and emotional data in real time, transmits this data to a server for analysis, and provides appropriate feedback to the user. Detailed embodiments of the system are described below.

[1613] First, the terminal is a device that the user operates in the virtual store, and collects behavioral data such as the user's gaze, movement trajectory, selected products, purchase history, etc. Furthermore, the terminal is equipped with sensors that collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires the user's emotional data.

[1614] The device then compresses the collected behavioral and emotional data and sends it to a server using a secure communication protocol (e.g., HTTPS). The server receives the data and stores it in a database.

[1615] The server formats the received data and inputs it into a pre-trained AI model and emotion engine to analyze the user's behavioral and emotional data. The AI ​​model compares the user data with professional data to evaluate the user's behavioral patterns, reaction time, and types of products they are interested in. The emotion engine also analyzes the collected emotional data to identify the user's current emotional state.

[1616] The server integrates these analysis results and generates optimal feedback for the user. This feedback includes specific improvements, recommended products, and customer support tailored to the user's emotional state. The generated feedback is sent to the device and displayed on the user's interface. The user can then make purchase decisions or change settings based on the presented feedback.

[1617] This section describes specific hardware and software for implementing this system. The hardware includes a VR head-mounted display (HMD) for collecting user behavior data, and a webcam and biometric sensors for collecting user emotion data. The software includes an AI model using TensorFlow / Keras for analyzing the data, an emotion engine for analyzing the emotion data, and the Requests library for data communication.

[1618] For example, when a user looks at a product in a virtual store, the HMD collects gaze data and information about the selected product, while the webcam collects facial expression data and sensors collect heart rate and skin potential. This data is sent to a server and analyzed by an AI model and emotion engine. For example, if a user looks at a particular product with excitement, the system will determine that the user is highly interested in that product and generate feedback recommending related products.

[1619] Below is an example of a prompt sentence using a generative AI model.

[1620] "Generate application code that collects user behavioral and sentiment data and provides optimal product recommendations and customer support within a virtual store."

[1621] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1622] Step 1:

[1623] The device collects user behavior data (eye gaze, movement trajectory, selected products, purchase history, etc.) in real time. The device is also equipped with sensors to collect the user's facial expressions, voice, and biometric information (heart rate, skin potential, etc.), and simultaneously acquires emotional data. This data is input from the collection sensors and stored in a database in real time.

[1624] Step 2:

[1625] The device compresses the collected behavioral and emotional data and transmits it to the server using a secure communication protocol (e.g., HTTPS). The transmitted data also includes metadata such as the user ID, session ID, and date and time. The compressed data is input to and received by the server via the transmission protocol.

[1626] Step 3:

[1627] The server stores the received data in a database. The stored data includes user behavior data, emotion data, and metadata. The data is stored appropriately in the database using the user ID as a key. This allows the required data to be quickly searched and analyzed.

[1628] Step 4:

[1629] The server formats the stored data and inputs it into the AI ​​model. The user's behavioral and emotional data is converted into a format that is easy for the AI ​​model to analyze. The formatted data is then input into the AI ​​model as user data. During this process, data cleaning and preprocessing are performed.

[1630] Step 5:

[1631] The AI ​​model installed on the server compares and analyzes user data with professional data. The AI ​​model evaluates the user's behavioral patterns, reaction time, and types of products they are interested in. Specifically, the AI ​​model analyzes the user's eye movements and product selection patterns and compares them with the professional data. The results of this analysis become input for the feedback generation process.

[1632] Step 6:

[1633] The emotion engine analyzes the user's emotion data to identify the user's current emotional state. For example, if the user is excited, the emotion engine detects that state based on changes in facial expressions and biometric data. The emotion analysis results are also used as input for the feedback generation process.

[1634] Step 7:

[1635] The server combines the analysis results of the AI ​​model and the emotion engine to generate optimal feedback for the user. This feedback can include specific improvements, recommended products, and customer support tailored to the user's emotional state. For example, if the user is excited, product recommendations will reflect that excitement.

[1636] Step 8:

[1637] The server transmits the generated feedback to the terminal, and the feedback is displayed in real time on the user's terminal, and the transmitted data includes a specific feedback message to be displayed to the user.

[1638] Step 9:

[1639] The user checks the feedback displayed on the device and reflects it in their actual actions. For example, they check the recommended product and purchase it, or decide on their next action based on the suggested improvements. They then take specific actions or make purchases based on the displayed feedback message.

[1640] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1641] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1642] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1643] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1644] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1645] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1646] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1647] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1648] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1649] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1650] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1651] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1652] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1653] 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.

[1654] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1655] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1656] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1657] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1658] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1659] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1660] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1661] The following is further disclosed regarding the above embodiment.

[1662] (Claim 1)

[1663] means for collecting user gameplay data in real time;

[1664] means for transmitting the collected data to a server;

[1665] A means including an AI model for comparing and analyzing the transmitted data with the play data of a professional gamer;

[1666] means for generating feedback to the user based on the comparative analysis results;

[1667] means for transmitting the generated feedback to a user terminal;

[1668] means for providing feedback displayed on the user's terminal;

[1669] A system including:

[1670] (Claim 2)

[1671] The system of claim 1, including detailed data such as user's actions while playing, reaction time, score, behavior patterns, characters and weapons used, etc.

[1672] (Claim 3)

[1673] The system of claim 1, wherein the AI ​​model evaluates reaction time, aiming position, movement patterns, and shooting accuracy when comparing and analyzing collected user data and professional gamer data.

[1674] "Example 1"

[1675] (Claim 1)

[1676] means for collecting user gameplay data in real time;

[1677] means for transmitting the collected data to a server;

[1678] A means of storing the transmitted data in a database and appropriately formatting it for analysis;

[1679] A means including an AI model for comparing and analyzing the formatted data with play data of professional gamers;

[1680] a means for generating feedback to a user based on the comparative analysis results obtained by the AI ​​model;

[1681] means for transmitting the generated feedback to a user terminal;

[1682] means for displaying the submitted feedback to the user;

[1683] A system including:

[1684] (Claim 2)

[1685] The system of claim 1, including detailed data such as user's actions while playing, reaction time, score, behavior patterns, characters and weapons used, etc.

[1686] (Claim 3)

[1687] The system of claim 1, further comprising means for evaluating reaction time, aiming position, movement patterns, and shooting accuracy when the AI ​​model compares and analyzes collected user data and professional gamer data.

[1688] "Application Example 1"

[1689] (Claim 1)

[1690] A means for collecting user work data in real time;

[1691] means for transmitting the collected data to a server;

[1692] A means including a generative AI model for comparing and analyzing the transmitted data with the work data of the Professional;

[1693] means for generating feedback to the user based on the comparative analysis results;

[1694] means for transmitting the generated feedback to a user terminal;

[1695] means for providing feedback displayed on the user's terminal;

[1696] A system including:

[1697] (Claim 2)

[1698] The system according to claim 1, including detailed data such as user's actions during work, processing time, scores, and behavioral patterns.

[1699] (Claim 3)

[1700] The system of claim 1, wherein the generative AI model evaluates processing time, movement patterns, and work accuracy when comparing and analyzing collected user data and professional data.

[1701] "Example 2: Combining Emotion Engines"

[1702] (Claim 1)

[1703] means for collecting user gameplay data in real time;

[1704] means for collecting facial, voice and biometric information to recognize the user's emotional state;

[1705] means for transmitting the collected gameplay data and emotion data to a server;

[1706] A means for storing and formatting the transmitted data in a database;

[1707] A means including an AI model for comparing and analyzing the formatted data with play data of professional gamers;

[1708] means for analyzing the emotion data and identifying an emotional state of the user, the emotional engine being included;

[1709] a means for generating feedback to the user based on the analysis results of the AI ​​model and the emotion engine;

[1710] means for transmitting the generated feedback to a user terminal;

[1711] means for providing feedback displayed on the user's terminal;

[1712] A system including:

[1713] (Claim 2)

[1714] The system of claim 1, wherein the user's movements during play, reaction time, score, behavioral patterns, characters and weapons used, as well as facial expressions, voice, and heart rate are included.

[1715] (Claim 3)

[1716] The system of claim 1, wherein the AI ​​model evaluates reaction time, aiming position, movement patterns, and shooting accuracy when comparing and analyzing collected user data and professional gamer data, and the emotion engine analyzes the emotion data to identify the user's emotions such as stress, tension, and joy.

[1717] "Application example 2 when combining emotion engines"

[1718] (Claim 1)

[1719] a means for collecting user behavior data in real time;

[1720] a means for collecting user emotion data in real time;

[1721] means for transmitting the collected behavioral data and emotion data to a server;

[1722] means including an AI model for comparing and analyzing the transmitted data with professional data;

[1723] means including an emotion engine for analyzing an emotional state of a user;

[1724] means for generating feedback to a user based on the comparative analysis result and the sentiment analysis result;

[1725] means for transmitting the generated feedback to a user terminal;

[1726] means for providing feedback displayed on the user's terminal;

[1727] A system including:

[1728] (Claim 2)

[1729] The system of claim 1, including detailed data such as the user's actions while playing, reaction time, score, behavioral patterns, selection items, and products in which the user is interested.

[1730] (Claim 3)

[1731] The system of claim 1, wherein the AI ​​model evaluates reaction time, looking position, movement patterns, and types of products of interest when comparing and analyzing collected user data and professional data. [Explanation of symbols]

[1732] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting user gameplay data in real time; means for transmitting the collected data to a server; A means including an AI model for comparing and analyzing the transmitted data with the play data of a professional gamer; means for generating feedback to the user based on the comparative analysis results; means for transmitting the generated feedback to a user terminal; means for providing feedback displayed on the user's terminal; A system including:

2. The system according to claim 1, wherein the system includes detailed data such as user's actions during play, reaction times, scores, behavior patterns, characters and weapons used, etc.

3. The system of claim 1, wherein the AI ​​model evaluates reaction time, aim position, movement patterns, and shooting accuracy when comparing and analyzing collected user data and professional gamer data.

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