System

Abstract

A system is provided.SOLUTION: A system comprising: means for recording game play data; means for transmitting the recorded game play data to a central processing unit; means for analyzing the game play data received at the central processing unit; means for evaluating a learning status and an attitude to a game based on a result of the analysis; and means for providing a result evaluated by the evaluating means to a parent as feedback.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, children's gaming time has increased, leading to problems such as gaming addiction and reduced study time. Furthermore, parents have few tools to monitor their children's gaming and study habits, making effective management difficult. Given this background, there is a need for a system that effectively combines gameplay and study to promote healthy gaming habits and help parents monitor their children's study status. [Means for solving the problem]

[0005] The present invention provides a system that solves the above problems by the following means: A system includes a means for recording gameplay data and a means for transmitting the recorded gameplay data to a central processing unit. The system also includes a means for evaluating the learning situation and attitude toward games based on the analysis results using an analysis means for analyzing the gameplay data received by the central processing unit. The system further includes a means for providing the results of the evaluation by the evaluation means as feedback to parents. This improves communication between parents and children and enables the development of healthy gaming habits. Furthermore, by including a means for calculating learning points based on the gameplay data and making the learning points exchangeable for services or items useful for learning, children's motivation to learn can be increased. Furthermore, the analysis means includes a function for analyzing the gameplay attitude and learning situation through dialogue with the user using natural language processing, enabling more accurate feedback to be provided.

[0006] "Gameplay Data" means information regarding a user's gameplay time, scores, and other actions.

[0007] "Central Processing Unit" means a server or cloud computing system that receives and analyzes data sent from a user terminal.

[0008] "Analysis means" means the function that uses the received data to apply specific algorithms or AI models to evaluate user behavior and attitudes.

[0009] "Evaluation means" refers to a function for evaluating the user's learning situation and attitude toward the game based on the analysis results of the analysis means.

[0010] "Feedback" means information or messages generated by an assessment instrument that are provided to parents or administrators regarding the assessment results.

[0011] "Learning Points" means points that a Child earns through gameplay that can later be exchanged for services or items that will aid in learning.

[0012] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate human language.

[0013] "Interaction with the user" refers to the process by which the system generates an appropriate response to a message entered by the user and communicates with the user.

[0014] "Attitude toward games" refers to the criteria used to evaluate a user's interest and behavioral patterns toward games. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] An embodiment of the present invention is a system that includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning situation and attitude toward the game, and provides the evaluation results to parents as feedback.

[0037] Recording and transmitting gameplay data

[0038] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[0039] Data reception and analysis

[0040] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[0041] Generating and Providing Feedback

[0042] The server generates feedback to provide to parents based on the evaluation results. The feedback includes information about the user's learning status and attitude toward the game and is sent to the parent's device, making it easier for parents to understand their child's gaming habits and learning status.

[0043] Calculating and exchanging learning points

[0044] Furthermore, the server calculates learning points based on gameplay data. Learning points are earned by users through gameplay and can be exchanged for services and items that are useful for learning. This helps children maintain their motivation to learn through gameplay.

[0045] Natural language processing dialogue

[0046] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[0047] Specific examples

[0048] For example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good" and their attitude toward the game is evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[0049] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily keep track of their children's learning progress.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user starts the game. As the user plays, the game time and score are measured in real time.

[0053] Step 2:

[0054] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[0055] Step 3:

[0056] The device sends the recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[0057] Step 4:

[0058] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[0059] Step 5:

[0060] The server analyzes the data and uses AI models and algorithms to evaluate the user's learning progress and attitude towards the game based on the received data.

[0061] Step 6:

[0062] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress and attitude towards the game.

[0063] Step 7:

[0064] The server sends the generated feedback to the parent device, which is sent as an HTTP POST request so that the parent can view it.

[0065] Step 8:

[0066] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[0067] Step 9:

[0068] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning by continuing to play the game.

[0069] Step 10:

[0070] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[0071] Through this process, the system can promote healthy gaming habits and provide an environment where parents can easily keep track of their children's learning progress.

[0072] Example 1

[0073] 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."

[0074] With conventional game systems, it is difficult for parents to grasp the user's playing and learning status, and the mechanisms for increasing motivation to learn through games are insufficient. For these reasons, there is a need for a means to effectively link games and learning.

[0075] 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.

[0076] In this invention, the server includes means for allowing users to play games, means for recording game play data, means for transmitting the recorded game play data to a central processing unit, means for analyzing the game play data received by the central processing unit, means for evaluating the learning situation and attitude toward the game based on the analysis results, means for providing the results of the evaluation by the evaluation means as feedback to parents, and means for the analysis means to analyze the original data through dialogue with the user using natural language processing. This allows parents to understand the learning situation and attitude of the user through game play, and can increase the user's motivation to learn through feedback and evaluation of learning points.

[0077] "User" refers to the person who plays the game, especially the children whose learning situation and attitude toward the game will be evaluated.

[0078] "Terminal" refers to an electronic device used by a user to play a game and that records and transmits gameplay data.

[0079] "Central Processing Unit" refers to a server that receives, analyzes, and evaluates gameplay data sent from user terminals.

[0080] "Gameplay data" refers to information generated while a user is playing a game, and specifically includes play time, scores, and behavior logs.

[0081] "Analysis means" refers to the algorithms or AI models that analyze gameplay data within the central processing unit and evaluate the user's learning status and attitude toward the game.

[0082] The "evaluation means" refers to a mechanism for evaluating the user's learning situation and attitude toward the game based on the analysis results obtained by the analysis means, and providing the results as feedback.

[0083] "Feedback" is evaluation information provided to parents based on the user's gameplay data, including the user's academic progress and attitude toward the game.

[0084] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used for interacting with users and analyzing data.

[0085] "Learning Points" are reward points calculated based on a user's gameplay data, and can be exchanged for services or items that are useful for learning.

[0086] "Generative AI Model" refers to an artificial intelligence model used to analyze a user's gameplay data and generate feedback.

[0087] A "prompt" is an instruction entered into a generative AI model, and refers to a sentence or phrase used for specific analysis or feedback generation.

[0088] This invention is a system in which a user plays a game and transmits the play data to a central processing unit, which evaluates the user's learning status and attitude toward the game and provides feedback to parents. This system is composed of a terminal, a central processing unit (server), a generative AI model, analysis means, evaluation means, and feedback means.

[0089] Recording and transmitting gameplay data

[0090] Users play games using dedicated devices (e.g., tablets, smartphones). Gameplay data, such as the user's playing time, scores, and behavioral information, is recorded by the device. The device then sends the recorded data to a central processing unit at regular intervals or when the game ends. Transmission is performed using the HTTP protocol.

[0091] Data reception and analysis

[0092] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means analyzes the data using a generative AI model (e.g., machine learning algorithm) or a specific algorithm. During the analysis process, the data can also be used to interact with the user using natural language processing technology.

[0093] Generating and Providing Feedback

[0094] The server generates feedback to provide to parents based on the results obtained by the analysis means. Using a generative AI model, it automatically generates appropriate feedback statements. For example, the feedback may include evaluation content based on the user's gameplay behavior and learning progress.

[0095] Examples of specific prompts include:

[0096] "A child with user ID 12345 played the game for 30 minutes and earned 200 points. Please analyze this play data, evaluate their learning progress and attitude toward the game, and generate feedback."

[0097] Calculating and exchanging learning points

[0098] The server calculates learning points to be awarded to users based on gameplay data. Using an algorithm, points are calculated based on play time and scores, and stored in a database. Users can exchange these learning points for services or items (e.g., educational materials, online courses) that will help them learn.

[0099] Specific examples

[0100] For example, if a child with user ID "12345" plays the game for 30 minutes and earns 200 points, the system will operate as follows:

[0101] 1. The user plays the game on the device, and the device records the gameplay data.

[0102] 2. The device sends the recorded data to the server.

[0103] 3. The server receives the data and stores it in a database.

[0104] 4. The analytical means (generative AI model) analyzes the received data and evaluates it as "good learning situation" and "positive attitude toward the game."

[0105] 5. The server generates a feedback statement based on the analysis results and sends it to the parent device.

[0106] 6. Parents receive feedback and see their child's gameplay and learning progress.

[0107] 7. The server calculates and awards 100 points to the user based on 30 minutes of play and 200 points.

[0108] 8. Users can use learning points to acquire items that will help them with their education.

[0109] In this way, the present invention effectively combines games and learning, promotes healthy gaming habits, and provides an environment where parents can easily keep track of their children's learning progress.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1:

[0112] The user launches the game and logs in. As the user begins playing, the device records play data (play time, score, action log, etc.) in real time.

[0113] Input: User's action to start the game

[0114] Output: Play data that has started recording (play time, score, action log)

[0115] Specific operation: A user launches the game app on their device and logs in using their unique user ID. After successful login, the game starts and gameplay data is recorded in real time in the device's memory.

[0116] Step 2:

[0117] The terminal transmits the recorded play data to a central processing unit (server) at regular intervals or when the game ends.

[0118] Input: Real-time recorded play data

[0119] Output: Play data sent to the server

[0120] Specific operation: The device sets a timer and sends play data to the server using an HTTP request at regular intervals or when it detects an action to end the game. The data sent includes the user ID, play time, score, and action log.

[0121] Step 3:

[0122] The server receives the game play data sent from the user terminal and stores it in a database.

[0123] Input: Play data sent from the device (user ID, play time, score, action log)

[0124] Output: Play data stored in the database

[0125] Specific operation: The server's receiving API receives the HTTP request, converts it into the appropriate data format, and inserts it into the database, where play data for each user is accumulated.

[0126] Step 4:

[0127] The server retrieves the received data from the database and passes it to the analysis means (generative AI model) for analysis.

[0128] Input: Play data retrieved from the database

[0129] Output: Analyzed learning situation and attitude towards the game

[0130] Specific operation: The server retrieves the target user's play data via a query and inputs it into the generative AI model. The model analyzes the user's learning status (e.g., learning progress, level of understanding) and attitude toward the game (e.g., motivation, engagement) from the play data.

[0131] Step 5:

[0132] The server generates feedback to provide to parents based on the analysis results, using a generative AI model to generate feedback statements that express the evaluation results in natural language.

[0133] Input: Learning situation and attitude toward the game obtained through analytical methods

[0134] Output: Generated feedback statement

[0135] Specific operation: The server uses the feedback template based on the evaluation results to construct a natural language feedback sentence using the generative AI model. For example, the generated feedback is "User 12345 is doing well in his studies and has a positive attitude towards the game."

[0136] Step 6:

[0137] The server sends the generated feedback to the parent device.

[0138] Input: Generated feedback sentence

[0139] Output: Feedback sent to the parent device

[0140] Specific behavior: The server uses the push notification service to send the generated feedback statement to the parent's device, which displays a notification and provides a link to view the details.

[0141] Step 7:

[0142] The server calculates learning points based on the game play data and awards them to the user.

[0143] Input: Play data (play time, score, action log)

[0144] Output: Calculated learning points

[0145] Specific operation: The server uses specific rules and algorithms to calculate learning points from play data, for example, 100 points based on 30 minutes of play time and 200 points, and adds them to the user's point account.

[0146] Step 8:

[0147] Users can use the learning points they earn to exchange them for learning items or services.

[0148] Input: User learning points

[0149] Output: Exchanged learning items or services

[0150] Specific operation: Users access the points exchange screen within the application and use their learning points to select and exchange offered learning items and services, such as online courses and learning materials.

[0151] The above is the specific processing flow of this system.

[0152] (Application example 1)

[0153] 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."

[0154] With conventional game systems, it is difficult to accurately grasp a child's learning situation and attitude toward the game, and this information is rarely effectively fed back to parents. Furthermore, there is no system that allows learning points earned through game play to be immediately used in physical stores, making it difficult to maintain children's motivation to study.

[0155] 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.

[0156] In this invention, the server includes a means for recording gameplay data, a means for transmitting the recorded gameplay data to a central processing unit, and an analysis means for analyzing the gameplay data received by the central processing unit. This makes it possible to analyze the gameplay data, evaluate the child's learning status and attitude toward the game, and provide the results as feedback to the parent. Furthermore, by including a means for instantly using the evaluated learning points to purchase products at a physical store, it is possible to maintain the child's motivation to learn.

[0157] "Game play data" refers to data generated when a user plays a game, including play time, scores, behavioral information, etc.

[0158] "Central Processing Unit" refers to the server and its associated systems for receiving and analyzing game play data transmitted from user terminals.

[0159] "Analysis means" refers to the algorithms or AI models that analyze the gameplay data received by the central processing unit and evaluate the user's learning status and attitude toward the game.

[0160] The "evaluation means" is a function that judges the user's learning situation and attitude toward the game based on the analysis results of the analysis means.

[0161] The "feedback means" refers to a communication means and a display device that reports the evaluation results obtained by the evaluation means to the parents.

[0162] "Learning points" are points calculated based on game play data, and are numerical values ​​related to the user's learning progress and performance.

[0163] "Means for immediate use in purchasing products at physical stores" refers to a system that allows assessed learning points to be used in real time to purchase products at physical stores.

[0164] "Natural language processing" is a computer processing technique for analyzing input messages from users and generating appropriate responses.

[0165] A form for implementing the present invention is a system in which a user plays a game, evaluates the learning situation and attitude toward the game based on gameplay data, provides feedback to parents, and instantly uses learning points to purchase products in a physical store.

[0166] When a user plays a game, gameplay data is recorded on the user's smartphone. The recorded data includes play time, scores, and other behavioral information. This data is sent to a central processing unit (server) at specified times or when the game ends. The server uses a cloud server or database (e.g., AWS (registered trademark) or Firebase) to process and store the received data.

[0167] The server analyzes the received gameplay data. AI models and algorithms are used to evaluate the user's learning status and attitude toward the game. Natural language processing technology is also used to generate appropriate responses to messages entered by the user. The specific technology used for this analysis is a machine learning library using Python.

[0168] The evaluation results are fed back to the parent's device. The feedback provides a detailed report of the user's learning progress and attitude towards the game, and is displayed through a dedicated application. Furthermore, analysis is used to calculate learning points, which can be immediately used to purchase products in physical stores. This process is carried out through a smartphone application, allowing points to be redeemed in real time.

[0169] As a concrete example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using an analysis means. As a result, the user's learning progress is evaluated as "good" and their attitude toward the game is "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is learning well and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to purchase educational toys at a physical store.

[0170] An example prompt might look like this:

[0171] "A child with user ID '12345' played the game for 30 minutes and earned 200 points. In this case, the play data will be sent to the server, and the analysis result will be 100 learning points. Please create a request to exchange this for an educational toy."

[0172] In this way, the system of the present invention effectively combines games and learning, promoting healthy gaming habits and making it easier for parents to keep track of their children's learning status. In addition, the instant points can be used to maintain and improve children's motivation to learn.

[0173] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The user plays the game.

[0176] The user starts the game on a device (smartphone or tablet), and behavioral data such as play time, scores, movements and actions are recorded.

[0177] Input: User gameplay data (play time, score, behavioral information)

[0178] Output: Recorded gameplay data

[0179] Step 2:

[0180] The terminal transmits gameplay data to the central processing unit.

[0181] At regular intervals or when the game ends, the terminal transmits the recorded gameplay data to the server.

[0182] Input: Recorded gameplay data

[0183] Output: Gameplay data sent to the server

[0184] Step 3:

[0185] The server analyzes the received data.

[0186] The server stores the received gameplay data in a database and analyzes it using AI models and analysis algorithms to evaluate the user's learning status and attitude toward the game.

[0187] Input: Gameplay data sent to the server

[0188] Output: Analysis results (user's learning status and attitude towards the game)

[0189] Step 4:

[0190] The server feeds back the evaluation results to the parent's device.

[0191] Based on the analysis results, the server generates feedback messages and sends them to the parents' devices. Through a dedicated application, parents can keep track of their children's learning progress and attitude toward the game.

[0192] Input: Analysis results (user's learning status and attitude towards the game)

[0193] Output: Feedback message sent to the parent device

[0194] Step 5:

[0195] The server calculates the learning points.

[0196] The server awards learning points to the user based on the game play data.

[0197] Input: Gameplay data and analysis results

[0198] Output: Calculated learning points

[0199] Step 6:

[0200] Users can use their learning points to purchase products in physical stores.

[0201] The user uses the application to instantly use the calculated learning points to purchase products at physical stores. The server receives the point usage request and confirms the point consumption.

[0202] Input: Learning points and user purchase request

[0203] Output: Purchase completed at physical store and points consumed

[0204] Through the above steps, the present invention provides a system that evaluates a user's learning situation and attitude toward games based on their gameplay data, provides feedback to parents, and allows them to instantly use learning points at physical stores.

[0205] 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.

[0206] In one embodiment of the present invention, a system includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. In addition, by combining an emotion engine, it becomes possible to recognize the user's emotions and reflect them in the evaluation.

[0207] Recording and transmitting gameplay data

[0208] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[0209] Data reception and analysis

[0210] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[0211] Emotion analysis using an emotion engine

[0212] When a user plays a game, the user device uses an emotion engine to analyze the user's facial expressions and voice to collect the user's emotion data, which is also transmitted to the central processing unit.

[0213] Reflecting emotional data in evaluations

[0214] The server receives the emotion data acquired from the emotion engine and passes it to the analysis means, which reflects this emotion data in the evaluation of the learning situation and attitude toward the game, thereby achieving a more accurate evaluation.

[0215] Generating and Providing Feedback

[0216] The server generates feedback to provide to parents based on the evaluation results. The feedback message includes information about the user's learning status, attitude toward the game, and emotions. This information is sent to the parent's device, allowing the parent to view the information and understand the overall situation of their child.

[0217] Calculating and exchanging learning points

[0218] Furthermore, the server calculates learning points based on gameplay data, which are then offered to users in exchange for services and items that are useful for learning, helping children maintain their motivation to learn through gameplay.

[0219] Natural language processing dialogue

[0220] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[0221] Specific examples

[0222] For example, consider a child with user ID "12345" who plays a game for 30 minutes, earns 200 points, and smiles frequently while playing. In this case, the user's device records emotional data along with play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good," their attitude toward the game is "positive," and their emotions are evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically, has a positive attitude toward the game, and had positive emotions while playing." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the user is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[0223] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily understand their children's learning status and emotions.

[0224] The processing flow will be explained below.

[0225] Step 1:

[0226] The user starts the game, and the game time and score are measured in real time.

[0227] Step 2:

[0228] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[0229] Step 3:

[0230] The device sends recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[0231] Step 4:

[0232] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[0233] Step 5:

[0234] The device records the user's emotions in real time while playing games, and uses an emotion engine to analyze and collect emotional data from the user's facial expressions and voice.

[0235] Step 6:

[0236] The device sends emotional data to the server, which then sends the emotional data along with gameplay data to the central processing unit.

[0237] Step 7:

[0238] The server receives the emotion data and stores it in a database, allowing gameplay data and emotion data to be analyzed together.

[0239] Step 8:

[0240] The server analyzes gameplay and emotional data, using AI models and algorithms to assess users' learning progress, attitudes towards the game, and emotions.

[0241] Step 9:

[0242] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress, attitude towards the game, and even emotions.

[0243] Step 10:

[0244] The server then sends the generated feedback to the parent device, where it is sent as an HTTP POST request and can be viewed by the parent.

[0245] Step 11:

[0246] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[0247] Step 12:

[0248] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning through gameplay.

[0249] Step 13:

[0250] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[0251] Example 2

[0252] 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."

[0253] Conventional educational game systems evaluate learning progress solely based on the user's gameplay data, making it difficult to accurately evaluate the user's learning progress by taking into account their emotions and reactions. Furthermore, if feedback is not provided appropriately to parents, they may not be able to accurately grasp their child's learning progress. Furthermore, the systems do not include a means to effectively reward users' learning efforts within the game, which can lead to a decrease in motivation to learn.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accumulating play record information when a user plays a game, means for transmitting the accumulated play record information to a central control device, analysis means for analyzing the play record information received in the central control device, means for collecting user emotion information using an emotion engine and transmitting the information to the central control device, and means for more accurately evaluating the learning progress and attitude toward the game based on the user emotion information. This enables highly accurate learning evaluation that takes user emotions into consideration, provision of appropriate feedback to parents, and rewards in the form of learning points.

[0255] "Game play record information" is data relating to play time, scores, and in-game actions collected when a user plays a game.

[0256] The "central control device" is a server that receives, stores, and analyzes data sent from user terminals.

[0257] "Analysis means" refers to a system element that includes an AI model or algorithm for analyzing data received at the central control unit and assessing the user's learning status and attitude toward the game.

[0258] An "emotion engine" is a software or hardware configuration that analyzes a user's facial expressions and voice in real time and collects emotional data.

[0259] "Feedback" refers to information notifications about the user's learning status and emotional state that are generated based on the evaluation results of the analysis means.

[0260] "Learning Points" are reward points calculated based on the user's gameplay and can be exchanged for items and services that are useful for learning.

[0261] "User" means an individual who plays the game and whose data is collected from the system.

[0262] A "parent" is a person whose role is to receive feedback on the user's learning progress and games.

[0263] In one embodiment of the present invention, a system is provided in which a user plays a game, and a server receives and analyzes the data to evaluate the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. This system also includes a function that recognizes the user's emotions by combining an emotion engine and reflects them in the evaluation.

[0264] First, when you play the game, your device will record the following data:

[0265] 1. Play Time: The time from starting the game to finishing it.

[0266] 2. Score: The total score earned by the user.

[0267] 3. Behavioral information: The series of actions a user takes within the game.

[0268] This data is collected at regular intervals or at the end of the game and sent to a central control device (server) along with the user ID.

[0269] The user device uses a built-in camera and microphone to analyze the user's facial expressions and voice in real time, and collects the user's emotional data through an emotion engine. Specifically, emotional indicators such as smiles and tone of voice are recorded. This emotional data is also sent to the server along with gameplay data.

[0270] The server receives data sent from the user's device in real time and stores it in a database. Here, the data is properly categorized and organized based on the user's ID. The stored data is then passed to the analysis means. The analysis means includes generative AI models and algorithms (e.g., TENSORFLOW (registered trademark) and PyTorch) that analyze the data and evaluate the user's learning progress and attitude toward the game.

[0271] The analysis method performs the following operations:

[0272] 1. Evaluating the user's learning progress (e.g., accuracy rate during games, task completion rate)

[0273] 2. Evaluation of attitude toward games (e.g., aggressive or not)

[0274] 3. Emotion assessment based on facial expressions and voice (e.g., positive, negative)

[0275] For example, if a child with user ID "12345" scores 200 points in 30 minutes of gameplay and is detected smiling a lot during that time, the server will use this data to assess that "user ID 12345 is doing well in his studies and has an active and positive attitude toward the game."

[0276] The server then generates a feedback message based on the analysis results and sends it to the parent's device. The feedback includes information about the user's learning progress, attitude toward the game, and emotional state. By receiving this feedback, parents can understand their child's overall learning progress and emotional state.

[0277] The server then calculates learning points based on gameplay data. For example, points are awarded based on the number of stages cleared or the score obtained. These points are then offered to users in exchange for items or services that will help their children learn.

[0278] Example prompt sentence:

[0279] "Please analyze the play data (play time: 30 minutes, score: 200 points) and emotional data (smile frequency) of user ID 12345 to evaluate their learning status, attitude toward the game, and emotions."

[0280] "Based on the analysis results, please generate a feedback message to send to the parent of user ID 12345."

[0281] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0282] Step 1: User gameplay

[0283] A user starts a game and plays it on a device such as a smartphone or tablet. The user's actions and movements within the game are recorded. Data input by the user includes the actions and choices made within the game. Based on this input, the device collects data such as play time, scores, and behavioral information.

[0284] Step 2: Record gameplay and emotion data

[0285] The device records data of users playing games in real time. This data includes play time, scores, and in-game behavior information. The device also uses a built-in camera and microphone to analyze the user's facial expressions and voice to collect emotional data. The input data is the user's operations, facial expressions, and voice, which the device analyzes to generate play data and emotional data.

[0286] Step 3: Sending data

[0287] The device transmits the recorded gameplay data and emotion data along with the user ID to the server. The timing of transmission is interval controlled or at the end of the game. The input data is the recorded play data and emotion data, and a data packet is generated based on this and transmitted to the server via the network.

[0288] Step 4: Receiving and storing data

[0289] The server receives data sent from user devices in real time. The received data is stored in a database and classified and organized. The input data consists of play data and emotional data sent from the device, and the server converts this into a format that is easy to analyze and stores it. Specifically, the data is classified for each user, and the play data and emotional data are tagged and stored in the database.

[0290] Step 5: Analyze the data

[0291] The server passes the saved data to the analysis means, which includes a generative AI model and algorithms that analyze the data to evaluate the user's learning progress and attitude toward the game. The input data is the saved play data and emotional data, and evaluation data is generated through the analysis algorithm. Specifically, the machine learning model analyzes the data and evaluates the user's performance and emotional state.

[0292] Step 6: Generate feedback messages

[0293] The server generates a feedback message based on the analysis results. This feedback message includes information about the user's learning progress, attitude toward the game, and emotional state. The input data is the analysis results, and the feedback message is generated based on these. Specifically, the message generated is, "User ID 12345's learning progress is good, and he has positive and proactive feelings toward the game."

[0294] Step 7: Submit and view feedback

[0295] The server sends the generated feedback message to the parent's device, which displays the received feedback message on its screen and allows the parent to check detailed information. The input data is the generated feedback message, and by sending it to the parent's device, the parent can understand their child's learning progress and emotional state.

[0296] Step 8: Calculating and Providing Learning Points

[0297] The server calculates learning points based on gameplay data. Input data is play data, and points are calculated based on this. For example, points are awarded based on the number of stages cleared and the score obtained. This allows children to earn points that can be exchanged for items or services that are useful for learning. Specifically, points are calculated based on the following rule: 10 points are awarded for each cleared stage, and 5 points for every 100 points scored.

[0298] (Application example 2)

[0299] 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."

[0300] Conventional factory worker evaluation systems only evaluate workers' work efficiency and the quality of their work products, making it difficult to evaluate workers' emotional state and overall work attitude. Furthermore, there were limited means to provide specific feedback that would contribute to improving worker motivation and the work environment. As a result, it was not possible to properly monitor workers' mental health and motivation, making it difficult to create an optimal work environment.

[0301] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0302] In this invention, the server includes a means for recording work data, a means for transmitting the recorded work data to a central processing unit, and an analysis means for analyzing the work data received by the central processing unit. This allows for real-time evaluation of the worker's work status and emotions and provides comprehensive feedback to the manager. Furthermore, by further including a means for calculating evaluation points based on the work data and converting the evaluation points into incentives for the worker, it is possible to contribute to improving worker motivation. Furthermore, by including a means for the analysis means for analyzing the worker's work attitude and emotions through dialogue with the worker using natural language processing, more accurate evaluations are possible.

[0303] "Work data" refers to data that includes information related to a worker's work, such as the worker's working hours, work efficiency, and quality of the work product.

[0304] The "central processing unit" is a computer system for analyzing received work data and generating evaluation results.

[0305] "Analysis tools" refers to algorithms and AI models that evaluate workers' work situations and emotions based on work data.

[0306] "Emotional state" is data that indicates a worker's mental reactions, feelings, and psychological state.

[0307] "Evaluation points" refer to a numerical representation of a worker's performance and attitude calculated based on work data.

[0308] "Incentives" are rewards, benefits, and incentives that are intended to increase worker motivation.

[0309] "Feedback" is the act of providing information including evaluation, advice, and improvement suggestions based on analysis results.

[0310] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used to understand work attitudes and emotions through dialogue with workers.

[0311] The present invention relates to a system for evaluating the work efficiency and emotional state of workers in a factory and providing feedback to managers. This system is realized using an application and a central processing unit installed on a factory robot.

[0312] Data collection

[0313] The factory robots are equipped with sensors that record each worker's work data (work time, work efficiency, quality of finished products, etc.) The robots are also equipped with cameras and microphones, and an emotion engine (e.g., Affectiva SDK) is used to analyze the facial expressions and voices of workers to collect emotional data.

[0314] Data transmission

[0315] The collected data is temporarily stored locally on the edge device and then transmitted to a central server at regular intervals using a lightweight messaging protocol such as MQTT.

[0316] Data analysis

[0317] The central processing unit (server) stores the received data in a database (e.g., MongoDB). It then analyzes the data using an AI model (e.g., TensorFlow) and analysis algorithms to evaluate each worker's work efficiency and emotional state. The AI ​​model uses a generative AI model to perform highly accurate analysis.

[0318] Feedback Generation

[0319] Based on the analysis results, a report is generated of each worker's work status and emotional state. The report includes information linked to the worker's ID and is displayed in an easy-to-understand manner for managers.

[0320] Feedback Notifications

[0321] The final feedback generated is sent to the administrator's device (smartphone or computer) and can be viewed via a dedicated mobile app or web app.

[0322] Examples and prompts

[0323] For example, consider a factory worker with ID 56789 who works eight hours during a daily shift, producing 100 parts at 90% efficiency. Furthermore, facial expression analysis reveals primarily positive emotions. This data is collected and analyzed as follows:

[0324] Specifically, the following prompt sentences are input into the generative AI model to generate evaluation and feedback:

[0325] Generate a report on worker performance and emotions based on the following data:

[0326] Working time: 8 hours

[0327] Efficiency: 90%

[0328] Deliverables: 100 pieces

[0329] Emotion: Positive

[0330] Generate a feedback message.

[0331] The server generates a feedback message like this:

[0332] Worker ID 56789 worked 8 hours during today's shift, producing 100 parts at 90% efficiency. He displayed mostly positive emotions while working and demonstrated good overall work performance.

[0333] In this way, the system can evaluate the work situation and emotions of workers in real time and provide comprehensive feedback to managers.In addition, by providing evaluation points as incentives to workers, it can improve motivation and the working environment.

[0334] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0335] Step 1:

[0336] The factory robot (terminal) records each worker's work data (e.g., work time, work efficiency, and quality of the finished product). Specifically, data is collected using work time sensors and product counters. The robot is also equipped with a camera and microphone, and an emotion engine (e.g., Affectiva SDK) is used to analyze the worker's facial expressions and voice to collect emotional data. The input is the worker's physical actions, facial expressions, and voice, and the output is the recorded work data and emotional data.

[0337] Step 2:

[0338] The collected data is temporarily stored on the local edge device. At appropriate intervals, or when a certain amount of data has accumulated, the data is sent to a central server. This communication is performed using a lightweight messaging protocol such as MQTT. The input is the task data and emotion data on the local edge device, and the output is the data to be sent to the central server.

[0339] Step 3:

[0340] The server (central processing unit) stores the received task data and emotion data in a database (e.g., MongoDB). Here, the data is organized and normalized to facilitate subsequent analysis. The input is the data sent to the central server, and the output is the integrated data stored in the database.

[0341] Step 4:

[0342] An AI analysis module (e.g., TensorFlow) and analysis algorithm installed on the server analyze the received data. Specifically, an AI model is used to evaluate work efficiency and emotional state, and the results are output in numerical or text format. The use of a generative AI model enables complex pattern recognition and prediction. The input is the integrated data in the database, and the output is the evaluation result.

[0343] Step 5:

[0344] The server generates feedback based on the evaluation results, again using a generative AI model to generate prompts such as:

[0345] Generate a report on worker performance and emotions based on the following data:

[0346] Working time: 8 hours

[0347] Efficiency: 90%

[0348] Deliverables: 100 pieces

[0349] Emotion: Positive

[0350] Generate a feedback message.

[0351] The generated feedback is output in the form of a report summarizing each worker's work status and emotional state. The input is the evaluation result, and the output is the generated feedback message.

[0352] Step 6:

[0353] The final feedback message is sent to the manager's device (smartphone or computer). The manager can check the feedback via a dedicated mobile app or web app and use it to improve the work environment or provide guidance to workers as needed. The input is the generated feedback message, and the output is the feedback information sent to the manager's device.

[0354] This allows for real-time assessment of workers' work status and emotions, providing comprehensive feedback to managers.

[0355] 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.

[0356] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0357] 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.

[0358] [Second embodiment]

[0359] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0360] 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.

[0361] 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).

[0362] 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.

[0363] 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.

[0364] 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).

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] 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.

[0370] 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."

[0371] An embodiment of the present invention is a system that includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning situation and attitude toward the game, and provides the evaluation results to parents as feedback.

[0372] Recording and transmitting gameplay data

[0373] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[0374] Data reception and analysis

[0375] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[0376] Generating and Providing Feedback

[0377] The server generates feedback to provide to parents based on the evaluation results. The feedback includes information about the user's learning status and attitude toward the game and is sent to the parent's device, making it easier for parents to understand their child's gaming habits and learning status.

[0378] Calculating and exchanging learning points

[0379] Furthermore, the server calculates learning points based on gameplay data. Learning points are earned by users through gameplay and can be exchanged for services and items that are useful for learning. This helps children maintain their motivation to learn through gameplay.

[0380] Natural language processing dialogue

[0381] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[0382] Specific examples

[0383] For example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good" and their attitude toward the game is evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[0384] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily keep track of their children's learning progress.

[0385] The processing flow will be explained below.

[0386] Step 1:

[0387] The user starts the game. As the user plays, the game time and score are measured in real time.

[0388] Step 2:

[0389] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[0390] Step 3:

[0391] The device sends the recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[0392] Step 4:

[0393] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[0394] Step 5:

[0395] The server analyzes the data and uses AI models and algorithms to evaluate the user's learning progress and attitude towards the game based on the received data.

[0396] Step 6:

[0397] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress and attitude towards the game.

[0398] Step 7:

[0399] The server sends the generated feedback to the parent device, which is sent as an HTTP POST request so that the parent can view it.

[0400] Step 8:

[0401] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[0402] Step 9:

[0403] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning by continuing to play the game.

[0404] Step 10:

[0405] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[0406] Through this process, the system can promote healthy gaming habits and provide an environment where parents can easily keep track of their children's learning progress.

[0407] Example 1

[0408] 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."

[0409] With conventional game systems, it is difficult for parents to grasp the user's playing and learning status, and the mechanisms for increasing motivation to learn through games are insufficient. For these reasons, there is a need for a means to effectively link games and learning.

[0410] 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.

[0411] In this invention, the server includes means for allowing users to play games, means for recording game play data, means for transmitting the recorded game play data to a central processing unit, means for analyzing the game play data received by the central processing unit, means for evaluating the learning situation and attitude toward the game based on the analysis results, means for providing the results of the evaluation by the evaluation means as feedback to parents, and means for the analysis means to analyze the original data through dialogue with the user using natural language processing. This allows parents to understand the learning situation and attitude of the user through game play, and can increase the user's motivation to learn through feedback and evaluation of learning points.

[0412] "User" refers to the person who plays the game, especially the children whose learning situation and attitude toward the game will be evaluated.

[0413] "Terminal" refers to an electronic device used by a user to play a game and that records and transmits gameplay data.

[0414] "Central Processing Unit" refers to a server that receives, analyzes, and evaluates gameplay data sent from user terminals.

[0415] "Gameplay data" refers to information generated while a user is playing a game, and specifically includes play time, scores, and behavior logs.

[0416] "Analysis means" refers to the algorithms or AI models that analyze gameplay data within the central processing unit and evaluate the user's learning status and attitude toward the game.

[0417] The "evaluation means" refers to a mechanism for evaluating the user's learning situation and attitude toward the game based on the analysis results obtained by the analysis means, and providing the results as feedback.

[0418] "Feedback" is evaluation information provided to parents based on the user's gameplay data, including the user's academic progress and attitude toward the game.

[0419] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used for interacting with users and analyzing data.

[0420] "Learning Points" are reward points calculated based on a user's gameplay data, and can be exchanged for services or items that are useful for learning.

[0421] "Generative AI Model" refers to an artificial intelligence model used to analyze a user's gameplay data and generate feedback.

[0422] A "prompt" is an instruction entered into a generative AI model, and refers to a sentence or phrase used for specific analysis or feedback generation.

[0423] This invention is a system in which a user plays a game and transmits the play data to a central processing unit, which evaluates the user's learning status and attitude toward the game and provides feedback to parents. This system is composed of a terminal, a central processing unit (server), a generative AI model, analysis means, evaluation means, and feedback means.

[0424] Recording and transmitting gameplay data

[0425] Users play games using dedicated devices (e.g., tablets, smartphones). Gameplay data, such as the user's playing time, scores, and behavioral information, is recorded by the device. The device then sends the recorded data to a central processing unit at regular intervals or when the game ends. Transmission is performed using the HTTP protocol.

[0426] Data reception and analysis

[0427] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means analyzes the data using a generative AI model (e.g., machine learning algorithm) or a specific algorithm. During the analysis process, the data can also be used to interact with the user using natural language processing technology.

[0428] Generating and Providing Feedback

[0429] The server generates feedback to provide to parents based on the results obtained by the analysis means. Using a generative AI model, it automatically generates appropriate feedback statements. For example, the feedback may include evaluation content based on the user's gameplay behavior and learning progress.

[0430] Examples of specific prompts include:

[0431] "A child with user ID 12345 played the game for 30 minutes and earned 200 points. Please analyze this play data, evaluate their learning progress and attitude toward the game, and generate feedback."

[0432] Calculating and exchanging learning points

[0433] The server calculates learning points to be awarded to users based on gameplay data. Using an algorithm, points are calculated based on play time and scores, and stored in a database. Users can exchange these learning points for services or items (e.g., educational materials, online courses) that will help them learn.

[0434] Specific examples

[0435] For example, if a child with user ID "12345" plays the game for 30 minutes and earns 200 points, the system will operate as follows:

[0436] 1. The user plays the game on the device, and the device records the gameplay data.

[0437] 2. The device sends the recorded data to the server.

[0438] 3. The server receives the data and stores it in a database.

[0439] 4. The analytical means (generative AI model) analyzes the received data and evaluates it as "good learning situation" and "positive attitude toward the game."

[0440] 5. The server generates a feedback statement based on the analysis results and sends it to the parent device.

[0441] 6. Parents receive feedback and see their child's gameplay and learning progress.

[0442] 7. The server calculates and awards 100 points to the user based on 30 minutes of play and 200 points.

[0443] 8. Users can use learning points to acquire items that will help them with their education.

[0444] In this way, the present invention effectively combines games and learning, promotes healthy gaming habits, and provides an environment where parents can easily keep track of their children's learning progress.

[0445] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0446] Step 1:

[0447] The user launches the game and logs in. As the user begins playing, the device records play data (play time, score, action log, etc.) in real time.

[0448] Input: User's action to start the game

[0449] Output: Play data that has started recording (play time, score, action log)

[0450] Specific operation: A user launches the game app on their device and logs in using their unique user ID. After successful login, the game starts and gameplay data is recorded in real time in the device's memory.

[0451] Step 2:

[0452] The terminal transmits the recorded play data to a central processing unit (server) at regular intervals or when the game ends.

[0453] Input: Real-time recorded play data

[0454] Output: Play data sent to the server

[0455] Specific operation: The device sets a timer and sends play data to the server using an HTTP request at regular intervals or when it detects an action to end the game. The data sent includes the user ID, play time, score, and action log.

[0456] Step 3:

[0457] The server receives the game play data sent from the user terminal and stores it in a database.

[0458] Input: Play data sent from the device (user ID, play time, score, action log)

[0459] Output: Play data stored in the database

[0460] Specific operation: The server's receiving API receives the HTTP request, converts it into the appropriate data format, and inserts it into the database, where play data for each user is accumulated.

[0461] Step 4:

[0462] The server retrieves the received data from the database and passes it to the analysis means (generative AI model) for analysis.

[0463] Input: Play data retrieved from the database

[0464] Output: Analyzed learning situation and attitude towards the game

[0465] Specific operation: The server retrieves the target user's play data via a query and inputs it into the generative AI model. The model analyzes the user's learning status (e.g., learning progress, level of understanding) and attitude toward the game (e.g., motivation, engagement) from the play data.

[0466] Step 5:

[0467] The server generates feedback to provide to parents based on the analysis results, using a generative AI model to generate feedback statements that express the evaluation results in natural language.

[0468] Input: Learning situation and attitude toward the game obtained through analytical methods

[0469] Output: Generated feedback statement

[0470] Specific operation: The server uses the feedback template based on the evaluation results to construct a natural language feedback sentence using the generative AI model. For example, the generated feedback is "User 12345 is doing well in his studies and has a positive attitude towards the game."

[0471] Step 6:

[0472] The server sends the generated feedback to the parent device.

[0473] Input: Generated feedback sentence

[0474] Output: Feedback sent to the parent device

[0475] Specific behavior: The server uses the push notification service to send the generated feedback statement to the parent's device, which displays a notification and provides a link to view the details.

[0476] Step 7:

[0477] The server calculates learning points based on the game play data and awards them to the user.

[0478] Input: Play data (play time, score, action log)

[0479] Output: Calculated learning points

[0480] Specific operation: The server uses specific rules and algorithms to calculate learning points from play data, for example, 100 points based on 30 minutes of play time and 200 points, and adds them to the user's point account.

[0481] Step 8:

[0482] Users can use the learning points they earn to exchange them for learning items or services.

[0483] Input: User learning points

[0484] Output: Exchanged learning items or services

[0485] Specific operation: Users access the points exchange screen within the application and use their learning points to select and exchange offered learning items and services, such as online courses and learning materials.

[0486] The above is the specific processing flow of this system.

[0487] (Application example 1)

[0488] 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."

[0489] With conventional game systems, it is difficult to accurately grasp a child's learning situation and attitude toward the game, and this information is rarely effectively fed back to parents. Furthermore, there is no system that allows learning points earned through game play to be immediately used in physical stores, making it difficult to maintain children's motivation to study.

[0490] 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.

[0491] In this invention, the server includes a means for recording gameplay data, a means for transmitting the recorded gameplay data to a central processing unit, and an analysis means for analyzing the gameplay data received by the central processing unit. This makes it possible to analyze the gameplay data, evaluate the child's learning status and attitude toward the game, and provide the results as feedback to the parent. Furthermore, by including a means for instantly using the evaluated learning points to purchase products at a physical store, it is possible to maintain the child's motivation to learn.

[0492] "Game play data" refers to data generated when a user plays a game, including play time, scores, behavioral information, etc.

[0493] "Central Processing Unit" refers to the server and its associated systems for receiving and analyzing game play data transmitted from user terminals.

[0494] "Analysis means" refers to the algorithms or AI models that analyze the gameplay data received by the central processing unit and evaluate the user's learning status and attitude toward the game.

[0495] The "evaluation means" is a function that judges the user's learning situation and attitude toward the game based on the analysis results of the analysis means.

[0496] The "feedback means" refers to a communication means and a display device that reports the evaluation results obtained by the evaluation means to the parents.

[0497] "Learning points" are points calculated based on game play data, and are numerical values ​​related to the user's learning progress and performance.

[0498] "Means for immediate use in purchasing products at physical stores" refers to a system that allows assessed learning points to be used in real time to purchase products at physical stores.

[0499] "Natural language processing" is a computer processing technique for analyzing input messages from users and generating appropriate responses.

[0500] A form for implementing the present invention is a system in which a user plays a game, evaluates the learning situation and attitude toward the game based on gameplay data, provides feedback to parents, and instantly uses learning points to purchase products in a physical store.

[0501] When a user plays a game, gameplay data is recorded on the user's smartphone. The recorded data includes play time, scores, and other behavioral information. This data is sent to a central processing unit (server) at specified times or when the game ends. The server uses a cloud server or database (e.g., AWS or Firebase) to process and store the received data.

[0502] The server analyzes the received gameplay data. AI models and algorithms are used to evaluate the user's learning status and attitude toward the game. Natural language processing technology is also used to generate appropriate responses to messages entered by the user. The specific technology used for this analysis is a machine learning library using Python.

[0503] The evaluation results are fed back to the parent's device. The feedback provides a detailed report of the user's learning progress and attitude towards the game, and is displayed through a dedicated application. Furthermore, analysis is used to calculate learning points, which can be immediately used to purchase products in physical stores. This process is carried out through a smartphone application, allowing points to be redeemed in real time.

[0504] As a concrete example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using an analysis means. As a result, the user's learning progress is evaluated as "good" and their attitude toward the game is "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is learning well and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to purchase educational toys at a physical store.

[0505] An example prompt might look like this:

[0506] "A child with user ID '12345' played the game for 30 minutes and earned 200 points. In this case, the play data will be sent to the server, and the analysis result will be 100 learning points. Please create a request to exchange this for an educational toy."

[0507] In this way, the system of the present invention effectively combines games and learning, promoting healthy gaming habits and making it easier for parents to keep track of their children's learning status. In addition, the instant points can be used to maintain and improve children's motivation to learn.

[0508] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0509] Step 1:

[0510] The user plays the game.

[0511] The user starts the game on a device (smartphone or tablet), and behavioral data such as play time, scores, movements and actions are recorded.

[0512] Input: User gameplay data (play time, score, behavioral information)

[0513] Output: Recorded gameplay data

[0514] Step 2:

[0515] The terminal transmits gameplay data to the central processing unit.

[0516] At regular intervals or when the game ends, the terminal transmits the recorded gameplay data to the server.

[0517] Input: Recorded gameplay data

[0518] Output: Gameplay data sent to the server

[0519] Step 3:

[0520] The server analyzes the received data.

[0521] The server stores the received gameplay data in a database and analyzes it using AI models and analysis algorithms to evaluate the user's learning status and attitude toward the game.

[0522] Input: Gameplay data sent to the server

[0523] Output: Analysis results (user's learning status and attitude towards the game)

[0524] Step 4:

[0525] The server feeds back the evaluation results to the parent's device.

[0526] Based on the analysis results, the server generates feedback messages and sends them to the parents' devices. Through a dedicated application, parents can keep track of their children's learning progress and attitude toward the game.

[0527] Input: Analysis results (user's learning status and attitude towards the game)

[0528] Output: Feedback message sent to the parent device

[0529] Step 5:

[0530] The server calculates the learning points.

[0531] The server awards learning points to the user based on the game play data.

[0532] Input: Gameplay data and analysis results

[0533] Output: Calculated learning points

[0534] Step 6:

[0535] Users can use their learning points to purchase products in physical stores.

[0536] The user uses the application to instantly use the calculated learning points to purchase products at physical stores. The server receives the point usage request and confirms the point consumption.

[0537] Input: Learning points and user purchase request

[0538] Output: Purchase completed at physical store and points consumed

[0539] Through the above steps, the present invention provides a system that evaluates a user's learning situation and attitude toward games based on their gameplay data, provides feedback to parents, and allows them to instantly use learning points at physical stores.

[0540] 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.

[0541] In one embodiment of the present invention, a system includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. In addition, by combining an emotion engine, it becomes possible to recognize the user's emotions and reflect them in the evaluation.

[0542] Recording and transmitting gameplay data

[0543] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[0544] Data reception and analysis

[0545] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[0546] Emotion analysis using an emotion engine

[0547] When a user plays a game, the user device uses an emotion engine to analyze the user's facial expressions and voice to collect the user's emotion data, which is also transmitted to the central processing unit.

[0548] Reflecting emotional data in evaluations

[0549] The server receives the emotion data acquired from the emotion engine and passes it to the analysis means, which reflects this emotion data in the evaluation of the learning situation and attitude toward the game, thereby achieving a more accurate evaluation.

[0550] Generating and Providing Feedback

[0551] The server generates feedback to provide to parents based on the evaluation results. The feedback message includes information about the user's learning status, attitude toward the game, and emotions. This information is sent to the parent's device, allowing the parent to view the information and understand the overall situation of their child.

[0552] Calculating and exchanging learning points

[0553] Furthermore, the server calculates learning points based on gameplay data, which are then offered to users in exchange for services and items that are useful for learning, helping children maintain their motivation to learn through gameplay.

[0554] Natural language processing dialogue

[0555] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[0556] Specific examples

[0557] For example, consider a child with user ID "12345" who plays a game for 30 minutes, earns 200 points, and smiles frequently while playing. In this case, the user's device records emotional data along with play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good," their attitude toward the game is "positive," and their emotions are evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically, has a positive attitude toward the game, and had positive emotions while playing." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the user is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[0558] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily understand their children's learning status and emotions.

[0559] The processing flow will be explained below.

[0560] Step 1:

[0561] The user starts the game, and the game time and score are measured in real time.

[0562] Step 2:

[0563] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[0564] Step 3:

[0565] The device sends recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[0566] Step 4:

[0567] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[0568] Step 5:

[0569] The device records the user's emotions in real time while playing games, and uses an emotion engine to analyze and collect emotional data from the user's facial expressions and voice.

[0570] Step 6:

[0571] The device sends emotional data to the server, which then sends the emotional data along with gameplay data to the central processing unit.

[0572] Step 7:

[0573] The server receives the emotion data and stores it in a database, allowing gameplay data and emotion data to be analyzed together.

[0574] Step 8:

[0575] The server analyzes gameplay and emotional data, using AI models and algorithms to assess users' learning progress, attitudes towards the game, and emotions.

[0576] Step 9:

[0577] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress, attitude towards the game, and even emotions.

[0578] Step 10:

[0579] The server then sends the generated feedback to the parent device, where it is sent as an HTTP POST request and can be viewed by the parent.

[0580] Step 11:

[0581] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[0582] Step 12:

[0583] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning through gameplay.

[0584] Step 13:

[0585] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[0586] Example 2

[0587] 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."

[0588] Conventional educational game systems evaluate learning progress solely based on the user's gameplay data, making it difficult to accurately evaluate the user's learning progress by taking into account their emotions and reactions. Furthermore, if feedback is not provided appropriately to parents, they may not be able to accurately grasp their child's learning progress. Furthermore, the systems do not include a means to effectively reward users' learning efforts within the game, which can lead to a decrease in motivation to learn.

[0589] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accumulating play record information when a user plays a game, means for transmitting the accumulated play record information to a central control device, analysis means for analyzing the play record information received in the central control device, means for collecting user emotion information using an emotion engine and transmitting the information to the central control device, and means for more accurately evaluating the learning progress and attitude toward the game based on the user emotion information. This enables highly accurate learning evaluation that takes user emotions into consideration, provision of appropriate feedback to parents, and rewards in the form of learning points.

[0590] "Game play record information" is data relating to play time, scores, and in-game actions collected when a user plays a game.

[0591] The "central control device" is a server that receives, stores, and analyzes data sent from user terminals.

[0592] "Analysis means" refers to a system element that includes an AI model or algorithm for analyzing data received at the central control unit and assessing the user's learning status and attitude toward the game.

[0593] An "emotion engine" is a software or hardware configuration that analyzes a user's facial expressions and voice in real time and collects emotional data.

[0594] "Feedback" refers to information notifications about the user's learning status and emotional state that are generated based on the evaluation results of the analysis means.

[0595] "Learning Points" are reward points calculated based on the user's gameplay and can be exchanged for items and services that are useful for learning.

[0596] "User" means an individual who plays the game and whose data is collected from the system.

[0597] A "parent" is a person whose role is to receive feedback on the user's learning progress and games.

[0598] In one embodiment of the present invention, a system is provided in which a user plays a game, and a server receives and analyzes the data to evaluate the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. This system also includes a function that recognizes the user's emotions by combining an emotion engine and reflects them in the evaluation.

[0599] First, when you play the game, your device will record the following data:

[0600] 1. Play Time: The time from starting the game to finishing it.

[0601] 2. Score: The total score earned by the user.

[0602] 3. Behavioral information: The series of actions a user takes within the game.

[0603] This data is collected at regular intervals or at the end of the game and sent to a central control device (server) along with the user ID.

[0604] The user device uses a built-in camera and microphone to analyze the user's facial expressions and voice in real time, and collects the user's emotional data through an emotion engine. Specifically, emotional indicators such as smiles and tone of voice are recorded. This emotional data is also sent to the server along with gameplay data.

[0605] The server receives data sent from the user's device in real time and stores it in a database. Here, the data is properly categorized and organized based on the user's ID. The stored data is then passed to an analysis tool, which includes a generative AI model or algorithm (e.g., TensorFlow or PyTorch) to analyze the data and evaluate the user's learning progress and attitude toward the game.

[0606] The analysis method performs the following operations:

[0607] 1. Evaluating the user's learning progress (e.g., accuracy rate during games, task completion rate)

[0608] 2. Evaluation of attitude toward games (e.g., aggressive or not)

[0609] 3. Emotion assessment based on facial expressions and voice (e.g., positive, negative)

[0610] For example, if a child with user ID "12345" scores 200 points in 30 minutes of gameplay and is detected smiling a lot during that time, the server will use this data to assess that "user ID 12345 is doing well in his studies and has an active and positive attitude toward the game."

[0611] The server then generates a feedback message based on the analysis results and sends it to the parent's device. The feedback includes information about the user's learning progress, attitude toward the game, and emotional state. By receiving this feedback, parents can understand their child's overall learning progress and emotional state.

[0612] The server then calculates learning points based on gameplay data. For example, points are awarded based on the number of stages cleared or the score obtained. These points are then offered to users in exchange for items or services that will help their children learn.

[0613] Example prompt sentence:

[0614] "Please analyze the play data (play time: 30 minutes, score: 200 points) and emotional data (smile frequency) of user ID 12345 to evaluate their learning status, attitude toward the game, and emotions."

[0615] "Based on the analysis results, please generate a feedback message to send to the parent of user ID 12345."

[0616] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0617] Step 1: User gameplay

[0618] A user starts a game and plays it on a device such as a smartphone or tablet. The user's actions and movements within the game are recorded. Data input by the user includes the actions and choices made within the game. Based on this input, the device collects data such as play time, scores, and behavioral information.

[0619] Step 2: Record gameplay and emotion data

[0620] The device records data of users playing games in real time. This data includes play time, scores, and in-game behavior information. The device also uses a built-in camera and microphone to analyze the user's facial expressions and voice to collect emotional data. The input data is the user's operations, facial expressions, and voice, which the device analyzes to generate play data and emotional data.

[0621] Step 3: Sending data

[0622] The device transmits the recorded gameplay data and emotion data along with the user ID to the server. The timing of transmission is interval controlled or at the end of the game. The input data is the recorded play data and emotion data, and a data packet is generated based on this and transmitted to the server via the network.

[0623] Step 4: Receiving and storing data

[0624] The server receives data sent from user devices in real time. The received data is stored in a database and classified and organized. The input data consists of play data and emotional data sent from the device, and the server converts this into a format that is easy to analyze and stores it. Specifically, the data is classified for each user, and the play data and emotional data are tagged and stored in the database.

[0625] Step 5: Analyze the data

[0626] The server passes the saved data to the analysis means, which includes a generative AI model and algorithms that analyze the data to evaluate the user's learning progress and attitude toward the game. The input data is the saved play data and emotional data, and evaluation data is generated through the analysis algorithm. Specifically, the machine learning model analyzes the data and evaluates the user's performance and emotional state.

[0627] Step 6: Generate feedback messages

[0628] The server generates a feedback message based on the analysis results. This feedback message includes information about the user's learning progress, attitude toward the game, and emotional state. The input data is the analysis results, and the feedback message is generated based on these. Specifically, the message generated is, "User ID 12345's learning progress is good, and he has positive and proactive feelings toward the game."

[0629] Step 7: Submit and view feedback

[0630] The server sends the generated feedback message to the parent's device, which displays the received feedback message on its screen and allows the parent to check detailed information. The input data is the generated feedback message, and by sending it to the parent's device, the parent can understand their child's learning progress and emotional state.

[0631] Step 8: Calculating and Providing Learning Points

[0632] The server calculates learning points based on gameplay data. Input data is play data, and points are calculated based on this. For example, points are awarded based on the number of stages cleared and the score obtained. This allows children to earn points that can be exchanged for items or services that are useful for learning. Specifically, points are calculated based on the following rule: 10 points are awarded for each cleared stage, and 5 points for every 100 points scored.

[0633] (Application example 2)

[0634] 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."

[0635] Conventional factory worker evaluation systems only evaluate workers' work efficiency and the quality of their work products, making it difficult to evaluate workers' emotional state and overall work attitude. Furthermore, there were limited means to provide specific feedback that would contribute to improving worker motivation and the work environment. As a result, it was not possible to properly monitor workers' mental health and motivation, making it difficult to create an optimal work environment.

[0636] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0637] In this invention, the server includes a means for recording work data, a means for transmitting the recorded work data to a central processing unit, and an analysis means for analyzing the work data received by the central processing unit. This allows for real-time evaluation of the worker's work status and emotions and provides comprehensive feedback to the manager. Furthermore, by further including a means for calculating evaluation points based on the work data and converting the evaluation points into incentives for the worker, it is possible to contribute to improving worker motivation. Furthermore, by including a means for the analysis means for analyzing the worker's work attitude and emotions through dialogue with the worker using natural language processing, more accurate evaluations are possible.

[0638] "Work data" refers to data that includes information related to a worker's work, such as the worker's working hours, work efficiency, and quality of the work product.

[0639] The "central processing unit" is a computer system for analyzing received work data and generating evaluation results.

[0640] "Analysis tools" refers to algorithms and AI models that evaluate workers' work situations and emotions based on work data.

[0641] "Emotional state" is data that indicates a worker's mental reactions, feelings, and psychological state.

[0642] "Evaluation points" refer to a numerical representation of a worker's performance and attitude calculated based on work data.

[0643] "Incentives" are rewards, benefits, and incentives that are intended to increase worker motivation.

[0644] "Feedback" is the act of providing information including evaluation, advice, and improvement suggestions based on analysis results.

[0645] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used to understand work attitudes and emotions through dialogue with workers.

[0646] The present invention relates to a system for evaluating the work efficiency and emotional state of workers in a factory and providing feedback to managers. This system is realized using an application and a central processing unit installed on a factory robot.

[0647] Data collection

[0648] The factory robots are equipped with sensors that record each worker's work data (work time, work efficiency, quality of finished products, etc.) The robots are also equipped with cameras and microphones, and an emotion engine (e.g., Affectiva SDK) is used to analyze the facial expressions and voices of workers to collect emotional data.

[0649] Data transmission

[0650] The collected data is temporarily stored locally on the edge device and then transmitted to a central server at regular intervals using a lightweight messaging protocol such as MQTT.

[0651] Data analysis

[0652] The central processing unit (server) stores the received data in a database (e.g., MongoDB). It then analyzes the data using an AI model (e.g., TensorFlow) and analysis algorithms to evaluate each worker's work efficiency and emotional state. The AI ​​model uses a generative AI model to perform highly accurate analysis.

[0653] Feedback Generation

[0654] Based on the analysis results, a report is generated of each worker's work status and emotional state. The report includes information linked to the worker's ID and is displayed in an easy-to-understand manner for managers.

[0655] Feedback Notifications

[0656] The final feedback generated is sent to the administrator's device (smartphone or computer) and can be viewed via a dedicated mobile app or web app.

[0657] Examples and prompts

[0658] For example, consider a factory worker with ID 56789 who works eight hours during a daily shift, producing 100 parts at 90% efficiency. Furthermore, facial expression analysis reveals primarily positive emotions. This data is collected and analyzed as follows:

[0659] Specifically, the following prompt sentences are input into the generative AI model to generate evaluation and feedback:

[0660] Generate a report on worker performance and emotions based on the following data:

[0661] Working time: 8 hours

[0662] Efficiency: 90%

[0663] Deliverables: 100 pieces

[0664] Emotion: Positive

[0665] Generate a feedback message.

[0666] The server generates a feedback message like this:

[0667] Worker ID 56789 worked 8 hours during today's shift, producing 100 parts at 90% efficiency. He displayed mostly positive emotions while working and demonstrated good overall work performance.

[0668] In this way, the system can evaluate the work situation and emotions of workers in real time and provide comprehensive feedback to managers.In addition, by providing evaluation points as incentives to workers, it can improve motivation and the working environment.

[0669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0670] Step 1:

[0671] The factory robot (terminal) records each worker's work data (e.g., work time, work efficiency, and quality of the finished product). Specifically, data is collected using work time sensors and product counters. The robot is also equipped with a camera and microphone, and an emotion engine (e.g., Affectiva SDK) is used to analyze the worker's facial expressions and voice to collect emotional data. The input is the worker's physical actions, facial expressions, and voice, and the output is the recorded work data and emotional data.

[0672] Step 2:

[0673] The collected data is temporarily stored on the local edge device. At appropriate intervals, or when a certain amount of data has accumulated, the data is sent to a central server. This communication is performed using a lightweight messaging protocol such as MQTT. The input is the task data and emotion data on the local edge device, and the output is the data to be sent to the central server.

[0674] Step 3:

[0675] The server (central processing unit) stores the received task data and emotion data in a database (e.g., MongoDB). Here, the data is organized and normalized to facilitate subsequent analysis. The input is the data sent to the central server, and the output is the integrated data stored in the database.

[0676] Step 4:

[0677] An AI analysis module (e.g., TensorFlow) and analysis algorithm installed on the server analyze the received data. Specifically, an AI model is used to evaluate work efficiency and emotional state, and the results are output in numerical or text format. The use of a generative AI model enables complex pattern recognition and prediction. The input is the integrated data in the database, and the output is the evaluation result.

[0678] Step 5:

[0679] The server generates feedback based on the evaluation results, again using a generative AI model to generate prompts such as:

[0680] Generate a report on worker performance and emotions based on the following data:

[0681] Working time: 8 hours

[0682] Efficiency: 90%

[0683] Deliverables: 100 pieces

[0684] Emotion: Positive

[0685] Generate a feedback message.

[0686] The generated feedback is output in the form of a report summarizing each worker's work status and emotional state. The input is the evaluation result, and the output is the generated feedback message.

[0687] Step 6:

[0688] The final feedback message is sent to the manager's device (smartphone or computer). The manager can check the feedback via a dedicated mobile app or web app and use it to improve the work environment or provide guidance to workers as needed. The input is the generated feedback message, and the output is the feedback information sent to the manager's device.

[0689] This allows for real-time assessment of workers' work status and emotions, providing comprehensive feedback to managers.

[0690] 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.

[0691] 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.

[0692] 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.

[0693] [Third embodiment]

[0694] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0695] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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).

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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.

[0704] 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.

[0705] 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."

[0706] An embodiment of the present invention is a system that includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning situation and attitude toward the game, and provides the evaluation results to parents as feedback.

[0707] Recording and transmitting gameplay data

[0708] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[0709] Data reception and analysis

[0710] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[0711] Generating and Providing Feedback

[0712] The server generates feedback to provide to parents based on the evaluation results. The feedback includes information about the user's learning status and attitude toward the game and is sent to the parent's device, making it easier for parents to understand their child's gaming habits and learning status.

[0713] Calculating and exchanging learning points

[0714] Furthermore, the server calculates learning points based on gameplay data. Learning points are earned by users through gameplay and can be exchanged for services and items that are useful for learning. This helps children maintain their motivation to learn through gameplay.

[0715] Natural language processing dialogue

[0716] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[0717] Specific examples

[0718] For example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good" and their attitude toward the game is evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[0719] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily keep track of their children's learning progress.

[0720] The processing flow will be explained below.

[0721] Step 1:

[0722] The user starts the game. As the user plays, the game time and score are measured in real time.

[0723] Step 2:

[0724] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[0725] Step 3:

[0726] The device sends the recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[0727] Step 4:

[0728] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[0729] Step 5:

[0730] The server analyzes the data and uses AI models and algorithms to evaluate the user's learning progress and attitude towards the game based on the received data.

[0731] Step 6:

[0732] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress and attitude towards the game.

[0733] Step 7:

[0734] The server sends the generated feedback to the parent device, which is sent as an HTTP POST request so that the parent can view it.

[0735] Step 8:

[0736] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[0737] Step 9:

[0738] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning by continuing to play the game.

[0739] Step 10:

[0740] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[0741] Through this process, the system can promote healthy gaming habits and provide an environment where parents can easily keep track of their children's learning progress.

[0742] Example 1

[0743] 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."

[0744] With conventional game systems, it is difficult for parents to grasp the user's playing and learning status, and the mechanisms for increasing motivation to learn through games are insufficient. For these reasons, there is a need for a means to effectively link games and learning.

[0745] 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.

[0746] In this invention, the server includes means for allowing users to play games, means for recording game play data, means for transmitting the recorded game play data to a central processing unit, means for analyzing the game play data received by the central processing unit, means for evaluating the learning situation and attitude toward the game based on the analysis results, means for providing the results of the evaluation by the evaluation means as feedback to parents, and means for the analysis means to analyze the original data through dialogue with the user using natural language processing. This allows parents to understand the learning situation and attitude of the user through game play, and can increase the user's motivation to learn through feedback and evaluation of learning points.

[0747] "User" refers to the person who plays the game, especially the children whose learning situation and attitude toward the game will be evaluated.

[0748] "Terminal" refers to an electronic device used by a user to play a game and that records and transmits gameplay data.

[0749] "Central Processing Unit" refers to a server that receives, analyzes, and evaluates gameplay data sent from user terminals.

[0750] "Gameplay data" refers to information generated while a user is playing a game, and specifically includes play time, scores, and behavior logs.

[0751] "Analysis means" refers to the algorithms or AI models that analyze gameplay data within the central processing unit and evaluate the user's learning status and attitude toward the game.

[0752] The "evaluation means" refers to a mechanism for evaluating the user's learning situation and attitude toward the game based on the analysis results obtained by the analysis means, and providing the results as feedback.

[0753] "Feedback" is evaluation information provided to parents based on the user's gameplay data, including the user's academic progress and attitude toward the game.

[0754] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used for interacting with users and analyzing data.

[0755] "Learning Points" are reward points calculated based on a user's gameplay data, and can be exchanged for services or items that are useful for learning.

[0756] "Generative AI Model" refers to an artificial intelligence model used to analyze a user's gameplay data and generate feedback.

[0757] A "prompt" is an instruction entered into a generative AI model, and refers to a sentence or phrase used for specific analysis or feedback generation.

[0758] This invention is a system in which a user plays a game and transmits the play data to a central processing unit, which evaluates the user's learning status and attitude toward the game and provides feedback to parents. This system is composed of a terminal, a central processing unit (server), a generative AI model, analysis means, evaluation means, and feedback means.

[0759] Recording and transmitting gameplay data

[0760] Users play games using dedicated devices (e.g., tablets, smartphones). Gameplay data, such as the user's playing time, scores, and behavioral information, is recorded by the device. The device then sends the recorded data to a central processing unit at regular intervals or when the game ends. Transmission is performed using the HTTP protocol.

[0761] Data reception and analysis

[0762] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means analyzes the data using a generative AI model (e.g., machine learning algorithm) or a specific algorithm. During the analysis process, the data can also be used to interact with the user using natural language processing technology.

[0763] Generating and Providing Feedback

[0764] The server generates feedback to provide to parents based on the results obtained by the analysis means. Using a generative AI model, it automatically generates appropriate feedback statements. For example, the feedback may include evaluation content based on the user's gameplay behavior and learning progress.

[0765] Examples of specific prompts include:

[0766] "A child with user ID 12345 played the game for 30 minutes and earned 200 points. Please analyze this play data, evaluate their learning progress and attitude toward the game, and generate feedback."

[0767] Calculating and exchanging learning points

[0768] The server calculates learning points to be awarded to users based on gameplay data. Using an algorithm, points are calculated based on play time and scores, and stored in a database. Users can exchange these learning points for services or items (e.g., educational materials, online courses) that will help them learn.

[0769] Specific examples

[0770] For example, if a child with user ID "12345" plays the game for 30 minutes and earns 200 points, the system will operate as follows:

[0771] 1. The user plays the game on the device, and the device records the gameplay data.

[0772] 2. The device sends the recorded data to the server.

[0773] 3. The server receives the data and stores it in a database.

[0774] 4. The analytical means (generative AI model) analyzes the received data and evaluates it as "good learning situation" and "positive attitude toward the game."

[0775] 5. The server generates a feedback statement based on the analysis results and sends it to the parent device.

[0776] 6. Parents receive feedback and see their child's gameplay and learning progress.

[0777] 7. The server calculates and awards 100 points to the user based on 30 minutes of play and 200 points.

[0778] 8. Users can use learning points to acquire items that will help them with their education.

[0779] In this way, the present invention effectively combines games and learning, promotes healthy gaming habits, and provides an environment where parents can easily keep track of their children's learning progress.

[0780] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0781] Step 1:

[0782] The user launches the game and logs in. As the user begins playing, the device records play data (play time, score, action log, etc.) in real time.

[0783] Input: User's action to start the game

[0784] Output: Play data that has started recording (play time, score, action log)

[0785] Specific operation: A user launches the game app on their device and logs in using their unique user ID. After successful login, the game starts and gameplay data is recorded in real time in the device's memory.

[0786] Step 2:

[0787] The terminal transmits the recorded play data to a central processing unit (server) at regular intervals or when the game ends.

[0788] Input: Real-time recorded play data

[0789] Output: Play data sent to the server

[0790] Specific operation: The device sets a timer and sends play data to the server using an HTTP request at regular intervals or when it detects an action to end the game. The data sent includes the user ID, play time, score, and action log.

[0791] Step 3:

[0792] The server receives the game play data sent from the user terminal and stores it in a database.

[0793] Input: Play data sent from the device (user ID, play time, score, action log)

[0794] Output: Play data stored in the database

[0795] Specific operation: The server's receiving API receives the HTTP request, converts it into the appropriate data format, and inserts it into the database, where play data for each user is accumulated.

[0796] Step 4:

[0797] The server retrieves the received data from the database and passes it to the analysis means (generative AI model) for analysis.

[0798] Input: Play data retrieved from the database

[0799] Output: Analyzed learning situation and attitude towards the game

[0800] Specific operation: The server retrieves the target user's play data via a query and inputs it into the generative AI model. The model analyzes the user's learning status (e.g., learning progress, level of understanding) and attitude toward the game (e.g., motivation, engagement) from the play data.

[0801] Step 5:

[0802] The server generates feedback to provide to parents based on the analysis results, using a generative AI model to generate feedback statements that express the evaluation results in natural language.

[0803] Input: Learning situation and attitude toward the game obtained through analytical methods

[0804] Output: Generated feedback statement

[0805] Specific operation: The server uses the feedback template based on the evaluation results to construct a natural language feedback sentence using the generative AI model. For example, the generated feedback is "User 12345 is doing well in his studies and has a positive attitude towards the game."

[0806] Step 6:

[0807] The server sends the generated feedback to the parent device.

[0808] Input: Generated feedback sentence

[0809] Output: Feedback sent to the parent device

[0810] Specific behavior: The server uses the push notification service to send the generated feedback statement to the parent's device, which displays a notification and provides a link to view the details.

[0811] Step 7:

[0812] The server calculates learning points based on the game play data and awards them to the user.

[0813] Input: Play data (play time, score, action log)

[0814] Output: Calculated learning points

[0815] Specific operation: The server uses specific rules and algorithms to calculate learning points from play data, for example, 100 points based on 30 minutes of play time and 200 points, and adds them to the user's point account.

[0816] Step 8:

[0817] Users can use the learning points they earn to exchange them for learning items or services.

[0818] Input: User learning points

[0819] Output: Exchanged learning items or services

[0820] Specific operation: Users access the points exchange screen within the application and use their learning points to select and exchange offered learning items and services, such as online courses and learning materials.

[0821] The above is the specific processing flow of this system.

[0822] (Application example 1)

[0823] 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."

[0824] With conventional game systems, it is difficult to accurately grasp a child's learning situation and attitude toward the game, and this information is rarely effectively fed back to parents. Furthermore, there is no system that allows learning points earned through game play to be immediately used in physical stores, making it difficult to maintain children's motivation to study.

[0825] 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.

[0826] In this invention, the server includes a means for recording gameplay data, a means for transmitting the recorded gameplay data to a central processing unit, and an analysis means for analyzing the gameplay data received by the central processing unit. This makes it possible to analyze the gameplay data, evaluate the child's learning status and attitude toward the game, and provide the results as feedback to the parent. Furthermore, by including a means for instantly using the evaluated learning points to purchase products at a physical store, it is possible to maintain the child's motivation to learn.

[0827] "Game play data" refers to data generated when a user plays a game, including play time, scores, behavioral information, etc.

[0828] "Central Processing Unit" refers to the server and its associated systems for receiving and analyzing game play data transmitted from user terminals.

[0829] "Analysis means" refers to the algorithms or AI models that analyze the gameplay data received by the central processing unit and evaluate the user's learning status and attitude toward the game.

[0830] The "evaluation means" is a function that judges the user's learning situation and attitude toward the game based on the analysis results of the analysis means.

[0831] The "feedback means" refers to a communication means and a display device that reports the evaluation results obtained by the evaluation means to the parents.

[0832] "Learning points" are points calculated based on game play data, and are numerical values ​​related to the user's learning progress and performance.

[0833] "Means for immediate use in purchasing products at physical stores" refers to a system that allows assessed learning points to be used in real time to purchase products at physical stores.

[0834] "Natural language processing" is a computer processing technique for analyzing input messages from users and generating appropriate responses.

[0835] A form for implementing the present invention is a system in which a user plays a game, evaluates the learning situation and attitude toward the game based on gameplay data, provides feedback to parents, and instantly uses learning points to purchase products in a physical store.

[0836] When a user plays a game, gameplay data is recorded on the user's smartphone. The recorded data includes play time, scores, and other behavioral information. This data is sent to a central processing unit (server) at specified times or when the game ends. The server uses a cloud server or database (e.g., AWS or Firebase) to process and store the received data.

[0837] The server analyzes the received gameplay data. AI models and algorithms are used to evaluate the user's learning status and attitude toward the game. Natural language processing technology is also used to generate appropriate responses to messages entered by the user. The specific technology used for this analysis is a machine learning library using Python.

[0838] The evaluation results are fed back to the parent's device. The feedback provides a detailed report of the user's learning progress and attitude towards the game, and is displayed through a dedicated application. Furthermore, analysis is used to calculate learning points, which can be immediately used to purchase products in physical stores. This process is carried out through a smartphone application, allowing points to be redeemed in real time.

[0839] As a concrete example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using an analysis means. As a result, the user's learning progress is evaluated as "good" and their attitude toward the game is "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is learning well and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to purchase educational toys at a physical store.

[0840] An example prompt might look like this:

[0841] "A child with user ID '12345' played the game for 30 minutes and earned 200 points. In this case, the play data will be sent to the server, and the analysis result will be 100 learning points. Please create a request to exchange this for an educational toy."

[0842] In this way, the system of the present invention effectively combines games and learning, promoting healthy gaming habits and making it easier for parents to keep track of their children's learning status. In addition, the instant points can be used to maintain and improve children's motivation to learn.

[0843] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0844] Step 1:

[0845] The user plays the game.

[0846] The user starts the game on a device (smartphone or tablet), and behavioral data such as play time, scores, movements and actions are recorded.

[0847] Input: User gameplay data (play time, score, behavioral information)

[0848] Output: Recorded gameplay data

[0849] Step 2:

[0850] The terminal transmits gameplay data to the central processing unit.

[0851] At regular intervals or when the game ends, the terminal transmits the recorded gameplay data to the server.

[0852] Input: Recorded gameplay data

[0853] Output: Gameplay data sent to the server

[0854] Step 3:

[0855] The server analyzes the received data.

[0856] The server stores the received gameplay data in a database and analyzes it using AI models and analysis algorithms to evaluate the user's learning status and attitude toward the game.

[0857] Input: Gameplay data sent to the server

[0858] Output: Analysis results (user's learning status and attitude towards the game)

[0859] Step 4:

[0860] The server feeds back the evaluation results to the parent's device.

[0861] Based on the analysis results, the server generates feedback messages and sends them to the parents' devices. Through a dedicated application, parents can keep track of their children's learning progress and attitude toward the game.

[0862] Input: Analysis results (user's learning status and attitude towards the game)

[0863] Output: Feedback message sent to the parent device

[0864] Step 5:

[0865] The server calculates the learning points.

[0866] The server awards learning points to the user based on the game play data.

[0867] Input: Gameplay data and analysis results

[0868] Output: Calculated learning points

[0869] Step 6:

[0870] Users can use their learning points to purchase products in physical stores.

[0871] The user uses the application to instantly use the calculated learning points to purchase products at physical stores. The server receives the point usage request and confirms the point consumption.

[0872] Input: Learning points and user purchase request

[0873] Output: Purchase completed at physical store and points consumed

[0874] Through the above steps, the present invention provides a system that evaluates a user's learning situation and attitude toward games based on their gameplay data, provides feedback to parents, and allows them to instantly use learning points at physical stores.

[0875] 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.

[0876] In one embodiment of the present invention, a system includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. In addition, by combining an emotion engine, it becomes possible to recognize the user's emotions and reflect them in the evaluation.

[0877] Recording and transmitting gameplay data

[0878] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[0879] Data reception and analysis

[0880] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[0881] Emotion analysis using an emotion engine

[0882] When a user plays a game, the user device uses an emotion engine to analyze the user's facial expressions and voice to collect the user's emotion data, which is also transmitted to the central processing unit.

[0883] Reflecting emotional data in evaluations

[0884] The server receives the emotion data acquired from the emotion engine and passes it to the analysis means, which reflects this emotion data in the evaluation of the learning situation and attitude toward the game, thereby achieving a more accurate evaluation.

[0885] Generating and Providing Feedback

[0886] The server generates feedback to provide to parents based on the evaluation results. The feedback message includes information about the user's learning status, attitude toward the game, and emotions. This information is sent to the parent's device, allowing the parent to view the information and understand the overall situation of their child.

[0887] Calculating and exchanging learning points

[0888] Furthermore, the server calculates learning points based on gameplay data, which are then offered to users in exchange for services and items that are useful for learning, helping children maintain their motivation to learn through gameplay.

[0889] Natural language processing dialogue

[0890] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[0891] Specific examples

[0892] For example, consider a child with user ID "12345" who plays a game for 30 minutes, earns 200 points, and smiles frequently while playing. In this case, the user's device records emotional data along with play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good," their attitude toward the game is "positive," and their emotions are evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically, has a positive attitude toward the game, and had positive emotions while playing." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the user is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[0893] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily understand their children's learning status and emotions.

[0894] The processing flow will be explained below.

[0895] Step 1:

[0896] The user starts the game, and the game time and score are measured in real time.

[0897] Step 2:

[0898] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[0899] Step 3:

[0900] The device sends recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[0901] Step 4:

[0902] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[0903] Step 5:

[0904] The device records the user's emotions in real time while playing games, and uses an emotion engine to analyze and collect emotional data from the user's facial expressions and voice.

[0905] Step 6:

[0906] The device sends emotional data to the server, which then sends the emotional data along with gameplay data to the central processing unit.

[0907] Step 7:

[0908] The server receives the emotion data and stores it in a database, allowing gameplay data and emotion data to be analyzed together.

[0909] Step 8:

[0910] The server analyzes gameplay and emotional data, using AI models and algorithms to assess users' learning progress, attitudes towards the game, and emotions.

[0911] Step 9:

[0912] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress, attitude towards the game, and even emotions.

[0913] Step 10:

[0914] The server then sends the generated feedback to the parent device, where it is sent as an HTTP POST request and can be viewed by the parent.

[0915] Step 11:

[0916] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[0917] Step 12:

[0918] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning through gameplay.

[0919] Step 13:

[0920] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[0921] Example 2

[0922] 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."

[0923] Conventional educational game systems evaluate learning progress solely based on the user's gameplay data, making it difficult to accurately evaluate the user's learning progress by taking into account their emotions and reactions. Furthermore, if feedback is not provided appropriately to parents, they may not be able to accurately grasp their child's learning progress. Furthermore, the systems do not include a means to effectively reward users' learning efforts within the game, which can lead to a decrease in motivation to learn.

[0924] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accumulating play record information when a user plays a game, means for transmitting the accumulated play record information to a central control device, analysis means for analyzing the play record information received in the central control device, means for collecting user emotion information using an emotion engine and transmitting the information to the central control device, and means for more accurately evaluating the learning progress and attitude toward the game based on the user emotion information. This enables highly accurate learning evaluation that takes user emotions into consideration, provision of appropriate feedback to parents, and rewards in the form of learning points.

[0925] "Game play record information" is data relating to play time, scores, and in-game actions collected when a user plays a game.

[0926] The "central control device" is a server that receives, stores, and analyzes data sent from user terminals.

[0927] "Analysis means" refers to a system element that includes an AI model or algorithm for analyzing data received at the central control unit and assessing the user's learning status and attitude toward the game.

[0928] An "emotion engine" is a software or hardware configuration that analyzes a user's facial expressions and voice in real time and collects emotional data.

[0929] "Feedback" refers to information notifications about the user's learning status and emotional state that are generated based on the evaluation results of the analysis means.

[0930] "Learning Points" are reward points calculated based on the user's gameplay and can be exchanged for items and services that are useful for learning.

[0931] "User" means an individual who plays the game and whose data is collected from the system.

[0932] A "parent" is a person whose role is to receive feedback on the user's learning progress and games.

[0933] In one embodiment of the present invention, a system is provided in which a user plays a game, and a server receives and analyzes the data to evaluate the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. This system also includes a function that recognizes the user's emotions by combining an emotion engine and reflects them in the evaluation.

[0934] First, when you play the game, your device will record the following data:

[0935] 1. Play Time: The time from starting the game to finishing it.

[0936] 2. Score: The total score earned by the user.

[0937] 3. Behavioral information: The series of actions a user takes within the game.

[0938] This data is collected at regular intervals or at the end of the game and sent to a central control device (server) along with the user ID.

[0939] The user device uses a built-in camera and microphone to analyze the user's facial expressions and voice in real time, and collects the user's emotional data through an emotion engine. Specifically, emotional indicators such as smiles and tone of voice are recorded. This emotional data is also sent to the server along with gameplay data.

[0940] The server receives data sent from the user's device in real time and stores it in a database. Here, the data is properly categorized and organized based on the user's ID. The stored data is then passed to an analysis tool, which includes a generative AI model or algorithm (e.g., TensorFlow or PyTorch) to analyze the data and evaluate the user's learning progress and attitude toward the game.

[0941] The analysis method performs the following operations:

[0942] 1. Evaluating the user's learning progress (e.g., accuracy rate during games, task completion rate)

[0943] 2. Evaluation of attitude toward games (e.g., aggressive or not)

[0944] 3. Emotion assessment based on facial expressions and voice (e.g., positive, negative)

[0945] For example, if a child with user ID "12345" scores 200 points in 30 minutes of gameplay and is detected smiling a lot during that time, the server will use this data to assess that "user ID 12345 is doing well in his studies and has an active and positive attitude toward the game."

[0946] The server then generates a feedback message based on the analysis results and sends it to the parent's device. The feedback includes information about the user's learning progress, attitude toward the game, and emotional state. By receiving this feedback, parents can understand their child's overall learning progress and emotional state.

[0947] The server then calculates learning points based on gameplay data. For example, points are awarded based on the number of stages cleared or the score obtained. These points are then offered to users in exchange for items or services that will help their children learn.

[0948] Example prompt sentence:

[0949] "Please analyze the play data (play time: 30 minutes, score: 200 points) and emotional data (smile frequency) of user ID 12345 to evaluate their learning status, attitude toward the game, and emotions."

[0950] "Based on the analysis results, please generate a feedback message to send to the parent of user ID 12345."

[0951] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0952] Step 1: User gameplay

[0953] A user starts a game and plays it on a device such as a smartphone or tablet. The user's actions and movements within the game are recorded. Data input by the user includes the actions and choices made within the game. Based on this input, the device collects data such as play time, scores, and behavioral information.

[0954] Step 2: Record gameplay and emotion data

[0955] The device records data of users playing games in real time. This data includes play time, scores, and in-game behavior information. The device also uses a built-in camera and microphone to analyze the user's facial expressions and voice to collect emotional data. The input data is the user's operations, facial expressions, and voice, which the device analyzes to generate play data and emotional data.

[0956] Step 3: Sending data

[0957] The device transmits the recorded gameplay data and emotion data along with the user ID to the server. The timing of transmission is interval controlled or at the end of the game. The input data is the recorded play data and emotion data, and a data packet is generated based on this and transmitted to the server via the network.

[0958] Step 4: Receiving and storing data

[0959] The server receives data sent from user devices in real time. The received data is stored in a database and classified and organized. The input data consists of play data and emotional data sent from the device, and the server converts this into a format that is easy to analyze and stores it. Specifically, the data is classified for each user, and the play data and emotional data are tagged and stored in the database.

[0960] Step 5: Analyze the data

[0961] The server passes the saved data to the analysis means, which includes a generative AI model and algorithms that analyze the data to evaluate the user's learning progress and attitude toward the game. The input data is the saved play data and emotional data, and evaluation data is generated through the analysis algorithm. Specifically, the machine learning model analyzes the data and evaluates the user's performance and emotional state.

[0962] Step 6: Generate feedback messages

[0963] The server generates a feedback message based on the analysis results. This feedback message includes information about the user's learning progress, attitude toward the game, and emotional state. The input data is the analysis results, and the feedback message is generated based on these. Specifically, the message generated is, "User ID 12345's learning progress is good, and he has positive and proactive feelings toward the game."

[0964] Step 7: Submit and view feedback

[0965] The server sends the generated feedback message to the parent's device, which displays the received feedback message on its screen and allows the parent to check detailed information. The input data is the generated feedback message, and by sending it to the parent's device, the parent can understand their child's learning progress and emotional state.

[0966] Step 8: Calculating and Providing Learning Points

[0967] The server calculates learning points based on gameplay data. Input data is play data, and points are calculated based on this. For example, points are awarded based on the number of stages cleared and the score obtained. This allows children to earn points that can be exchanged for items or services that are useful for learning. Specifically, points are calculated based on the following rule: 10 points are awarded for each cleared stage, and 5 points for every 100 points scored.

[0968] (Application example 2)

[0969] 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."

[0970] Conventional factory worker evaluation systems only evaluate workers' work efficiency and the quality of their work products, making it difficult to evaluate workers' emotional state and overall work attitude. Furthermore, there were limited means to provide specific feedback that would contribute to improving worker motivation and the work environment. As a result, it was not possible to properly monitor workers' mental health and motivation, making it difficult to create an optimal work environment.

[0971] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0972] In this invention, the server includes a means for recording work data, a means for transmitting the recorded work data to a central processing unit, and an analysis means for analyzing the work data received by the central processing unit. This allows for real-time evaluation of the worker's work status and emotions and provides comprehensive feedback to the manager. Furthermore, by further including a means for calculating evaluation points based on the work data and converting the evaluation points into incentives for the worker, it is possible to contribute to improving worker motivation. Furthermore, by including a means for the analysis means for analyzing the worker's work attitude and emotions through dialogue with the worker using natural language processing, more accurate evaluations are possible.

[0973] "Work data" refers to data that includes information related to a worker's work, such as the worker's working hours, work efficiency, and quality of the work product.

[0974] The "central processing unit" is a computer system for analyzing received work data and generating evaluation results.

[0975] "Analysis tools" refers to algorithms and AI models that evaluate workers' work situations and emotions based on work data.

[0976] "Emotional state" is data that indicates a worker's mental reactions, feelings, and psychological state.

[0977] "Evaluation points" refer to a numerical representation of a worker's performance and attitude calculated based on work data.

[0978] "Incentives" are rewards, benefits, and incentives that are intended to increase worker motivation.

[0979] "Feedback" is the act of providing information including evaluation, advice, and improvement suggestions based on analysis results.

[0980] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used to understand work attitudes and emotions through dialogue with workers.

[0981] The present invention relates to a system for evaluating the work efficiency and emotional state of workers in a factory and providing feedback to managers. This system is realized using an application and a central processing unit installed on a factory robot.

[0982] Data collection

[0983] The factory robots are equipped with sensors that record each worker's work data (work time, work efficiency, quality of finished products, etc.) The robots are also equipped with cameras and microphones, and an emotion engine (e.g., Affectiva SDK) is used to analyze the facial expressions and voices of workers to collect emotional data.

[0984] Data transmission

[0985] The collected data is temporarily stored locally on the edge device and then transmitted to a central server at regular intervals using a lightweight messaging protocol such as MQTT.

[0986] Data analysis

[0987] The central processing unit (server) stores the received data in a database (e.g., MongoDB). It then analyzes the data using an AI model (e.g., TensorFlow) and analysis algorithms to evaluate each worker's work efficiency and emotional state. The AI ​​model uses a generative AI model to perform highly accurate analysis.

[0988] Feedback Generation

[0989] Based on the analysis results, a report is generated of each worker's work status and emotional state. The report includes information linked to the worker's ID and is displayed in an easy-to-understand manner for managers.

[0990] Feedback Notifications

[0991] The final feedback generated is sent to the administrator's device (smartphone or computer) and can be viewed via a dedicated mobile app or web app.

[0992] Examples and prompts

[0993] For example, consider a factory worker with ID 56789 who works eight hours during a daily shift, producing 100 parts at 90% efficiency. Furthermore, facial expression analysis reveals primarily positive emotions. This data is collected and analyzed as follows:

[0994] Specifically, the following prompt sentences are input into the generative AI model to generate evaluation and feedback:

[0995] Generate a report on worker performance and emotions based on the following data:

[0996] Working time: 8 hours

[0997] Efficiency: 90%

[0998] Deliverables: 100 pieces

[0999] Emotion: Positive

[1000] Generate a feedback message.

[1001] The server generates a feedback message like this:

[1002] Worker ID 56789 worked 8 hours during today's shift, producing 100 parts at 90% efficiency. He displayed mostly positive emotions while working and demonstrated good overall work performance.

[1003] In this way, the system can evaluate the work situation and emotions of workers in real time and provide comprehensive feedback to managers.In addition, by providing evaluation points as incentives to workers, it can improve motivation and the working environment.

[1004] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1005] Step 1:

[1006] The factory robot (terminal) records each worker's work data (e.g., work time, work efficiency, and quality of the finished product). Specifically, data is collected using work time sensors and product counters. The robot is also equipped with a camera and microphone, and an emotion engine (e.g., Affectiva SDK) is used to analyze the worker's facial expressions and voice to collect emotional data. The input is the worker's physical actions, facial expressions, and voice, and the output is the recorded work data and emotional data.

[1007] Step 2:

[1008] The collected data is temporarily stored on the local edge device. At appropriate intervals, or when a certain amount of data has accumulated, the data is sent to a central server. This communication is performed using a lightweight messaging protocol such as MQTT. The input is the task data and emotion data on the local edge device, and the output is the data to be sent to the central server.

[1009] Step 3:

[1010] The server (central processing unit) stores the received task data and emotion data in a database (e.g., MongoDB). Here, the data is organized and normalized to facilitate subsequent analysis. The input is the data sent to the central server, and the output is the integrated data stored in the database.

[1011] Step 4:

[1012] An AI analysis module (e.g., TensorFlow) and analysis algorithm installed on the server analyze the received data. Specifically, an AI model is used to evaluate work efficiency and emotional state, and the results are output in numerical or text format. The use of a generative AI model enables complex pattern recognition and prediction. The input is the integrated data in the database, and the output is the evaluation result.

[1013] Step 5:

[1014] The server generates feedback based on the evaluation results, again using a generative AI model to generate prompts such as:

[1015] Generate a report on worker performance and emotions based on the following data:

[1016] Working time: 8 hours

[1017] Efficiency: 90%

[1018] Deliverables: 100 pieces

[1019] Emotion: Positive

[1020] Generate a feedback message.

[1021] The generated feedback is output in the form of a report summarizing each worker's work status and emotional state. The input is the evaluation result, and the output is the generated feedback message.

[1022] Step 6:

[1023] The final feedback message is sent to the manager's device (smartphone or computer). The manager can check the feedback via a dedicated mobile app or web app and use it to improve the work environment or provide guidance to workers as needed. The input is the generated feedback message, and the output is the feedback information sent to the manager's device.

[1024] This allows for real-time assessment of workers' work status and emotions, providing comprehensive feedback to managers.

[1025] 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.

[1026] 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.

[1027] 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.

[1028] [Fourth embodiment]

[1029] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1030] 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.

[1031] 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).

[1032] 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.

[1033] 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.

[1034] 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).

[1035] 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.

[1036] 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.

[1037] 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.

[1038] 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.

[1039] 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.

[1040] 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.

[1041] 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."

[1042] An embodiment of the present invention is a system that includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning situation and attitude toward the game, and provides the evaluation results to parents as feedback.

[1043] Recording and transmitting gameplay data

[1044] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[1045] Data reception and analysis

[1046] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[1047] Generating and Providing Feedback

[1048] The server generates feedback to provide to parents based on the evaluation results. The feedback includes information about the user's learning status and attitude toward the game and is sent to the parent's device, making it easier for parents to understand their child's gaming habits and learning status.

[1049] Calculating and exchanging learning points

[1050] Furthermore, the server calculates learning points based on gameplay data. Learning points are earned by users through gameplay and can be exchanged for services and items that are useful for learning. This helps children maintain their motivation to learn through gameplay.

[1051] Natural language processing dialogue

[1052] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[1053] Specific examples

[1054] For example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good" and their attitude toward the game is evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[1055] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily keep track of their children's learning progress.

[1056] The processing flow will be explained below.

[1057] Step 1:

[1058] The user starts the game. As the user plays, the game time and score are measured in real time.

[1059] Step 2:

[1060] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[1061] Step 3:

[1062] The device sends the recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[1063] Step 4:

[1064] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[1065] Step 5:

[1066] The server analyzes the data and uses AI models and algorithms to evaluate the user's learning progress and attitude towards the game based on the received data.

[1067] Step 6:

[1068] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress and attitude towards the game.

[1069] Step 7:

[1070] The server sends the generated feedback to the parent device, which is sent as an HTTP POST request so that the parent can view it.

[1071] Step 8:

[1072] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[1073] Step 9:

[1074] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning by continuing to play the game.

[1075] Step 10:

[1076] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[1077] Through this process, the system can promote healthy gaming habits and provide an environment where parents can easily keep track of their children's learning progress.

[1078] Example 1

[1079] 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."

[1080] With conventional game systems, it is difficult for parents to grasp the user's playing and learning status, and the mechanisms for increasing motivation to learn through games are insufficient. For these reasons, there is a need for a means to effectively link games and learning.

[1081] 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.

[1082] In this invention, the server includes means for allowing users to play games, means for recording game play data, means for transmitting the recorded game play data to a central processing unit, means for analyzing the game play data received by the central processing unit, means for evaluating the learning situation and attitude toward the game based on the analysis results, means for providing the results of the evaluation by the evaluation means as feedback to parents, and means for the analysis means to analyze the original data through dialogue with the user using natural language processing. This allows parents to understand the learning situation and attitude of the user through game play, and can increase the user's motivation to learn through feedback and evaluation of learning points.

[1083] "User" refers to the person who plays the game, especially the children whose learning situation and attitude toward the game will be evaluated.

[1084] "Terminal" refers to an electronic device used by a user to play a game and that records and transmits gameplay data.

[1085] "Central Processing Unit" refers to a server that receives, analyzes, and evaluates gameplay data sent from user terminals.

[1086] "Gameplay data" refers to information generated while a user is playing a game, and specifically includes play time, scores, and behavior logs.

[1087] "Analysis means" refers to the algorithms or AI models that analyze gameplay data within the central processing unit and evaluate the user's learning status and attitude toward the game.

[1088] The "evaluation means" refers to a mechanism for evaluating the user's learning situation and attitude toward the game based on the analysis results obtained by the analysis means, and providing the results as feedback.

[1089] "Feedback" is evaluation information provided to parents based on the user's gameplay data, including the user's academic progress and attitude toward the game.

[1090] "Natural language processing" is a technology that enables computers to understand and generate human language, and is used for interacting with users and analyzing data.

[1091] "Learning Points" are reward points calculated based on a user's gameplay data, and can be exchanged for services or items that are useful for learning.

[1092] "Generative AI Model" refers to an artificial intelligence model used to analyze a user's gameplay data and generate feedback.

[1093] A "prompt" is an instruction entered into a generative AI model, and refers to a sentence or phrase used for specific analysis or feedback generation.

[1094] This invention is a system in which a user plays a game and transmits the play data to a central processing unit, which evaluates the user's learning status and attitude toward the game and provides feedback to parents. This system is composed of a terminal, a central processing unit (server), a generative AI model, analysis means, evaluation means, and feedback means.

[1095] Recording and transmitting gameplay data

[1096] Users play games using dedicated devices (e.g., tablets, smartphones). Gameplay data, such as the user's playing time, scores, and behavioral information, is recorded by the device. The device then sends the recorded data to a central processing unit at regular intervals or when the game ends. Transmission is performed using the HTTP protocol.

[1097] Data reception and analysis

[1098] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means analyzes the data using a generative AI model (e.g., machine learning algorithm) or a specific algorithm. During the analysis process, the data can also be used to interact with the user using natural language processing technology.

[1099] Generating and Providing Feedback

[1100] The server generates feedback to provide to parents based on the results obtained by the analysis means. Using a generative AI model, it automatically generates appropriate feedback statements. For example, the feedback may include evaluation content based on the user's gameplay behavior and learning progress.

[1101] Examples of specific prompts include:

[1102] "A child with user ID 12345 played the game for 30 minutes and earned 200 points. Please analyze this play data, evaluate their learning progress and attitude toward the game, and generate feedback."

[1103] Calculating and exchanging learning points

[1104] The server calculates learning points to be awarded to users based on gameplay data. Using an algorithm, points are calculated based on play time and scores, and stored in a database. Users can exchange these learning points for services or items (e.g., educational materials, online courses) that will help them learn.

[1105] Specific examples

[1106] For example, if a child with user ID "12345" plays the game for 30 minutes and earns 200 points, the system will operate as follows:

[1107] 1. The user plays the game on the device, and the device records the gameplay data.

[1108] 2. The device sends the recorded data to the server.

[1109] 3. The server receives the data and stores it in a database.

[1110] 4. The analytical means (generative AI model) analyzes the received data and evaluates it as "good learning situation" and "positive attitude toward the game."

[1111] 5. The server generates a feedback statement based on the analysis results and sends it to the parent device.

[1112] 6. Parents receive feedback and see their child's gameplay and learning progress.

[1113] 7. The server calculates and awards 100 points to the user based on 30 minutes of play and 200 points.

[1114] 8. Users can use learning points to acquire items that will help them with their education.

[1115] In this way, the present invention effectively combines games and learning, promotes healthy gaming habits, and provides an environment where parents can easily keep track of their children's learning progress.

[1116] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1117] Step 1:

[1118] The user launches the game and logs in. As the user begins playing, the device records play data (play time, score, action log, etc.) in real time.

[1119] Input: User's action to start the game

[1120] Output: Play data that has started recording (play time, score, action log)

[1121] Specific operation: A user launches the game app on their device and logs in using their unique user ID. After successful login, the game starts and gameplay data is recorded in real time in the device's memory.

[1122] Step 2:

[1123] The terminal transmits the recorded play data to a central processing unit (server) at regular intervals or when the game ends.

[1124] Input: Real-time recorded play data

[1125] Output: Play data sent to the server

[1126] Specific operation: The device sets a timer and sends play data to the server using an HTTP request at regular intervals or when it detects an action to end the game. The data sent includes the user ID, play time, score, and action log.

[1127] Step 3:

[1128] The server receives the game play data sent from the user terminal and stores it in a database.

[1129] Input: Play data sent from the device (user ID, play time, score, action log)

[1130] Output: Play data stored in the database

[1131] Specific operation: The server's receiving API receives the HTTP request, converts it into the appropriate data format, and inserts it into the database, where play data for each user is accumulated.

[1132] Step 4:

[1133] The server retrieves the received data from the database and passes it to the analysis means (generative AI model) for analysis.

[1134] Input: Play data retrieved from the database

[1135] Output: Analyzed learning situation and attitude towards the game

[1136] Specific operation: The server retrieves the target user's play data via a query and inputs it into the generative AI model. The model analyzes the user's learning status (e.g., learning progress, level of understanding) and attitude toward the game (e.g., motivation, engagement) from the play data.

[1137] Step 5:

[1138] The server generates feedback to provide to parents based on the analysis results, using a generative AI model to generate feedback statements that express the evaluation results in natural language.

[1139] Input: Learning situation and attitude toward the game obtained through analytical methods

[1140] Output: Generated feedback statement

[1141] Specific operation: The server uses the feedback template based on the evaluation results to construct a natural language feedback sentence using the generative AI model. For example, the generated feedback is "User 12345 is doing well in his studies and has a positive attitude towards the game."

[1142] Step 6:

[1143] The server sends the generated feedback to the parent device.

[1144] Input: Generated feedback sentence

[1145] Output: Feedback sent to the parent device

[1146] Specific behavior: The server uses the push notification service to send the generated feedback statement to the parent's device, which displays a notification and provides a link to view the details.

[1147] Step 7:

[1148] The server calculates learning points based on the game play data and awards them to the user.

[1149] Input: Play data (play time, score, action log)

[1150] Output: Calculated learning points

[1151] Specific operation: The server uses specific rules and algorithms to calculate learning points from play data, for example, 100 points based on 30 minutes of play time and 200 points, and adds them to the user's point account.

[1152] Step 8:

[1153] Users can use the learning points they earn to exchange them for learning items or services.

[1154] Input: User learning points

[1155] Output: Exchanged learning items or services

[1156] Specific operation: Users access the points exchange screen within the application and use their learning points to select and exchange offered learning items and services, such as online courses and learning materials.

[1157] The above is the specific processing flow of this system.

[1158] (Application example 1)

[1159] 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."

[1160] With conventional game systems, it is difficult to accurately grasp a child's learning situation and attitude toward the game, and this information is rarely effectively fed back to parents. Furthermore, there is no system that allows learning points earned through game play to be immediately used in physical stores, making it difficult to maintain children's motivation to study.

[1161] 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.

[1162] In this invention, the server includes a means for recording gameplay data, a means for transmitting the recorded gameplay data to a central processing unit, and an analysis means for analyzing the gameplay data received by the central processing unit. This makes it possible to analyze the gameplay data, evaluate the child's learning status and attitude toward the game, and provide the results as feedback to the parent. Furthermore, by including a means for instantly using the evaluated learning points to purchase products at a physical store, it is possible to maintain the child's motivation to learn.

[1163] "Game play data" refers to data generated when a user plays a game, including play time, scores, behavioral information, etc.

[1164] "Central Processing Unit" refers to the server and its associated systems for receiving and analyzing game play data transmitted from user terminals.

[1165] "Analysis means" refers to the algorithms or AI models that analyze the gameplay data received by the central processing unit and evaluate the user's learning status and attitude toward the game.

[1166] The "evaluation means" is a function that judges the user's learning situation and attitude toward the game based on the analysis results of the analysis means.

[1167] The "feedback means" refers to a communication means and a display device that reports the evaluation results obtained by the evaluation means to the parents.

[1168] "Learning points" are points calculated based on game play data, and are numerical values ​​related to the user's learning progress and performance.

[1169] "Means for immediate use in purchasing products at physical stores" refers to a system that allows assessed learning points to be used in real time to purchase products at physical stores.

[1170] "Natural language processing" is a computer processing technique for analyzing input messages from users and generating appropriate responses.

[1171] A form for implementing the present invention is a system in which a user plays a game, evaluates the learning situation and attitude toward the game based on gameplay data, provides feedback to parents, and instantly uses learning points to purchase products in a physical store.

[1172] When a user plays a game, gameplay data is recorded on the user's smartphone. The recorded data includes play time, scores, and other behavioral information. This data is sent to a central processing unit (server) at specified times or when the game ends. The server uses a cloud server or database (e.g., AWS or Firebase) to process and store the received data.

[1173] The server analyzes the received gameplay data. AI models and algorithms are used to evaluate the user's learning status and attitude toward the game. Natural language processing technology is also used to generate appropriate responses to messages entered by the user. The specific technology used for this analysis is a machine learning library using Python.

[1174] The evaluation results are fed back to the parent's device. The feedback provides a detailed report of the user's learning progress and attitude towards the game, and is displayed through a dedicated application. Furthermore, analysis is used to calculate learning points, which can be immediately used to purchase products in physical stores. This process is carried out through a smartphone application, allowing points to be redeemed in real time.

[1175] As a concrete example, consider a case where a child with user ID "12345" plays a game for 30 minutes and scores 200 points. In this case, the user's device records the play data and sends it to the server. The server receives the data and analyzes it using an analysis means. As a result, the user's learning progress is evaluated as "good" and their attitude toward the game is "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is learning well and has a positive attitude toward the game." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the child is awarded, for example, 100 points. The user can use these points to purchase educational toys at a physical store.

[1176] An example prompt might look like this:

[1177] "A child with user ID '12345' played the game for 30 minutes and earned 200 points. In this case, the play data will be sent to the server, and the analysis result will be 100 learning points. Please create a request to exchange this for an educational toy."

[1178] In this way, the system of the present invention effectively combines games and learning, promoting healthy gaming habits and making it easier for parents to keep track of their children's learning status. In addition, the instant points can be used to maintain and improve children's motivation to learn.

[1179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1180] Step 1:

[1181] The user plays the game.

[1182] The user starts the game on a device (smartphone or tablet), and behavioral data such as play time, scores, movements and actions are recorded.

[1183] Input: User gameplay data (play time, score, behavioral information)

[1184] Output: Recorded gameplay data

[1185] Step 2:

[1186] The terminal transmits gameplay data to the central processing unit.

[1187] At regular intervals or when the game ends, the terminal transmits the recorded gameplay data to the server.

[1188] Input: Recorded gameplay data

[1189] Output: Gameplay data sent to the server

[1190] Step 3:

[1191] The server analyzes the received data.

[1192] The server stores the received gameplay data in a database and analyzes it using AI models and analysis algorithms to evaluate the user's learning status and attitude toward the game.

[1193] Input: Gameplay data sent to the server

[1194] Output: Analysis results (user's learning status and attitude towards the game)

[1195] Step 4:

[1196] The server feeds back the evaluation results to the parent's device.

[1197] Based on the analysis results, the server generates feedback messages and sends them to the parents' devices. Through a dedicated application, parents can keep track of their children's learning progress and attitude toward the game.

[1198] Input: Analysis results (user's learning status and attitude towards the game)

[1199] Output: Feedback message sent to the parent device

[1200] Step 5:

[1201] The server calculates the learning points.

[1202] The server awards learning points to the user based on the game play data.

[1203] Input: Gameplay data and analysis results

[1204] Output: Calculated learning points

[1205] Step 6:

[1206] Users can use their learning points to purchase products in physical stores.

[1207] The user uses the application to instantly use the calculated learning points to purchase products at physical stores. The server receives the point usage request and confirms the point consumption.

[1208] Input: Learning points and user purchase request

[1209] Output: Purchase completed at physical store and points consumed

[1210] Through the above steps, the present invention provides a system that evaluates a user's learning situation and attitude toward games based on their gameplay data, provides feedback to parents, and allows them to instantly use learning points at physical stores.

[1211] 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.

[1212] In one embodiment of the present invention, a system includes a series of processes in which a user plays a game, a central processing unit receives and analyzes the data, evaluates the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. In addition, by combining an emotion engine, it becomes possible to recognize the user's emotions and reflect them in the evaluation.

[1213] Recording and transmitting gameplay data

[1214] When a user plays a game, the user's device records gameplay data, including play time, scores, and other behavioral information. The gameplay data is collected at regular intervals or at the end of the game. The collected gameplay data is sent to a central processing unit along with the user's ID.

[1215] Data reception and analysis

[1216] The server receives gameplay data sent from the user's device. The received data is stored in a database and passed to the analysis means. The analysis means uses AI models and algorithms to analyze the received data and evaluate the user's learning progress and attitude toward the game.

[1217] Emotion analysis using an emotion engine

[1218] When a user plays a game, the user device uses an emotion engine to analyze the user's facial expressions and voice to collect the user's emotion data, which is also transmitted to the central processing unit.

[1219] Reflecting emotional data in evaluations

[1220] The server receives the emotion data acquired from the emotion engine and passes it to the analysis means, which reflects this emotion data in the evaluation of the learning situation and attitude toward the game, thereby achieving a more accurate evaluation.

[1221] Generating and Providing Feedback

[1222] The server generates feedback to provide to parents based on the evaluation results. The feedback message includes information about the user's learning status, attitude toward the game, and emotions. This information is sent to the parent's device, allowing the parent to view the information and understand the overall situation of their child.

[1223] Calculating and exchanging learning points

[1224] Furthermore, the server calculates learning points based on gameplay data, which are then offered to users in exchange for services and items that are useful for learning, helping children maintain their motivation to learn through gameplay.

[1225] Natural language processing dialogue

[1226] The analysis method uses natural language processing to interact with users, enabling more accurate evaluations. The system generates appropriate responses to messages entered by users, gaining a deeper understanding of the user's attitude toward the game and their learning status.

[1227] Specific examples

[1228] For example, consider a child with user ID "12345" who plays a game for 30 minutes, earns 200 points, and smiles frequently while playing. In this case, the user's device records emotional data along with play data and sends it to the server. The server receives the data and analyzes it using its analysis means. As a result, the user's academic performance is evaluated as "good," their attitude toward the game is "positive," and their emotions are evaluated as "positive." The server generates feedback based on this evaluation and notifies the parent, "User 12345 is doing well academically, has a positive attitude toward the game, and had positive emotions while playing." This feedback is sent to the parent's device, where the parent can view it. Learning points are also calculated based on the play data, and the user is awarded, for example, 100 points. The user can use these points to acquire items that will help them with their studies.

[1229] In this way, the present invention effectively combines games and learning, promoting healthy gaming habits and providing an environment where parents can easily understand their children's learning status and emotions.

[1230] The processing flow will be explained below.

[1231] Step 1:

[1232] The user starts the game, and the game time and score are measured in real time.

[1233] Step 2:

[1234] The device records gameplay data, collecting information such as user ID, play time, and score, and compiling this data.

[1235] Step 3:

[1236] The device sends recorded gameplay data to the server, which sends the data as an HTTP POST request to the server's API endpoint.

[1237] Step 4:

[1238] The server stores the received gameplay data in a database, including user IDs, play time, scores, etc.

[1239] Step 5:

[1240] The device records the user's emotions in real time while playing games, and uses an emotion engine to analyze and collect emotional data from the user's facial expressions and voice.

[1241] Step 6:

[1242] The device sends emotional data to the server, which then sends the emotional data along with gameplay data to the central processing unit.

[1243] Step 7:

[1244] The server receives the emotion data and stores it in a database, allowing gameplay data and emotion data to be analyzed together.

[1245] Step 8:

[1246] The server analyzes gameplay and emotional data, using AI models and algorithms to assess users' learning progress, attitudes towards the game, and emotions.

[1247] Step 9:

[1248] The server generates feedback based on the analysis results, and the feedback message contains information about the user's learning progress, attitude towards the game, and even emotions.

[1249] Step 10:

[1250] The server then sends the generated feedback to the parent device, where it is sent as an HTTP POST request and can be viewed by the parent.

[1251] Step 11:

[1252] The server calculates learning points based on gameplay data, and the points are provided to users in exchange for services and items that are useful for learning.

[1253] Step 12:

[1254] Users can exchange their learning points for services and items, allowing them to enjoy the benefits of contributing to learning through gameplay.

[1255] Step 13:

[1256] The server will use natural language processing to interact with the user as needed, allowing for a more precise assessment of the user's gameplay and learning progress.

[1257] Example 2

[1258] 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."

[1259] Conventional educational game systems evaluate learning progress solely based on the user's gameplay data, making it difficult to accurately evaluate the user's learning progress by taking into account their emotions and reactions. Furthermore, if feedback is not provided appropriately to parents, they may not be able to accurately grasp their child's learning progress. Furthermore, the systems do not include a means to effectively reward users' learning efforts within the game, which can lead to a decrease in motivation to learn.

[1260] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for accumulating play record information when a user plays a game, means for transmitting the accumulated play record information to a central control device, analysis means for analyzing the play record information received in the central control device, means for collecting user emotion information using an emotion engine and transmitting the information to the central control device, and means for more accurately evaluating the learning progress and attitude toward the game based on the user emotion information. This enables highly accurate learning evaluation that takes user emotions into consideration, provision of appropriate feedback to parents, and rewards in the form of learning points.

[1261] "Game play record information" is data relating to play time, scores, and in-game actions collected when a user plays a game.

[1262] The "central control device" is a server that receives, stores, and analyzes data sent from user terminals.

[1263] "Analysis means" refers to a system element that includes an AI model or algorithm for analyzing data received at the central control unit and assessing the user's learning status and attitude toward the game.

[1264] An "emotion engine" is a software or hardware configuration that analyzes a user's facial expressions and voice in real time and collects emotional data.

[1265] "Feedback" refers to information notifications about the user's learning status and emotional state that are generated based on the evaluation results of the analysis means.

[1266] "Learning Points" are reward points calculated based on the user's gameplay and can be exchanged for items and services that are useful for learning.

[1267] "User" means an individual who plays the game and whose data is collected from the system.

[1268] A "parent" is a person whose role is to receive feedback on the user's learning progress and games.

[1269] In one embodiment of the present invention, a system is provided in which a user plays a game, and a server receives and analyzes the data to evaluate the user's learning status and attitude and emotions toward the game, and provides the evaluation results to parents. This system also includes a function that recognizes the user's emotions by combining an emotion engine and reflects them in the evaluation.

[1270] First, when you play the game, your device will record the following data:

[1271] 1. Play Time: The time from starting the game to finishing it.

[1272] 2. Score: The total score earned by the user.

[1273] 3. Behavioral information: The series of actions a user takes within the game.

[1274] This data is collected at regular intervals or at the end of the game and sent to a central control device (server) along with the user ID.

[1275] The user device uses a built-in camera and microphone to analyze the user's facial expressions and voice in real time, and collects the user's emotional data through an emotion engine. Specifically, emotional indicators such as smiles and tone of voice are recorded. This emotional data is also sent to the server along with gameplay data.

[1276] The server receives data sent from the user's device in real time and stores it in a database. Here, the data is properly categorized and organized based on the user's ID. The stored data is then passed to an analysis tool, which includes a generative AI model or algorithm (e.g., TensorFlow or PyTorch) to analyze the data and evaluate the user's learning progress and attitude toward the game.

[1277] The analysis method performs the following operations:

[1278] 1. Evaluating the user's learning progress (e.g., accuracy rate during games, task completion rate)

[1279] 2. Evaluation of attitude toward games (e.g., aggressive or not)

[1280] 3. Emotion assessment based on facial expressions and voice (e.g., positive, negative)

[1281] For example, if a child with user ID "12345" scores 200 points in 30 minutes of gameplay and is detected smiling a lot during that time, the server will use this data to assess that "user ID 12345 is doing well in his studies and has an active and positive attitude toward the game."

[1282] The server then generates a feedback message based on the analysis results and sends it to the parent's device. The feedback includes information about the user's learning progress, attitude toward the game, and emotional state. By receiving this feedback, parents can understand their child's overall learning progress and emotional state.

[1283] The server then calculates learning points based on gameplay data. For example, points are awarded based on the number of stages cleared or the score obtained. These points are then offered to users in exchange for items or services that will help their children learn.

[1284] Example prompt sentence:

[1285] "Please analyze the play data (play time: 30 minutes, score: 200 points) and emotional data (smile frequency) of user ID 12345 to evaluate their learning status, attitude toward the game, and emotions."

[1286] "Based on the analysis results, please generate a feedback message to send to the parent of user ID 12345."

[1287] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1288] Step 1: User gameplay

[1289] A user starts a game and plays it on a device such as a smartphone or tablet. The user's actions and movements within the game are recorded. Data input by the user includes the actions and choices made within the game. Based on this input, the device collects data such as play time, scores, and behavioral information.

[1290] Step 2: Record gameplay and emotion data

[1291] The device records data of users playing games in real time. This data includes play time, scores, and in-game behavior information. The device also uses a built-in camera and microphone to analyze the user's facial expressions and voice to collect emotional data. The input data is the user's operations, facial expressions, and voice, which the device analyzes to generate play data and emotional data.

[1292] Step 3: Sending data

[1293] The device transmits the recorded gameplay data and emotion data along with the user ID to the server. The timing of transmission is interval controlled or at the end of the game. The input data is the recorded play data and emotion data, and a data packet is generated based on this and transmitted to the server via the network.

[1294] Step 4: Receiving and storing data

[1295] The server receives data sent from user devices in real time. The received data is stored in a database and classified and organized. The input data consists of play data and emotional data sent from the device, and the server converts this into a format that is easy to analyze and stores it. Specifically, the data is classified for each user, and the play data and emotional data are tagged and stored in the database.

[1296] Step 5: Analyze the data

[1297] The server passes the saved data to the analysis means, which includes a generative AI model and algorithms that analyze the data to evaluate the user's learning progress and attitude toward the game. The input data is the saved play data and emotional data, and evaluation data is generated through the analysis algorithm. Specifically, the machine learning model analyzes the data and evaluates the user's performance and emotional state.

[1298] Step 6: Generate feedback messages

[1299] The server generates a feedback message based on the analysis results. This feedback message includes information about the user's learning progress, attitude toward the game, and emotional state. The input data is the analysis results, and the feedback message is generated based on these. Specifically, the message generated is, "User ID 12345's learning progress is good, and he has positive and proactive feelings toward the game."

[1300] Step 7: Submit and view feedback

[1301] The server sends the generated feedback message to the parent's device, which displays the received feedback message on its screen and allows the parent to check detailed information. The input data is the generated feedback message, and by sending it to the parent's device, the parent can understand their child's learning progress and emotional state.

[1302] Step 8: Calculating and Providing Learning Points

[1303] The server calculates learning points based on gameplay data. Input data is play data, and points are calculated based on this. For example, points are awarded based on the number of stages cleared and the score obtained. This allows children to earn points that can be exchanged for items or services that are useful for learning. Specifically, points are calculated based on the following rule: 10 points are awarded for each cleared stage, and 5 points for every 100 points scored.

[1304] (Application example 2)

[1305] 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."

[1306] Conventional factory worker evaluation systems only evaluate workers' work efficiency and the quality of their work products, making it difficult to evaluate workers' emotional state and overall work attitude. Furthermore, there were limited means to provide specific feedback that would contribute to improving worker motivation and the work environment. As a result, it was not possible to properly monitor workers' mental health and motivation, making it difficult to create an optimal work environment.

[1307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1308] In this invention, the server includes a means for recording work data, a means for transmitting the recorded work data to a central processing unit, and an analysis means for analyzing the work data received by the central processing unit. This allows for real-time evaluation of the worker's work status and emotions and provides comprehensive feedback to the manager. Furthermore, by further including a means for calculating evaluation points based on the work data and converting the evaluation points into incentives for the worker, it is possible to contribute to improving worker motivation. Furthermore, by including a means for the analysis means for analyzing the worker's work attitude and emotions through dialogue with the worker using natural language processing, more accurate evaluations are possible.

[1309] "Work data" refers to data that includes information related to a worker's work, such as the worker's working hours, work efficiency, and quality of the work product.

[1310] The "central processing unit" is a computer system for analyzing received work data and generating evaluation results.

[1311] "Analysis tools" refers to algorithms and AI models that evaluate workers' work situations and emotions based on work data.

[1312] "Emotional state" is data that indicates a worker's mental reactions, feelings, and psychological state.

[1313] "Evaluation points" refer to a numerical representation of a worker's performance and attitude calculated based on work data.

[1314] "Incentives" are rewards, benefits, and incentives that are intended to increase worker motivation.

[1315] "Feedback" is the act of providing information including evaluation, advice, and improvement suggestions based on analysis results.

[1316] "Natural language processing" is a technology that allows computers to understand and analyze human language, and is used to understand work attitudes and emotions through dialogue with workers.

[1317] The present invention relates to a system for evaluating the work efficiency and emotional state of workers in a factory and providing feedback to managers. This system is realized using an application and a central processing unit installed on a factory robot.

[1318] Data collection

[1319] The factory robots are equipped with sensors that record each worker's work data (work time, work efficiency, quality of finished products, etc.) The robots are also equipped with cameras and microphones, and an emotion engine (e.g., Affectiva SDK) is used to analyze the facial expressions and voices of workers to collect emotional data.

[1320] Data transmission

[1321] The collected data is temporarily stored locally on the edge device and then transmitted to a central server at regular intervals using a lightweight messaging protocol such as MQTT.

[1322] Data analysis

[1323] The central processing unit (server) stores the received data in a database (e.g., MongoDB). It then analyzes the data using an AI model (e.g., TensorFlow) and analysis algorithms to evaluate each worker's work efficiency and emotional state. The AI ​​model uses a generative AI model to perform highly accurate analysis.

[1324] Feedback Generation

[1325] Based on the analysis results, a report is generated of each worker's work status and emotional state. The report includes information linked to the worker's ID and is displayed in an easy-to-understand manner for managers.

[1326] Feedback Notifications

[1327] The final feedback generated is sent to the administrator's device (smartphone or computer) and can be viewed via a dedicated mobile app or web app.

[1328] Examples and prompts

[1329] For example, consider a factory worker with ID 56789 who works eight hours during a daily shift, producing 100 parts at 90% efficiency. Furthermore, facial expression analysis reveals primarily positive emotions. This data is collected and analyzed as follows:

[1330] Specifically, the following prompt sentences are input into the generative AI model to generate evaluation and feedback:

[1331] Generate a report on worker performance and emotions based on the following data:

[1332] Working time: 8 hours

[1333] Efficiency: 90%

[1334] Deliverables: 100 pieces

[1335] Emotion: Positive

[1336] Generate a feedback message.

[1337] The server generates a feedback message like this:

[1338] Worker ID 56789 worked 8 hours during today's shift, producing 100 parts at 90% efficiency. He displayed mostly positive emotions while working and demonstrated good overall work performance.

[1339] In this way, the system can evaluate the work situation and emotions of workers in real time and provide comprehensive feedback to managers.In addition, by providing evaluation points as incentives to workers, it can improve motivation and the working environment.

[1340] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1341] Step 1:

[1342] The factory robot (terminal) records each worker's work data (e.g., work time, work efficiency, and quality of the finished product). Specifically, data is collected using work time sensors and product counters. The robot is also equipped with a camera and microphone, and an emotion engine (e.g., Affectiva SDK) is used to analyze the worker's facial expressions and voice to collect emotional data. The input is the worker's physical actions, facial expressions, and voice, and the output is the recorded work data and emotional data.

[1343] Step 2:

[1344] The collected data is temporarily stored on the local edge device. At appropriate intervals, or when a certain amount of data has accumulated, the data is sent to a central server. This communication is performed using a lightweight messaging protocol such as MQTT. The input is the task data and emotion data on the local edge device, and the output is the data to be sent to the central server.

[1345] Step 3:

[1346] The server (central processing unit) stores the received task data and emotion data in a database (e.g., MongoDB). Here, the data is organized and normalized to facilitate subsequent analysis. The input is the data sent to the central server, and the output is the integrated data stored in the database.

[1347] Step 4:

[1348] An AI analysis module (e.g., TensorFlow) and analysis algorithm installed on the server analyze the received data. Specifically, an AI model is used to evaluate work efficiency and emotional state, and the results are output in numerical or text format. The use of a generative AI model enables complex pattern recognition and prediction. The input is the integrated data in the database, and the output is the evaluation result.

[1349] Step 5:

[1350] The server generates feedback based on the evaluation results, again using a generative AI model to generate prompts such as:

[1351] Generate a report on worker performance and emotions based on the following data:

[1352] Working time: 8 hours

[1353] Efficiency: 90%

[1354] Deliverables: 100 pieces

[1355] Emotion: Positive

[1356] Generate a feedback message.

[1357] The generated feedback is output in the form of a report summarizing each worker's work status and emotional state. The input is the evaluation result, and the output is the generated feedback message.

[1358] Step 6:

[1359] The final feedback message is sent to the manager's device (smartphone or computer). The manager can check the feedback via a dedicated mobile app or web app and use it to improve the work environment or provide guidance to workers as needed. The input is the generated feedback message, and the output is the feedback information sent to the manager's device.

[1360] This allows for real-time assessment of workers' work status and emotions, providing comprehensive feedback to managers.

[1361] 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.

[1362] 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.

[1363] 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 robot 414.

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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).

[1368] 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.

[1369] 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."

[1370] 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.

[1371] 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).

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] 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.

[1377] 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.

[1378] 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.

[1379] 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.

[1380] 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.

[1381] 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.

[1382] The following is further disclosed regarding the above embodiment.

[1383] (Claim 1)

[1384] a means for recording gameplay data;

[1385] means for transmitting the recorded game play data to a central processing unit;

[1386] analysis means for analyzing the received game play data in the central processing unit;

[1387] A means for evaluating the learning situation and attitude toward the game based on the analysis results;

[1388] a means for providing the result of the evaluation by the evaluation means as feedback to the parent;

[1389] A system including:

[1390] (Claim 2)

[1391] 10. The system of claim 1, further comprising means for calculating learning points based on the game play data and making the learning points exchangeable for services or items useful for learning.

[1392] (Claim 3)

[1393] 2. The system according to claim 1, wherein the analyzing means includes means for analyzing game playing attitudes and learning situations through dialogue with a user using natural language processing.

[1394] "Example 1"

[1395] (Claim 1)

[1396] a means by which users can play the game;

[1397] a means for recording gameplay data;

[1398] means for transmitting the recorded game play data to a central processing unit;

[1399] analysis means for analyzing the received game play data in the central processing unit;

[1400] A means for evaluating the learning situation and attitude toward the game based on the analysis results;

[1401] a means for providing the result of the evaluation by the evaluation means as feedback to the parent;

[1402] a means for analyzing the original data by using natural language processing through a dialogue with a user;

[1403] A system including:

[1404] (Claim 2)

[1405] 10. The system of claim 1, further comprising means for calculating learning points based on the game play data and making the learning points exchangeable for services or items useful for learning.

[1406] (Claim 3)

[1407] 10. The system of claim 1, further comprising means for collecting and transmitting game play data to a central processing unit at regular intervals or at the end of a game.

[1408] (Claim 4)

[1409] 10. The system of claim 1, further comprising means for generating the feedback in natural language using a generative AI model.

[1410] "Application Example 1"

[1411] (Claim 1)

[1412] a means for recording gameplay data;

[1413] means for transmitting the recorded game play data to a central processing unit;

[1414] analysis means for analyzing the received game play data in the central processing unit;

[1415] A means for evaluating the learning situation and attitude toward the game based on the analysis results;

[1416] a means for providing the result of the evaluation by the evaluation means as feedback to the parent;

[1417] A means to immediately use the assessed learning points to purchase products in physical stores,

[1418] A system including:

[1419] (Claim 2)

[1420] 10. The system of claim 1, further comprising means for calculating learning points based on the game play data and making the learning points exchangeable for services or items useful for learning.

[1421] (Claim 3)

[1422] 2. The system according to claim 1, wherein the analyzing means includes means for analyzing game playing attitudes and learning situations through dialogue with a user using natural language processing.

[1423] "Example 2: Combining Emotion Engines"

[1424] (Claim 1)

[1425] A means for storing game record information when a user plays a game;

[1426] means for transmitting the accumulated play record information to a central control device;

[1427] an analysis means for analyzing the received game record information in the central control device;

[1428] A means for evaluating the learning progress and attitude towards play based on the analysis results;

[1429] a means for providing the result of the evaluation by the evaluation means as feedback to the parent;

[1430] means for collecting user emotion information using an emotion engine and transmitting the information to a central control device;

[1431] a means for more accurately evaluating the learning progress and attitude toward play based on the emotional information;

[1432] A system including:

[1433] (Claim 2)

[1434] 2. The system according to claim 1, further comprising means for calculating learning points based on the play record information and making the learning points exchangeable for services or goods useful for learning.

[1435] (Claim 3)

[1436] 2. The system according to claim 1, wherein the analyzing means includes means for analyzing playing behavior and learning progress through dialogue with a user using natural language processing.

[1437] "Application example 2 when combining emotion engines"

[1438] New Claims

[1439] (Claim 1)

[1440] A means for recording work data;

[1441] means for transmitting the recorded work data to a central processing unit;

[1442] an analysis means for analyzing the received operation data in the central processing unit;

[1443] A means for evaluating the work situation and emotions of the worker based on the analysis results;

[1444] a means for providing the results of the evaluation by the evaluation means to a manager as feedback;

[1445] A system including:

[1446] (Claim 2)

[1447] 10. The system of claim 1, further comprising means for calculating evaluation points based on the work data and making the evaluation points exchangeable for worker incentives.

[1448] (Claim 3)

[1449] 2. The system of claim 1, wherein the analyzing means includes means for analyzing work attitudes and emotions through dialogue with workers using natural language processing. [Explanation of symbols]

[1450] 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. a means for recording gameplay data; means for transmitting the recorded game play data to a central processing unit; analysis means for analyzing the received game play data in the central processing unit; A means for evaluating the learning situation and attitude toward the game based on the analysis results; a means for providing the result of the evaluation by the evaluation means as feedback to the parent; A system including:

2. 2. The system according to claim 1, further comprising means for calculating learning points based on the game play data and making the learning points exchangeable for services or items useful for learning.

3. The system according to claim 1 , wherein the analyzing means includes means for analyzing the game playing attitude and learning situation through a dialogue with the user using natural language processing.

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