system

Abstract

We provide the system. [Solution] An input method for entering lifestyle habits specified by the parents, A generation means for generating games based on input lifestyle habits, A means of presenting the generated game to parents and children, A means of collecting feedback from parents after implementation, A means of saving feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, making children acquire living habits has often been a cause of stress for parents, and there has been a problem that it is difficult to find a specific method. In particular, there is a lack of a mechanism that allows children to acquire living habits while having fun playing, and both parents and children may feel burdened. It is desired to solve such problems.

Means for Solving the Problems

[0005] This invention solves the above problem by providing a system that includes an input means for inputting lifestyle habits specified by the parent, a generation means for generating games using machine learning based on the input lifestyle habits, a presentation means for presenting the generated games to the parent and child, and a means for collecting and storing feedback obtained from the parent. With this system, parents can make learning lifestyle habits fun for their children, and children themselves can acquire habits independently through play.

[0006] "Input means" refers to a device or method that provides an interface for parents to input lifestyle habits they want their children to acquire into a system.

[0007] "Generative means" refers to a device or method that executes algorithms or processes for constructing games using machine learning techniques based on input information about lifestyle habits.

[0008] "Presentation means" refers to a device or method that provides parents or children with the details and procedures of the generated play visually or audibly.

[0009] "Collection means" refers to a device or method for receiving feedback or reactions from parents regarding the results of play activities.

[0010] "Storage means" refers to a device or method for retaining collected feedback data and storing it for later use or analysis. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0025] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] This invention relates to an educational platform system that enables children to acquire specific life habits while having fun. The program processing and specific examples of this system are shown below.

[0033] First, the user (parent) uses a specific application on the device to input the daily habits they want their child to develop. For example, they can select habits such as "brushing teeth every morning" or "putting away toys." The device prepares this information as digital data and sends it to the server.

[0034] Upon receiving this input data, the server generates games using predetermined generation methods. Specifically, it utilizes machine learning models to devise creative and educational games related to the input lifestyle habits. For example, games such as "a game where you sing while brushing your teeth to make brushing time more enjoyable" or "a challenge to put toys away in a box within a time limit" are generated.

[0035] Next, the generated game information is sent from the server to the terminal. The terminal receives this information and presents it to the user through the user interface. Parents can use this information to play games with their children.

[0036] After completing a game, the user enters feedback about the results into a terminal. The terminal sends this feedback to a server, which stores it in a database using a storage mechanism. This data is used to create future games and improve the system.

[0037] Thus, the system has a process that generates play activities tailored to the lifestyle habits specified by the parents and provides information to carry them out. As a result, children can learn habits while having fun through play, and parent-child time becomes more fulfilling.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the application on their device and selects or enters the lifestyle habits they want their child to develop. Specifically, they can choose from a list of lifestyle habits provided within the app, or enter new, specific habits in text format.

[0041] Step 2:

[0042] The device converts the user's entered lifestyle data into a digital format. This data includes details of selected lifestyle habits and any additional requests the user may have made.

[0043] Step 3:

[0044] The device transmits digitally converted lifestyle data to the server via an appropriate communication protocol. This transmission includes error checking to verify the accuracy and reliability of the data.

[0045] Step 4:

[0046] The server receives lifestyle data sent from the terminal. The received data is analyzed and used as parameters to determine what category of play to generate.

[0047] Step 5:

[0048] The server uses a machine learning model as a generation tool to generate games based on the received parameters. In doing so, it considers previously accumulated data and feedback to suggest more effective gameplay.

[0049] Step 6:

[0050] The generated game data is sent from the server to the terminal. This data includes specific game rules, execution procedures, and a list of necessary items.

[0051] Step 7:

[0052] The terminal displays play information received from the server to the user through a user interface. The user can then view this information to see how to conduct the play session with their child.

[0053] Step 8:

[0054] The user performs the suggested game with the child. After the activity, they input feedback into the application about the results of the game and the child's reactions.

[0055] Step 9:

[0056] The device collects feedback data entered by the user and sends it to the server. This data is stored in a database for later analysis and to improve the quality of the gameplay.

[0057] Step 10:

[0058] The server stores the received feedback data and analyzes it to help generate future gameplay. The analysis results are used for continuous improvement of the system.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] It is difficult for children to develop good habits on their own, and it is not easy for parents to support them in establishing these habits. In particular, it is necessary to devise ways to help children develop daily habits as part of their growth process while having fun, and there is a need for an educational platform that allows parents and children to spend enjoyable and meaningful time together.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes an information input means for specifying lifestyle habits, a generation algorithm for generating games based on the input information, and an information presentation means for presenting the generated games to the parent and child. This allows parents to provide their children with a fun learning environment and to help them naturally acquire lifestyle habits through play.

[0064] "Lifestyle habits" refer to actions and routines that are repeatedly performed in daily life.

[0065] "Information input means" refers to a method or device for a user to input data, which is done via software on a terminal.

[0066] A "generative algorithm" is a procedure or system that uses a machine learning model to devise new games based on input data.

[0067] "Information presentation means" refers to devices or methods for visually or audibly communicating the content and procedures of a generated game to parents and children.

[0068] "Data collection means" refers to systems and devices used to collect information from users, particularly feedback.

[0069] "Information storage means" refers to a method or device for securely recording collected data and preparing it for future use.

[0070] A "visualization device" refers to equipment used to display information visually, such as displays and monitors.

[0071] This invention relates to an educational platform system that helps children develop lifestyle habits specified by their parents in an enjoyable way. The system consists of users, terminals, and a server, each working together to achieve its functions.

[0072] The user (parent) first specifies daily routines using a specific application on the device. These routines include, for example, "brushing teeth every morning" and "putting away toys." The device processes the entered information and sends it to the server as digital data. This data is transmitted securely using the HTTP or HTTPS protocol.

[0073] The server uses a generative AI model based on the received data to generate new games. Specifically, it leverages machine learning algorithms to design creative games based on data. This process generates creative ideas related to the inputted daily habits. For example, games such as "a game where you sing a song while brushing your teeth" or "a challenge to put away toys within a time limit" are devised.

[0074] The generated game information is sent from the server to the terminal, which then displays it to the user through a visualization device. This allows parents to review the details and steps of the generated game and participate with their children. The system incorporates elements that capture children's interest, making habit-forming learning enjoyable.

[0075] After completing a game, the user enters feedback about the results into a terminal, which then sends this feedback to the server. The server stores the feedback in a database and uses it to generate future games and improve the system. Through this process, the system continuously evolves, enabling it to provide games that are more adapted to user needs.

[0076] A concrete example of a prompt is, "Generate a game to encourage children to develop a 'cleaning habit' in a fun way." Based on this prompt, the system generates a relevant game.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user uses a device to access an educational application and inputs the lifestyle habits they want their child to develop. This input concerns specific habits such as "brushing teeth every morning" or "putting away toys." The device converts this information into digital data and prepares it for the next processing step. The input data includes the selected habit name and related conditions. Formatted digital data is generated as output.

[0080] Step 2:

[0081] The terminal sends the formatted digital data to the server. Data security is ensured through the HTTP or HTTPS protocol. Once data transmission is complete, server-side processing can begin. The input is the digital data sent from the terminal, and the output is the server's confirmation of receipt.

[0082] Step 3:

[0083] The server generates new games using a generative AI model based on the received data. Specifically, a machine learning algorithm within the server analyzes the input data and creates the optimal game. This algorithm also takes into account the results of analyzing past feedback data. The input is lifestyle data received by the server, and the output is the specifications of the generated game.

[0084] Step 4:

[0085] The server repackages the generated game specifications as data and sends it to the terminal. Upon receiving this information, the terminal prepares to present it to the parent again through the user interface. The input is the game specifications sent from the server, and the output is the terminal's confirmation of receipt.

[0086] Step 5:

[0087] The device presents the game to the parent in presentation mode through a visualization device. Specifically, the steps and methods of participation in the game are explained to the parent and child in an easy-to-understand manner using the screen and audio. The input is the game specifications, and the output is the presented game information.

[0088] Step 6:

[0089] After playing, the user inputs feedback on the results into the device. This feedback includes how much the child enjoyed it and areas for improvement. The device converts this information into digital data and prepares it for the next processing step. The input is the user's feedback, and the output is the formatted feedback data.

[0090] Step 7:

[0091] The terminal sends feedback data to the server. The server stores this information in a database and uses it to generate future gameplay and improve the system. The input is the feedback data sent from the terminal, and the output is the server's confirmation of data storage.

[0092] (Application Example 1)

[0093] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0094] There is a need for educational methods that allow children to naturally acquire specific lifestyle habits while having fun. However, traditional methods make it difficult for parents to provide appropriate play for their children, and there is a lack of effective learning support utilizing virtual environments.

[0095] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0096] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and provision means for providing the generated content on a virtual store platform. This makes it possible for parents to easily obtain learning content suitable for their children, and for children to develop habits while remaining interested.

[0097] An "input method for entering parent-specified lifestyle habits" is a device that allows parents to input specific habit information they want their children to acquire in a digital format.

[0098] A "generation method for generating games based on inputted lifestyle habits" is a mechanism that automatically devises and generates games that will attract children's interest based on the received data on lifestyle habits.

[0099] A "presentation method for showing generated games to parents and children" is a device that displays the details of the generated game and the steps for performing it in an easy-to-understand manner for parents and children.

[0100] A "collection method for gathering feedback from parents after the activity" is a device that collects feedback information such as evaluations and impressions from parents as a result of their children engaging in play.

[0101] A "means of storing feedback" refers to a memory device that effectively stores collected feedback data for later use.

[0102] "A means of providing content generated on a virtual store platform" refers to a mechanism that makes generated learning content accessible and usable by parents and children in a virtual environment.

[0103] The educational platform system implementing this invention is designed to enable parents to efficiently generate and implement play activities for their children's daily routines. It utilizes user devices such as smartphones and tablets, and a server connected to the internet.

[0104] The server operates by integrating the following means:

[0105] First, as an input method, parents can input lifestyle habits they want their children to develop (e.g., "brushing teeth every morning") through the user interface. This information is then transmitted to the server in digital format.

[0106] Next, the server uses a generative AI model based on the input lifestyle habits to generate relevant play and educational content. In this process, machine learning techniques are used to personalize the content, for example, by creating an animated song to make brushing teeth more fun.

[0107] The delivery method allows parents to easily access the generated content on the virtual store platform. This process helps parents use the content at the right time and supports their children in developing good habits while having fun.

[0108] Furthermore, through the presentation method, this content is visually presented to parents and children via the device's display. Based on this information, parents can incorporate it into their daily activities, thereby helping their children acquire good lifestyle habits.

[0109] After the game is played, a data collection system activates, inputting feedback from the parent into the device and sending it to the server. This feedback is managed by a storage system as important data for system improvement.

[0110] For example, if a parent selects "wash your hands every night" on the device, the generated educational content will be a "handwashing song and animation," and they can send feedback evaluating the results.

[0111] Examples of prompt statements used for generative AI models include the following:

[0112] "Please provide elements for creating songs and animations that help children learn to wash their hands in a fun way."

[0113] "Please suggest visual content that is ideal for establishing a handwashing routine after dinner for 3-year-olds."

[0114] In this way, this invention provides a method for effectively shaping children's lifestyle habits while parents and children enjoy themselves together.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user uses a device to input the daily habits they want their child to develop (e.g., "brushing teeth every morning") through an input interface. The input data is sent to the server in a digital format. The server then receives specific habit information tailored to the parent's needs.

[0118] Step 2:

[0119] The server uses received lifestyle data to generate playful and educational content using generation methods. Here, a generation AI model is used to generate prompts (e.g., "Please provide game elements to make brushing teeth fun for children"), and personalized content is devised based on these prompts. The generated content may include songs and animations.

[0120] Step 3:

[0121] The generated content is delivered by the server through a virtual store platform. Parents can access this information via their devices and download or stream the content. This allows parents to obtain habit-forming content suitable for their children.

[0122] Step 4:

[0123] The user uses the device's display to play the generated game together with the child. Here, the device presents content visually and aurally, guiding the play process. It is expected that the child will learn habits while having fun through the interactive content.

[0124] Step 5:

[0125] After a user completes a game, the device collects parental ratings and comments through a feedback input interface. This feedback data is sent to a server in digital format. The server receives the feedback and stores it in a database using a storage mechanism. This information is used to improve future content.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention incorporates an emotion engine that recognizes user emotions into an educational platform system designed to help children acquire good habits while having fun. This system generates games based on user-specified habits and adjusts the content using the emotion engine, thereby providing a more effective and enjoyable learning experience.

[0128] First, the user (parent) uses a device to specify the lifestyle habits they want their child to develop. The device converts this information into digital data and sends it to the server. The server uses a generation mechanism to generate games based on the received data. The user's emotional state, obtained from an emotion engine, is taken into consideration when generating the games. Specifically, a machine learning algorithm analyzes the user's voice and facial expressions to adjust the content and difficulty level of the games appropriately.

[0129] Next, the game information generated on the server is sent to the terminal. The terminal presents the generated game to the user through a user interface, and here too, the display content and interaction methods are optimized based on the analysis results of the emotion engine. This ensures that the game is tailored to the child's interests and emotions.

[0130] After execution, the user enters feedback on the child's reactions and the outcome of the play into the device. During this process, the emotion engine collects data in real time, recording changes in emotions during the play. The device sends the feedback and emotion data to a server, where it is stored in a database to help optimize future play sessions.

[0131] For example, when generating a game to help children develop the habit of "putting away toys," the emotional engine can determine whether the child is enjoying themselves or feeling bored, and adjust the game's tempo and reward system accordingly. This allows children to participate more actively, and parents can also benefit from a more satisfying learning experience.

[0132] In this way, this system analyzes and utilizes users' emotions to provide a personalized learning environment and supports children in naturally developing good habits.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user uses their device to input the lifestyle habits they want their child to develop through the interface. Examples of habits they can select include "brushing teeth every morning" and "tidying up before bed."

[0136] Step 2:

[0137] The terminal digitizes the entered lifestyle data and sends it to the server. This transmitted data includes selected habits and any additional requests from the user.

[0138] Step 3:

[0139] The server receives data sent from the terminal and activates the emotion engine. The emotion engine collects sensor data necessary to analyze the user's voice and facial expressions.

[0140] Step 4:

[0141] The server uses analysis results from the emotion engine to determine the user's current emotional state. This result is then used as a factor in determining what kind of gameplay is optimal.

[0142] Step 5:

[0143] The server generates games using a generation mechanism. Machine learning algorithms are used to adjust the difficulty and content of the games according to the user's emotional state.

[0144] Step 6:

[0145] Detailed information about the generated game is sent from the server to the terminal. This information includes the rules of the game, the steps to be taken, and the recommended forms of interaction.

[0146] Step 7:

[0147] The device displays received game information through the user interface. Here too, the presentation method is customized to reflect the analysis of the emotion engine and maintain the user's interest.

[0148] Step 8:

[0149] The users (parent and child) actually play the suggested game. During the game, the emotion engine analyzes the users' reactions in real time and sends adjustment requests to the server as needed.

[0150] Step 9:

[0151] After the play session, the user inputs the child's reactions and their evaluation of the play on their device. The emotion engine then collects additional final emotion data to create comprehensive feedback.

[0152] Step 10:

[0153] The device sends user feedback and emotional data to the server. The server receives this data, stores it in a database, and uses it to optimize future game development.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] Traditionally, learning methods for developing good habits have faced the problem of children losing interest and not sticking with them. Furthermore, customization to individual children was difficult, resulting in ineffective learning. Additionally, the lack of systems to utilize feedback and children's emotional states in the learning process prevented the provision of effective learning experiences.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes input means for inputting habits specified by the parent, generation means for generating activities based on the input habits, and emotion analysis means for analyzing emotional states and adjusting activities. This makes it possible to provide an effective and enjoyable learning environment tailored to the child.

[0159] A "parent" is someone who uses this system to help their child develop good habits.

[0160] "Habits" are the behaviors and skills that children need to acquire in their daily lives.

[0161] An "input method" is a device or system used by parents to input habits they want their children to acquire into a system.

[0162] "Generative means" refers to technologies and methods for generating activities for children based on inputted habits.

[0163] An "activity" is a game or task generated by the system to help children learn habits in a fun way.

[0164] "Presentation methods" refer to devices or systems used to show the generated activity to parents and children.

[0165] "Emotional analysis methods" refer to techniques and methods for adjusting a child's activities by analyzing their emotional state.

[0166] "Means of collection" refers to devices or methods for collecting feedback from parents.

[0167] "Storage methods" refer to the technologies and methods for recording and storing collected feedback and analytical data.

[0168] This invention is an educational support system that helps parents instill good habits in their children in an enjoyable way. The system provides parents with the goal of instilling specific habits in their children through activities based on the child's interests and emotions.

[0169] First, the user (parent) uses a device to input specific habits they want their child to develop. This input is done using digital devices such as tablets and smartphones. The input information is processed by the device and converted into digital data. This data is then transmitted to a server via the internet.

[0170] The server designs activities using a generation mechanism based on the received digital data. This generation mechanism utilizes machine learning and artificial intelligence technologies, with a generation AI model at its core. An emotion analysis mechanism is incorporated, analyzing emotional data such as the user's (child's) voice and facial expressions in real time. Based on this analysis, the tempo and content of the activities are dynamically adjusted.

[0171] Information about the activities generated by the server is sent to the terminal and presented to the user (child) on the terminal. This uses a user interface that utilizes animation and sound effects, making it interactive and visually engaging.

[0172] After execution, the user (parent) can input responses regarding the activity's effectiveness and the child's reaction into the device. This collected data is then sent back to the server and stored in a database. This data will be used as reference for future activity design and generation processes.

[0173] For example, if you want to instill the habit of "putting away toys," you can input "putting away toys" into the device, and the server will automatically generate related activities. In this process, the system operates a generation AI model based on a prompt message such as "Lifestyle: Putting away toys. What kind of game will you create for the child to enjoy? Adjust the game's tempo and rewards based on the emotion engine," providing an interactive and fun activity.

[0174] This system allows children to naturally acquire good habits in a fun and efficient way.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user uses their device to input specific habits they want their child to develop. In this process, the user selects habits such as "putting away toys."

[0178] The input is the habits chosen by the parents, and the output is this information converted into digital data format.

[0179] The terminal receives this digital data as input using a touchscreen or voice input system, and sends that data to the server. This transmission delivers habitual information to the server via the network.

[0180] Step 2:

[0181] The server analyzes the digital data received from the terminal and prepares to utilize a generative AI model for activity design.

[0182] The input is digital data of habits submitted by the user, and the output is an activity generation prompt statement.

[0183] The server activates the sentiment analysis system and references previously collected sentiment data. Based on this, it generates activity generation prompts and prepares to design the activity generation logic.

[0184] Step 3:

[0185] The server uses a generative AI model to design activities that will interest children, based on activity generation prompts.

[0186] The input consists of activity generation prompts and pre-collected sentiment data, while the output is a customized activity.

[0187] The server generates activities optimized for the user (child), including adjusting the difficulty level and designing the reward system. The generated activities are then sent to the device.

[0188] Step 4:

[0189] The device receives activity data generated from the server and presents it to the child through a user interface.

[0190] The input is the activity content sent from the server, and the output is the game or task presented to the child visually and aurally.

[0191] The device presents activities using animation and sound features. A real-time emotion analysis system monitors the child's responses and optimizes their interaction with the activity.

[0192] Step 5:

[0193] After the activity is completed, the user enters their thoughts and feedback on the child's reaction and the effects into the device.

[0194] The input is feedback information after the activity is completed, and the output is data that organizes this information and is used for future improvements.

[0195] The device uses collection methods to obtain feedback and sends that data to the server. This feedback is stored on the server and used as reference information when generating the next activity.

[0196] (Application Example 2)

[0197] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0198] In modern retail settings, providing children with learning opportunities to naturally develop good habits is not easy. In particular, engaging children's interests while addressing their individual emotional states is a challenge within limited timeframes. Traditional methods have struggled to analyze children's emotions in real time and dynamically adjust play activities accordingly.

[0199] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0200] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and adjustment means for analyzing the user's emotions using emotion recognition technology and dynamically adjusting the presented content based on the results. This makes it possible to provide an interactive learning experience that effectively helps children acquire habits while maintaining their interest, according to their individual emotional state.

[0201] "Input means" refers to a device or method for inputting information about lifestyle habits specified by the parent.

[0202] "Generative means" refers to a device or method for designing play based on inputted lifestyle habits.

[0203] "Presentation means" refers to a device or method for presenting the generated play to parents and children visually or aurally.

[0204] "Collection means" refers to a device or method for collecting feedback from parents after a play session has taken place.

[0205] "Storage means" refers to an apparatus or method for recording and storing collected feedback information.

[0206] "Emotion recognition technology" is a technology that analyzes voice and facial expression data in order to analyze the user's emotions.

[0207] "Adjustment means" refers to a device or method for dynamically changing the presented content based on the analysis results obtained by emotion recognition technology.

[0208] This invention is a system that helps children learn good habits in an enjoyable way. Based on the habits specified by the parents, this system generates games for children and dynamically adjusts the content and difficulty level of the games according to the child's emotional state, thereby improving learning efficiency.

[0209] The system comprises a terminal, a server, and emotion recognition technology. The terminal provides an interface for inputting lifestyle habits specified by the parent, and transmits this information to the server as digital data. The server has a generation mechanism, which generates games using machine learning algorithms and performs data analysis using emotion recognition technology.

[0210] To analyze voice and facial expression data, the device uses its camera and microphone to capture the child's reactions. This analysis is performed using AWS® Rekognition and Google® Cloud Vision APIs, and the resulting emotional data is sent to a server. The server then adjusts the generated play content based on this data to provide a learning experience tailored to the child's individual emotional state.

[0211] For example, if a child is playing a game themed around "tidying up toys," the system uses emotion recognition technology to determine whether the child is having fun or is bored. If the child is having fun, the system can speed up the game's pace and implement a reward system, such as giving virtual badges to enhance their sense of accomplishment.

[0212] An example of a prompt message would be: "Analyze this child's emotions from their facial expressions and voice. If they are currently enjoying themselves, set the difficulty level for the next game stage and award a virtual badge as a reward upon completion." This message is then input into a generating AI model to optimize the game's output.

[0213] Overall, this system is designed to naturally enhance children's motivation to learn and provide a highly satisfying educational environment for parents.

[0214] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0215] Step 1:

[0216] The user (parent) uses a terminal to specify lifestyle habits. The terminal converts these specifications into digital data and sends it to the server. The input is data related to the specified lifestyle habits, and the output is the digital data sent to the server.

[0217] Step 2:

[0218] The server analyzes the received digital data and begins designing the game using generation tools. Here, the game's theme and story, corresponding to daily life habits, are determined. The input is digital data, and the output is a generated prototype of the game.

[0219] Step 3:

[0220] The device's camera and microphone are used to capture the user's (child's) voice and facial expressions in real time. This data is then analyzed using emotion recognition technology. The input is the child's voice and visual data, and the output is the analyzed emotion data.

[0221] Step 4:

[0222] The server dynamically adjusts the game content and difficulty level based on the analyzed emotional data. This provides an optimal learning experience tailored to the user's emotional state. The input is the analyzed emotional data, and the output is the adjusted game content.

[0223] Step 5:

[0224] The adjusted gameplay is presented to the user (child) via the device. Interactive elements and reward systems of the game are implemented here. The input is the adjusted gameplay, and the output is visual and auditory feedback to the child.

[0225] Step 6:

[0226] After the game ends, the user (parent) enters feedback into the device, and this data is sent to the server. The server then uses this feedback to store it in a database for future game design. The input is the feedback information from the parent, and the output is the stored feedback data.

[0227] Step 7:

[0228] Based on the saved data, the server uses a generative AI model to generate prompts to make the next gameplay more effective. The input is the saved feedback data, and the output is the prompts for the generative AI model.

[0229] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention relates to an educational platform system that enables children to acquire specific life habits while having fun. The program processing and specific examples of this system are shown below.

[0246] First, the user (parent) uses a specific application on the device to input the daily habits they want their child to develop. For example, they can select habits such as "brushing teeth every morning" or "putting away toys." The device prepares this information as digital data and sends it to the server.

[0247] Upon receiving this input data, the server generates games using predetermined generation methods. Specifically, it utilizes machine learning models to devise creative and educational games related to the input lifestyle habits. For example, games such as "a game where you sing while brushing your teeth to make brushing time more enjoyable" or "a challenge to put toys away in a box within a time limit" are generated.

[0248] Next, the generated game information is sent from the server to the terminal. The terminal receives this information and presents it to the user through the user interface. Parents can use this information to play games with their children.

[0249] After completing a game, the user enters feedback about the results into a terminal. The terminal sends this feedback to a server, which stores it in a database using a storage mechanism. This data is used to create future games and improve the system.

[0250] Thus, the system has a process that generates play activities tailored to the lifestyle habits specified by the parents and provides information to carry them out. As a result, children can learn habits while having fun through play, and parent-child time becomes more fulfilling.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] The user launches the application on their device and selects or enters the lifestyle habits they want their child to develop. Specifically, they can choose from a list of lifestyle habits provided within the app, or enter new, specific habits in text format.

[0254] Step 2:

[0255] The device converts the user's entered lifestyle data into a digital format. This data includes details of selected lifestyle habits and any additional requests the user may have made.

[0256] Step 3:

[0257] The device transmits digitally converted lifestyle data to the server via an appropriate communication protocol. This transmission includes error checking to verify the accuracy and reliability of the data.

[0258] Step 4:

[0259] The server receives lifestyle data sent from the terminal. The received data is analyzed and used as parameters to determine what category of play to generate.

[0260] Step 5:

[0261] The server uses a machine learning model as a generation tool to generate games based on the received parameters. In doing so, it considers previously accumulated data and feedback to suggest more effective gameplay.

[0262] Step 6:

[0263] The generated game data is sent from the server to the terminal. This data includes specific game rules, execution procedures, and a list of necessary items.

[0264] Step 7:

[0265] The terminal displays play information received from the server to the user through a user interface. The user can then view this information to see how to conduct the play session with their child.

[0266] Step 8:

[0267] The user performs the suggested game with the child. After the activity, they input feedback into the application about the results of the game and the child's reactions.

[0268] Step 9:

[0269] The device collects feedback data entered by the user and sends it to the server. This data is stored in a database for later analysis and to improve the quality of the gameplay.

[0270] Step 10:

[0271] The server stores the received feedback data and analyzes it to help generate future gameplay. The analysis results are used for continuous improvement of the system.

[0272] (Example 1)

[0273] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0274] It is difficult for children to develop good habits on their own, and it is not easy for parents to support them in establishing these habits. In particular, it is necessary to devise ways to help children develop daily habits as part of their growth process while having fun, and there is a need for an educational platform that allows parents and children to spend enjoyable and meaningful time together.

[0275] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0276] In this invention, the server includes an information input means for specifying lifestyle habits, a generation algorithm for generating games based on the input information, and an information presentation means for presenting the generated games to the parent and child. This allows parents to provide their children with a fun learning environment and to help them naturally acquire lifestyle habits through play.

[0277] "Living habits" refer to actions and routines that are repeatedly carried out in daily life.

[0278] "Information input means" refers to a method or device for a user to input data, which is carried out via software on a terminal.

[0279] "Generation algorithm" refers to a procedure or system that devises new games based on data input using a machine learning model.

[0280] "Information presentation means" refers to a device or method for visually or audibly conveying the content and procedures of the generated games to parents and children.

[0281] "Data collection means" refers to a system or device for collecting information obtained from users, especially feedback.

[0282] "Information storage means" refers to a method or device for safely recording the collected data for later use.

[0283] "Visualization device" refers to a device used to visually display information, such as a display or monitor.

[0284] This invention relates to an educational platform system for parents to happily teach their children specific living habits. The system consists of a user, a terminal, and a server, and each collaborates to achieve the functions.

[0285] The user (parent) first specifies living habits by using a specific application on the terminal. This specification includes habits such as "brushing teeth every morning" and "putting away toys" as specific examples. The terminal processes the input information and transmits it to the server as digital data. This data is securely transmitted using the HTTP or HTTPS protocol.

[0286] Based on the received data, the server utilizes a generative AI model to generate new games. Specifically, it leverages machine learning algorithms to design creative games based on the data. In this process, creative ideas related to the input lifestyle habits are generated. For example, games like "singing while brushing teeth" or "a challenge to tidy up toys within a time limit" are devised.

[0287] The information of the generated games is transmitted from the server to the terminal, and the terminal presents it to the user through a visualization device. Through this presentation, parents can check the details and procedures of the generated games and implement them with their children. By including elements that attract children's interest, the system aims to make habit learning enjoyable.

[0288] After the execution of the game, the user inputs feedback about the execution result into the terminal, and the terminal transmits the feedback to the server. The server saves the feedback in the database and utilizes it for future game generation and system improvement. Through this process, the system can continuously evolve and provide games adapted to user needs.

[0289] As a specific example of the prompt text, there is "Please generate a game to promote the 'cleaning habit' that children can enjoy learning." Based on this prompt, the system generates relevant games.

[0290] The flow of the specific process in Example 1 will be described using Figure 11.

[0291] Step 1:

[0292] The user uses a device to access an educational application and inputs the lifestyle habits they want their child to develop. This input concerns specific habits such as "brushing teeth every morning" or "putting away toys." The device converts this information into digital data and prepares it for the next processing step. The input data includes the selected habit name and related conditions. Formatted digital data is generated as output.

[0293] Step 2:

[0294] The terminal sends the formatted digital data to the server. Data security is ensured through the HTTP or HTTPS protocol. Once data transmission is complete, server-side processing can begin. The input is the digital data sent from the terminal, and the output is the server's confirmation of receipt.

[0295] Step 3:

[0296] The server generates new games using a generative AI model based on the received data. Specifically, a machine learning algorithm within the server analyzes the input data and creates the optimal game. This algorithm also takes into account the results of analyzing past feedback data. The input is lifestyle data received by the server, and the output is the specifications of the generated game.

[0297] Step 4:

[0298] The server repackages the generated game specifications as data and sends it to the terminal. Upon receiving this information, the terminal prepares to present it to the parent again through the user interface. The input is the game specifications sent from the server, and the output is the terminal's confirmation of receipt.

[0299] Step 5:

[0300] The terminal presents the play to the parent in presentation mode through a visualization device. Specifically, the play procedures and participation methods are clearly explained to the parent and child using the screen and voice. The input is the play specification, and the output is the presented play information.

[0301] Step 6:

[0302] After the user plays, the user inputs the feedback on the result to the terminal. This feedback includes the degree to which the child enjoyed the play and areas for improvement. The terminal converts this information into digital data and prepares it for the next process. The input is the user's feedback, and the output is the formatted feedback data.

[0303] Step 7:

[0304] The terminal sends the feedback data to the server. The server accumulates this information in a database and utilizes it for play generation and system improvement in subsequent sessions. The input is the feedback data sent from the terminal, and the output is the confirmation of data storage on the server side.

[0305] (Application Example 1)

[0306] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0307] There is a need for an educational method that allows children to naturally acquire specific living habits while enjoying them. However, with conventional methods, it is difficult for parents to provide appropriate play for their children, and there is a lack of effective learning support utilizing virtual environments.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0309] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and provision means for providing the generated content on a virtual store platform. This makes it possible for parents to easily obtain learning content suitable for their children, and for children to develop habits while remaining interested.

[0310] An "input method for entering parent-specified lifestyle habits" is a device that allows parents to input specific habit information they want their children to acquire in a digital format.

[0311] A "generation method for generating games based on inputted lifestyle habits" is a mechanism that automatically devises and generates games that will attract children's interest based on the received data on lifestyle habits.

[0312] A "presentation method for showing generated games to parents and children" is a device that displays the details of the generated game and the steps for performing it in an easy-to-understand manner for parents and children.

[0313] A "collection method for gathering feedback from parents after the activity" is a device that collects feedback information such as evaluations and impressions from parents as a result of their children engaging in play.

[0314] A "means of storing feedback" refers to a memory device that effectively stores collected feedback data for later use.

[0315] "A means of providing content generated on a virtual store platform" refers to a mechanism that makes generated learning content accessible and usable by parents and children in a virtual environment.

[0316] The educational platform system implementing this invention is designed to enable parents to efficiently generate and implement play activities for their children's daily routines. It utilizes user devices such as smartphones and tablets, and a server connected to the internet.

[0317] The server operates by integrating the following means:

[0318] First, as an input method, parents can input lifestyle habits they want their children to develop (e.g., "brushing teeth every morning") through the user interface. This information is then transmitted to the server in digital format.

[0319] Next, the server uses a generative AI model based on the input lifestyle habits to generate relevant play and educational content. In this process, machine learning techniques are used to personalize the content, for example, by creating an animated song to make brushing teeth more fun.

[0320] The delivery method allows parents to easily access the generated content on the virtual store platform. This process helps parents use the content at the right time and supports their children in developing good habits while having fun.

[0321] Furthermore, through the presentation method, this content is visually presented to parents and children via the device's display. Based on this information, parents can incorporate it into their daily activities, thereby helping their children acquire good lifestyle habits.

[0322] After the game is played, a data collection system activates, inputting feedback from the parent into the device and sending it to the server. This feedback is managed by a storage system as important data for system improvement.

[0323] For example, if a parent selects "wash your hands every night" on the device, the generated educational content will be a "handwashing song and animation," and they can send feedback evaluating the results.

[0324] Examples of prompt statements used for generative AI models include the following:

[0325] "Please provide elements for creating songs and animations that help children learn to wash their hands in a fun way."

[0326] "Please suggest visual content that is ideal for establishing a handwashing routine after dinner for 3-year-olds."

[0327] In this way, this invention provides a method for effectively shaping children's lifestyle habits while parents and children enjoy themselves together.

[0328] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0329] Step 1:

[0330] The user uses a device to input the daily habits they want their child to develop (e.g., "brushing teeth every morning") through an input interface. The input data is sent to the server in a digital format. The server then receives specific habit information tailored to the parent's needs.

[0331] Step 2:

[0332] The server uses received lifestyle data to generate playful and educational content using generation methods. Here, a generation AI model is used to generate prompts (e.g., "Please provide game elements to make brushing teeth fun for children"), and personalized content is devised based on these prompts. The generated content may include songs and animations.

[0333] Step 3:

[0334] The generated content is delivered by the server through a virtual store platform. Parents can access this information via their devices and download or stream the content. This allows parents to obtain habit-forming content suitable for their children.

[0335] Step 4:

[0336] The user uses the device's display to play the generated game together with the child. Here, the device presents content visually and aurally, guiding the play process. It is expected that the child will learn habits while having fun through the interactive content.

[0337] Step 5:

[0338] After a user completes a game, the device collects parental ratings and comments through a feedback input interface. This feedback data is sent to a server in digital format. The server receives the feedback and stores it in a database using a storage mechanism. This information is used to improve future content.

[0339] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0340] This invention incorporates an emotion engine that recognizes user emotions into an educational platform system designed to help children acquire good habits while having fun. This system generates games based on user-specified habits and adjusts the content using the emotion engine, thereby providing a more effective and enjoyable learning experience.

[0341] First, the user (parent) uses a device to specify the lifestyle habits they want their child to develop. The device converts this information into digital data and sends it to the server. The server uses a generation mechanism to generate games based on the received data. The user's emotional state, obtained from an emotion engine, is taken into consideration when generating the games. Specifically, a machine learning algorithm analyzes the user's voice and facial expressions to adjust the content and difficulty level of the games appropriately.

[0342] Next, the game information generated on the server is sent to the terminal. The terminal presents the generated game to the user through a user interface, and here too, the display content and interaction methods are optimized based on the analysis results of the emotion engine. This ensures that the game is tailored to the child's interests and emotions.

[0343] After execution, the user enters feedback on the child's reactions and the outcome of the play into the device. During this process, the emotion engine collects data in real time, recording changes in emotions during the play. The device sends the feedback and emotion data to a server, where it is stored in a database to help optimize future play sessions.

[0344] For example, when generating a game to help children develop the habit of "putting away toys," the emotional engine can determine whether the child is enjoying themselves or feeling bored, and adjust the game's tempo and reward system accordingly. This allows children to participate more actively, and parents can also benefit from a more satisfying learning experience.

[0345] In this way, this system analyzes and utilizes users' emotions to provide a personalized learning environment and supports children in naturally developing good habits.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The user uses their device to input the lifestyle habits they want their child to develop through the interface. Examples of habits they can select include "brushing teeth every morning" and "tidying up before bed."

[0349] Step 2:

[0350] The terminal digitizes the entered lifestyle data and sends it to the server. This transmitted data includes selected habits and any additional requests from the user.

[0351] Step 3:

[0352] The server receives data sent from the terminal and activates the emotion engine. The emotion engine collects sensor data necessary to analyze the user's voice and facial expressions.

[0353] Step 4:

[0354] The server uses analysis results from the emotion engine to determine the user's current emotional state. This result is then used as a factor in determining what kind of gameplay is optimal.

[0355] Step 5:

[0356] The server generates games using a generation mechanism. Machine learning algorithms are used to adjust the difficulty and content of the games according to the user's emotional state.

[0357] Step 6:

[0358] Detailed information about the generated game is sent from the server to the terminal. This information includes the rules of the game, the steps to be taken, and the recommended forms of interaction.

[0359] Step 7:

[0360] The device displays received game information through the user interface. Here too, the presentation method is customized to reflect the analysis of the emotion engine and maintain the user's interest.

[0361] Step 8:

[0362] The users (parent and child) actually play the suggested game. During the game, the emotion engine analyzes the users' reactions in real time and sends adjustment requests to the server as needed.

[0363] Step 9:

[0364] After the play session, the user inputs the child's reactions and their evaluation of the play on their device. The emotion engine then collects additional final emotion data to create comprehensive feedback.

[0365] Step 10:

[0366] The device sends user feedback and emotional data to the server. The server receives this data, stores it in a database, and uses it to optimize future game development.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] Traditionally, learning methods for developing good habits have faced the problem of children losing interest and not sticking with them. Furthermore, customization to individual children was difficult, resulting in ineffective learning. Additionally, the lack of systems to utilize feedback and children's emotional states in the learning process prevented the provision of effective learning experiences.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes input means for inputting habits specified by the parent, generation means for generating activities based on the input habits, and emotion analysis means for analyzing emotional states and adjusting activities. This makes it possible to provide an effective and enjoyable learning environment tailored to the child.

[0372] A "parent" is someone who uses this system to help their child develop good habits.

[0373] "Habits" are the behaviors and skills that children need to acquire in their daily lives.

[0374] An "input method" is a device or system used by parents to input habits they want their children to acquire into a system.

[0375] "Generative means" refers to technologies and methods for generating activities for children based on inputted habits.

[0376] An "activity" is a game or task generated by the system to help children learn habits in a fun way.

[0377] "Presentation methods" refer to devices or systems used to show the generated activity to parents and children.

[0378] "Emotional analysis methods" refer to techniques and methods for adjusting a child's activities by analyzing their emotional state.

[0379] "Means of collection" refers to devices or methods for collecting feedback from parents.

[0380] "Storage methods" refer to the technologies and methods for recording and storing collected feedback and analytical data.

[0381] This invention is an educational support system that helps parents instill good habits in their children in an enjoyable way. The system provides parents with the goal of instilling specific habits in their children through activities based on the child's interests and emotions.

[0382] First, the user (parent) uses a device to input specific habits they want their child to develop. This input is done using digital devices such as tablets and smartphones. The input information is processed by the device and converted into digital data. This data is then transmitted to a server via the internet.

[0383] The server designs activities using a generation mechanism based on the received digital data. This generation mechanism utilizes machine learning and artificial intelligence technologies, with a generation AI model at its core. An emotion analysis mechanism is incorporated, analyzing emotional data such as the user's (child's) voice and facial expressions in real time. Based on this analysis, the tempo and content of the activities are dynamically adjusted.

[0384] Information about the activities generated by the server is sent to the terminal and presented to the user (child) on the terminal. This uses a user interface that utilizes animation and sound effects, making it interactive and visually engaging.

[0385] After execution, the user (parent) can input responses regarding the activity's effectiveness and the child's reaction into the device. This collected data is then sent back to the server and stored in a database. This data will be used as reference for future activity design and generation processes.

[0386] For example, if you want to instill the habit of "putting away toys," you can input "putting away toys" into the device, and the server will automatically generate related activities. In this process, the system operates a generation AI model based on a prompt message such as "Lifestyle: Putting away toys. What kind of game will you create for the child to enjoy? Adjust the game's tempo and rewards based on the emotion engine," providing an interactive and fun activity.

[0387] This system allows children to naturally acquire good habits in a fun and efficient way.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The user uses their device to input specific habits they want their child to develop. In this process, the user selects habits such as "putting away toys."

[0391] The input is the habits chosen by the parents, and the output is this information converted into digital data format.

[0392] The terminal receives this digital data as input using a touchscreen or voice input system, and sends that data to the server. This transmission delivers habitual information to the server via the network.

[0393] Step 2:

[0394] The server analyzes the digital data received from the terminal and prepares to utilize a generative AI model for activity design.

[0395] The input is digital data of habits submitted by the user, and the output is an activity generation prompt statement.

[0396] The server activates the sentiment analysis system and references previously collected sentiment data. Based on this, it generates activity generation prompts and prepares to design the activity generation logic.

[0397] Step 3:

[0398] The server uses a generative AI model to design activities that will interest children, based on activity generation prompts.

[0399] The input consists of activity generation prompts and pre-collected sentiment data, while the output is a customized activity.

[0400] The server generates activities optimized for the user (child), including adjusting the difficulty level and designing the reward system. The generated activities are then sent to the device.

[0401] Step 4:

[0402] The device receives activity data generated from the server and presents it to the child through a user interface.

[0403] The input is the activity content sent from the server, and the output is the game or task presented to the child visually and aurally.

[0404] The device presents activities using animation and sound features. A real-time emotion analysis system monitors the child's responses and optimizes their interaction with the activity.

[0405] Step 5:

[0406] After the activity is completed, the user enters their thoughts and feedback on the child's reaction and the effects into the device.

[0407] The input is feedback information after the activity is completed, and the output is data that organizes this information and is used for future improvements.

[0408] The device uses collection methods to obtain feedback and sends that data to the server. This feedback is stored on the server and used as reference information when generating the next activity.

[0409] (Application Example 2)

[0410] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0411] In modern retail settings, providing children with learning opportunities to naturally develop good habits is not easy. In particular, engaging children's interests while addressing their individual emotional states is a challenge within limited timeframes. Traditional methods have struggled to analyze children's emotions in real time and dynamically adjust play activities accordingly.

[0412] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0413] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and adjustment means for analyzing the user's emotions using emotion recognition technology and dynamically adjusting the presented content based on the results. This makes it possible to provide an interactive learning experience that effectively helps children acquire habits while maintaining their interest, according to their individual emotional state.

[0414] "Input means" refers to a device or method for inputting information about lifestyle habits specified by the parent.

[0415] "Generative means" refers to a device or method for designing play based on inputted lifestyle habits.

[0416] "Presentation means" refers to a device or method for presenting the generated play to parents and children visually or aurally.

[0417] "Collection means" refers to a device or method for collecting feedback from parents after a play session has taken place.

[0418] "Storage means" refers to an apparatus or method for recording and storing collected feedback information.

[0419] "Emotion recognition technology" is a technology that analyzes voice and facial expression data in order to analyze the user's emotions.

[0420] "Adjustment means" refers to a device or method for dynamically changing the presented content based on the analysis results obtained by emotion recognition technology.

[0421] This invention is a system that helps children learn good habits in an enjoyable way. Based on the habits specified by the parents, this system generates games for children and dynamically adjusts the content and difficulty level of the games according to the child's emotional state, thereby improving learning efficiency.

[0422] The system comprises a terminal, a server, and emotion recognition technology. The terminal provides an interface for inputting lifestyle habits specified by the parent, and transmits this information to the server as digital data. The server has a generation mechanism, which generates games using machine learning algorithms and performs data analysis using emotion recognition technology.

[0423] To analyze voice and facial expression data, the device uses its camera and microphone to capture the child's reactions. This analysis is performed using AWS Rekognition and Google Cloud Vision APIs, and the resulting emotional data is sent to a server. The server then adjusts the generated play content based on this data to provide a learning experience tailored to the child's individual emotional state.

[0424] For example, if a child is playing a game themed around "tidying up toys," the system uses emotion recognition technology to determine whether the child is having fun or is bored. If the child is having fun, the system can speed up the game's pace and implement a reward system, such as giving virtual badges to enhance their sense of accomplishment.

[0425] An example of a prompt message would be: "Analyze this child's emotions from their facial expressions and voice. If they are currently enjoying themselves, set the difficulty level for the next game stage and award a virtual badge as a reward upon completion." This message is then input into a generating AI model to optimize the game's output.

[0426] Overall, this system is designed to naturally enhance children's motivation to learn and provide a highly satisfying educational environment for parents.

[0427] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0428] Step 1:

[0429] The user (parent) uses a terminal to specify lifestyle habits. The terminal converts these specifications into digital data and sends it to the server. The input is data related to the specified lifestyle habits, and the output is the digital data sent to the server.

[0430] Step 2:

[0431] The server analyzes the received digital data and begins designing the game using generation tools. Here, the game's theme and story, corresponding to daily life habits, are determined. The input is digital data, and the output is a generated prototype of the game.

[0432] Step 3:

[0433] The device's camera and microphone are used to capture the user's (child's) voice and facial expressions in real time. This data is then analyzed using emotion recognition technology. The input is the child's voice and visual data, and the output is the analyzed emotion data.

[0434] Step 4:

[0435] The server dynamically adjusts the game content and difficulty level based on the analyzed emotional data. This provides an optimal learning experience tailored to the user's emotional state. The input is the analyzed emotional data, and the output is the adjusted game content.

[0436] Step 5:

[0437] The adjusted gameplay is presented to the user (child) via the device. Interactive elements and reward systems of the game are implemented here. The input is the adjusted gameplay, and the output is visual and auditory feedback to the child.

[0438] Step 6:

[0439] After the game ends, the user (parent) enters feedback into the device, and this data is sent to the server. The server then uses this feedback to store it in a database for future game design. The input is the feedback information from the parent, and the output is the stored feedback data.

[0440] Step 7:

[0441] Based on the saved data, the server uses a generative AI model to generate prompts to make the next gameplay more effective. The input is the saved feedback data, and the output is the prompts for the generative AI model.

[0442] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0443] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0444] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0445] [Third Embodiment]

[0446] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0447] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0448] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0449] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0450] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0451] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0452] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0453] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0454] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0455] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0456] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0457] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0458] This invention relates to an educational platform system that enables children to acquire specific life habits while having fun. The program processing and specific examples of this system are shown below.

[0459] First, the user (parent) uses a specific application on the device to input the daily habits they want their child to develop. For example, they can select habits such as "brushing teeth every morning" or "putting away toys." The device prepares this information as digital data and sends it to the server.

[0460] Upon receiving this input data, the server generates games using predetermined generation methods. Specifically, it utilizes machine learning models to devise creative and educational games related to the input lifestyle habits. For example, games such as "a game where you sing while brushing your teeth to make brushing time more enjoyable" or "a challenge to put toys away in a box within a time limit" are generated.

[0461] Next, the generated game information is sent from the server to the terminal. The terminal receives this information and presents it to the user through the user interface. Parents can use this information to play games with their children.

[0462] After completing a game, the user enters feedback about the results into a terminal. The terminal sends this feedback to a server, which stores it in a database using a storage mechanism. This data is used to create future games and improve the system.

[0463] Thus, the system has a process that generates play activities tailored to the lifestyle habits specified by the parents and provides information to carry them out. As a result, children can learn habits while having fun through play, and parent-child time becomes more fulfilling.

[0464] The following describes the processing flow.

[0465] Step 1:

[0466] The user launches the application on their device and selects or enters the lifestyle habits they want their child to develop. Specifically, they can choose from a list of lifestyle habits provided within the app, or enter new, specific habits in text format.

[0467] Step 2:

[0468] The device converts the user's entered lifestyle data into a digital format. This data includes details of selected lifestyle habits and any additional requests the user may have made.

[0469] Step 3:

[0470] The device transmits digitally converted lifestyle data to the server via an appropriate communication protocol. This transmission includes error checking to verify the accuracy and reliability of the data.

[0471] Step 4:

[0472] The server receives lifestyle data sent from the terminal. The received data is analyzed and used as parameters to determine what category of play to generate.

[0473] Step 5:

[0474] The server uses a machine learning model as a generation tool to generate games based on the received parameters. In doing so, it considers previously accumulated data and feedback to suggest more effective gameplay.

[0475] Step 6:

[0476] The generated game data is sent from the server to the terminal. This data includes specific game rules, execution procedures, and a list of necessary items.

[0477] Step 7:

[0478] The terminal displays play information received from the server to the user through a user interface. The user can then view this information to see how to conduct the play session with their child.

[0479] Step 8:

[0480] The user performs the suggested game with the child. After the activity, they input feedback into the application about the results of the game and the child's reactions.

[0481] Step 9:

[0482] The device collects feedback data entered by the user and sends it to the server. This data is stored in a database for later analysis and to improve the quality of the gameplay.

[0483] Step 10:

[0484] The server stores the received feedback data and analyzes it to help generate future gameplay. The analysis results are used for continuous improvement of the system.

[0485] (Example 1)

[0486] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] It is difficult for children to develop good habits on their own, and it is not easy for parents to support them in establishing these habits. In particular, it is necessary to devise ways to help children develop daily habits as part of their growth process while having fun, and there is a need for an educational platform that allows parents and children to spend enjoyable and meaningful time together.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0489] In this invention, the server includes an information input means for specifying lifestyle habits, a generation algorithm for generating games based on the input information, and an information presentation means for presenting the generated games to the parent and child. This allows parents to provide their children with a fun learning environment and to help them naturally acquire lifestyle habits through play.

[0490] "Lifestyle habits" refer to actions and routines that are repeatedly performed in daily life.

[0491] "Information input means" refers to a method or device for a user to input data, which is done via software on a terminal.

[0492] A "generative algorithm" is a procedure or system that uses a machine learning model to devise new games based on input data.

[0493] "Information presentation means" refers to devices or methods for visually or audibly communicating the content and procedures of a generated game to parents and children.

[0494] "Data collection means" refers to systems and devices used to collect information from users, particularly feedback.

[0495] "Information storage means" refers to a method or device for securely recording collected data and preparing it for future use.

[0496] A "visualization device" refers to equipment used to display information visually, such as displays and monitors.

[0497] This invention relates to an educational platform system that helps children develop lifestyle habits specified by their parents in an enjoyable way. The system consists of users, terminals, and a server, each working together to achieve its functions.

[0498] The user (parent) first specifies daily routines using a specific application on the device. These routines include, for example, "brushing teeth every morning" and "putting away toys." The device processes the entered information and sends it to the server as digital data. This data is transmitted securely using the HTTP or HTTPS protocol.

[0499] The server uses a generative AI model based on the received data to generate new games. Specifically, it leverages machine learning algorithms to design creative games based on data. This process generates creative ideas related to the inputted daily habits. For example, games such as "a game where you sing a song while brushing your teeth" or "a challenge to put away toys within a time limit" are devised.

[0500] The generated game information is sent from the server to the terminal, which then displays it to the user through a visualization device. This allows parents to review the details and steps of the generated game and participate with their children. The system incorporates elements that capture children's interest, making habit-forming learning enjoyable.

[0501] After completing a game, the user enters feedback about the results into a terminal, which then sends this feedback to the server. The server stores the feedback in a database and uses it to generate future games and improve the system. Through this process, the system continuously evolves, enabling it to provide games that are more adapted to user needs.

[0502] A concrete example of a prompt is, "Generate a game to encourage children to develop a 'cleaning habit' in a fun way." Based on this prompt, the system generates a relevant game.

[0503] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0504] Step 1:

[0505] The user uses a device to access an educational application and inputs the lifestyle habits they want their child to develop. This input concerns specific habits such as "brushing teeth every morning" or "putting away toys." The device converts this information into digital data and prepares it for the next processing step. The input data includes the selected habit name and related conditions. Formatted digital data is generated as output.

[0506] Step 2:

[0507] The terminal sends the formatted digital data to the server. Data security is ensured through the HTTP or HTTPS protocol. Once data transmission is complete, server-side processing can begin. The input is the digital data sent from the terminal, and the output is the server's confirmation of receipt.

[0508] Step 3:

[0509] The server generates new games using a generative AI model based on the received data. Specifically, a machine learning algorithm within the server analyzes the input data and creates the optimal game. This algorithm also takes into account the results of analyzing past feedback data. The input is lifestyle data received by the server, and the output is the specifications of the generated game.

[0510] Step 4:

[0511] The server repackages the generated game specifications as data and sends it to the terminal. Upon receiving this information, the terminal prepares to present it to the parent again through the user interface. The input is the game specifications sent from the server, and the output is the terminal's confirmation of receipt.

[0512] Step 5:

[0513] The device presents the game to the parent in presentation mode through a visualization device. Specifically, the steps and methods of participation in the game are explained to the parent and child in an easy-to-understand manner using the screen and audio. The input is the game specifications, and the output is the presented game information.

[0514] Step 6:

[0515] After playing, the user inputs feedback on the results into the device. This feedback includes how much the child enjoyed it and areas for improvement. The device converts this information into digital data and prepares it for the next processing step. The input is the user's feedback, and the output is the formatted feedback data.

[0516] Step 7:

[0517] The terminal sends feedback data to the server. The server stores this information in a database and uses it to generate future gameplay and improve the system. The input is the feedback data sent from the terminal, and the output is the server's confirmation of data storage.

[0518] (Application Example 1)

[0519] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0520] There is a need for educational methods that allow children to naturally acquire specific lifestyle habits while having fun. However, traditional methods make it difficult for parents to provide appropriate play for their children, and there is a lack of effective learning support utilizing virtual environments.

[0521] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0522] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and provision means for providing the generated content on a virtual store platform. This makes it possible for parents to easily obtain learning content suitable for their children, and for children to develop habits while remaining interested.

[0523] An "input method for entering parent-specified lifestyle habits" is a device that allows parents to input specific habit information they want their children to acquire in a digital format.

[0524] A "generation method for generating games based on inputted lifestyle habits" is a mechanism that automatically devises and generates games that will attract children's interest based on the received data on lifestyle habits.

[0525] A "presentation method for showing generated games to parents and children" is a device that displays the details of the generated game and the steps for performing it in an easy-to-understand manner for parents and children.

[0526] A "collection method for gathering feedback from parents after the activity" is a device that collects feedback information such as evaluations and impressions from parents as a result of their children engaging in play.

[0527] A "means of storing feedback" refers to a memory device that effectively stores collected feedback data for later use.

[0528] "A means of providing content generated on a virtual store platform" refers to a mechanism that makes generated learning content accessible and usable by parents and children in a virtual environment.

[0529] The educational platform system implementing this invention is designed to enable parents to efficiently generate and implement play activities for their children's daily routines. It utilizes user devices such as smartphones and tablets, and a server connected to the internet.

[0530] The server operates by integrating the following means:

[0531] First, as an input method, parents can input lifestyle habits they want their children to develop (e.g., "brushing teeth every morning") through the user interface. This information is then transmitted to the server in digital format.

[0532] Next, the server uses a generative AI model based on the input lifestyle habits to generate relevant play and educational content. In this process, machine learning techniques are used to personalize the content, for example, by creating an animated song to make brushing teeth more fun.

[0533] The delivery method allows parents to easily access the generated content on the virtual store platform. This process helps parents use the content at the right time and supports their children in developing good habits while having fun.

[0534] Furthermore, through the presentation method, this content is visually presented to parents and children via the device's display. Based on this information, parents can incorporate it into their daily activities, thereby helping their children acquire good lifestyle habits.

[0535] After the game is played, a data collection system activates, inputting feedback from the parent into the device and sending it to the server. This feedback is managed by a storage system as important data for system improvement.

[0536] For example, if a parent selects "wash your hands every night" on the device, the generated educational content will be a "handwashing song and animation," and they can send feedback evaluating the results.

[0537] Examples of prompt statements used for generative AI models include the following:

[0538] "Please provide elements for creating songs and animations that help children learn to wash their hands in a fun way."

[0539] "Please suggest visual content that is ideal for establishing a handwashing routine after dinner for 3-year-olds."

[0540] In this way, this invention provides a method for effectively shaping children's lifestyle habits while parents and children enjoy themselves together.

[0541] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0542] Step 1:

[0543] The user uses a device to input the daily habits they want their child to develop (e.g., "brushing teeth every morning") through an input interface. The input data is sent to the server in a digital format. The server then receives specific habit information tailored to the parent's needs.

[0544] Step 2:

[0545] The server uses received lifestyle data to generate playful and educational content using generation methods. Here, a generation AI model is used to generate prompts (e.g., "Please provide game elements to make brushing teeth fun for children"), and personalized content is devised based on these prompts. The generated content may include songs and animations.

[0546] Step 3:

[0547] The generated content is delivered by the server through a virtual store platform. Parents can access this information via their devices and download or stream the content. This allows parents to obtain habit-forming content suitable for their children.

[0548] Step 4:

[0549] The user uses the device's display to play the generated game together with the child. Here, the device presents content visually and aurally, guiding the play process. It is expected that the child will learn habits while having fun through the interactive content.

[0550] Step 5:

[0551] After a user completes a game, the device collects parental ratings and comments through a feedback input interface. This feedback data is sent to a server in digital format. The server receives the feedback and stores it in a database using a storage mechanism. This information is used to improve future content.

[0552] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0553] This invention incorporates an emotion engine that recognizes user emotions into an educational platform system designed to help children acquire good habits while having fun. This system generates games based on user-specified habits and adjusts the content using the emotion engine, thereby providing a more effective and enjoyable learning experience.

[0554] First, the user (parent) uses a device to specify the lifestyle habits they want their child to develop. The device converts this information into digital data and sends it to the server. The server uses a generation mechanism to generate games based on the received data. The user's emotional state, obtained from an emotion engine, is taken into consideration when generating the games. Specifically, a machine learning algorithm analyzes the user's voice and facial expressions to adjust the content and difficulty level of the games appropriately.

[0555] Next, the game information generated on the server is sent to the terminal. The terminal presents the generated game to the user through a user interface, and here too, the display content and interaction methods are optimized based on the analysis results of the emotion engine. This ensures that the game is tailored to the child's interests and emotions.

[0556] After execution, the user enters feedback on the child's reactions and the outcome of the play into the device. During this process, the emotion engine collects data in real time, recording changes in emotions during the play. The device sends the feedback and emotion data to a server, where it is stored in a database to help optimize future play sessions.

[0557] For example, when generating a game to help children develop the habit of "putting away toys," the emotional engine can determine whether the child is enjoying themselves or feeling bored, and adjust the game's tempo and reward system accordingly. This allows children to participate more actively, and parents can also benefit from a more satisfying learning experience.

[0558] In this way, this system analyzes and utilizes users' emotions to provide a personalized learning environment and supports children in naturally developing good habits.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The user uses their device to input the lifestyle habits they want their child to develop through the interface. Examples of habits they can select include "brushing teeth every morning" and "tidying up before bed."

[0562] Step 2:

[0563] The terminal digitizes the entered lifestyle data and sends it to the server. This transmitted data includes selected habits and any additional requests from the user.

[0564] Step 3:

[0565] The server receives data sent from the terminal and activates the emotion engine. The emotion engine collects sensor data necessary to analyze the user's voice and facial expressions.

[0566] Step 4:

[0567] The server uses analysis results from the emotion engine to determine the user's current emotional state. This result is then used as a factor in determining what kind of gameplay is optimal.

[0568] Step 5:

[0569] The server generates games using a generation mechanism. Machine learning algorithms are used to adjust the difficulty and content of the games according to the user's emotional state.

[0570] Step 6:

[0571] Detailed information about the generated game is sent from the server to the terminal. This information includes the rules of the game, the steps to be taken, and the recommended forms of interaction.

[0572] Step 7:

[0573] The device displays received game information through the user interface. Here too, the presentation method is customized to reflect the analysis of the emotion engine and maintain the user's interest.

[0574] Step 8:

[0575] The users (parent and child) actually play the suggested game. During the game, the emotion engine analyzes the users' reactions in real time and sends adjustment requests to the server as needed.

[0576] Step 9:

[0577] After the play session, the user inputs the child's reactions and their evaluation of the play on their device. The emotion engine then collects additional final emotion data to create comprehensive feedback.

[0578] Step 10:

[0579] The device sends user feedback and emotional data to the server. The server receives this data, stores it in a database, and uses it to optimize future game development.

[0580] (Example 2)

[0581] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] Traditionally, learning methods for developing good habits have faced the problem of children losing interest and not sticking with them. Furthermore, customization to individual children was difficult, resulting in ineffective learning. Additionally, the lack of systems to utilize feedback and children's emotional states in the learning process prevented the provision of effective learning experiences.

[0583] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0584] In this invention, the server includes input means for inputting habits specified by the parent, generation means for generating activities based on the input habits, and emotion analysis means for analyzing emotional states and adjusting activities. This makes it possible to provide an effective and enjoyable learning environment tailored to the child.

[0585] A "parent" is someone who uses this system to help their child develop good habits.

[0586] "Habits" are the behaviors and skills that children need to acquire in their daily lives.

[0587] An "input method" is a device or system used by parents to input habits they want their children to acquire into a system.

[0588] "Generative means" refers to technologies and methods for generating activities for children based on inputted habits.

[0589] An "activity" is a game or task generated by the system to help children learn habits in a fun way.

[0590] "Presentation methods" refer to devices or systems used to show the generated activity to parents and children.

[0591] "Emotional analysis methods" refer to techniques and methods for adjusting a child's activities by analyzing their emotional state.

[0592] "Means of collection" refers to devices or methods for collecting feedback from parents.

[0593] "Storage methods" refer to the technologies and methods for recording and storing collected feedback and analytical data.

[0594] This invention is an educational support system that helps parents instill good habits in their children in an enjoyable way. The system provides parents with the goal of instilling specific habits in their children through activities based on the child's interests and emotions.

[0595] First, the user (parent) uses a device to input specific habits they want their child to develop. This input is done using digital devices such as tablets and smartphones. The input information is processed by the device and converted into digital data. This data is then transmitted to a server via the internet.

[0596] The server designs activities using a generation mechanism based on the received digital data. This generation mechanism utilizes machine learning and artificial intelligence technologies, with a generation AI model at its core. An emotion analysis mechanism is incorporated, analyzing emotional data such as the user's (child's) voice and facial expressions in real time. Based on this analysis, the tempo and content of the activities are dynamically adjusted.

[0597] Information about the activities generated by the server is sent to the terminal and presented to the user (child) on the terminal. This uses a user interface that utilizes animation and sound effects, making it interactive and visually engaging.

[0598] After execution, the user (parent) can input responses regarding the activity's effectiveness and the child's reaction into the device. This collected data is then sent back to the server and stored in a database. This data will be used as reference for future activity design and generation processes.

[0599] For example, if you want to instill the habit of "putting away toys," you can input "putting away toys" into the device, and the server will automatically generate related activities. In this process, the system operates a generation AI model based on a prompt message such as "Lifestyle: Putting away toys. What kind of game will you create for the child to enjoy? Adjust the game's tempo and rewards based on the emotion engine," providing an interactive and fun activity.

[0600] This system allows children to naturally acquire good habits in a fun and efficient way.

[0601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0602] Step 1:

[0603] The user uses their device to input specific habits they want their child to develop. In this process, the user selects habits such as "putting away toys."

[0604] The input is the habits chosen by the parents, and the output is this information converted into digital data format.

[0605] The terminal receives this digital data as input using a touchscreen or voice input system, and sends that data to the server. This transmission delivers habitual information to the server via the network.

[0606] Step 2:

[0607] The server analyzes the digital data received from the terminal and prepares to utilize a generative AI model for activity design.

[0608] The input is digital data of habits submitted by the user, and the output is an activity generation prompt statement.

[0609] The server activates the sentiment analysis system and references previously collected sentiment data. Based on this, it generates activity generation prompts and prepares to design the activity generation logic.

[0610] Step 3:

[0611] The server uses a generative AI model to design activities that will interest children, based on activity generation prompts.

[0612] The input consists of activity generation prompts and pre-collected sentiment data, while the output is a customized activity.

[0613] The server generates activities optimized for the user (child), including adjusting the difficulty level and designing the reward system. The generated activities are then sent to the device.

[0614] Step 4:

[0615] The device receives activity data generated from the server and presents it to the child through a user interface.

[0616] The input is the activity content sent from the server, and the output is the game or task presented to the child visually and aurally.

[0617] The device presents activities using animation and sound features. A real-time emotion analysis system monitors the child's responses and optimizes their interaction with the activity.

[0618] Step 5:

[0619] After the activity is completed, the user enters their thoughts and feedback on the child's reaction and the effects into the device.

[0620] The input is feedback information after the activity is completed, and the output is data that organizes this information and is used for future improvements.

[0621] The device uses collection methods to obtain feedback and sends that data to the server. This feedback is stored on the server and used as reference information when generating the next activity.

[0622] (Application Example 2)

[0623] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0624] In modern retail settings, providing children with learning opportunities to naturally develop good habits is not easy. In particular, engaging children's interests while addressing their individual emotional states is a challenge within limited timeframes. Traditional methods have struggled to analyze children's emotions in real time and dynamically adjust play activities accordingly.

[0625] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0626] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and adjustment means for analyzing the user's emotions using emotion recognition technology and dynamically adjusting the presented content based on the results. This makes it possible to provide an interactive learning experience that effectively helps children acquire habits while maintaining their interest, according to their individual emotional state.

[0627] "Input means" refers to a device or method for inputting information about lifestyle habits specified by the parent.

[0628] "Generative means" refers to a device or method for designing play based on inputted lifestyle habits.

[0629] "Presentation means" refers to a device or method for presenting the generated play to parents and children visually or aurally.

[0630] "Collection means" refers to a device or method for collecting feedback from parents after a play session has taken place.

[0631] "Storage means" refers to an apparatus or method for recording and storing collected feedback information.

[0632] "Emotion recognition technology" is a technology that analyzes voice and facial expression data in order to analyze the user's emotions.

[0633] "Adjustment means" refers to a device or method for dynamically changing the presented content based on the analysis results obtained by emotion recognition technology.

[0634] This invention is a system that helps children learn good habits in an enjoyable way. Based on the habits specified by the parents, this system generates games for children and dynamically adjusts the content and difficulty level of the games according to the child's emotional state, thereby improving learning efficiency.

[0635] The system comprises a terminal, a server, and emotion recognition technology. The terminal provides an interface for inputting lifestyle habits specified by the parent, and transmits this information to the server as digital data. The server has a generation mechanism, which generates games using machine learning algorithms and performs data analysis using emotion recognition technology.

[0636] To analyze voice and facial expression data, the device uses its camera and microphone to capture the child's reactions. This analysis is performed using AWS Rekognition and Google Cloud Vision APIs, and the resulting emotional data is sent to a server. The server then adjusts the generated play content based on this data to provide a learning experience tailored to the child's individual emotional state.

[0637] For example, if a child is playing a game themed around "tidying up toys," the system uses emotion recognition technology to determine whether the child is having fun or is bored. If the child is having fun, the system can speed up the game's pace and implement a reward system, such as giving virtual badges to enhance their sense of accomplishment.

[0638] An example of a prompt message would be: "Analyze this child's emotions from their facial expressions and voice. If they are currently enjoying themselves, set the difficulty level for the next game stage and award a virtual badge as a reward upon completion." This message is then input into a generating AI model to optimize the game's output.

[0639] Overall, this system is designed to naturally enhance children's motivation to learn and provide a highly satisfying educational environment for parents.

[0640] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0641] Step 1:

[0642] The user (parent) uses a terminal to specify lifestyle habits. The terminal converts these specifications into digital data and sends it to the server. The input is data related to the specified lifestyle habits, and the output is the digital data sent to the server.

[0643] Step 2:

[0644] The server analyzes the received digital data and begins designing the game using generation tools. Here, the game's theme and story, corresponding to daily life habits, are determined. The input is digital data, and the output is a generated prototype of the game.

[0645] Step 3:

[0646] The device's camera and microphone are used to capture the user's (child's) voice and facial expressions in real time. This data is then analyzed using emotion recognition technology. The input is the child's voice and visual data, and the output is the analyzed emotion data.

[0647] Step 4:

[0648] The server dynamically adjusts the game content and difficulty level based on the analyzed emotional data. This provides an optimal learning experience tailored to the user's emotional state. The input is the analyzed emotional data, and the output is the adjusted game content.

[0649] Step 5:

[0650] The adjusted gameplay is presented to the user (child) via the device. Interactive elements and reward systems of the game are implemented here. The input is the adjusted gameplay, and the output is visual and auditory feedback to the child.

[0651] Step 6:

[0652] After the game ends, the user (parent) enters feedback into the device, and this data is sent to the server. The server then uses this feedback to store it in a database for future game design. The input is the feedback information from the parent, and the output is the stored feedback data.

[0653] Step 7:

[0654] Based on the saved data, the server uses a generative AI model to generate prompts to make the next gameplay more effective. The input is the saved feedback data, and the output is the prompts for the generative AI model.

[0655] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0656] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0657] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0661] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0662] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0663] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0664] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0665] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0666] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0667] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0668] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0671] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0672] This invention relates to an educational platform system that enables children to acquire specific life habits while having fun. The program processing and specific examples of this system are shown below.

[0673] First, the user (parent) uses a specific application on the device to input the daily habits they want their child to develop. For example, they can select habits such as "brushing teeth every morning" or "putting away toys." The device prepares this information as digital data and sends it to the server.

[0674] Upon receiving this input data, the server generates games using predetermined generation methods. Specifically, it utilizes machine learning models to devise creative and educational games related to the input lifestyle habits. For example, games such as "a game where you sing while brushing your teeth to make brushing time more enjoyable" or "a challenge to put toys away in a box within a time limit" are generated.

[0675] Next, the generated game information is sent from the server to the terminal. The terminal receives this information and presents it to the user through the user interface. Parents can use this information to play games with their children.

[0676] After completing a game, the user enters feedback about the results into a terminal. The terminal sends this feedback to a server, which stores it in a database using a storage mechanism. This data is used to create future games and improve the system.

[0677] Thus, the system has a process that generates play activities tailored to the lifestyle habits specified by the parents and provides information to carry them out. As a result, children can learn habits while having fun through play, and parent-child time becomes more fulfilling.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The user launches the application on their device and selects or enters the lifestyle habits they want their child to develop. Specifically, they can choose from a list of lifestyle habits provided within the app, or enter new, specific habits in text format.

[0681] Step 2:

[0682] The device converts the user's entered lifestyle data into a digital format. This data includes details of selected lifestyle habits and any additional requests the user may have made.

[0683] Step 3:

[0684] The device transmits digitally converted lifestyle data to the server via an appropriate communication protocol. This transmission includes error checking to verify the accuracy and reliability of the data.

[0685] Step 4:

[0686] The server receives lifestyle data sent from the terminal. The received data is analyzed and used as parameters to determine what category of play to generate.

[0687] Step 5:

[0688] The server uses a machine learning model as a generation tool to generate games based on the received parameters. In doing so, it considers previously accumulated data and feedback to suggest more effective gameplay.

[0689] Step 6:

[0690] The generated game data is sent from the server to the terminal. This data includes specific game rules, execution procedures, and a list of necessary items.

[0691] Step 7:

[0692] The terminal displays play information received from the server to the user through a user interface. The user can then view this information to see how to conduct the play session with their child.

[0693] Step 8:

[0694] The user performs the suggested game with the child. After the activity, they input feedback into the application about the results of the game and the child's reactions.

[0695] Step 9:

[0696] The device collects feedback data entered by the user and sends it to the server. This data is stored in a database for later analysis and to improve the quality of the gameplay.

[0697] Step 10:

[0698] The server stores the received feedback data and analyzes it to help generate future gameplay. The analysis results are used for continuous improvement of the system.

[0699] (Example 1)

[0700] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] It is difficult for children to develop good habits on their own, and it is not easy for parents to support them in establishing these habits. In particular, it is necessary to devise ways to help children develop daily habits as part of their growth process while having fun, and there is a need for an educational platform that allows parents and children to spend enjoyable and meaningful time together.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0703] In this invention, the server includes an information input means for specifying lifestyle habits, a generation algorithm for generating games based on the input information, and an information presentation means for presenting the generated games to the parent and child. This allows parents to provide their children with a fun learning environment and to help them naturally acquire lifestyle habits through play.

[0704] "Lifestyle habits" refer to actions and routines that are repeatedly performed in daily life.

[0705] "Information input means" refers to a method or device for a user to input data, which is done via software on a terminal.

[0706] A "generative algorithm" is a procedure or system that uses a machine learning model to devise new games based on input data.

[0707] "Information presentation means" refers to devices or methods for visually or audibly communicating the content and procedures of a generated game to parents and children.

[0708] "Data collection means" refers to systems and devices used to collect information from users, particularly feedback.

[0709] "Information storage means" refers to a method or device for securely recording collected data and preparing it for future use.

[0710] A "visualization device" refers to equipment used to display information visually, such as displays and monitors.

[0711] This invention relates to an educational platform system that helps children develop lifestyle habits specified by their parents in an enjoyable way. The system consists of users, terminals, and a server, each working together to achieve its functions.

[0712] The user (parent) first specifies daily routines using a specific application on the device. These routines include, for example, "brushing teeth every morning" and "putting away toys." The device processes the entered information and sends it to the server as digital data. This data is transmitted securely using the HTTP or HTTPS protocol.

[0713] The server uses a generative AI model based on the received data to generate new games. Specifically, it leverages machine learning algorithms to design creative games based on data. This process generates creative ideas related to the inputted daily habits. For example, games such as "a game where you sing a song while brushing your teeth" or "a challenge to put away toys within a time limit" are devised.

[0714] The generated game information is sent from the server to the terminal, which then displays it to the user through a visualization device. This allows parents to review the details and steps of the generated game and participate with their children. The system incorporates elements that capture children's interest, making habit-forming learning enjoyable.

[0715] After completing a game, the user enters feedback about the results into a terminal, which then sends this feedback to the server. The server stores the feedback in a database and uses it to generate future games and improve the system. Through this process, the system continuously evolves, enabling it to provide games that are more adapted to user needs.

[0716] A concrete example of a prompt is, "Generate a game to encourage children to develop a 'cleaning habit' in a fun way." Based on this prompt, the system generates a relevant game.

[0717] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0718] Step 1:

[0719] The user uses a device to access an educational application and inputs the lifestyle habits they want their child to develop. This input concerns specific habits such as "brushing teeth every morning" or "putting away toys." The device converts this information into digital data and prepares it for the next processing step. The input data includes the selected habit name and related conditions. Formatted digital data is generated as output.

[0720] Step 2:

[0721] The terminal sends the formatted digital data to the server. Data security is ensured through the HTTP or HTTPS protocol. Once data transmission is complete, server-side processing can begin. The input is the digital data sent from the terminal, and the output is the server's confirmation of receipt.

[0722] Step 3:

[0723] The server generates new games using a generative AI model based on the received data. Specifically, a machine learning algorithm within the server analyzes the input data and creates the optimal game. This algorithm also takes into account the results of analyzing past feedback data. The input is lifestyle data received by the server, and the output is the specifications of the generated game.

[0724] Step 4:

[0725] The server repackages the generated game specifications as data and sends it to the terminal. Upon receiving this information, the terminal prepares to present it to the parent again through the user interface. The input is the game specifications sent from the server, and the output is the terminal's confirmation of receipt.

[0726] Step 5:

[0727] The device presents the game to the parent in presentation mode through a visualization device. Specifically, the steps and methods of participation in the game are explained to the parent and child in an easy-to-understand manner using the screen and audio. The input is the game specifications, and the output is the presented game information.

[0728] Step 6:

[0729] After playing, the user inputs feedback on the results into the device. This feedback includes how much the child enjoyed it and areas for improvement. The device converts this information into digital data and prepares it for the next processing step. The input is the user's feedback, and the output is the formatted feedback data.

[0730] Step 7:

[0731] The terminal sends feedback data to the server. The server stores this information in a database and uses it to generate future gameplay and improve the system. The input is the feedback data sent from the terminal, and the output is the server's confirmation of data storage.

[0732] (Application Example 1)

[0733] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0734] There is a need for educational methods that allow children to naturally acquire specific lifestyle habits while having fun. However, traditional methods make it difficult for parents to provide appropriate play for their children, and there is a lack of effective learning support utilizing virtual environments.

[0735] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0736] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and provision means for providing the generated content on a virtual store platform. This makes it possible for parents to easily obtain learning content suitable for their children, and for children to develop habits while remaining interested.

[0737] An "input method for entering parent-specified lifestyle habits" is a device that allows parents to input specific habit information they want their children to acquire in a digital format.

[0738] A "generation method for generating games based on inputted lifestyle habits" is a mechanism that automatically devises and generates games that will attract children's interest based on the received data on lifestyle habits.

[0739] A "presentation method for showing generated games to parents and children" is a device that displays the details of the generated game and the steps for performing it in an easy-to-understand manner for parents and children.

[0740] A "collection method for gathering feedback from parents after the activity" is a device that collects feedback information such as evaluations and impressions from parents as a result of their children engaging in play.

[0741] A "means of storing feedback" refers to a memory device that effectively stores collected feedback data for later use.

[0742] "A means of providing content generated on a virtual store platform" refers to a mechanism that makes generated learning content accessible and usable by parents and children in a virtual environment.

[0743] The educational platform system implementing this invention is designed to enable parents to efficiently generate and implement play activities for their children's daily routines. It utilizes user devices such as smartphones and tablets, and a server connected to the internet.

[0744] The server operates by integrating the following means:

[0745] First, as an input method, parents can input lifestyle habits they want their children to develop (e.g., "brushing teeth every morning") through the user interface. This information is then transmitted to the server in digital format.

[0746] Next, the server uses a generative AI model based on the input lifestyle habits to generate relevant play and educational content. In this process, machine learning techniques are used to personalize the content, for example, by creating an animated song to make brushing teeth more fun.

[0747] The delivery method allows parents to easily access the generated content on the virtual store platform. This process helps parents use the content at the right time and supports their children in developing good habits while having fun.

[0748] Furthermore, through the presentation method, this content is visually presented to parents and children via the device's display. Based on this information, parents can incorporate it into their daily activities, thereby helping their children acquire good lifestyle habits.

[0749] After the game is played, a data collection system activates, inputting feedback from the parent into the device and sending it to the server. This feedback is managed by a storage system as important data for system improvement.

[0750] For example, if a parent selects "wash your hands every night" on the device, the generated educational content will be a "handwashing song and animation," and they can send feedback evaluating the results.

[0751] Examples of prompt statements used for generative AI models include the following:

[0752] "Please provide elements for creating songs and animations that help children learn to wash their hands in a fun way."

[0753] "Please suggest visual content that is ideal for establishing a handwashing routine after dinner for 3-year-olds."

[0754] In this way, this invention provides a method for effectively shaping children's lifestyle habits while parents and children enjoy themselves together.

[0755] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0756] Step 1:

[0757] The user uses a device to input the daily habits they want their child to develop (e.g., "brushing teeth every morning") through an input interface. The input data is sent to the server in a digital format. The server then receives specific habit information tailored to the parent's needs.

[0758] Step 2:

[0759] The server uses received lifestyle data to generate playful and educational content using generation methods. Here, a generation AI model is used to generate prompts (e.g., "Please provide game elements to make brushing teeth fun for children"), and personalized content is devised based on these prompts. The generated content may include songs and animations.

[0760] Step 3:

[0761] The generated content is delivered by the server through a virtual store platform. Parents can access this information via their devices and download or stream the content. This allows parents to obtain habit-forming content suitable for their children.

[0762] Step 4:

[0763] The user uses the device's display to play the generated game together with the child. Here, the device presents content visually and aurally, guiding the play process. It is expected that the child will learn habits while having fun through the interactive content.

[0764] Step 5:

[0765] After a user completes a game, the device collects parental ratings and comments through a feedback input interface. This feedback data is sent to a server in digital format. The server receives the feedback and stores it in a database using a storage mechanism. This information is used to improve future content.

[0766] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0767] This invention incorporates an emotion engine that recognizes user emotions into an educational platform system designed to help children acquire good habits while having fun. This system generates games based on user-specified habits and adjusts the content using the emotion engine, thereby providing a more effective and enjoyable learning experience.

[0768] First, the user (parent) uses a device to specify the lifestyle habits they want their child to develop. The device converts this information into digital data and sends it to the server. The server uses a generation mechanism to generate games based on the received data. The user's emotional state, obtained from an emotion engine, is taken into consideration when generating the games. Specifically, a machine learning algorithm analyzes the user's voice and facial expressions to adjust the content and difficulty level of the games appropriately.

[0769] Next, the game information generated on the server is sent to the terminal. The terminal presents the generated game to the user through a user interface, and here too, the display content and interaction methods are optimized based on the analysis results of the emotion engine. This ensures that the game is tailored to the child's interests and emotions.

[0770] After execution, the user enters feedback on the child's reactions and the outcome of the play into the device. During this process, the emotion engine collects data in real time, recording changes in emotions during the play. The device sends the feedback and emotion data to a server, where it is stored in a database to help optimize future play sessions.

[0771] For example, when generating a game to help children develop the habit of "putting away toys," the emotional engine can determine whether the child is enjoying themselves or feeling bored, and adjust the game's tempo and reward system accordingly. This allows children to participate more actively, and parents can also benefit from a more satisfying learning experience.

[0772] In this way, this system analyzes and utilizes users' emotions to provide a personalized learning environment and supports children in naturally developing good habits.

[0773] The following describes the processing flow.

[0774] Step 1:

[0775] The user uses their device to input the lifestyle habits they want their child to develop through the interface. Examples of habits they can select include "brushing teeth every morning" and "tidying up before bed."

[0776] Step 2:

[0777] The terminal digitizes the entered lifestyle data and sends it to the server. This transmitted data includes selected habits and any additional requests from the user.

[0778] Step 3:

[0779] The server receives data sent from the terminal and activates the emotion engine. The emotion engine collects sensor data necessary to analyze the user's voice and facial expressions.

[0780] Step 4:

[0781] The server uses analysis results from the emotion engine to determine the user's current emotional state. This result is then used as a factor in determining what kind of gameplay is optimal.

[0782] Step 5:

[0783] The server generates games using a generation mechanism. Machine learning algorithms are used to adjust the difficulty and content of the games according to the user's emotional state.

[0784] Step 6:

[0785] Detailed information about the generated game is sent from the server to the terminal. This information includes the rules of the game, the steps to be taken, and the recommended forms of interaction.

[0786] Step 7:

[0787] The device displays received game information through the user interface. Here too, the presentation method is customized to reflect the analysis of the emotion engine and maintain the user's interest.

[0788] Step 8:

[0789] The users (parent and child) actually play the suggested game. During the game, the emotion engine analyzes the users' reactions in real time and sends adjustment requests to the server as needed.

[0790] Step 9:

[0791] After the play session, the user inputs the child's reactions and their evaluation of the play on their device. The emotion engine then collects additional final emotion data to create comprehensive feedback.

[0792] Step 10:

[0793] The device sends user feedback and emotional data to the server. The server receives this data, stores it in a database, and uses it to optimize future game development.

[0794] (Example 2)

[0795] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0796] Traditionally, learning methods for developing good habits have faced the problem of children losing interest and not sticking with them. Furthermore, customization to individual children was difficult, resulting in ineffective learning. Additionally, the lack of systems to utilize feedback and children's emotional states in the learning process prevented the provision of effective learning experiences.

[0797] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0798] In this invention, the server includes input means for inputting habits specified by the parent, generation means for generating activities based on the input habits, and emotion analysis means for analyzing emotional states and adjusting activities. This makes it possible to provide an effective and enjoyable learning environment tailored to the child.

[0799] A "parent" is someone who uses this system to help their child develop good habits.

[0800] "Habits" are the behaviors and skills that children need to acquire in their daily lives.

[0801] An "input method" is a device or system used by parents to input habits they want their children to acquire into a system.

[0802] "Generative means" refers to technologies and methods for generating activities for children based on inputted habits.

[0803] An "activity" is a game or task generated by the system to help children learn habits in a fun way.

[0804] "Presentation methods" refer to devices or systems used to show the generated activity to parents and children.

[0805] "Emotional analysis methods" refer to techniques and methods for adjusting a child's activities by analyzing their emotional state.

[0806] "Means of collection" refers to devices or methods for collecting feedback from parents.

[0807] "Storage methods" refer to the technologies and methods for recording and storing collected feedback and analytical data.

[0808] This invention is an educational support system that helps parents instill good habits in their children in an enjoyable way. The system provides parents with the goal of instilling specific habits in their children through activities based on the child's interests and emotions.

[0809] First, the user (parent) uses a device to input specific habits they want their child to develop. This input is done using digital devices such as tablets and smartphones. The input information is processed by the device and converted into digital data. This data is then transmitted to a server via the internet.

[0810] The server designs activities using a generation mechanism based on the received digital data. This generation mechanism utilizes machine learning and artificial intelligence technologies, with a generation AI model at its core. An emotion analysis mechanism is incorporated, analyzing emotional data such as the user's (child's) voice and facial expressions in real time. Based on this analysis, the tempo and content of the activities are dynamically adjusted.

[0811] Information about the activities generated by the server is sent to the terminal and presented to the user (child) on the terminal. This uses a user interface that utilizes animation and sound effects, making it interactive and visually engaging.

[0812] After execution, the user (parent) can input responses regarding the activity's effectiveness and the child's reaction into the device. This collected data is then sent back to the server and stored in a database. This data will be used as reference for future activity design and generation processes.

[0813] For example, if you want to instill the habit of "putting away toys," you can input "putting away toys" into the device, and the server will automatically generate related activities. In this process, the system operates a generation AI model based on a prompt message such as "Lifestyle: Putting away toys. What kind of game will you create for the child to enjoy? Adjust the game's tempo and rewards based on the emotion engine," providing an interactive and fun activity.

[0814] This system allows children to naturally acquire good habits in a fun and efficient way.

[0815] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0816] Step 1:

[0817] The user uses their device to input specific habits they want their child to develop. In this process, the user selects habits such as "putting away toys."

[0818] The input is the habits chosen by the parents, and the output is this information converted into digital data format.

[0819] The terminal receives this digital data as input using a touchscreen or voice input system, and sends that data to the server. This transmission delivers habitual information to the server via the network.

[0820] Step 2:

[0821] The server analyzes the digital data received from the terminal and prepares to utilize a generative AI model for activity design.

[0822] The input is digital data of habits submitted by the user, and the output is an activity generation prompt statement.

[0823] The server activates the sentiment analysis system and references previously collected sentiment data. Based on this, it generates activity generation prompts and prepares to design the activity generation logic.

[0824] Step 3:

[0825] The server uses a generative AI model to design activities that will interest children, based on activity generation prompts.

[0826] The input consists of activity generation prompts and pre-collected sentiment data, while the output is a customized activity.

[0827] The server generates activities optimized for the user (child), including adjusting the difficulty level and designing the reward system. The generated activities are then sent to the device.

[0828] Step 4:

[0829] The device receives activity data generated from the server and presents it to the child through a user interface.

[0830] The input is the activity content sent from the server, and the output is the game or task presented to the child visually and aurally.

[0831] The device presents activities using animation and sound features. A real-time emotion analysis system monitors the child's responses and optimizes their interaction with the activity.

[0832] Step 5:

[0833] After the activity is completed, the user enters their thoughts and feedback on the child's reaction and the effects into the device.

[0834] The input is feedback information after the activity is completed, and the output is data that organizes this information and is used for future improvements.

[0835] The device uses collection methods to obtain feedback and sends that data to the server. This feedback is stored on the server and used as reference information when generating the next activity.

[0836] (Application Example 2)

[0837] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0838] In modern retail settings, providing children with learning opportunities to naturally develop good habits is not easy. In particular, engaging children's interests while addressing their individual emotional states is a challenge within limited timeframes. Traditional methods have struggled to analyze children's emotions in real time and dynamically adjust play activities accordingly.

[0839] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0840] In this invention, the server includes input means for inputting lifestyle habits specified by the parent, generation means for generating games based on the input lifestyle habits, and adjustment means for analyzing the user's emotions using emotion recognition technology and dynamically adjusting the presented content based on the results. This makes it possible to provide an interactive learning experience that effectively helps children acquire habits while maintaining their interest, according to their individual emotional state.

[0841] "Input means" refers to a device or method for inputting information about lifestyle habits specified by the parent.

[0842] "Generative means" refers to a device or method for designing play based on inputted lifestyle habits.

[0843] "Presentation means" refers to a device or method for presenting the generated play to parents and children visually or aurally.

[0844] "Collection means" refers to a device or method for collecting feedback from parents after a play session has taken place.

[0845] "Storage means" refers to an apparatus or method for recording and storing collected feedback information.

[0846] "Emotion recognition technology" is a technology that analyzes voice and facial expression data in order to analyze the user's emotions.

[0847] "Adjustment means" refers to a device or method for dynamically changing the presented content based on the analysis results obtained by emotion recognition technology.

[0848] This invention is a system that helps children learn good habits in an enjoyable way. Based on the habits specified by the parents, this system generates games for children and dynamically adjusts the content and difficulty level of the games according to the child's emotional state, thereby improving learning efficiency.

[0849] The system comprises a terminal, a server, and emotion recognition technology. The terminal provides an interface for inputting lifestyle habits specified by the parent, and transmits this information to the server as digital data. The server has a generation mechanism, which generates games using machine learning algorithms and performs data analysis using emotion recognition technology.

[0850] To analyze voice and facial expression data, the device uses its camera and microphone to capture the child's reactions. This analysis is performed using AWS Rekognition and Google Cloud Vision APIs, and the resulting emotional data is sent to a server. The server then adjusts the generated play content based on this data to provide a learning experience tailored to the child's individual emotional state.

[0851] For example, if a child is playing a game themed around "tidying up toys," the system uses emotion recognition technology to determine whether the child is having fun or is bored. If the child is having fun, the system can speed up the game's pace and implement a reward system, such as giving virtual badges to enhance their sense of accomplishment.

[0852] An example of a prompt message would be: "Analyze this child's emotions from their facial expressions and voice. If they are currently enjoying themselves, set the difficulty level for the next game stage and award a virtual badge as a reward upon completion." This message is then input into a generating AI model to optimize the game's output.

[0853] Overall, this system is designed to naturally enhance children's motivation to learn and provide a highly satisfying educational environment for parents.

[0854] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0855] Step 1:

[0856] The user (parent) uses a terminal to specify lifestyle habits. The terminal converts these specifications into digital data and sends it to the server. The input is data related to the specified lifestyle habits, and the output is the digital data sent to the server.

[0857] Step 2:

[0858] The server analyzes the received digital data and begins designing the game using generation tools. Here, the game's theme and story, corresponding to daily life habits, are determined. The input is digital data, and the output is a generated prototype of the game.

[0859] Step 3:

[0860] The device's camera and microphone are used to capture the user's (child's) voice and facial expressions in real time. This data is then analyzed using emotion recognition technology. The input is the child's voice and visual data, and the output is the analyzed emotion data.

[0861] Step 4:

[0862] The server dynamically adjusts the game content and difficulty level based on the analyzed emotional data. This provides an optimal learning experience tailored to the user's emotional state. The input is the analyzed emotional data, and the output is the adjusted game content.

[0863] Step 5:

[0864] The adjusted gameplay is presented to the user (child) via the device. Interactive elements and reward systems of the game are implemented here. The input is the adjusted gameplay, and the output is visual and auditory feedback to the child.

[0865] Step 6:

[0866] After the game ends, the user (parent) enters feedback into the device, and this data is sent to the server. The server then uses this feedback to store it in a database for future game design. The input is the feedback information from the parent, and the output is the stored feedback data.

[0867] Step 7:

[0868] Based on the saved data, the server uses a generative AI model to generate prompts to make the next gameplay more effective. The input is the saved feedback data, and the output is the prompts for the generative AI model.

[0869] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0870] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0871] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0872] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0873] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0874] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0875] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0876] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0877] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0878] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0879] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0880] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0881] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0882] 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.

[0883] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0884] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0885] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0886] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0887] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0888] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0889] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0890] The following is further disclosed regarding the embodiments described above.

[0891] (Claim 1)

[0892] An input method for entering lifestyle habits specified by the parents,

[0893] A generation means for generating games based on input lifestyle habits,

[0894] A means of presenting the generated game to parents and children,

[0895] A means of collecting feedback from parents after implementation,

[0896] A means of saving feedback,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, wherein the generation means comprises a generation algorithm that generates games using machine learning.

[0900] (Claim 3)

[0901] The system according to claim 1, wherein the presentation means presents details of the game generated via a display device to the parent and displays the execution procedure.

[0902] "Example 1"

[0903] (Claim 1)

[0904] A means of inputting information to specify lifestyle habits,

[0905] A generation algorithm that generates games based on input information,

[0906] A means of presenting information to parents and children about the games that have been generated,

[0907] A data collection method for aggregating feedback provided by parents,

[0908] A means of storing information to accumulate the collected feedback,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, wherein the generation algorithm is configured to create games using a machine learning model.

[0912] (Claim 3)

[0913] The system according to claim 1, wherein the information presentation means presents the specifications of a game generated using a visualization device to the parent and guides them on how to execute it.

[0914] "Application Example 1"

[0915] (Claim 1)

[0916] An input method for entering lifestyle habits specified by the parents,

[0917] A generation means for generating games based on input lifestyle habits,

[0918] A means of presenting the generated game to parents and children,

[0919] A means of collecting feedback from parents after implementation,

[0920] A means of saving feedback,

[0921] A means of providing content generated on a virtual store platform,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, wherein the generation means includes an algorithm that generates games using machine learning and generates educational content in a form accessible within a virtual store.

[0925] (Claim 3)

[0926] The system according to claim 1, wherein the presentation means presents to the parent details and execution procedures of a game generated via a display device, and further displays related stories and visual content from a virtual store.

[0927] "Example 2 of combining an emotion engine"

[0928] (Claim 1)

[0929] An input method for entering habits specified by the parent,

[0930] A generation means that generates activities based on input habits,

[0931] A means of presenting the generated activity to the parent and child,

[0932] An emotional analysis tool for analyzing emotional states and adjusting activities,

[0933] A collection method for collecting responses from the parent after execution,

[0934] A storage means for storing collected responses and analysis data,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, wherein the generation means comprises a generation logic that generates activities using a learning algorithm.

[0938] (Claim 3)

[0939] The system according to claim 1, wherein the presentation means presents details of an activity generated via a display device to the parent and displays an execution instruction.

[0940] "Application example 2 when combining with an emotional engine"

[0941] (Claim 1)

[0942] An input method for entering lifestyle habits specified by the parents,

[0943] A generation means for generating games based on input lifestyle habits,

[0944] A means of presenting the generated game to parents and children,

[0945] A means of collecting feedback from parents after implementation,

[0946] A means of saving feedback,

[0947] An adjustment means that analyzes the user's emotions using emotion recognition technology and dynamically adjusts the presented content based on the results,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, wherein the generation means comprises a generation algorithm that generates games using machine learning.

[0951] (Claim 3)

[0952] The system according to claim 1, wherein the presentation means presents details of the game generated via a display device to the parent and dynamically changes the execution procedure based on the results of the emotional analysis. [Explanation of symbols]

[0953] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An input method for entering lifestyle habits specified by the parents, A generation means for generating games based on input lifestyle habits, A means of presenting the generated game to parents and children, A means of collecting feedback from parents after implementation, A means of saving feedback, A system that includes this.

2. The system according to claim 1, wherein the generation means comprises a generation algorithm that generates games using machine learning.

3. The system according to claim 1, wherein the presentation means presents the details of the game generated via the display device to the parent and displays the execution procedure.

Images