system

Abstract

The system according to this embodiment aims to help children learn to tidy up in a fun way and maintain their motivation. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a monitoring unit, and a reward unit. The analysis unit analyzes the state of the room. The proposal unit proposes a tidying task based on the results analyzed by the analysis unit. The monitoring unit monitors the progress of the tidying task proposed by the proposal unit. The reward unit provides a reward based on the progress monitored by the monitoring unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

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[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, the method including 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 that responds 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] Conventional technologies do not sufficiently provide an effective method for children to enjoy learning to tidy up, and there is room for improvement.

[0005] The system according to the embodiment aims to enable children to enjoy learning to tidy up and maintain motivation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a monitoring unit, and a reward unit. The analysis unit analyzes the state of the room. The proposal unit proposes a tidying task based on the results of the analysis by the analysis unit. The monitoring unit monitors the progress of the tidying task proposed by the proposal unit. The reward unit provides a reward based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment allows children to learn how to tidy up in a fun way and maintain their motivation. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The tidying support system according to an embodiment of the present invention is a game-like AI agent that makes learning tidying up in stages fun. This tidying support system can be operated with a smartphone or tablet and analyzes the state of the room using a camera and sensors. The tidying support system instantly grasps the state of the room and proposes tidying tasks tailored to the child's age and personality. For example, it presents specific tasks such as putting away a particular toy or organizing clothes. As the child makes progress in tidying up, the tidying support system monitors the progress in real time and advises on when and how to praise the child as needed. For example, when tidying up is completed, it displays a message such as "Well done!" and provides rewards such as badges and level-ups. This allows children to learn tidying up while having fun. The tidying support system also reports the child's progress to the parents and advises on when and how to praise the child as needed. This facilitates parent-child communication and maintains motivation for tidying up. This mechanism can create an opportunity for children to enjoy tidying up, especially in dual-income households with children, particularly those with children of elementary school age or younger. Furthermore, the tidying support system instantly grasps the state of the room and proposes tidying tasks in a game-like manner, creating a world where small household tasks contribute to the overall happiness of the family's habits. This allows children to learn about tidying up while having fun.

[0029] The tidying support system according to this embodiment comprises an analysis unit, a suggestion unit, a monitoring unit, and a reward unit. The analysis unit analyzes the state of the room. The analysis unit analyzes the state of the room using, for example, a camera or sensors. The analysis unit can analyze the cleanliness of the room, the degree of tidiness, the arrangement of objects, etc. The suggestion unit proposes tidying tasks based on the results analyzed by the analysis unit. The suggestion unit proposes tidying tasks tailored to the child's age and personality, for example. The suggestion unit can present specific tasks such as tidying up certain toys or organizing clothes. The monitoring unit monitors the progress of the tidying tasks proposed by the suggestion unit. The monitoring unit monitors, for example, the child's tidying progress in real time. The monitoring unit can monitor the degree of completion of tidying, the passage of time, etc. The reward unit provides rewards based on the progress monitored by the monitoring unit. The reward unit provides rewards such as badges or level-ups according to the achievement status. The reward unit can provide rewards such as badges, points, and level-ups. As a result, the tidying-up support system according to this embodiment allows children to learn how to tidy up while having fun.

[0030] The analysis unit analyzes the state of the room. For example, the analysis unit uses cameras and sensors to analyze the state of the room. Specifically, cameras installed in the room capture images of the entire room, and sensors collect environmental data such as temperature, humidity, and illuminance. This data is transmitted in real time to a central database for the analysis unit to access. The analysis unit uses AI to analyze this data and evaluate the cleanliness of the room, the degree of tidiness, and the arrangement of objects. For example, the AI ​​uses image recognition technology to detect toys and clothes scattered on the floor and identify their location and number. It also detects the presence or absence of dust and debris to evaluate the cleanliness of the room, and analyzes the arrangement and storage of objects to evaluate the degree of tidiness. Furthermore, the analysis unit can also use past data and statistical information to analyze changes and trends in the state of the room. For example, based on past data, it can predict fluctuations in the state of the room at specific times of day or on specific days of the week and evaluate the need for future tidying. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term condition management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0031] The proposal department proposes tidying tasks based on the results analyzed by the analysis department. For example, the proposal department proposes tidying tasks tailored to the child's age and personality. Specifically, based on room condition data provided by the analysis department, the AI ​​generates the optimal tidying task considering the child's age, personality, and past tidying history. For example, it suggests simple toy tidying tasks for toddlers and more complex tasks such as organizing clothes and tidying bookshelves for elementary school children. The proposal department can also design tasks that make tidying fun and game-like, according to the child's interests and preferences. For example, it can present challenges such as sorting specific toys by color or tidying a designated area within a time limit. Furthermore, the proposal department can propose the next task in stages according to the child's progress and level of achievement. This allows children to tidy up without difficulty and develop tidying habits while feeling a sense of accomplishment. The proposal department can also collect feedback from parents and guardians and continuously improve the content and difficulty of the tasks. For example, it can adjust specific tasks or add new ones based on parental opinions to provide optimal tidying support for children. This allows the proposal department to provide flexible and effective support so that children can learn to tidy up while having fun.

[0032] The monitoring unit monitors the progress of the tidying tasks proposed by the proposal unit. For example, the monitoring unit monitors the child's tidying progress in real time. Specifically, it uses cameras and sensors installed in the room to monitor the child's tidying work. The cameras capture the child's movements and tidying process, while the sensors detect changes in the movement and placement of objects. This data is transmitted in real time to a central database for the monitoring unit to access. The monitoring unit uses AI to analyze this data and evaluate the progress and completion rate of the tidying. For example, the AI ​​uses image recognition technology to count the number of toys and clothes that have been put away and evaluate how much of a designated area has been tidied up. It also monitors the passage of time and records how long it took to complete the tidying work. Furthermore, the monitoring unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. For example, if a child abandons tidying or becomes distracted by other activities, the monitoring unit can immediately notify the parent or guardian and prompt appropriate action. This allows the monitoring department to accurately grasp the progress of the tidying up and support the children so that they can tidy up efficiently.

[0033] The rewards department provides rewards based on progress monitored by the monitoring department. For example, the rewards department provides rewards such as badges and level-ups depending on the achievement level. Specifically, based on the tidying progress data provided by the monitoring department, AI evaluates the child's achievement level and generates appropriate rewards. For example, if a child completes a specific tidying task, they are awarded a badge and given a certain number of points. By completing multiple tasks, they can level up and earn new rewards and benefits. The rewards department can devise ways to enhance the child's motivation by changing the types and content of rewards. For example, it can provide rewards that children will enjoy, such as digital items or participation rights in special events, in addition to badges and points. The rewards department can also collect feedback from parents and guardians and continuously improve the content and method of providing rewards. For example, based on parental opinions, it can increase the types of rewards or adjust the conditions for earning rewards to provide the optimal reward system for children. Furthermore, the rewards department can maintain the child's motivation and promote consistent tidying habits by adjusting the frequency and timing of reward provision. In this way, the rewards department can provide an effective reward system that allows children to learn to tidy up while having fun.

[0034] The analysis unit can analyze the state of a room using cameras and sensors. For example, the analysis unit can use a fixed camera to capture the state of the entire room and use image analysis technology to analyze the cleanliness and tidiness of the room. The analysis unit can also use motion detection sensors to analyze the arrangement of objects in the room. For example, the analysis unit can detect moving objects in the room and analyze their position and movement. Furthermore, the analysis unit can collect environmental data of the room using temperature and humidity sensors and incorporate it into the analysis. For example, the analysis unit can analyze whether the temperature and humidity of the room affect tidiness. In this way, the state of the room can be accurately analyzed using cameras and sensors. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data captured by a camera into a generating AI and have the generating AI perform the process of analyzing the state of the room from the image data.

[0035] The suggestion function can propose tidying tasks tailored to a child's age and personality. For example, it can suggest tidying tasks based on the child's developmental stage by age. For instance, it might suggest easy tidying tasks for toddlers and slightly more difficult tasks for elementary school children. The suggestion function can also suggest tidying tasks based on personality assessment results. For example, it might suggest tasks that introverted children can do alone and tasks that extroverted children can do with their families. Furthermore, the suggestion function can customize tidying tasks based on the child's interests and preferences. For example, it might suggest tidying tasks using the child's favorite characters. By suggesting tasks tailored to the child's age and personality, children can learn to tidy up in a fun way. Some or all of the above processes in the suggestion function may be performed using AI, or not. For example, the suggestion function can input the child's age and personality data into a generating AI, which then proposes the most suitable tidying task.

[0036] The monitoring unit can monitor the child's tidying progress in real time. The monitoring unit can monitor the child's tidying progress using, for example, cameras and sensors. The monitoring unit can monitor the degree of completion and the passage of time in real time. For example, the monitoring unit can analyze image data captured by a camera to determine how far along the tidying is. The monitoring unit can also use sensors to detect the child's movements and monitor the tidying progress. For example, the monitoring unit can use motion detection sensors to track the child's movements and understand the tidying progress in real time. Furthermore, the monitoring unit can report the child's tidying progress to the parents and provide feedback as needed. For example, the monitoring unit can notify the parents when the tidying is complete and advise on the timing and method of praise. This allows for timely feedback by monitoring progress in real time. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input image data captured by a camera into a generating AI, which can then perform the process of analyzing the tidying progress.

[0037] The rewards unit can provide rewards such as badges and level-ups based on achievement. For example, the rewards unit can provide a badge when tidying up is completed. For example, it can award a "Tidying Master" badge when a specific tidying task is completed. The rewards unit can also level up based on the progress of tidying up. For example, it can award experience points based on the progress of tidying up, and level up when a certain amount of experience points are accumulated. Furthermore, the rewards unit can award points and provide rewards based on the points accumulated. For example, it can award points based on the progress of tidying up, and rewards can be received by accumulating points. This helps maintain children's motivation by providing rewards based on their achievements. Some or all of the above processes in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input tidying progress data into a generating AI, and the generating AI can execute the process of providing rewards.

[0038] The suggestion unit can present specific tasks, such as putting away certain toys or organizing clothes. For example, the suggestion unit can present the task of putting away certain toys. For example, the suggestion unit can present the task, "Put all the blocks in the toy box." The suggestion unit can also present the task of organizing clothes. For example, the suggestion unit can present the task, "Put all the clothes in the closet." Furthermore, the suggestion unit can present the task of organizing the entire room. For example, the suggestion unit can present the task, "Clean up the entire room." By presenting specific tasks, it becomes easier for children to take specific actions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input room state data into a generating AI, and the generating AI can execute a process that proposes specific tidying tasks.

[0039] The analysis unit can improve its analysis accuracy by referring to past room condition data. For example, the analysis unit can predict the tidying status of a specific area based on past room condition data. For example, the analysis unit can refer to past data to identify areas that frequently become cluttered and analyze them intensively. The analysis unit can also use past data to compare the progress of tidying and find areas for improvement. For example, the analysis unit can make suggestions to improve the efficiency of tidying based on past tidying data. In this way, the analysis accuracy is improved by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past room condition data into a generating AI, and the generating AI can perform processes to improve the analysis accuracy.

[0040] The analysis unit can change its analysis method depending on the room's purpose of use and time of day. For example, when the room is used as a play area, the analysis unit will focus on analyzing the scattered toys. For example, when the room is used as a bedroom, the analysis unit will focus on analyzing the tidiness around the bed. The analysis unit can also focus on analyzing the tidiness around the desk when the room is used as a study room. For example, the analysis unit can change its analysis method depending on the room's purpose of use and time of day to perform a more appropriate analysis. This allows for a more appropriate analysis by changing the analysis method depending on the room's purpose of use and time of day. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room purpose of use and time of day data into a generating AI, which can then perform a process to change the analysis method.

[0041] The analysis unit can perform analysis while considering environmental data such as room temperature and humidity. For example, if the room temperature is high, the analysis unit will consider the possibility that the progress of cleaning will be delayed. For example, if the room humidity is high, the analysis unit will consider the risk of mold growth. The analysis unit can also apply normal analysis methods when the room temperature and humidity are appropriate. For example, the analysis unit will analyze whether the room temperature and humidity affect cleaning. This allows for more accurate analysis by considering environmental data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room temperature and humidity data into a generating AI, which can then perform analysis while considering the environmental data.

[0042] The analysis unit can perform analysis while considering the furniture arrangement and interior design of the room. For example, the analysis unit can analyze the flow of movement that makes tidying up easier, taking into account the furniture arrangement of the room. For example, the analysis unit can propose a tidying method that does not spoil the aesthetics, taking into account the interior design. Furthermore, if the analysis unit needs to change the furniture arrangement, it can display the analysis results including a suggestion for such a change. For example, the analysis unit can analyze the impact of the furniture arrangement and interior design of the room on tidying up. This allows for more practical analysis by considering the furniture arrangement and interior design. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the room's furniture arrangement and interior design data into a generating AI, and the generating AI can perform the analysis.

[0043] The suggestion unit can select the most suitable task by referring to past tidying history. For example, the suggestion unit can propose similar tasks based on past successful tidying tasks. For example, the suggestion unit can propose tasks that take into account areas for improvement based on past unsuccessful tidying tasks. The suggestion unit can also analyze past tidying history and select tasks of appropriate difficulty. For example, the suggestion unit can select tasks that are easy for children to tackle based on past tidying history. In this way, the most suitable task can be selected by referring to past history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input past tidying history data into a generating AI, which can then execute the process of selecting the most suitable task.

[0044] The suggestion function can customize the task content based on the child's interests and preferences. For example, the suggestion function can suggest a tidying task using a character the child likes. For example, the suggestion function can suggest a tidying task related to a theme the child is interested in. The suggestion function can also suggest a tidying task that can be approached in a playful way, tailored to the child's interests. For example, the suggestion function can suggest a tidying game using a character the child likes. By customizing the task based on the child's interests and preferences, the child can enjoy tidying up. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can input data on the child's interests and preferences into a generating AI, which can then perform the process of customizing the task content.

[0045] The suggestion unit can apply different suggestion methods depending on the child's learning style and preferences. For example, for a child with a visual learning style, the suggestion unit can provide suggestion methods using images and videos. For example, for a child with an auditory learning style, the suggestion unit can provide suggestion methods using audio guides. Furthermore, for a child with a tactile learning style, the suggestion unit can also suggest tasks that involve actual hand movements. For example, the suggestion unit selects the most suitable suggestion method according to the child's learning style and preferences. This allows children to learn to tidy up more effectively by applying suggestion methods tailored to their learning style and preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the child's learning style and preferences into a generating AI, which can then execute a process to apply different suggestion methods.

[0046] The suggestion unit can propose collaborative tasks that encourage the participation of the entire family. For example, the suggestion unit could propose a tidying-up task that the whole family works on together and accomplishes through cooperation. For example, the suggestion unit could propose a task that promotes communication through the participation of the entire family. The suggestion unit could also propose a game-like tidying-up task that the whole family can enjoy. For example, the suggestion unit could propose a tidying-up game that the whole family can play together. This would promote communication and make tidying up fun as the whole family works on it together. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input participation data for all family members into a generating AI, and the generating AI can perform the process of proposing collaborative tasks.

[0047] The monitoring unit can improve monitoring accuracy by referring to past progress data. For example, the monitoring unit can predict the tidying status of a specific area based on past progress data. For example, the monitoring unit can refer to past data to identify areas that frequently become cluttered and monitor them intensively. The monitoring unit can also use past data to compare tidying progress and find areas for improvement. For example, the monitoring unit can make suggestions to improve tidying efficiency based on past tidying data. This improves monitoring accuracy by referring to past data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input past progress data into a generating AI, and the generating AI can perform processes to improve monitoring accuracy.

[0048] The monitoring unit can change its monitoring methods by considering the child's behavior patterns. For example, the monitoring unit can analyze the child's behavior patterns and set appropriate monitoring timings. For example, the monitoring unit can adjust the frequency and method of monitoring based on the child's behavior patterns. The monitoring unit can also propose effective monitoring methods by considering the child's behavior patterns. For example, the monitoring unit can monitor the progress of tidying up based on the child's behavior patterns. This allows for more effective monitoring by considering behavior patterns. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input child behavior pattern data into a generating AI, which can then perform the process of changing the monitoring method.

[0049] The monitoring unit can perform monitoring while considering environmental factors such as room lighting and music. For example, if the room lighting is bright, the monitoring unit can provide a highly visible monitoring method. For example, if music is playing in the room, the monitoring unit can provide a monitoring method using voice guidance. The monitoring unit can also select an appropriate monitoring method while considering the environmental factors of the room. For example, the monitoring unit can analyze the impact of room lighting and music on tidying up and propose the optimal monitoring method. This allows for more accurate monitoring by considering environmental factors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input room lighting and music data into a generating AI, which can then perform the processing of monitoring while considering environmental factors.

[0050] The monitoring unit can incorporate feedback from parents and other family members into its monitoring. For example, the monitoring unit can adjust its monitoring methods based on feedback from parents and other family members. For example, the monitoring unit can incorporate family opinions and propose effective monitoring methods. The monitoring unit can also reflect feedback from parents and other family members and display the monitoring results. For example, the monitoring unit can monitor the progress of tidying up based on feedback from parents and other family members. This allows for more effective monitoring by incorporating family feedback. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input feedback data from parents and other family members into a generating AI, which can then perform monitoring while reflecting the feedback.

[0051] The reward unit can select the optimal reward by referring to past reward history. For example, the reward unit may provide a similar reward based on past successful rewards. For example, the reward unit may provide a reward that takes into account areas for improvement based on past unsuccessful rewards. The reward unit can also analyze past reward history and select an appropriate reward. For example, the reward unit may select a reward that will please a child based on past reward history. In this way, the optimal reward can be selected by referring to past history. Some or all of the above processes in the reward unit may be performed using AI, for example, or without AI. For example, the reward unit may input past reward history data into a generating AI, which may then perform the process of selecting the optimal reward.

[0052] The reward unit can customize rewards based on the child's preferences and interests. For example, the reward unit can offer badges of the child's favorite characters as rewards. For example, the reward unit can offer rewards related to themes the child is interested in. The reward unit can also offer rewards that are fun and engaging, tailored to the child's preferences. For example, the reward unit can offer merchandise of the child's favorite characters as rewards. By providing rewards that match the child's preferences, the reward unit can increase the child's motivation. Some or all of the above processes in the reward unit may be performed using AI, for example, or not. For example, the reward unit can input data on the child's preferences and interests into a generating AI, which can then perform the process of customizing the rewards.

[0053] The rewards unit can provide rewards that can be shared by the whole family. For example, the rewards unit can provide games or activities that the whole family can enjoy as rewards. For example, the rewards unit can provide events or experiences that the whole family can participate in as rewards. The rewards unit can also provide perks or services that can be shared by the whole family as rewards. For example, the rewards unit can provide coupons that the whole family can use. This promotes family communication by providing rewards that can be shared by the whole family. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input reward data that can be shared by the whole family into a generating AI, and the generating AI can perform the process of providing rewards.

[0054] The rewards unit can propose long-term reward plans that are tailored to the child's learning outcomes and growth. For example, the rewards unit can provide a tiered reward plan based on the child's learning outcomes. For example, the rewards unit can set long-term goals in line with the child's growth and provide rewards accordingly. The rewards unit can also propose reward plans that take into account the child's progress and maintain continuous motivation. For example, the rewards unit can provide monthly goal achievement rewards and annual performance rewards. By providing a long-term reward plan, the child's continuous motivation can be maintained. Some or all of the above processes in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input the child's learning outcomes and growth data into a generating AI, which can then execute a process to propose a long-term reward plan.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can consider the room's lighting conditions when analyzing the room's state. For example, if the room is brightly lit, the analysis unit can more accurately analyze the arrangement of objects and cleanliness. Furthermore, if the room is dimly lit, the analysis unit can suggest auxiliary lighting to improve the accuracy of the analysis. In addition, the analysis unit can analyze the impact of the lighting's color temperature on the progress of tidying up and suggest optimal lighting conditions. This allows for more accurate analysis by considering lighting conditions.

[0057] The proposal department can adjust the way tidying-up tasks are presented according to the child's learning style. For example, tasks using images or videos can be presented to children who are visual learners. Tasks using audio guides can also be presented to children who are auditory learners. Furthermore, tasks that involve hands-on activity can be proposed for children who are tactile learners. By presenting tasks that match the child's learning style, children can learn to tidy up more effectively.

[0058] The monitoring unit can consider the child's behavior patterns when monitoring the child's progress in tidying up. For example, the monitoring unit can analyze the child's behavior patterns and set appropriate monitoring timings. Furthermore, the monitoring unit can adjust the frequency and method of monitoring based on the child's behavior patterns. In addition, the monitoring unit can suggest effective monitoring methods, taking behavior patterns into consideration. This allows for more effective monitoring.

[0059] The reward system allows for customization of rewards based on children's preferences and interests. For example, rewards can include badges of characters children like. Rewards related to themes children are interested in can also be provided. Furthermore, rewards can be tailored to children's preferences, making them enjoyable through play. This allows for increased motivation by providing rewards that match children's interests.

[0060] The rewards section can offer rewards that can be shared by the whole family. For example, games or activities that the whole family can enjoy can be offered as rewards. Events or experiences that the whole family can participate in can also be offered as rewards. Furthermore, perks or services that can be shared by the whole family can be offered as rewards. This promotes family communication by providing rewards that can be shared by the whole family.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes the condition of the room. The analysis unit can, for example, use cameras and sensors to analyze the cleanliness of the room, the degree of tidiness, the arrangement of objects, etc. Step 2: The proposal unit proposes tidying tasks based on the results analyzed by the analysis unit. For example, the proposal unit can propose tidying tasks tailored to the child's age and personality, and can present specific tasks such as tidying up certain toys or organizing clothes. Step 3: The monitoring unit monitors the progress of the tidying-up tasks proposed by the proposal unit. For example, the monitoring unit can monitor the progress of children tidying up in real time, and monitor the degree of completion and the passage of time. Step 4: The rewards department provides rewards based on the progress monitored by the monitoring department. The rewards department provides rewards such as badges and level-ups depending on the achievement status. The rewards department can provide rewards such as badges, points, and level-ups.

[0063] (Example of form 2) The tidying support system according to an embodiment of the present invention is a game-like AI agent that makes learning tidying up in stages fun. This tidying support system can be operated with a smartphone or tablet and analyzes the state of the room using a camera and sensors. The tidying support system instantly grasps the state of the room and proposes tidying tasks tailored to the child's age and personality. For example, it presents specific tasks such as putting away a particular toy or organizing clothes. As the child makes progress in tidying up, the tidying support system monitors the progress in real time and advises on when and how to praise the child as needed. For example, when tidying up is completed, it displays a message such as "Well done!" and provides rewards such as badges and level-ups. This allows children to learn tidying up while having fun. The tidying support system also reports the child's progress to the parents and advises on when and how to praise the child as needed. This facilitates parent-child communication and maintains motivation for tidying up. This mechanism can create an opportunity for children to enjoy tidying up, especially in dual-income households with children, particularly those with children of elementary school age or younger. Furthermore, the tidying support system instantly grasps the state of the room and proposes tidying tasks in a game-like manner, creating a world where small household tasks contribute to the overall happiness of the family's habits. This allows children to learn about tidying up while having fun.

[0064] The tidying support system according to this embodiment comprises an analysis unit, a suggestion unit, a monitoring unit, and a reward unit. The analysis unit analyzes the state of the room. The analysis unit analyzes the state of the room using, for example, a camera or sensors. The analysis unit can analyze the cleanliness of the room, the degree of tidiness, the arrangement of objects, etc. The suggestion unit proposes tidying tasks based on the results analyzed by the analysis unit. The suggestion unit proposes tidying tasks tailored to the child's age and personality, for example. The suggestion unit can present specific tasks such as tidying up certain toys or organizing clothes. The monitoring unit monitors the progress of the tidying tasks proposed by the suggestion unit. The monitoring unit monitors, for example, the child's tidying progress in real time. The monitoring unit can monitor the degree of completion of tidying, the passage of time, etc. The reward unit provides rewards based on the progress monitored by the monitoring unit. The reward unit provides rewards such as badges or level-ups according to the achievement status. The reward unit can provide rewards such as badges, points, and level-ups. As a result, the tidying-up support system according to this embodiment allows children to learn how to tidy up while having fun.

[0065] The analysis unit analyzes the state of the room. For example, the analysis unit uses cameras and sensors to analyze the state of the room. Specifically, cameras installed in the room capture images of the entire room, and sensors collect environmental data such as temperature, humidity, and illuminance. This data is transmitted in real time to a central database for the analysis unit to access. The analysis unit uses AI to analyze this data and evaluate the cleanliness of the room, the degree of tidiness, and the arrangement of objects. For example, the AI ​​uses image recognition technology to detect toys and clothes scattered on the floor and identify their location and number. It also detects the presence or absence of dust and debris to evaluate the cleanliness of the room, and analyzes the arrangement and storage of objects to evaluate the degree of tidiness. Furthermore, the analysis unit can also use past data and statistical information to analyze changes and trends in the state of the room. For example, based on past data, it can predict fluctuations in the state of the room at specific times of day or on specific days of the week and evaluate the need for future tidying. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term condition management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The proposal department proposes tidying tasks based on the results analyzed by the analysis department. For example, the proposal department proposes tidying tasks tailored to the child's age and personality. Specifically, based on room condition data provided by the analysis department, the AI ​​generates the optimal tidying task considering the child's age, personality, and past tidying history. For example, it suggests simple toy tidying tasks for toddlers and more complex tasks such as organizing clothes and tidying bookshelves for elementary school children. The proposal department can also design tasks that make tidying fun and game-like, according to the child's interests and preferences. For example, it can present challenges such as sorting specific toys by color or tidying a designated area within a time limit. Furthermore, the proposal department can propose the next task in stages according to the child's progress and level of achievement. This allows children to tidy up without difficulty and develop tidying habits while feeling a sense of accomplishment. The proposal department can also collect feedback from parents and guardians and continuously improve the content and difficulty of the tasks. For example, it can adjust specific tasks or add new ones based on parental opinions to provide optimal tidying support for children. This allows the proposal department to provide flexible and effective support so that children can learn to tidy up while having fun.

[0067] The monitoring unit monitors the progress of the tidying tasks proposed by the proposal unit. For example, the monitoring unit monitors the child's tidying progress in real time. Specifically, it uses cameras and sensors installed in the room to monitor the child's tidying work. The cameras capture the child's movements and tidying process, while the sensors detect changes in the movement and placement of objects. This data is transmitted in real time to a central database for the monitoring unit to access. The monitoring unit uses AI to analyze this data and evaluate the progress and completion rate of the tidying. For example, the AI ​​uses image recognition technology to count the number of toys and clothes that have been put away and evaluate how much of a designated area has been tidied up. It also monitors the passage of time and records how long it took to complete the tidying work. Furthermore, the monitoring unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue warnings early. For example, if a child abandons tidying or becomes distracted by other activities, the monitoring unit can immediately notify the parent or guardian and prompt appropriate action. This allows the monitoring department to accurately grasp the progress of the tidying up and support the children so that they can tidy up efficiently.

[0068] The rewards department provides rewards based on progress monitored by the monitoring department. For example, the rewards department provides rewards such as badges and level-ups depending on the achievement level. Specifically, based on the tidying progress data provided by the monitoring department, AI evaluates the child's achievement level and generates appropriate rewards. For example, if a child completes a specific tidying task, they are awarded a badge and given a certain number of points. By completing multiple tasks, they can level up and earn new rewards and benefits. The rewards department can devise ways to enhance the child's motivation by changing the types and content of rewards. For example, it can provide rewards that children will enjoy, such as digital items or participation rights in special events, in addition to badges and points. The rewards department can also collect feedback from parents and guardians and continuously improve the content and method of providing rewards. For example, based on parental opinions, it can increase the types of rewards or adjust the conditions for earning rewards to provide the optimal reward system for children. Furthermore, the rewards department can maintain the child's motivation and promote consistent tidying habits by adjusting the frequency and timing of reward provision. In this way, the rewards department can provide an effective reward system that allows children to learn to tidy up while having fun.

[0069] The analysis unit can analyze the state of a room using cameras and sensors. For example, the analysis unit can use a fixed camera to capture the state of the entire room and use image analysis technology to analyze the cleanliness and tidiness of the room. The analysis unit can also use motion detection sensors to analyze the arrangement of objects in the room. For example, the analysis unit can detect moving objects in the room and analyze their position and movement. Furthermore, the analysis unit can collect environmental data of the room using temperature and humidity sensors and incorporate it into the analysis. For example, the analysis unit can analyze whether the temperature and humidity of the room affect tidiness. In this way, the state of the room can be accurately analyzed using cameras and sensors. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data captured by a camera into a generating AI and have the generating AI perform the process of analyzing the state of the room from the image data.

[0070] The suggestion function can propose tidying tasks tailored to a child's age and personality. For example, it can suggest tidying tasks based on the child's developmental stage by age. For instance, it might suggest easy tidying tasks for toddlers and slightly more difficult tasks for elementary school children. The suggestion function can also suggest tidying tasks based on personality assessment results. For example, it might suggest tasks that introverted children can do alone and tasks that extroverted children can do with their families. Furthermore, the suggestion function can customize tidying tasks based on the child's interests and preferences. For example, it might suggest tidying tasks using the child's favorite characters. By suggesting tasks tailored to the child's age and personality, children can learn to tidy up in a fun way. Some or all of the above processes in the suggestion function may be performed using AI, or not. For example, the suggestion function can input the child's age and personality data into a generating AI, which then proposes the most suitable tidying task.

[0071] The monitoring unit can monitor the child's tidying progress in real time. The monitoring unit can monitor the child's tidying progress using, for example, cameras and sensors. The monitoring unit can monitor the degree of completion and the passage of time in real time. For example, the monitoring unit can analyze image data captured by a camera to determine how far along the tidying is. The monitoring unit can also use sensors to detect the child's movements and monitor the tidying progress. For example, the monitoring unit can use motion detection sensors to track the child's movements and understand the tidying progress in real time. Furthermore, the monitoring unit can report the child's tidying progress to the parents and provide feedback as needed. For example, the monitoring unit can notify the parents when the tidying is complete and advise on the timing and method of praise. This allows for timely feedback by monitoring progress in real time. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input image data captured by a camera into a generating AI, which can then perform the process of analyzing the tidying progress.

[0072] The rewards unit can provide rewards such as badges and level-ups based on achievement. For example, the rewards unit can provide a badge when tidying up is completed. For example, it can award a "Tidying Master" badge when a specific tidying task is completed. The rewards unit can also level up based on the progress of tidying up. For example, it can award experience points based on the progress of tidying up, and level up when a certain amount of experience points are accumulated. Furthermore, the rewards unit can award points and provide rewards based on the points accumulated. For example, it can award points based on the progress of tidying up, and rewards can be received by accumulating points. This helps maintain children's motivation by providing rewards based on their achievements. Some or all of the above processes in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input tidying progress data into a generating AI, and the generating AI can execute the process of providing rewards.

[0073] The suggestion unit can present specific tasks, such as putting away certain toys or organizing clothes. For example, the suggestion unit can present the task of putting away certain toys. For example, the suggestion unit can present the task, "Put all the blocks in the toy box." The suggestion unit can also present the task of organizing clothes. For example, the suggestion unit can present the task, "Put all the clothes in the closet." Furthermore, the suggestion unit can present the task of organizing the entire room. For example, the suggestion unit can present the task, "Clean up the entire room." By presenting specific tasks, it becomes easier for children to take specific actions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input room state data into a generating AI, and the generating AI can execute a process that proposes specific tidying tasks.

[0074] The analysis unit can estimate a child's emotions and adjust the timing of the room state analysis based on the estimated emotions. For example, the analysis unit can capture a child's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expression. The analysis unit can also record a child's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the child to tidy up without difficulty by adjusting the timing of the analysis according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of a child captured by a camera into a generating AI, which can then perform a process to estimate the child's emotions.

[0075] The analysis unit can improve its analysis accuracy by referring to past room condition data. For example, the analysis unit can predict the tidying status of a specific area based on past room condition data. For example, the analysis unit can refer to past data to identify areas that frequently become cluttered and analyze them intensively. The analysis unit can also use past data to compare the progress of tidying and find areas for improvement. For example, the analysis unit can make suggestions to improve the efficiency of tidying based on past tidying data. In this way, the analysis accuracy is improved by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past room condition data into a generating AI, and the generating AI can perform processes to improve the analysis accuracy.

[0076] The analysis unit can change its analysis method depending on the room's purpose of use and time of day. For example, when the room is used as a play area, the analysis unit will focus on analyzing the scattered toys. For example, when the room is used as a bedroom, the analysis unit will focus on analyzing the tidiness around the bed. The analysis unit can also focus on analyzing the tidiness around the desk when the room is used as a study room. For example, the analysis unit can change its analysis method depending on the room's purpose of use and time of day to perform a more appropriate analysis. This allows for a more appropriate analysis by changing the analysis method depending on the room's purpose of use and time of day. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room purpose of use and time of day data into a generating AI, which can then perform a process to change the analysis method.

[0077] The analysis unit can estimate a child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can capture a child's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expression. The analysis unit can also record a child's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows for a display that is easy for children to understand by adjusting the display method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of a child captured by a camera into a generating AI, which can then perform a process to estimate the child's emotions.

[0078] The analysis unit can perform analysis while considering environmental data such as room temperature and humidity. For example, if the room temperature is high, the analysis unit will consider the possibility that the progress of cleaning will be delayed. For example, if the room humidity is high, the analysis unit will consider the risk of mold growth. The analysis unit can also apply normal analysis methods when the room temperature and humidity are appropriate. For example, the analysis unit will analyze whether the room temperature and humidity affect cleaning. This allows for more accurate analysis by considering environmental data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input room temperature and humidity data into a generating AI, which can then perform analysis while considering the environmental data.

[0079] The analysis unit can perform analysis while considering the furniture arrangement and interior design of the room. For example, the analysis unit can analyze the flow of movement that makes tidying up easier, taking into account the furniture arrangement of the room. For example, the analysis unit can propose a tidying method that does not spoil the aesthetics, taking into account the interior design. Furthermore, if the analysis unit needs to change the furniture arrangement, it can display the analysis results including a suggestion for such a change. For example, the analysis unit can analyze the impact of the furniture arrangement and interior design of the room on tidying up. This allows for more practical analysis by considering the furniture arrangement and interior design. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the room's furniture arrangement and interior design data into a generating AI, and the generating AI can perform the analysis.

[0080] The proposed system can estimate a child's emotions and adjust the difficulty level of a tidying task based on the estimated emotions. For example, the system can capture a child's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the system can calculate an emotion score based on changes in facial expression. The system can also record a child's voice and estimate their emotions using voice analysis technology. For example, the system can analyze the tone and speed of their voice and calculate an emotion score. The system can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the system can calculate an emotion score based on fluctuations in heart rate. This allows the system to adjust the difficulty level of the task according to the child's emotions, enabling the child to tidy up without difficulty. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed system may be performed using AI, for example, or without AI. For example, the proposal unit can input image data of a child taken with a camera into a generating AI, which then estimates the child's emotions and performs a process to adjust the difficulty level of the task.

[0081] The suggestion unit can select the most suitable task by referring to past tidying history. For example, the suggestion unit can propose similar tasks based on past successful tidying tasks. For example, the suggestion unit can propose tasks that take into account areas for improvement based on past unsuccessful tidying tasks. The suggestion unit can also analyze past tidying history and select tasks of appropriate difficulty. For example, the suggestion unit can select tasks that are easy for children to tackle based on past tidying history. In this way, the most suitable task can be selected by referring to past history. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input past tidying history data into a generating AI, which can then execute the process of selecting the most suitable task.

[0082] The suggestion function can customize the task content based on the child's interests and preferences. For example, the suggestion function can suggest a tidying task using a character the child likes. For example, the suggestion function can suggest a tidying task related to a theme the child is interested in. The suggestion function can also suggest a tidying task that can be approached in a playful way, tailored to the child's interests. For example, the suggestion function can suggest a tidying game using a character the child likes. By customizing the task based on the child's interests and preferences, the child can enjoy tidying up. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can input data on the child's interests and preferences into a generating AI, which can then perform the process of customizing the task content.

[0083] The proposed system can estimate a child's emotions and adjust the presentation method of tasks based on the estimated emotions. For example, the system can capture a child's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the system can calculate an emotion score based on changes in facial expressions. The system can also record a child's voice and estimate their emotions using voice analysis technology. For example, the system can analyze the tone and speed of the voice and calculate an emotion score. The system can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the system can calculate an emotion score based on fluctuations in heart rate. This allows for presentations that are easier for children to understand by adjusting the presentation method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed system may be performed using AI, for example, or without AI. For example, the proposal unit can input image data of a child taken with a camera into a generating AI, which then estimates the child's emotions and performs a process to adjust the way the task is presented.

[0084] The suggestion unit can apply different suggestion methods depending on the child's learning style and preferences. For example, for a child with a visual learning style, the suggestion unit can provide suggestion methods using images and videos. For example, for a child with an auditory learning style, the suggestion unit can provide suggestion methods using audio guides. Furthermore, for a child with a tactile learning style, the suggestion unit can also suggest tasks that involve actual hand movements. For example, the suggestion unit selects the most suitable suggestion method according to the child's learning style and preferences. This allows children to learn to tidy up more effectively by applying suggestion methods tailored to their learning style and preferences. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the child's learning style and preferences into a generating AI, which can then execute a process to apply different suggestion methods.

[0085] The suggestion unit can propose collaborative tasks that encourage the participation of the entire family. For example, the suggestion unit could propose a tidying-up task that the whole family works on together and accomplishes through cooperation. For example, the suggestion unit could propose a task that promotes communication through the participation of the entire family. The suggestion unit could also propose a game-like tidying-up task that the whole family can enjoy. For example, the suggestion unit could propose a tidying-up game that the whole family can play together. This would promote communication and make tidying up fun as the whole family works on it together. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input participation data for all family members into a generating AI, and the generating AI can perform the process of proposing collaborative tasks.

[0086] The monitoring unit can estimate the child's emotions and adjust the monitoring frequency based on the estimated emotions. For example, the monitoring unit can capture the child's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expressions. The monitoring unit can also record the child's voice and estimate their emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also collect the child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on fluctuations in heart rate. This allows the monitoring frequency to be adjusted according to the child's emotions, enabling the child to tidy up without undue stress. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input image data of the child captured by the camera into a generating AI, which can then estimate the child's emotions and perform a process to adjust the monitoring frequency.

[0087] The monitoring unit can improve monitoring accuracy by referring to past progress data. For example, the monitoring unit can predict the tidying status of a specific area based on past progress data. For example, the monitoring unit can refer to past data to identify areas that frequently become cluttered and monitor them intensively. The monitoring unit can also use past data to compare tidying progress and find areas for improvement. For example, the monitoring unit can make suggestions to improve tidying efficiency based on past tidying data. This improves monitoring accuracy by referring to past data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input past progress data into a generating AI, and the generating AI can perform processes to improve monitoring accuracy.

[0088] The monitoring unit can change its monitoring methods by considering the child's behavior patterns. For example, the monitoring unit can analyze the child's behavior patterns and set appropriate monitoring timings. For example, the monitoring unit can adjust the frequency and method of monitoring based on the child's behavior patterns. The monitoring unit can also propose effective monitoring methods by considering the child's behavior patterns. For example, the monitoring unit can monitor the progress of tidying up based on the child's behavior patterns. This allows for more effective monitoring by considering behavior patterns. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input child behavior pattern data into a generating AI, which can then perform the process of changing the monitoring method.

[0089] The monitoring unit can estimate a child's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, the monitoring unit can capture a child's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on changes in facial expression. The monitoring unit can also record a child's voice and estimate the emotion using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the voice and calculate an emotion score. The monitoring unit can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, the monitoring unit can calculate an emotion score based on fluctuations in heart rate. This allows for a display that is easy for children to understand by adjusting the display method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input image data of a child captured by a camera into a generating AI, which can then estimate the child's emotions and perform processing to adjust how the monitoring results are displayed.

[0090] The monitoring unit can perform monitoring while considering environmental factors such as room lighting and music. For example, if the room lighting is bright, the monitoring unit can provide a highly visible monitoring method. For example, if music is playing in the room, the monitoring unit can provide a monitoring method using voice guidance. The monitoring unit can also select an appropriate monitoring method while considering the environmental factors of the room. For example, the monitoring unit can analyze the impact of room lighting and music on tidying up and propose the optimal monitoring method. This allows for more accurate monitoring by considering environmental factors. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input room lighting and music data into a generating AI, which can then perform the processing of monitoring while considering environmental factors.

[0091] The monitoring unit can incorporate feedback from parents and other family members into its monitoring. For example, the monitoring unit can adjust its monitoring methods based on feedback from parents and other family members. For example, the monitoring unit can incorporate family opinions and propose effective monitoring methods. The monitoring unit can also reflect feedback from parents and other family members and display the monitoring results. For example, the monitoring unit can monitor the progress of tidying up based on feedback from parents and other family members. This allows for more effective monitoring by incorporating family feedback. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input feedback data from parents and other family members into a generating AI, which can then perform monitoring while reflecting the feedback.

[0092] The reward unit can estimate a child's emotions and adjust the type and timing of rewards based on the estimated emotions. For example, the reward unit can capture a child's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reward unit can calculate an emotion score based on changes in facial expression. The reward unit can also record a child's voice and estimate their emotions using voice analysis technology. For example, the reward unit can analyze the tone and speed of the voice and calculate an emotion score. The reward unit can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reward unit can calculate an emotion score based on fluctuations in heart rate. This allows the child's motivation to be maintained by adjusting the type and timing of rewards according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reward unit may be performed using AI, for example, or without AI. For example, the reward unit can input image data of the child captured by the camera into a generating AI, which can then estimate the child's emotions and perform processing to adjust the type and timing of the reward.

[0093] The reward unit can select the optimal reward by referring to past reward history. For example, the reward unit may provide a similar reward based on past successful rewards. For example, the reward unit may provide a reward that takes into account areas for improvement based on past unsuccessful rewards. The reward unit can also analyze past reward history and select an appropriate reward. For example, the reward unit may select a reward that will please a child based on past reward history. In this way, the optimal reward can be selected by referring to past history. Some or all of the above processes in the reward unit may be performed using AI, for example, or without AI. For example, the reward unit may input past reward history data into a generating AI, which may then perform the process of selecting the optimal reward.

[0094] The reward unit can customize rewards based on the child's preferences and interests. For example, the reward unit can offer badges of the child's favorite characters as rewards. For example, the reward unit can offer rewards related to themes the child is interested in. The reward unit can also offer rewards that are fun and engaging, tailored to the child's preferences. For example, the reward unit can offer merchandise of the child's favorite characters as rewards. By providing rewards that match the child's preferences, the reward unit can increase the child's motivation. Some or all of the above processes in the reward unit may be performed using AI, for example, or not. For example, the reward unit can input data on the child's preferences and interests into a generating AI, which can then perform the process of customizing the rewards.

[0095] The reward unit can estimate a child's emotions and adjust the reward display method based on the estimated emotions. For example, the reward unit can capture a child's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the reward unit can calculate an emotion score based on changes in facial expression. The reward unit can also record a child's voice and estimate the emotion using voice analysis technology. For example, the reward unit can analyze the tone and speed of the voice and calculate an emotion score. The reward unit can also collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. For example, the reward unit can calculate an emotion score based on fluctuations in heart rate. This allows for displays that are easy for children to understand by adjusting the display method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward unit may be performed using AI, for example, or without AI. For example, the reward unit can input image data of a child captured by a camera into a generating AI, which can then estimate the child's emotions and adjust how the reward is displayed.

[0096] The rewards unit can provide rewards that can be shared by the whole family. For example, the rewards unit can provide games or activities that the whole family can enjoy as rewards. For example, the rewards unit can provide events or experiences that the whole family can participate in as rewards. The rewards unit can also provide perks or services that can be shared by the whole family as rewards. For example, the rewards unit can provide coupons that the whole family can use. This promotes family communication by providing rewards that can be shared by the whole family. Some or all of the above processing in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input reward data that can be shared by the whole family into a generating AI, and the generating AI can perform the process of providing rewards.

[0097] The rewards unit can propose long-term reward plans that are tailored to the child's learning outcomes and growth. For example, the rewards unit can provide a tiered reward plan based on the child's learning outcomes. For example, the rewards unit can set long-term goals in line with the child's growth and provide rewards accordingly. The rewards unit can also propose reward plans that take into account the child's progress and maintain continuous motivation. For example, the rewards unit can provide monthly goal achievement rewards and annual performance rewards. By providing a long-term reward plan, the child's continuous motivation can be maintained. Some or all of the above processes in the rewards unit may be performed using AI, for example, or not using AI. For example, the rewards unit can input the child's learning outcomes and growth data into a generating AI, which can then execute a process to propose a long-term reward plan.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can consider the room's lighting conditions when analyzing the room's state. For example, if the room is brightly lit, the analysis unit can more accurately analyze the arrangement of objects and cleanliness. Furthermore, if the room is dimly lit, the analysis unit can suggest auxiliary lighting to improve the accuracy of the analysis. In addition, the analysis unit can analyze the impact of the lighting's color temperature on the progress of tidying up and suggest optimal lighting conditions. This allows for more accurate analysis by considering lighting conditions.

[0100] The proposal department can adjust the way tidying-up tasks are presented according to the child's learning style. For example, tasks using images or videos can be presented to children who are visual learners. Tasks using audio guides can also be presented to children who are auditory learners. Furthermore, tasks that involve hands-on activity can be proposed for children who are tactile learners. By presenting tasks that match the child's learning style, children can learn to tidy up more effectively.

[0101] The monitoring unit can consider the child's behavior patterns when monitoring the child's progress in tidying up. For example, the monitoring unit can analyze the child's behavior patterns and set appropriate monitoring timings. Furthermore, the monitoring unit can adjust the frequency and method of monitoring based on the child's behavior patterns. In addition, the monitoring unit can suggest effective monitoring methods, taking behavior patterns into consideration. This allows for more effective monitoring.

[0102] The reward system allows for customization of rewards based on children's preferences and interests. For example, rewards can include badges of characters children like. Rewards related to themes children are interested in can also be provided. Furthermore, rewards can be tailored to children's preferences, making them enjoyable through play. This allows for increased motivation by providing rewards that match children's interests.

[0103] The proposed system can estimate a child's emotions and adjust the difficulty of tidying tasks based on those emotions. For example, a child's facial expressions can be captured with a camera, and their emotions can be estimated using an emotion estimation algorithm. Alternatively, a child's voice can be recorded, and their emotions can be estimated using voice analysis technology. Furthermore, a child's biometric data (heart rate and skin electrical activity) can be collected with sensors, and their emotions can be estimated using the emotion estimation algorithm. This allows the difficulty of tasks to be adjusted according to the child's emotions, enabling them to tidy up without undue stress.

[0104] The monitoring unit can estimate a child's emotions and adjust the monitoring frequency based on the estimated emotions. For example, it can capture a child's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record a child's voice and estimate their emotions using voice analysis technology. Furthermore, it can collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the monitoring frequency according to the child's emotions, the child can proceed with tidying up without feeling pressured.

[0105] The reward unit can estimate a child's emotions and adjust the type and timing of rewards based on those estimates. For example, it can capture a child's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record a child's voice and estimate their emotions using voice analysis technology. Furthermore, it can collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to maintain a child's motivation by adjusting the type and timing of rewards according to their emotions.

[0106] The proposed system can estimate a child's emotions and adjust the presentation method of tasks based on those estimated emotions. For example, a child's facial expressions can be captured with a camera, and their emotions can be estimated using an emotion estimation algorithm. Alternatively, a child's voice can be recorded, and their emotions can be estimated using voice analysis technology. Furthermore, a child's biometric data (heart rate and skin electrical activity) can be collected with sensors, and their emotions can be estimated using an emotion estimation algorithm. This allows for presentations that are easier for children to understand by adjusting the presentation method according to their emotions.

[0107] The monitoring unit can estimate a child's emotions and adjust the display method of the monitoring results based on the estimated emotions. For example, it can capture a child's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. It can also record a child's voice and estimate their emotions using voice analysis technology. Furthermore, it can collect a child's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the display method according to the child's emotions, it becomes possible to display information in a way that is easy for children to understand.

[0108] The rewards section can offer rewards that can be shared by the whole family. For example, games or activities that the whole family can enjoy can be offered as rewards. Events or experiences that the whole family can participate in can also be offered as rewards. Furthermore, perks or services that can be shared by the whole family can be offered as rewards. This promotes family communication by providing rewards that can be shared by the whole family.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The analysis unit analyzes the condition of the room. The analysis unit can, for example, use cameras and sensors to analyze the cleanliness of the room, the degree of tidiness, the arrangement of objects, etc. Step 2: The proposal unit proposes tidying tasks based on the results analyzed by the analysis unit. For example, the proposal unit can propose tidying tasks tailored to the child's age and personality, and can present specific tasks such as tidying up certain toys or organizing clothes. Step 3: The monitoring unit monitors the progress of the tidying-up tasks proposed by the proposal unit. For example, the monitoring unit can monitor the progress of children tidying up in real time, and monitor the degree of completion and the passage of time. Step 4: The rewards department provides rewards based on the progress monitored by the monitoring department. The rewards department provides rewards such as badges and level-ups depending on the achievement status. The rewards department can provide rewards such as badges, points, and level-ups.

[0111] 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.

[0112] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the analysis unit, proposal unit, monitoring unit, and reward unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the state of the room using the camera 42 and sensors of the smart device 14 and processes the analysis results with the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a tidying task based on the analysis results. The monitoring unit is implemented in the specific processing unit 46A of the smart device 14 and monitors the child's tidying progress in real time. The reward unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides a reward according to the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0120] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] 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.

[0122] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the analysis unit, proposal unit, monitoring unit, and reward unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the state of the room using the camera 42 and sensors of the smart glasses 214 and processes the analysis results with the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a tidying task based on the analysis results. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 and monitors the child's tidying progress in real time. The reward unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides a reward according to the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0136] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the analysis unit, proposal unit, monitoring unit, and reward unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes the state of the room using the camera 42 and sensors of the headset terminal 314 and processes the analysis results with the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a tidying task based on the analysis results. The monitoring unit is implemented in the specific processing unit 46A of the headset terminal 314 and monitors the child's tidying progress in real time. The reward unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides a reward according to the progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0152] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] 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.

[0154] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] 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.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the analysis unit, proposal unit, monitoring unit, and reward unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit analyzes the state of the room using the camera 42 and sensors of the robot 414, and the control unit 46A processes the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes a tidying task based on the analysis results. The monitoring unit is implemented in the specific processing unit 46A of the robot 414 and monitors the child's tidying progress in real time. The reward unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides a reward according to the progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] Figure 9 shows the 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.

[0166] 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.

[0167] 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.

[0168] 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, and motorcycles, 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 based, for example, 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.

[0169] 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."

[0170] 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.

[0171] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] 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 other things 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.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An analysis unit that analyzes the condition of the room, A proposal unit proposes a tidying-up task based on the results of the analysis performed by the aforementioned analysis unit, A monitoring unit monitors the progress of the tidying-up tasks proposed by the aforementioned proposal unit, The system includes a reward unit that provides rewards based on the progress monitored by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the room's condition using cameras and sensors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose tidying-up tasks tailored to the child's age and personality. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, Monitor your child's tidying-up progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned compensation unit is, Rewards such as badges and level-ups are provided based on achievement status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Present specific tasks, such as putting away certain toys or organizing clothes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the timing of the room condition analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Improve analysis accuracy by referencing past room condition data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The analysis method is changed depending on the purpose of use and time of day of the room. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The analysis takes into account environmental data such as room temperature and humidity. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The analysis takes into account the room's furniture arrangement and interior design. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, The system estimates the child's emotions and adjusts the difficulty level of the tidying-up task based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, Refer to past decluttering history to select the most suitable task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, Customize the assignment content based on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the child's emotions and adjusts the way tasks are presented based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, Apply different suggestion methods depending on the child's learning style and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, Propose a collaborative task to encourage participation from the whole family. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, The system estimates the child's emotions and adjusts the frequency of monitoring based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, Improve monitoring accuracy by referring to past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, Change monitoring methods to take into account the child's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, The system estimates the child's emotions and adjusts how the monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned monitoring unit, Monitoring is performed while taking into account environmental factors such as room lighting and music. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned monitoring unit, Monitoring is conducted by incorporating feedback from parents and other family members. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned compensation unit is, The system estimates the child's emotions and adjusts the type and timing of rewards based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned compensation unit is, Select the optimal compensation by referring to past compensation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned compensation unit is, Customize rewards based on the child's preferences and interests. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned compensation unit is, The system estimates the child's emotions and adjusts the reward display method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned compensation unit is, Provide rewards that can be shared by the whole family. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned compensation unit is, We propose a long-term reward plan that is tailored to the child's learning achievements and growth. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An analysis unit that analyzes the condition of the room, A proposal unit proposes a tidying-up task based on the results of the analysis performed by the aforementioned analysis unit, A monitoring unit monitors the progress of the tidying-up tasks proposed by the aforementioned proposal unit, The system includes a reward unit that provides rewards based on the progress monitored by the monitoring unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the room's condition using cameras and sensors. The system according to feature 1.

3. The aforementioned proposal section is, We propose tidying-up tasks tailored to the child's age and personality. The system according to feature 1.

4. The aforementioned monitoring unit, Monitor your child's tidying-up progress in real time. The system according to feature 1.

5. The aforementioned compensation unit is, Rewards such as badges and level-ups are provided based on achievement status. The system according to feature 1.

6. The aforementioned proposal section is, Present specific tasks, such as putting away certain toys or organizing clothes. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates the child's emotions and adjusts the timing of the room condition analysis based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, Improve analysis accuracy by referencing past room condition data. The system according to feature 1.

9. The aforementioned analysis unit, The analysis method is changed depending on the purpose of use and time of day of the room. The system according to feature 1.

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