system

Abstract

The system according to this embodiment aims to classify types of waste in real time, respond quickly to allergens, and propose the optimal cleaning time and area. [Solution] The system according to the embodiment comprises a classification unit, an alert unit, an analysis unit, and a scheduling unit. The classification unit classifies the types of waste in real time. The alert unit notifies an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit. The analysis unit analyzes the data collected based on the alerts notified by the alert unit. The scheduling unit proposes the optimal cleaning time and area based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to grasp the types and amounts of garbage, and there are problems such as difficulty in quickly responding to allergens, efficiently selecting cleaning timing and areas.

[0005] The system according to the embodiment aims to classify the types of garbage in real time, quickly respond to allergens, and propose optimal cleaning time and areas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a classification unit, an alert unit, an analysis unit, and a scheduling unit. The classification unit classifies the type of waste in real time. The alert unit notifies an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit. The analysis unit analyzes the data collected based on the alerts notified by the alert unit. The scheduling unit proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can classify the type of waste in real time, respond quickly to allergens, and suggest the optimal cleaning time and area. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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) An embodiment of the present invention provides an indoor environment optimization system that optimizes the indoor environment using AI to provide a healthy living space. The indoor environment optimization system uses sensors to classify types of debris such as dust, pet hair, mites, and pollen in real time. The indoor environment optimization system has a function to notify the user of an alert when a specific allergen exceeds a threshold. The indoor environment optimization system analyzes the collected data to identify areas with high levels of dirt and areas with high allergen concentrations. The indoor environment optimization system has a scheduling optimization function that proposes the optimal cleaning time and area based on time-of-day data. For example, the indoor environment optimization system uses sensors to classify types of debris such as dust, pet hair, mites, and pollen in real time. At this time, the sensors identify the type of debris and transmit the data to the classification unit. Next, based on the data classified by the classification unit, the alert unit notifies the user of an alert when a specific allergen exceeds a threshold. Furthermore, the analysis unit analyzes the collected data to identify areas with high levels of dirt and areas with high allergen concentrations. Finally, the scheduling unit proposes the optimal cleaning time and area based on time-of-day data. This enables the indoor environment optimization system to maintain a healthier indoor environment, provide peace of mind by responding quickly to allergens, and realize an efficient cleaning plan optimized for each household. In this way, the indoor environment optimization system can provide a healthy living space.

[0029] The indoor environment optimization system according to this embodiment comprises a classification unit, an alert unit, an analysis unit, and a scheduling unit. The classification unit classifies the type of waste in real time. The classification unit identifies and classifies the type of waste, such as dust, pet hair, mites, and pollen, in real time, for example, using sensors. The classification unit can identify the type of waste using, for example, optical sensors. The classification unit can also analyze the components of the waste using, for example, chemical sensors to identify the type. Furthermore, the classification unit can also identify the type of waste using, for example, AI. The alert unit notifies an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit. The alert unit notifies the user of, for example, when the concentration of an allergen exceeds a certain threshold. The alert unit can notify the user using, for example, an audio alert. The alert unit can also notify the user using, for example, a visual alert. Furthermore, the alert unit can also notify the user through, for example, a smartphone application. The analysis unit analyzes the data collected based on the alerts notified by the alert unit. The analysis unit can, for example, identify areas with high levels of dirt based on collected data. The analysis unit can, for example, use AI to analyze the data and identify areas with high levels of dirt. The analysis unit can also, for example, identify areas with high allergen concentrations. Furthermore, the analysis unit can, for example, analyze dirt generation patterns based on the data. The scheduling unit proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. The scheduling unit can, for example, propose the optimal cleaning time based on time-of-day data. The scheduling unit can, for example, use AI to propose the optimal cleaning time. Furthermore, the scheduling unit can, for example, propose the optimal cleaning area based on areas with high levels of dirt. Furthermore, the scheduling unit can, for example, propose the optimal cleaning time and area considering the user's lifestyle patterns. As a result, the indoor environment optimization system according to this embodiment can classify types of waste in real time, respond quickly to allergens, and provide an efficient cleaning plan.

[0030] The classification unit classifies waste types in real time. For example, it uses sensors to identify and classify different types of waste, such as dust, pet hair, mites, and pollen, in real time. Specifically, optical sensors analyze the shape and color of waste to distinguish between different types, such as dust and pet hair. Optical sensors, for example, use laser or LED light sources to detect reflected light from waste and analyze its pattern to identify the type of waste. Chemical sensors analyze the components of waste to detect the presence of specific chemical substances. For example, chemical sensors collect airborne particles and analyze their components to identify allergens such as mites and pollen. Furthermore, the AI-powered classification unit uses machine learning algorithms to identify waste types. AI can learn from large amounts of data and identify waste characteristics with high accuracy. For example, AI uses image recognition technology to analyze images of waste and identify the type of waste based on its shape, color, and texture. This allows the classification unit to combine diverse sensor technologies with AI to classify waste types with high accuracy and in real time.

[0031] The alert unit notifies the user of an alert when a specific allergen exceeds a threshold based on data classified by the classification unit. For example, the alert unit notifies the user of an alert when the concentration of an allergen exceeds a certain threshold. Specifically, the alert unit monitors the concentration of allergens in real time and immediately notifies the user when the threshold is exceeded. Audio alerts emit warning sounds or voice messages to the user through a speaker to inform them of the presence of an allergen. Visual alerts use LED lights or displays to warn the user through flashing or color changes. Furthermore, when notifying via a smartphone app, the alert unit sends a push notification to the app and displays allergen information on the user's smartphone. The app provides detailed information such as the type and concentration of the allergen and the location of occurrence, enabling the user to respond quickly. In this way, the alert unit can quickly and reliably notify the user of allergen information using a variety of means, including audio, visual, and smartphone apps.

[0032] The analysis unit analyzes data collected based on alerts notified by the alert unit. For example, the analysis unit can identify areas with high levels of dirt based on the collected data. Specifically, the analysis unit integrates data such as the type and concentration of waste and the location of origin to identify areas with high levels of dirt. The AI-powered analysis unit uses machine learning algorithms to analyze the data and identify areas with high levels of dirt and allergens. For example, the AI ​​can learn from past data and predict areas where dirt is likely to occur at specific times and under specific conditions. The analysis unit can also analyze dirt occurrence patterns based on the data. For example, if there is a tendency for dirt to increase on specific days of the week or at specific times, it can analyze that pattern and propose preventive measures. In this way, the analysis unit can analyze the collected data from multiple angles, identify areas with high levels of dirt and allergens, and develop an efficient cleaning plan.

[0033] The scheduling unit proposes the optimal cleaning time and area based on data analyzed by the analysis unit. For example, the scheduling unit can propose the optimal cleaning time based on time-of-day data. Specifically, the scheduling unit proposes the optimal cleaning time considering the user's lifestyle patterns and room usage. The AI-powered scheduling unit uses machine learning algorithms to learn from past data and propose the optimal cleaning time and area. For example, the AI ​​analyzes the user's lifestyle patterns to identify the times and areas where the room is most likely to get dirty, and then creates a cleaning schedule based on that information. The scheduling unit can also propose the optimal cleaning area based on areas with high levels of dirt. For example, it can set the schedule to prioritize cleaning the areas with high levels of dirt identified by the analysis unit. Furthermore, the scheduling unit can collect user feedback and continuously improve the accuracy and effectiveness of the schedule. As a result, the scheduling unit can provide an efficient cleaning plan that takes into account the user's lifestyle patterns and room usage, optimizing the indoor environment.

[0034] The classification unit can classify types of waste, such as dust, pet hair, mites, and pollen, in real time using sensors. The classification unit can identify types of waste, for example, using optical sensors. The classification unit can also analyze the components of waste and identify types, for example, using chemical sensors. The classification unit can also identify types of waste, for example, using AI. This allows for accurate classification of waste types using sensors. The specific time range and update frequency of "real time" can be, for example, seconds or minutes. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input data acquired by sensors into a generating AI and have the generating AI perform the classification of waste types.

[0035] The alert unit can notify the user of an alert when a specific allergen exceeds a threshold. For example, the alert unit can notify the user of an alert when the concentration of an allergen exceeds a certain threshold. The alert unit can notify the user using, for example, an audio alert. The alert unit can also notify the user using, for example, a visual alert. The alert unit can also notify the user through, for example, a smartphone app. This allows the user to be quickly notified when an allergen exceeds a threshold. The specific numerical value and setting method of the threshold may be, for example, a specific numerical value for the allergen concentration. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input allergen concentration data into a generating AI and have the generating AI execute the alert notification.

[0036] The analysis unit can analyze the collected data and identify areas with high levels of dirt and areas with high allergen concentrations. For example, the analysis unit can identify areas with high levels of dirt based on the collected data. For example, the analysis unit can use AI to analyze the data and identify areas with high levels of dirt. The analysis unit can also identify areas with high levels of allergen concentrations. For example, the analysis unit can analyze the dirt occurrence patterns based on the data. This allows for more efficient cleaning by identifying areas with high levels of dirt and areas with high allergen concentrations. Specific criteria and methods for identifying areas with high levels of dirt include, for example, the frequency of dirt and the type of dirt. Specific criteria and methods for identifying areas with high levels of allergen concentrations include, for example, specific numerical values ​​of allergen concentrations. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI identify areas with high levels of dirt and areas with high allergen concentrations.

[0037] The scheduling unit can propose the optimal cleaning time and area based on time-of-day data. For example, the scheduling unit can propose the optimal cleaning time based on time-of-day data. For example, the scheduling unit can propose the optimal cleaning time using AI. For example, the scheduling unit can also propose the optimal cleaning area based on areas with high levels of dirt. For example, the scheduling unit can propose the optimal cleaning time and area considering the user's lifestyle patterns. This enables efficient cleaning by proposing the optimal cleaning time and area based on time-of-day data. Specific criteria and methods for determining the optimal cleaning time include, for example, the user's lifestyle patterns and the frequency of dirt occurrence. Specific criteria and methods for determining the optimal area include, for example, areas with high levels of dirt and areas with high allergen concentrations. Some or all of the above-described processes in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input time-of-day data into a generating AI and have the generating AI propose the optimal cleaning time and area.

[0038] The classification unit can identify the source of waste and classify it according to its source. For example, the classification unit can identify waste generated from the kitchen and classify it as food waste. The classification unit can also identify waste generated from the living room and classify it as general waste. The classification unit can also identify waste generated from the bathroom and classify it as sanitary waste. This allows for more detailed classification by identifying the source of the waste. Specific methods and criteria for identifying the source of waste include, for example, the location of the room and the type of activity. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input waste source data into a generating AI and have the generating AI perform classification according to the source.

[0039] The classification unit can optimize its classification algorithm based on the size and shape of the waste. For example, the classification unit can classify large waste as bulky waste and apply a specific processing method. The classification unit can also classify small waste as general waste and apply a standard processing method. The classification unit can also classify waste with a specific shape as recyclable waste and apply a recycling process. This allows for more accurate classification by optimizing the classification algorithm based on the size and shape of the waste. Specific measurement methods and standards for waste size include, for example, millimeters or centimeters. Specific identification methods and standards for waste shape include, for example, round or square. Some or all of the above processing in the classification unit may be performed using AI, or not. For example, the classification unit can input waste size and shape data into a generating AI and have the generating AI optimize the classification algorithm.

[0040] The classification unit can improve classification accuracy by taking into account the indoor temperature and humidity. For example, in a hot and humid environment, the classification unit can classify waste quickly to prevent decomposition. For example, in a cold and dry environment, the classification unit can also classify waste while considering its storage condition. The classification unit can also adjust the waste classification algorithm according to the indoor temperature and humidity. This improves classification accuracy by taking indoor temperature and humidity into account. Specific measurement methods and standards for indoor temperature include, for example, Celsius and Fahrenheit. Specific measurement methods and standards for indoor humidity include, for example, relative humidity and absolute humidity. Some or all of the above processing in the classification unit may be performed using, for example, AI, or without AI. For example, the classification unit can input indoor temperature and humidity data into a generating AI and have the generating AI perform the task of improving classification accuracy.

[0041] The classification unit can identify and classify waste based on its color and texture. For example, the classification unit can identify waste of different colors and classify it as recyclable waste. The classification unit can also identify waste of different textures and apply specific processing methods to it. The classification unit can also optimize its classification algorithm based on the color and texture of the waste. This allows for more detailed classification by identifying the color and texture of the waste. Specific methods and criteria for identifying the color of waste include, for example, RGB values ​​and hue. Specific methods and criteria for identifying the texture of waste include, for example, surface roughness and hardness. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input waste color and texture data into a generating AI and have the generating AI perform the classification.

[0042] The alert unit can set different alert levels depending on the type of allergen. For example, in the case of pollen allergens, the alert unit will notify a mild alert. For example, in the case of dust mite allergens, the alert unit may also notify a moderate alert. For example, in the case of pet dander allergens, the alert unit may also notify a severe alert. This allows for more appropriate alerts by setting different alert levels depending on the type of allergen. Specific classification methods and criteria for allergen types include, for example, pollen, dust mites, and pet dander. Specific setting methods and criteria for alert levels include, for example, low, medium, and high. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input allergen type data into a generating AI and have the generating AI set the alert level.

[0043] The alert unit can determine alert priorities by referring to the user's past allergen reaction history. For example, the alert unit will prioritize alerts for allergens that the user has reacted strongly to in the past. The alert unit can also, for example, send low-priority alerts for allergens that the user has reacted mildly to in the past. The alert unit can also, for example, analyze the user's past allergen reaction history to determine the optimal alert priority. This allows for more appropriate alerts by referring to the user's past allergen reaction history. Specific methods and criteria for referring to the user's past allergen reaction history include, for example, records of past allergy attacks and medical data. Specific methods and criteria for determining alert priority include, for example, the risk level of the allergen and the user's health status. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the user's past allergen reaction history data into a generating AI and have the generating AI determine the alert priority.

[0044] The alert unit can select the optimal notification timing by considering the user's current activity status. For example, if the user is working, the alert unit may send an alert during a break. If the user is exercising, the alert unit may send an alert after the exercise. If the user is sleeping, the alert unit may send an alert after waking up. This allows for more appropriate notification timing by considering the user's current activity status. Specific methods and criteria for determining the user's current activity status include, for example, exercising or resting. Specific methods and criteria for selecting the optimal notification timing include, for example, the user's activity status and time of day. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user activity data into a generating AI and have the generating AI select the notification timing.

[0045] The alert unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the alert unit will send an alert via push notification. If the user is using a tablet, the alert unit can also send an alert as a pop-up on the screen. If the user is using a smartwatch, the alert unit can also send an alert via vibration. This allows for a more appropriate notification method by considering the user's device information. Specific methods and criteria for referencing the user's device information include, for example, smartphones, tablets, and smartwatches. Specific methods and criteria for selecting the optimal notification method include, for example, voice notifications, vibrations, and screen displays. Some or all of the above processing in the alert unit may be performed using, for example, AI, or without AI. For example, the alert unit can input user device information data into a generating AI and have the generating AI select the notification method.

[0046] The analysis unit can predict current dirt and allergen trends by referring to past data. For example, the analysis unit can predict seasonal allergen trends based on past data. The analysis unit can also predict dirt trends during specific time periods based on past data. The analysis unit can also predict dirt and allergen trends in specific areas based on past data. In this way, current dirt and allergen trends can be predicted by referring to past data. Specific methods and criteria for referring to past data include, for example, past cleaning history and allergen concentration records. Specific methods and criteria for predicting current dirt and allergen trends include, for example, seasonal fluctuations and user activity patterns. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform predictions of current dirt and allergen trends.

[0047] The analysis unit can apply different analysis algorithms to each type of waste. For example, for dust, the analysis unit can apply an algorithm that detects fine particles. For example, for pet hair, the analysis unit can apply an algorithm that identifies length and texture. For example, for mites, the analysis unit can apply an algorithm that detects specific shapes and movements. This enables optimal analysis for each type of waste. Specific classification methods and criteria for waste types include, for example, dust, pet hair, mites, and pollen. Specific types and application methods for analysis algorithms include, for example, machine learning algorithms and statistical analysis algorithms. 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 waste type data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0048] The analysis unit can identify the distribution of dirt and allergens by considering the room layout information. For example, the analysis unit can identify areas where dirt tends to accumulate by considering the arrangement of furniture in the room. The analysis unit can also identify the distribution of allergens by considering the location of ventilation openings in the room. The analysis unit can also analyze the distribution of dirt and allergens based on the room layout information. This allows for accurate identification of the distribution of dirt and allergens by considering the room layout information. Specific methods and criteria for acquiring room layout information include, for example, the room layout and furniture arrangement. Specific methods and criteria for identifying the distribution of dirt and allergens include, for example, the degree of dirt concentration and the concentration of allergens. 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 room layout information data into a generating AI and have the generating AI perform the identification of the distribution of dirt and allergens.

[0049] The analysis unit can analyze the effects of indoor dirt and allergens by referring to external environmental data. For example, the analysis unit can analyze the effects of indoor dirt and allergens by referring to external weather data. The analysis unit can also analyze the effects of indoor dirt and allergens by referring to external air quality data. The analysis unit can also analyze the effects of indoor dirt and allergens based on external environmental data. This allows for an accurate analysis of the effects of indoor dirt and allergens by referring to external environmental data. Specific methods and criteria for referencing external environmental data include, for example, weather data and air quality data. Specific methods and criteria for analyzing the effects of indoor dirt and allergens include, for example, correlation with the external environment and degree of influence. 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 external environmental data into a generating AI and have the generating AI perform an analysis of the effects of indoor dirt and allergens.

[0050] The scheduling unit can suggest the optimal cleaning time and area by referring to past cleaning history. For example, the scheduling unit can suggest the optimal cleaning time based on the time slots the user has cleaned in the past. The scheduling unit can also suggest the optimal cleaning area based on the areas the user has cleaned in the past. For example, the scheduling unit can analyze the user's past cleaning history and suggest the most efficient cleaning time and area. In this way, the optimal cleaning time and area can be suggested by referring to past cleaning history. Specific methods and criteria for referring to past cleaning history include, for example, cleaning frequency and cleaning content. Specific criteria and methods for determining the optimal cleaning time include, for example, the user's lifestyle pattern and the frequency of dirt occurrence. Specific criteria and methods for determining the optimal area include, for example, areas with a lot of dirt and areas with high allergen concentrations. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input past cleaning history data into a generating AI and have the generating AI suggest the optimal cleaning time and area.

[0051] The scheduling unit can suggest different cleaning methods depending on the type and amount of waste. For example, if there is a lot of dust, the scheduling unit may suggest a cleaning method using a vacuum cleaner with strong suction power. For example, if there is a lot of pet hair, the scheduling unit may suggest a cleaning method using a vacuum cleaner with a brush attachment. For example, if there are a lot of dust mites, the scheduling unit may suggest a cleaning method using a vacuum cleaner with a sterilization function. This allows the system to suggest the optimal cleaning method depending on the type and amount of waste. Specific classification methods and criteria for waste types include, for example, dust, pet hair, dust mites, and pollen. Specific measurement methods and criteria for waste amounts include, for example, weight and volume. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input waste type and amount data into a generating AI and have the generating AI execute the cleaning method suggestion.

[0052] The scheduling unit can suggest the optimal cleaning time by considering the user's lifestyle patterns. For example, the scheduling unit can suggest cleaning before the user leaves for work. For example, the scheduling unit can suggest cleaning during the user's relaxation time after returning home. For example, the scheduling unit can analyze the user's lifestyle patterns and suggest the optimal cleaning time. In this way, the optimal cleaning time can be suggested by considering the user's lifestyle patterns. Specific methods and criteria for understanding the user's lifestyle patterns include, for example, daily activity patterns and daily rhythms. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input the user's lifestyle pattern data into a generating AI and have the generating AI suggest the optimal cleaning time.

[0053] The scheduling unit can propose the optimal cleaning area by considering the user's device information. For example, if the user is using a smartphone, the scheduling unit will propose a cleaning area based on the smartphone's location information. If the user is using a tablet, the scheduling unit can also propose a cleaning area based on the tablet's location information. The scheduling unit can also propose the optimal cleaning area based on the user's device information. In this way, the optimal cleaning area can be proposed by considering the user's device information. Specific methods and criteria for referencing the user's device information include, for example, smartphones, tablets, and smartwatches. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input user device information data into a generating AI and have the generating AI propose the optimal cleaning area.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The classification unit can adjust its classification accuracy by considering the frequency and seasonality of waste generation when identifying types of waste. For example, it can improve the accuracy of pollen identification in spring when pollen levels are high, and improve the accuracy of dust identification in winter. It can also predict the types of waste generated after specific events and improve the identification accuracy for those types. This enables optimal waste classification according to the season and events.

[0056] The alert function can adjust the content of alerts based on the user's health condition. For example, if a user has allergies, an alert can be sent even if the concentration of the allergen is low. Also, if a user has a cold, an alert can be sent with stricter criteria than usual. This enables appropriate alert notifications tailored to the user's health condition.

[0057] The classification unit can identify the source of waste and classify it according to its origin. For example, it can identify waste generated in the kitchen and classify it as food waste. It can also identify waste generated in the living room and classify it as general waste. It can also identify waste generated in the bathroom and classify it as sanitary waste. This allows for more detailed classification by identifying the source of the waste.

[0058] The classification unit can optimize its classification algorithm based on the size and shape of the waste. For example, large waste can be classified as bulky waste and treated with a specific disposal method. Small waste can be classified as general waste and treated with a standard disposal method. Waste with a specific shape can be classified as recyclable waste and treated with a recycling process. By optimizing the classification algorithm based on the size and shape of the waste, more accurate classification becomes possible.

[0059] The analysis unit can predict current trends in dirt and allergens by referring to past data. For example, it can predict seasonal allergen trends based on past data. It can also predict dirt trends during specific time periods. It can also predict dirt and allergen trends in specific areas. In this way, current trends in dirt and allergens can be predicted by referring to past data.

[0060] The scheduling unit can suggest different cleaning methods depending on the type and amount of waste. For example, if there is a lot of dust, it can suggest using a vacuum cleaner with strong suction. If there is a lot of pet hair, it can suggest using a vacuum cleaner with a brush attachment. If there are a lot of dust mites, it can suggest using a vacuum cleaner with a sterilization function. This allows the system to suggest the optimal cleaning method according to the type and amount of waste.

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

[0062] Step 1: The classification unit classifies the type of waste in real time. The classification unit uses sensors to identify and classify different types of waste, such as dust, pet hair, mites, and pollen, in real time. Optical sensors, chemical sensors, and AI can be used to identify the type of waste. Step 2: The alert unit notifies the user of an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit. The alert unit notifies the user of an alert when the concentration of an allergen exceeds a certain threshold. Notifications can be sent to the user via audio alerts, visual alerts, or a smartphone app. Step 3: The analysis unit analyzes the data collected based on the alerts notified by the alert unit. Based on the collected data, the analysis unit identifies areas with high levels of dirt and areas with high allergen concentrations. AI can also be used to analyze the data and analyze the patterns of dirt occurrence. Step 4: The scheduling unit proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. The scheduling unit proposes the optimal cleaning time and area considering time-of-day data and the user's lifestyle patterns. It can also propose the optimal cleaning time using AI.

[0063] (Example of form 2) An embodiment of the present invention provides an indoor environment optimization system that optimizes the indoor environment using AI to provide a healthy living space. The indoor environment optimization system uses sensors to classify types of debris such as dust, pet hair, mites, and pollen in real time. The indoor environment optimization system has a function to notify the user of an alert when a specific allergen exceeds a threshold. The indoor environment optimization system analyzes the collected data to identify areas with high levels of dirt and areas with high allergen concentrations. The indoor environment optimization system has a scheduling optimization function that proposes the optimal cleaning time and area based on time-of-day data. For example, the indoor environment optimization system uses sensors to classify types of debris such as dust, pet hair, mites, and pollen in real time. At this time, the sensors identify the type of debris and transmit the data to the classification unit. Next, based on the data classified by the classification unit, the alert unit notifies the user of an alert when a specific allergen exceeds a threshold. Furthermore, the analysis unit analyzes the collected data to identify areas with high levels of dirt and areas with high allergen concentrations. Finally, the scheduling unit proposes the optimal cleaning time and area based on time-of-day data. This enables the indoor environment optimization system to maintain a healthier indoor environment, provide peace of mind by responding quickly to allergens, and realize an efficient cleaning plan optimized for each household. In this way, the indoor environment optimization system can provide a healthy living space.

[0064] The indoor environment optimization system according to this embodiment comprises a classification unit, an alert unit, an analysis unit, and a scheduling unit. The classification unit classifies the type of waste in real time. The classification unit identifies and classifies the type of waste, such as dust, pet hair, mites, and pollen, in real time, for example, using sensors. The classification unit can identify the type of waste using, for example, optical sensors. The classification unit can also analyze the components of the waste using, for example, chemical sensors to identify the type. Furthermore, the classification unit can also identify the type of waste using, for example, AI. The alert unit notifies an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit. The alert unit notifies the user of, for example, when the concentration of an allergen exceeds a certain threshold. The alert unit can notify the user using, for example, an audio alert. The alert unit can also notify the user using, for example, a visual alert. Furthermore, the alert unit can also notify the user through, for example, a smartphone application. The analysis unit analyzes the data collected based on the alerts notified by the alert unit. The analysis unit can, for example, identify areas with high levels of dirt based on collected data. The analysis unit can, for example, use AI to analyze the data and identify areas with high levels of dirt. The analysis unit can also, for example, identify areas with high allergen concentrations. Furthermore, the analysis unit can, for example, analyze dirt generation patterns based on the data. The scheduling unit proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. The scheduling unit can, for example, propose the optimal cleaning time based on time-of-day data. The scheduling unit can, for example, use AI to propose the optimal cleaning time. Furthermore, the scheduling unit can, for example, propose the optimal cleaning area based on areas with high levels of dirt. Furthermore, the scheduling unit can, for example, propose the optimal cleaning time and area considering the user's lifestyle patterns. As a result, the indoor environment optimization system according to this embodiment can classify types of waste in real time, respond quickly to allergens, and provide an efficient cleaning plan.

[0065] The classification unit classifies waste types in real time. For example, it uses sensors to identify and classify different types of waste, such as dust, pet hair, mites, and pollen, in real time. Specifically, optical sensors analyze the shape and color of waste to distinguish between different types, such as dust and pet hair. Optical sensors, for example, use laser or LED light sources to detect reflected light from waste and analyze its pattern to identify the type of waste. Chemical sensors analyze the components of waste to detect the presence of specific chemical substances. For example, chemical sensors collect airborne particles and analyze their components to identify allergens such as mites and pollen. Furthermore, the AI-powered classification unit uses machine learning algorithms to identify waste types. AI can learn from large amounts of data and identify waste characteristics with high accuracy. For example, AI uses image recognition technology to analyze images of waste and identify the type of waste based on its shape, color, and texture. This allows the classification unit to combine diverse sensor technologies with AI to classify waste types with high accuracy and in real time.

[0066] The alert unit notifies the user of an alert when a specific allergen exceeds a threshold based on data classified by the classification unit. For example, the alert unit notifies the user of an alert when the concentration of an allergen exceeds a certain threshold. Specifically, the alert unit monitors the concentration of allergens in real time and immediately notifies the user when the threshold is exceeded. Audio alerts emit warning sounds or voice messages to the user through a speaker to inform them of the presence of an allergen. Visual alerts use LED lights or displays to warn the user through flashing or color changes. Furthermore, when notifying via a smartphone app, the alert unit sends a push notification to the app and displays allergen information on the user's smartphone. The app provides detailed information such as the type and concentration of the allergen and the location of occurrence, enabling the user to respond quickly. In this way, the alert unit can quickly and reliably notify the user of allergen information using a variety of means, including audio, visual, and smartphone apps.

[0067] The analysis unit analyzes data collected based on alerts notified by the alert unit. For example, the analysis unit can identify areas with high levels of dirt based on the collected data. Specifically, the analysis unit integrates data such as the type and concentration of waste and the location of origin to identify areas with high levels of dirt. The AI-powered analysis unit uses machine learning algorithms to analyze the data and identify areas with high levels of dirt and allergens. For example, the AI ​​can learn from past data and predict areas where dirt is likely to occur at specific times and under specific conditions. The analysis unit can also analyze dirt occurrence patterns based on the data. For example, if there is a tendency for dirt to increase on specific days of the week or at specific times, it can analyze that pattern and propose preventive measures. In this way, the analysis unit can analyze the collected data from multiple angles, identify areas with high levels of dirt and allergens, and develop an efficient cleaning plan.

[0068] The scheduling unit proposes the optimal cleaning time and area based on data analyzed by the analysis unit. For example, the scheduling unit can propose the optimal cleaning time based on time-of-day data. Specifically, the scheduling unit proposes the optimal cleaning time considering the user's lifestyle patterns and room usage. The AI-powered scheduling unit uses machine learning algorithms to learn from past data and propose the optimal cleaning time and area. For example, the AI ​​analyzes the user's lifestyle patterns to identify the times and areas where the room is most likely to get dirty, and then creates a cleaning schedule based on that information. The scheduling unit can also propose the optimal cleaning area based on areas with high levels of dirt. For example, it can set the schedule to prioritize cleaning the areas with high levels of dirt identified by the analysis unit. Furthermore, the scheduling unit can collect user feedback and continuously improve the accuracy and effectiveness of the schedule. As a result, the scheduling unit can provide an efficient cleaning plan that takes into account the user's lifestyle patterns and room usage, optimizing the indoor environment.

[0069] The classification unit can classify types of waste, such as dust, pet hair, mites, and pollen, in real time using sensors. The classification unit can identify types of waste, for example, using optical sensors. The classification unit can also analyze the components of waste and identify types, for example, using chemical sensors. The classification unit can also identify types of waste, for example, using AI. This allows for accurate classification of waste types using sensors. The specific time range and update frequency of "real time" can be, for example, seconds or minutes. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input data acquired by sensors into a generating AI and have the generating AI perform the classification of waste types.

[0070] The alert unit can notify the user of an alert when a specific allergen exceeds a threshold. For example, the alert unit can notify the user of an alert when the concentration of an allergen exceeds a certain threshold. The alert unit can notify the user using, for example, an audio alert. The alert unit can also notify the user using, for example, a visual alert. The alert unit can also notify the user through, for example, a smartphone app. This allows the user to be quickly notified when an allergen exceeds a threshold. The specific numerical value and setting method of the threshold may be, for example, a specific numerical value for the allergen concentration. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input allergen concentration data into a generating AI and have the generating AI execute the alert notification.

[0071] The analysis unit can analyze the collected data and identify areas with high levels of dirt and areas with high allergen concentrations. For example, the analysis unit can identify areas with high levels of dirt based on the collected data. For example, the analysis unit can use AI to analyze the data and identify areas with high levels of dirt. The analysis unit can also identify areas with high levels of allergen concentrations. For example, the analysis unit can analyze the dirt occurrence patterns based on the data. This allows for more efficient cleaning by identifying areas with high levels of dirt and areas with high allergen concentrations. Specific criteria and methods for identifying areas with high levels of dirt include, for example, the frequency of dirt and the type of dirt. Specific criteria and methods for identifying areas with high levels of allergen concentrations include, for example, specific numerical values ​​of allergen concentrations. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI identify areas with high levels of dirt and areas with high allergen concentrations.

[0072] The scheduling unit can propose the optimal cleaning time and area based on time-of-day data. For example, the scheduling unit can propose the optimal cleaning time based on time-of-day data. For example, the scheduling unit can propose the optimal cleaning time using AI. For example, the scheduling unit can also propose the optimal cleaning area based on areas with high levels of dirt. For example, the scheduling unit can propose the optimal cleaning time and area considering the user's lifestyle patterns. This enables efficient cleaning by proposing the optimal cleaning time and area based on time-of-day data. Specific criteria and methods for determining the optimal cleaning time include, for example, the user's lifestyle patterns and the frequency of dirt occurrence. Specific criteria and methods for determining the optimal area include, for example, areas with high levels of dirt and areas with high allergen concentrations. Some or all of the above-described processes in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input time-of-day data into a generating AI and have the generating AI propose the optimal cleaning time and area.

[0073] The classification unit can estimate the user's emotions and adjust the accuracy of waste classification based on the estimated emotions. For example, if the user is stressed, the classification unit can increase the classification accuracy to identify the type of waste in detail. For example, if the user is relaxed, the classification unit can loosen the classification accuracy and perform a rougher classification. For example, if the user is in a hurry, the classification unit can prioritize classification speed and quickly identify the type of waste. This allows for more appropriate classification by adjusting the waste classification accuracy according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific evaluation criteria and adjustment methods for classification accuracy include, for example, classification accuracy and misclassification rate. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input user emotion data into the generating AI and have the generating AI adjust the accuracy of the waste classification.

[0074] The classification unit can identify the source of waste and classify it according to its source. For example, the classification unit can identify waste generated from the kitchen and classify it as food waste. The classification unit can also identify waste generated from the living room and classify it as general waste. The classification unit can also identify waste generated from the bathroom and classify it as sanitary waste. This allows for more detailed classification by identifying the source of the waste. Specific methods and criteria for identifying the source of waste include, for example, the location of the room and the type of activity. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input waste source data into a generating AI and have the generating AI perform classification according to the source.

[0075] The classification unit can optimize its classification algorithm based on the size and shape of the waste. For example, the classification unit can classify large waste as bulky waste and apply a specific processing method. The classification unit can also classify small waste as general waste and apply a standard processing method. The classification unit can also classify waste with a specific shape as recyclable waste and apply a recycling process. This allows for more accurate classification by optimizing the classification algorithm based on the size and shape of the waste. Specific measurement methods and standards for waste size include, for example, millimeters or centimeters. Specific identification methods and standards for waste shape include, for example, round or square. Some or all of the above processing in the classification unit may be performed using AI, or not. For example, the classification unit can input waste size and shape data into a generating AI and have the generating AI optimize the classification algorithm.

[0076] The classification unit can estimate the user's emotions and adjust the display method of the classification results based on the estimated user emotions. For example, if the user is tense, the classification unit provides a simple and highly visible display method. For example, if the user is relaxed, the classification unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the classification unit can also provide a display method that gets straight to the point. By adjusting the display method of the classification results according to the user's emotions, a more appropriate display becomes possible. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific methods and criteria for adjusting the display method of the classification results include, for example, color coding and graph display. 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) and multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input user emotion data into the generating AI and have the generating AI adjust how the classification results are displayed.

[0077] The classification unit can improve classification accuracy by taking into account the indoor temperature and humidity. For example, in a hot and humid environment, the classification unit can classify waste quickly to prevent decomposition. For example, in a cold and dry environment, the classification unit can also classify waste while considering its storage condition. The classification unit can also adjust the waste classification algorithm according to the indoor temperature and humidity. This improves classification accuracy by taking indoor temperature and humidity into account. Specific measurement methods and standards for indoor temperature include, for example, Celsius and Fahrenheit. Specific measurement methods and standards for indoor humidity include, for example, relative humidity and absolute humidity. Some or all of the above processing in the classification unit may be performed using, for example, AI, or without AI. For example, the classification unit can input indoor temperature and humidity data into a generating AI and have the generating AI perform the task of improving classification accuracy.

[0078] The classification unit can identify and classify waste based on its color and texture. For example, the classification unit can identify waste of different colors and classify it as recyclable waste. The classification unit can also identify waste of different textures and apply specific processing methods to it. The classification unit can also optimize its classification algorithm based on the color and texture of the waste. This allows for more detailed classification by identifying the color and texture of the waste. Specific methods and criteria for identifying the color of waste include, for example, RGB values ​​and hue. Specific methods and criteria for identifying the texture of waste include, for example, surface roughness and hardness. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input waste color and texture data into a generating AI and have the generating AI perform the classification.

[0079] The alert unit can estimate the user's emotions and adjust the alert notification method based on the estimated emotions. For example, if the user is tense, the alert unit may notify the user with a calm voice. For example, if the user is relaxed, the alert unit may notify the user with a cheerful voice. For example, if the user is in a hurry, the alert unit may notify the user with a quick and concise voice. By adjusting the alert notification method according to the user's emotions, more appropriate notifications can be made. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific methods and criteria for adjusting the alert notification method include, for example, voice notification and vibration. 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) and multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user emotion data into a generating AI and have the AI ​​adjust the alert notification method.

[0080] The alert unit can set different alert levels depending on the type of allergen. For example, in the case of pollen allergens, the alert unit will notify a mild alert. For example, in the case of dust mite allergens, the alert unit may also notify a moderate alert. For example, in the case of pet dander allergens, the alert unit may also notify a severe alert. This allows for more appropriate alerts by setting different alert levels depending on the type of allergen. Specific classification methods and criteria for allergen types include, for example, pollen, dust mites, and pet dander. Specific setting methods and criteria for alert levels include, for example, low, medium, and high. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input allergen type data into a generating AI and have the generating AI set the alert level.

[0081] The alert unit can determine alert priorities by referring to the user's past allergen reaction history. For example, the alert unit will prioritize alerts for allergens that the user has reacted strongly to in the past. The alert unit can also, for example, send low-priority alerts for allergens that the user has reacted mildly to in the past. The alert unit can also, for example, analyze the user's past allergen reaction history to determine the optimal alert priority. This allows for more appropriate alerts by referring to the user's past allergen reaction history. Specific methods and criteria for referring to the user's past allergen reaction history include, for example, records of past allergy attacks and medical data. Specific methods and criteria for determining alert priority include, for example, the risk level of the allergen and the user's health status. Some or all of the above processing in the alert unit may be performed using, for example, AI, or not using AI. For example, the alert unit can input the user's past allergen reaction history data into a generating AI and have the generating AI determine the alert priority.

[0082] The alert unit can estimate the user's emotions and customize the alert content based on the estimated emotions. For example, if the user is nervous, the alert unit can provide a detailed alert. For example, if the user is relaxed, the alert unit can provide a concise alert. For example, if the user is in a hurry, the alert unit can provide a to-the-point alert. This allows for more appropriate notifications by customizing the alert content according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific methods and criteria for customizing the alert content include, for example, the content of the notification message and the type of warning sound. Emotion estimation is achieved using an emotion estimation function, for example, 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 alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user emotion data into the generative AI and have the generative AI customize the alert content.

[0083] The alert unit can select the optimal notification timing by considering the user's current activity status. For example, if the user is working, the alert unit may send an alert during a break. If the user is exercising, the alert unit may send an alert after the exercise. If the user is sleeping, the alert unit may send an alert after waking up. This allows for more appropriate notification timing by considering the user's current activity status. Specific methods and criteria for determining the user's current activity status include, for example, exercising or resting. Specific methods and criteria for selecting the optimal notification timing include, for example, the user's activity status and time of day. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user activity data into a generating AI and have the generating AI select the notification timing.

[0084] The alert unit can select the optimal notification method by considering the user's device information. For example, if the user is using a smartphone, the alert unit will send an alert via push notification. If the user is using a tablet, the alert unit can also send an alert as a pop-up on the screen. If the user is using a smartwatch, the alert unit can also send an alert via vibration. This allows for a more appropriate notification method by considering the user's device information. Specific methods and criteria for referencing the user's device information include, for example, smartphones, tablets, and smartwatches. Specific methods and criteria for selecting the optimal notification method include, for example, voice notifications, vibrations, and screen displays. Some or all of the above processing in the alert unit may be performed using, for example, AI, or without AI. For example, the alert unit can input user device information data into a generating AI and have the generating AI select the notification method.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific methods and criteria for adjusting the display method of the analysis results include, for example, color coding and graph display. 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) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust how the analysis results are displayed.

[0086] The analysis unit can predict current dirt and allergen trends by referring to past data. For example, the analysis unit can predict seasonal allergen trends based on past data. The analysis unit can also predict dirt trends during specific time periods based on past data. The analysis unit can also predict dirt and allergen trends in specific areas based on past data. In this way, current dirt and allergen trends can be predicted by referring to past data. Specific methods and criteria for referring to past data include, for example, past cleaning history and allergen concentration records. Specific methods and criteria for predicting current dirt and allergen trends include, for example, seasonal fluctuations and user activity patterns. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform predictions of current dirt and allergen trends.

[0087] The analysis unit can apply different analysis algorithms to each type of waste. For example, for dust, the analysis unit can apply an algorithm that detects fine particles. For example, for pet hair, the analysis unit can apply an algorithm that identifies length and texture. For example, for mites, the analysis unit can apply an algorithm that detects specific shapes and movements. This enables optimal analysis for each type of waste. Specific classification methods and criteria for waste types include, for example, dust, pet hair, mites, and pollen. Specific types and application methods for analysis algorithms include, for example, machine learning algorithms and statistical analysis algorithms. 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 waste type data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the importance of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit will prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit may also display detailed analysis results. For example, if the user is in a hurry, the analysis unit may also display concise analysis results. This allows for the provision of more appropriate information by adjusting the importance of the analysis results according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific methods and criteria for adjusting the importance of the analysis results include, for example, high allergen concentration and frequency of soiling. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the importance of the analysis results.

[0089] The analysis unit can identify the distribution of dirt and allergens by considering the room layout information. For example, the analysis unit can identify areas where dirt tends to accumulate by considering the arrangement of furniture in the room. The analysis unit can also identify the distribution of allergens by considering the location of ventilation openings in the room. The analysis unit can also analyze the distribution of dirt and allergens based on the room layout information. This allows for accurate identification of the distribution of dirt and allergens by considering the room layout information. Specific methods and criteria for acquiring room layout information include, for example, the room layout and furniture arrangement. Specific methods and criteria for identifying the distribution of dirt and allergens include, for example, the degree of dirt concentration and the concentration of allergens. 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 room layout information data into a generating AI and have the generating AI perform the identification of the distribution of dirt and allergens.

[0090] The analysis unit can analyze the effects of indoor dirt and allergens by referring to external environmental data. For example, the analysis unit can analyze the effects of indoor dirt and allergens by referring to external weather data. The analysis unit can also analyze the effects of indoor dirt and allergens by referring to external air quality data. The analysis unit can also analyze the effects of indoor dirt and allergens based on external environmental data. This allows for an accurate analysis of the effects of indoor dirt and allergens by referring to external environmental data. Specific methods and criteria for referencing external environmental data include, for example, weather data and air quality data. Specific methods and criteria for analyzing the effects of indoor dirt and allergens include, for example, correlation with the external environment and degree of influence. 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 external environmental data into a generating AI and have the generating AI perform an analysis of the effects of indoor dirt and allergens.

[0091] The scheduling unit can estimate the user's emotions and adjust the cleaning schedule suggestion method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can provide a simple and easy-to-understand suggestion method. For example, if the user is relaxed, the scheduling unit can also provide a suggestion method that includes detailed information. For example, if the user is in a hurry, the scheduling unit can also provide a suggestion method that gets straight to the point. By adjusting the cleaning schedule suggestion method according to the user's emotions, more appropriate suggestions can be made. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Specific methods and criteria for adjusting the cleaning schedule suggestion method include, for example, the timing of notifications and the details of the suggestion content. Emotion estimation is achieved using an emotion estimation function, for example, 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 scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input user emotion data into a generating AI and have the AI ​​adjust how it suggests cleaning schedules.

[0092] The scheduling unit can suggest the optimal cleaning time and area by referring to past cleaning history. For example, the scheduling unit can suggest the optimal cleaning time based on the time slots the user has cleaned in the past. The scheduling unit can also suggest the optimal cleaning area based on the areas the user has cleaned in the past. For example, the scheduling unit can analyze the user's past cleaning history and suggest the most efficient cleaning time and area. In this way, the optimal cleaning time and area can be suggested by referring to past cleaning history. Specific methods and criteria for referring to past cleaning history include, for example, cleaning frequency and cleaning content. Specific criteria and methods for determining the optimal cleaning time include, for example, the user's lifestyle pattern and the frequency of dirt occurrence. Specific criteria and methods for determining the optimal area include, for example, areas with a lot of dirt and areas with high allergen concentrations. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input past cleaning history data into a generating AI and have the generating AI suggest the optimal cleaning time and area.

[0093] The scheduling unit can suggest different cleaning methods depending on the type and amount of waste. For example, if there is a lot of dust, the scheduling unit may suggest a cleaning method using a vacuum cleaner with strong suction power. For example, if there is a lot of pet hair, the scheduling unit may suggest a cleaning method using a vacuum cleaner with a brush attachment. For example, if there are a lot of dust mites, the scheduling unit may suggest a cleaning method using a vacuum cleaner with a sterilization function. This allows the system to suggest the optimal cleaning method depending on the type and amount of waste. Specific classification methods and criteria for waste types include, for example, dust, pet hair, dust mites, and pollen. Specific measurement methods and criteria for waste amounts include, for example, weight and volume. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input waste type and amount data into a generating AI and have the generating AI execute the cleaning method suggestion.

[0094] The scheduling unit can estimate the user's emotions and determine the priority of the cleaning schedule based on the estimated emotions. For example, if the user is stressed, the scheduling unit will prioritize important cleaning tasks. For example, if the user is relaxed, the scheduling unit can also suggest a detailed cleaning schedule. For example, if the user is in a hurry, the scheduling unit can also suggest a concise cleaning schedule. This allows for a more appropriate schedule by determining the priority of the cleaning schedule according to the user's emotions. Specific methods and criteria for estimating the user's emotions include, for example, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, such as 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 scheduling unit may be performed using AI, or not. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI determine the priority of the cleaning schedule.

[0095] The scheduling unit can suggest the optimal cleaning time by considering the user's lifestyle patterns. For example, the scheduling unit can suggest cleaning before the user leaves for work. For example, the scheduling unit can suggest cleaning during the user's relaxation time after returning home. For example, the scheduling unit can analyze the user's lifestyle patterns and suggest the optimal cleaning time. In this way, the optimal cleaning time can be suggested by considering the user's lifestyle patterns. Specific methods and criteria for understanding the user's lifestyle patterns include, for example, daily activity patterns and daily rhythms. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input the user's lifestyle pattern data into a generating AI and have the generating AI suggest the optimal cleaning time.

[0096] The scheduling unit can propose the optimal cleaning area by considering the user's device information. For example, if the user is using a smartphone, the scheduling unit will propose a cleaning area based on the smartphone's location information. If the user is using a tablet, the scheduling unit can also propose a cleaning area based on the tablet's location information. The scheduling unit can also propose the optimal cleaning area based on the user's device information. In this way, the optimal cleaning area can be proposed by considering the user's device information. Specific methods and criteria for referencing the user's device information include, for example, smartphones, tablets, and smartwatches. Some or all of the above processing in the scheduling unit may be performed using, for example, AI, or without AI. For example, the scheduling unit can input user device information data into a generating AI and have the generating AI propose the optimal cleaning area.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The classification unit can adjust its classification accuracy by considering the frequency and seasonality of waste generation when identifying types of waste. For example, it can improve the accuracy of pollen identification in spring when pollen levels are high, and improve the accuracy of dust identification in winter. It can also predict the types of waste generated after specific events and improve the identification accuracy for those types. This enables optimal waste classification according to the season and events.

[0099] The alert function can adjust the content of alerts based on the user's health condition. For example, if a user has allergies, an alert can be sent even if the concentration of the allergen is low. Also, if a user has a cold, an alert can be sent with stricter criteria than usual. This enables appropriate alert notifications tailored to the user's health condition.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible.

[0101] The scheduling unit can estimate the user's emotions and adjust the way cleaning schedules are suggested based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-understand suggestion. If the user is relaxed, it can provide a suggestion that includes more detailed information. If the user is in a hurry, it can provide a suggestion that gets straight to the point. By adjusting the way cleaning schedules are suggested according to the user's emotions, more appropriate suggestions can be made.

[0102] The classification unit can identify the source of waste and classify it according to its origin. For example, it can identify waste generated in the kitchen and classify it as food waste. It can also identify waste generated in the living room and classify it as general waste. It can also identify waste generated in the bathroom and classify it as sanitary waste. This allows for more detailed classification by identifying the source of the waste.

[0103] The classification unit can optimize its classification algorithm based on the size and shape of the waste. For example, large waste can be classified as bulky waste and treated with a specific disposal method. Small waste can be classified as general waste and treated with a standard disposal method. Waste with a specific shape can be classified as recyclable waste and treated with a recycling process. By optimizing the classification algorithm based on the size and shape of the waste, more accurate classification becomes possible.

[0104] The alert unit can estimate the user's emotions and adjust the alert notification method based on those emotions. For example, if the user is stressed, the alert can be delivered in a calm voice. If the user is relaxed, the alert can be delivered in a cheerful voice. If the user is in a hurry, the alert can be delivered quickly and concisely. By adjusting the alert notification method according to the user's emotions, more appropriate notifications can be provided.

[0105] The analysis unit can predict current trends in dirt and allergens by referring to past data. For example, it can predict seasonal allergen trends based on past data. It can also predict dirt trends during specific time periods. It can also predict dirt and allergen trends in specific areas. In this way, current trends in dirt and allergens can be predicted by referring to past data.

[0106] The scheduling unit can estimate the user's emotions and prioritize cleaning schedules based on those emotions. For example, if the user is stressed, it will prioritize important cleaning tasks. If the user is relaxed, it can suggest a detailed cleaning schedule. If the user is in a hurry, it can suggest a concise cleaning schedule. By prioritizing cleaning schedules according to the user's emotions, a more appropriate schedule can be created.

[0107] The scheduling unit can suggest different cleaning methods depending on the type and amount of waste. For example, if there is a lot of dust, it can suggest using a vacuum cleaner with strong suction. If there is a lot of pet hair, it can suggest using a vacuum cleaner with a brush attachment. If there are a lot of dust mites, it can suggest using a vacuum cleaner with a sterilization function. This allows the system to suggest the optimal cleaning method according to the type and amount of waste.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The classification unit classifies the type of waste in real time. The classification unit uses sensors to identify and classify different types of waste, such as dust, pet hair, mites, and pollen, in real time. Optical sensors, chemical sensors, and AI can be used to identify the type of waste. Step 2: The alert unit notifies the user of an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit. The alert unit notifies the user of an alert when the concentration of an allergen exceeds a certain threshold. Notifications can be sent to the user via audio alerts, visual alerts, or a smartphone app. Step 3: The analysis unit analyzes the data collected based on the alerts notified by the alert unit. Based on the collected data, the analysis unit identifies areas with high levels of dirt and areas with high allergen concentrations. AI can also be used to analyze the data and analyze the patterns of dirt occurrence. Step 4: The scheduling unit proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. The scheduling unit proposes the optimal cleaning time and area considering time-of-day data and the user's lifestyle patterns. It can also propose the optimal cleaning time using AI.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the classification unit, alert unit, analysis unit, and scheduling unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the classification unit identifies the type of waste using the sensors of the smart device 14 and classifies it in real time. The alert unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and notifies the user of an alert when a specific allergen exceeds a threshold. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify areas with high levels of dirt or high concentrations of allergens. The scheduling unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes the optimal cleaning time and area based on time-of-day data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the classification unit, alert unit, analysis unit, and scheduling unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the classification unit uses the sensors of the smart glasses 214 to identify the type of waste and classify it in real time. The alert unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and notifies the user of an alert when a specific allergen exceeds a threshold. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify areas with high levels of dirt or high concentrations of allergens. The scheduling unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes the optimal cleaning time and area based on time-of-day data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the classification unit, alert unit, analysis unit, and scheduling unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the classification unit identifies the type of waste using the sensors of the headset terminal 314 and classifies it in real time. The alert unit is implemented in the identification processing unit 290 of the data processing unit 12 and notifies the user of an alert when a specific allergen exceeds a threshold. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to identify areas with high levels of dirt or high concentrations of allergens. The scheduling unit is implemented in the identification processing unit 290 of the data processing unit 12 and proposes the optimal cleaning time and area based on time-of-day data. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the classification unit, alert unit, analysis unit, and scheduling unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the classification unit uses the sensors of the robot 414 to identify the type of waste and classify it in real time. The alert unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and notifies the user of an alert when a specific allergen exceeds a threshold. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify areas with high levels of dirt or high concentrations of allergens. The scheduling unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes the optimal cleaning time and area based on time-of-day data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A sorting unit that sorts the type of waste in real time, An alert unit that notifies an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit, An analysis unit analyzes data collected based on alerts notified by the alert unit, The system includes a scheduling unit that proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned classification unit is Sensors are used to classify types of debris such as dust, pet hair, mites, and pollen in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The alert unit is, The system alerts the user if a specific allergen exceeds a certain threshold. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The collected data is analyzed to identify areas with high levels of dirt and high concentrations of allergens. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned scheduling unit, Based on time-of-day data, we propose the optimal cleaning time and area. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned classification unit is It estimates the user's emotions and adjusts the accuracy of waste sorting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned classification unit is Identify the source of the waste and classify it according to its source. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned classification unit is Optimize the classification algorithm based on the size and shape of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned classification unit is It estimates the user's emotions and adjusts how the classification results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned classification unit is Improve classification accuracy by taking indoor temperature and humidity into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned classification unit is Identify and classify the color and texture of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 12) The alert unit is, It estimates the user's emotions and adjusts how alerts are notified based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The alert unit is, Set different alert levels depending on the type of allergen. The system described in Appendix 1, characterized by the features described herein. (Note 14) The alert unit is, Prioritizing alerts is determined by referencing the user's past allergen reaction history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The alert unit is, It estimates the user's emotions and customizes the content of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The alert unit is, The optimal notification timing is selected based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 17) The alert unit is, The optimal notification method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user'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 19) The aforementioned analysis unit, By referring to past data, we can predict current trends in dirt and allergens. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, Apply different analysis algorithms to each type of waste. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the importance of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, Identify the distribution of dirt and allergens by considering the room layout information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, Analyze indoor pollution and allergen impacts by referencing external environmental data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scheduling unit, The system estimates the user's emotions and adjusts how cleaning schedules are suggested based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scheduling unit, Based on past cleaning history, we suggest the optimal cleaning time and area. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned scheduling unit, We suggest different cleaning methods depending on the type and amount of waste. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned scheduling unit, It estimates the user's emotions and prioritizes the cleaning schedule based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned scheduling unit, We suggest the optimal cleaning time, taking into account the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned scheduling unit, It suggests the optimal cleaning area, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. A sorting unit that sorts waste types in real time, An alert unit that notifies an alert when a specific allergen exceeds a threshold based on the data classified by the classification unit, An analysis unit analyzes data collected based on alerts notified by the alert unit, The system includes a scheduling unit that proposes the optimal cleaning time and area based on the data analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned classification unit is Sensors are used to classify types of debris such as dust, pet hair, mites, and pollen in real time. The system according to feature 1.

3. The alert unit is, The system alerts the user if a specific allergen exceeds a certain threshold. The system according to feature 1.

4. The aforementioned analysis unit, The collected data is analyzed to identify areas with high levels of dirt and high concentrations of allergens. The system according to feature 1.

5. The aforementioned scheduling unit, Based on time-of-day data, we propose the optimal cleaning time and area. The system according to feature 1.

6. The aforementioned classification unit is It estimates the user's emotions and adjusts the accuracy of waste sorting based on those estimated emotions. The system according to feature 1.

7. The aforementioned classification unit is Identify the source of the waste and classify it according to its source. The system according to feature 1.

8. The aforementioned classification unit is Optimize the classification algorithm based on the size and shape of the waste. The system according to feature 1.

9. The aforementioned classification unit is It estimates the user's emotions and adjusts how the classification results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned classification unit is Improve classification accuracy by taking indoor temperature and humidity into consideration. The system according to feature 1.

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