System

Abstract

An object of a system according to an embodiment is to automate and efficiently and accurately perform sorting of garbage.SOLUTION: A system according to an embodiment includes an analysis unit, an adjustment unit, a monitoring unit, and a notification unit. The analysis unit determines a type of dust. The adjustment unit adjusts the partition based on the type of dust determined by the analysis unit. The monitoring part monitors the amount of dust. The notification unit performs the notification when the capacity of the trash can reaches 80% or more.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In conventional technology, waste sorting is done manually, which is inefficient and can lead to incorrect sorting.

[0005] The system according to the embodiment aims to automate the separation of garbage efficiently and accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an adjustment unit, a monitoring unit, and a notification unit. The analysis unit determines the type of garbage. The adjustment unit adjusts the dividers based on the type of garbage determined by the analysis unit. The monitoring unit monitors the amount of garbage. The notification unit issues a notification when the capacity of the garbage bin reaches 80% or more. [Effects of the Invention]

[0007] The system according to the embodiment automates the separation of waste efficiently and accurately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An automatic trash sorting bin according to an embodiment of the present invention is a system that automatically identifies the type of trash and properly separates it. When trash is placed in the automatic trash sorting bin, AI scans the trash and determines its type. Then, depending on the determined type, the dividers inside the bin automatically adjust to separate the trash into the appropriate locations. Furthermore, multiple sensors are installed inside the bin to monitor the amount and type of trash in real time. This allows a user to be notified before the bin is full. For example, when trash is placed in the automatic trash sorting bin, AI scans the trash and analyzes its shape, material, color, etc. to determine its type. Then, the dividers inside the bin automatically adjust according to the determined type of trash. For example, when plastic trash is placed in the bin, the divider for plastic opens and adjusts so that the trash falls into the appropriate location. Furthermore, sensors inside the bin monitor the amount and type of trash in real time and can send a notification to a smartphone before the bin is full. This allows users to simply place their trash in the bin and the system automatically separates it, eliminating the need for manual intervention. In addition, accurate garbage sorting improves recycling efficiency and contributes to environmental protection. As a result, automatic garbage sorting bins automatically separate garbage when users simply put it in, saving them time and effort. In addition, accurate garbage sorting improves recycling efficiency and contributes to environmental protection. For example, proper sorting of plastic garbage makes it easier to process it at recycling plants, improving the recycling rate.

[0029] The automatic trash sorting bin according to the embodiment includes an analysis unit, an adjustment unit, a monitoring unit, and a notification unit. The analysis unit determines the type of trash. For example, the analysis unit analyzes the shape, material, and color of the trash to determine the type of trash. For example, the analysis unit analyzes the shape of the trash using image analysis technology. The analysis unit can also analyze the material of the trash using an optical sensor. The analysis unit can also analyze the color of the trash using an RGB sensor. For example, the analysis unit 3D scans the shape of the trash using image analysis technology and determines the type of trash based on the shape data. The analysis unit can also identify the material of the trash using an optical sensor and determine the type of trash based on the material data. The analysis unit can also detect the color of the trash using an RGB sensor and determine the type of trash based on the color data. The adjustment unit adjusts the dividers based on the determined type of trash. For example, the adjustment unit adjusts the position of the dividers using a motor. For example, if plastic trash is determined, the adjustment unit opens the plastic divider. If paper trash is determined, the adjustment unit can open the paper divider. The adjustment unit can also open the metal partition when metal waste is determined. For example, the adjustment unit adjusts the position of the partition using a motor, and opens the plastic partition when plastic waste is determined. The adjustment unit can also open the paper partition when paper waste is determined. The adjustment unit can also open the metal partition when metal waste is determined. The monitoring unit monitors the amount of waste. The monitoring unit measures the amount of waste using, for example, a weight sensor. The monitoring unit monitors the amount of waste in real time using, for example, a sensor inside the trash can. The monitoring unit can also monitor the type of waste. For example, the monitoring unit measures the amount of waste using a weight sensor and notifies the user before the trash can is full. The monitoring unit can also monitor the type of waste using a sensor inside the trash can and notifies the user before the trash can is full. The notification unit notifies the user when the capacity of the trash can reaches 80% or more. The notification unit sends a notification to, for example, a smartphone. The notification unit can also sound an alarm before the trash can is full. The notification section can also turn on an LED light before the trash can is full.For example, the notification unit sends a notification to the smartphone when the trash can's capacity reaches 80% or more. The notification unit can also sound an alarm before the trash can is full. The notification unit can also turn on an LED light before the trash can is full. In this way, the automatic trash sorting bin according to the embodiment can automatically determine the type of trash and properly separate it. For example, the analysis unit analyzes the shape, material, and color of the trash to determine the type of trash. The adjustment unit adjusts the dividers based on the determined type of trash. The monitoring unit monitors the amount of trash and sends a notification before the trash can is full. The notification unit sends a notification when the trash can's capacity reaches 80% or more. In this way, the automatic trash sorting bin can automatically determine the type of trash and properly separate it.

[0030] The analysis unit can analyze the shape, material, and color of the garbage and determine the type of garbage. The analysis unit can analyze the shape of the garbage using, for example, image analysis technology. For example, the analysis unit can 3D scan the shape of the garbage using image analysis technology and determine the type of garbage based on the shape data. The analysis unit can also analyze the material of the garbage using an optical sensor. For example, the analysis unit can identify the material of the garbage using an optical sensor and determine the type of garbage based on the material data. The analysis unit can also analyze the color of the garbage using an RGB sensor. For example, the analysis unit can detect the color of the garbage using an RGB sensor and determine the type of garbage based on the color data. This allows for more accurate determination of the type of garbage by analyzing the shape, material, and color of the garbage. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the shape, material, and color of the garbage to a generation AI and have the generation AI determine the type of garbage.

[0031] The adjustment unit can adjust the dividers inside the trash can according to the determined type of trash. The adjustment unit adjusts the position of the dividers using, for example, a motor. For example, when plastic trash is determined, the adjustment unit opens the plastic divider. Furthermore, when paper trash is determined, the adjustment unit can also open the paper divider. Furthermore, when metal trash is determined, the adjustment unit can also open the metal divider. For example, the adjustment unit adjusts the position of the dividers using a motor, and when plastic trash is determined, the adjustment unit opens the plastic divider. Furthermore, when paper trash is determined, the adjustment unit can also open the paper divider. Furthermore, when metal trash is determined, the adjustment unit can also open the metal divider. In this way, by adjusting the dividers according to the type of trash, appropriate sorting is possible. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit inputs data on the determined type of trash into the generation AI and causes the generation AI to adjust the dividers.

[0032] The monitoring unit can monitor the amount and type of garbage every second using a sensor inside the trash can. The monitoring unit can measure the amount of garbage using, for example, a weight sensor. For example, the monitoring unit can monitor the amount of garbage in real time using a sensor inside the trash can. The monitoring unit can also monitor the type of garbage. For example, the monitoring unit can measure the amount of garbage using a weight sensor and issue a notification before the trash can becomes full. The monitoring unit can also monitor the type of garbage using a sensor inside the trash can and issue a notification before the trash can becomes full. In this way, by monitoring the amount and type of garbage in real time, notifications can be issued at the appropriate time. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input data on the amount and type of garbage into a generation AI to improve the accuracy of monitoring.

[0033] The notification unit can notify the user before the trash can becomes full. The notification unit, for example, sends a notification to a smartphone. For example, the notification unit can sound an alarm before the trash can becomes full. The notification unit can also turn on an LED light before the trash can becomes full. For example, the notification unit sends a notification to the smartphone when the capacity of the trash can reaches 80% or more. The notification unit can also sound an alarm before the trash can becomes full. The notification unit can also turn on an LED light before the trash can becomes full. In this way, by notifying the user before the trash can becomes full, it is possible to encourage the user to collect the trash. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input trash can capacity data to a generation AI and have the generation AI execute the timing of the notification.

[0034] The notification unit can send a notification to the smartphone. The notification unit, for example, sends a notification to the smartphone. For example, the notification unit can sound an alarm before the trash can is full. The notification unit can also turn on an LED light before the trash can is full. For example, the notification unit sends a notification to the smartphone when the trash can's capacity reaches 80% or more. The notification unit can sound an alarm before the trash can is full. The notification unit can also turn on an LED light before the trash can is full. By sending a notification to the smartphone, the user can take immediate action. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input trash can capacity data to a generation AI and have the generation AI execute the notification timing.

[0035] The analysis unit can analyze the shape, material, and color of the garbage, as well as the odor and temperature of the garbage to determine its type. For example, the analysis unit may detect the odor of the garbage using a sensor and determine whether it is food waste. For example, the analysis unit may detect the odor of the garbage using a gas sensor and determine whether it is food waste. The analysis unit may also measure the temperature of the garbage using a sensor and determine whether warm garbage is food waste. For example, the analysis unit may measure the temperature of the garbage using an infrared sensor and determine whether warm garbage is food waste. The analysis unit may also analyze the odor and temperature of the garbage in combination to more accurately determine the type of garbage. For example, the analysis unit may analyze the odor and temperature of the garbage using a gas sensor and an infrared sensor in combination to determine whether it is food waste. In this way, analyzing the odor and temperature of the garbage allows for more accurate determination of the type of garbage. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input data on the odor and temperature of the garbage into the generation AI and have the generation AI determine the type of garbage.

[0036] When determining the type of garbage, the analysis unit can improve the accuracy of the determination by referring to past analysis data. The analysis unit, for example, can more quickly determine the same type of garbage based on data analyzed in the past. For example, the analysis unit stores past analysis data in a database and quickly determines the type of garbage when the same type of garbage is thrown in. The analysis unit can also refer to past analysis data to determine similar types of garbage. For example, the analysis unit can determine similar types of garbage based on past analysis data. The analysis unit can also reduce errors when determining the type of garbage by using past analysis data. For example, the analysis unit can reduce errors when determining the type of garbage based on past analysis data. As a result, referring to past analysis data improves the accuracy of the determination. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to improve the accuracy of the determination.

[0037] When determining the type of garbage, the analysis unit can analyze the sound made when the garbage is thrown in and determine the type. For example, the analysis unit detects the sound made when the garbage is thrown in using a sensor, and if a hard sound is heard, it determines that the garbage is metal garbage. For example, the analysis unit can analyze the sound made when the garbage is thrown in using voice recognition technology, and if a hard sound is heard, it determines that the garbage is metal garbage. The analysis unit can also analyze the sound made when the garbage is thrown in and if a soft sound like paper is heard, it determines that the garbage is paper garbage. For example, the analysis unit can analyze the sound made when the garbage is thrown in using voice recognition technology, and if a soft sound like paper is heard, it determines that the garbage is paper garbage. The analysis unit can also analyze the sound made when the garbage is thrown in and if a medium hard sound like plastic is heard, it determines that the garbage is plastic garbage. For example, the analysis unit can analyze the sound made when the garbage is thrown in using voice recognition technology, and if a medium hard sound like plastic is heard, it determines that the garbage is plastic garbage. In this way, by analyzing the sound made when the garbage is thrown in, the type of garbage can be determined more accurately. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the sound made when garbage is thrown into the generation AI and have the generation AI determine the type of garbage.

[0038] The analysis unit can analyze the weight of the garbage in addition to the shape, material, and color of the garbage to determine its type. For example, the analysis unit measures the weight of the garbage using a sensor and determines whether heavy garbage is metal. For example, the analysis unit can measure the weight of the garbage using a weight sensor and determine whether heavy garbage is metal. The analysis unit can also analyze the weight of the garbage and determine whether light garbage is paper. For example, the analysis unit can measure the weight of the garbage using a weight sensor and determine whether light garbage is paper. The analysis unit can also analyze the weight of the garbage in combination with the shape, material, and color of the garbage to more accurately determine the type of garbage. For example, the analysis unit measures the weight of the garbage using a weight sensor and determines the type of garbage by combining the weight, material, and color data. In this way, analyzing the weight of the garbage allows for more accurate determination of the type of garbage. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input garbage weight data into the generation AI and have the generation AI determine the type of garbage.

[0039] When determining the type of garbage, the analysis unit can determine the priority of the determination taking into account the frequency of garbage disposal. The analysis unit, for example, prioritizes the determination of types of garbage that are frequently disposed of. For example, the analysis unit analyzes the frequency of garbage disposal and quickly determines the types of garbage that are frequently disposed of. The analysis unit can also determine the priority and perform the analysis taking into account the frequency of garbage disposal. For example, the analysis unit determines the priority and performs the analysis based on the frequency of garbage disposal. In this way, the priority can be determined and the analysis can be performed by taking the frequency of garbage disposal into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency of garbage disposal into the generation AI and have the generation AI determine the priority of the determination.

[0040] When determining the type of garbage, the analysis unit can make the determination taking into account attribute information of the garbage putter. For example, if the garbage putter produces a lot of household garbage, the analysis unit makes the determination based on that attribute information. For example, the analysis unit prioritizes household garbage based on the attribute information of the garbage putter. Furthermore, if the garbage putter is a company, the analysis unit can also make the determination taking into account the type of garbage specific to the company. For example, the analysis unit determines the type of garbage specific to the company based on the attribute information of the garbage putter. Furthermore, the analysis unit can prioritize specific types of garbage based on the attribute information of the garbage putter. For example, the analysis unit prioritizes specific types of garbage based on the attribute information of the garbage putter. This allows for more accurate determination of the type of garbage by taking into account the attribute information of the garbage putter. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input attribute information of the garbage putter into the generation AI and cause the generation AI to determine the type of garbage.

[0041] The adjustment unit can fine-tune the opening and closing angle of the partition depending on the determined type of trash. For example, in the case of plastic trash, the adjustment unit sets the opening and closing angle of the partition wider. For example, when plastic trash is determined, the adjustment unit sets the opening and closing angle of the partition wider. The adjustment unit can also set the opening and closing angle of the partition narrower when paper trash is determined. For example, when paper trash is determined, the adjustment unit can also set the opening and closing angle of the partition narrower. The adjustment unit can also set the opening and closing angle of the partition to a medium level when metal trash is determined. For example, when metal trash is determined, the adjustment unit can also set the opening and closing angle of the partition to a medium level. This allows for more accurate trash sorting by fine-tuning the opening and closing angle of the partition. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to fine-tune the opening and closing angle of the partition.

[0042] The adjustment unit can automatically change the material and shape of the partitions depending on the type of trash. For example, in the case of plastic trash, the adjustment unit changes the material of the partitions to plastic. For example, if the adjustment unit determines that the trash is plastic, the adjustment unit changes the material of the partitions to plastic. The adjustment unit can also change the material of the partitions to paper in the case of paper trash. For example, if the adjustment unit determines that the trash is paper, the adjustment unit can also change the material of the partitions to paper. The adjustment unit can also change the material of the partitions to metal in the case of metal trash. For example, if the adjustment unit determines that the trash is metal, the adjustment unit can also change the material of the partitions to metal. This allows for more appropriate trash sorting by changing the material and shape of the partitions. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input data on the determined type of trash into the generation AI and cause the generation AI to change the material and shape of the partitions.

[0043] The adjustment unit can adjust the timing of opening and closing the partitions depending on the type of waste. For example, in the case of plastic waste, the adjustment unit sets the timing of opening and closing the partitions earlier. For example, if the adjustment unit determines that the waste is plastic, the adjustment unit sets the timing of opening and closing the partitions earlier. The adjustment unit can also set the timing of opening and closing the partitions later in the case of paper waste. For example, if the adjustment unit determines that the waste is paper, the adjustment unit can also set the timing of opening and closing the partitions later. The adjustment unit can also set the timing of opening and closing the partitions to a medium level in the case of metal waste. For example, if the adjustment unit determines that the waste is metal, the adjustment unit can also set the timing of opening and closing the partitions to a medium level. This allows for more appropriate waste sorting by adjusting the timing of opening and closing the partitions. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of waste to the generation AI and cause the generation AI to adjust the timing of opening and closing the partitions.

[0044] The adjustment unit can change the opening / closing direction of the partition depending on the determined type of trash. For example, in the case of plastic trash, the adjustment unit sets the partition to open to the left. For example, if plastic trash is determined, the adjustment unit sets the partition to open to the left. Furthermore, the adjustment unit can also set the partition to open to the right in the case of paper trash. For example, if paper trash is determined, the adjustment unit can also set the partition to open to the right. Furthermore, the adjustment unit can also set the partition to open up and down in the case of metal trash. For example, if metal trash is determined, the adjustment unit can also set the partition to open up and down. In this way, by changing the opening / closing direction of the partition, trash can be more appropriately separated. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to change the opening / closing direction of the partition.

[0045] The adjustment unit can adjust the speed at which the partitions open and close depending on the type of trash. For example, in the case of plastic trash, the adjustment unit sets the speed at which the partitions open and close faster. For example, when plastic trash is determined, the adjustment unit sets the speed at which the partitions open and close faster. The adjustment unit can also set the speed at which the partitions open and close slower in the case of paper trash. For example, when paper trash is determined, the adjustment unit can also set the speed at which the partitions open and close slower. The adjustment unit can also set the speed at which the partitions open and close medium in the case of metal trash. For example, when metal trash is determined, the adjustment unit can also set the speed at which the partitions open and close medium. This allows for more appropriate trash sorting by adjusting the speed at which the partitions open and close. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to adjust the speed at which the partitions open and close.

[0046] The adjustment unit can adjust the number of times the partition is opened and closed depending on the type of trash. For example, in the case of plastic trash, the adjustment unit sets the number of times the partition is opened and closed to one. For example, if plastic trash is determined, the adjustment unit sets the number of times the partition is opened and closed to one. Furthermore, the adjustment unit can set the number of times the partition is opened and closed to two for paper trash. For example, if paper trash is determined, the adjustment unit can set the number of times the partition is opened and closed to two. Furthermore, the adjustment unit can set the number of times the partition is opened and closed to three for metal trash. For example, if metal trash is determined, the adjustment unit can set the number of times the partition is opened and closed to three. In this way, by adjusting the number of times the partition is opened and closed, trash can be more appropriately sorted. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to adjust the number of times the partition is opened and closed.

[0047] The monitoring unit can monitor the amount and type of garbage using external sensors in addition to sensors inside the trash can. The monitoring unit, for example, monitors the amount of garbage using sensors installed outside the trash can. For example, the monitoring unit monitors the amount of garbage using an infrared sensor. The monitoring unit can also monitor the type of garbage using an external sensor and perform analysis in cooperation with the internal sensor. For example, the monitoring unit can monitor the type of garbage using an ultrasonic sensor and perform analysis in cooperation with the internal sensor. The monitoring unit can also collect environmental information around the trash can using an external sensor to improve the accuracy of monitoring. For example, the monitoring unit can collect temperature information around the trash can using a temperature sensor to improve the accuracy of monitoring. In this way, the use of external sensors can more accurately monitor the amount and type of garbage. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data from an external sensor into the generation AI and cause the generation AI to monitor the amount and type of garbage.

[0048] When monitoring the amount and type of garbage, the monitoring unit can improve the accuracy of the monitoring by referring to past monitoring data. The monitoring unit, for example, predicts the amount of the same type of garbage based on past monitoring data. For example, the monitoring unit stores past monitoring data in a database and predicts the amount of the same type of garbage. The monitoring unit can also refer to past monitoring data to monitor similar types of garbage. For example, the monitoring unit monitors similar types of garbage based on past monitoring data. The monitoring unit can also reduce errors when monitoring the amount and type of garbage by using past monitoring data. For example, the monitoring unit reduces errors when monitoring the amount and type of garbage based on past monitoring data. As a result, the accuracy of the monitoring is improved by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0049] The monitoring unit can monitor the amount and type of garbage by analyzing vibrations generated when garbage is inserted. For example, the monitoring unit may use a sensor to detect vibrations generated when garbage is inserted and monitor the amount of garbage. For example, the monitoring unit may use an acceleration sensor to detect vibrations generated when garbage is inserted and monitor the amount of garbage. The monitoring unit can also analyze the vibrations generated when garbage is inserted and monitor the type of garbage. For example, the monitoring unit can use a vibration analysis algorithm to analyze the vibrations generated when garbage is inserted and monitor the type of garbage. The monitoring unit can also analyze the vibrations generated when garbage is inserted and accurately monitor the amount and type of garbage. For example, the monitoring unit may combine an acceleration sensor and a vibration analysis algorithm to analyze the vibrations generated when garbage is inserted and accurately monitor the amount and type of garbage. This allows for more accurate monitoring of the amount and type of garbage by analyzing the vibrations generated when garbage is inserted. Some or all of the above-described processing performed by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input vibration data generated when garbage is inserted to a generation AI and cause the generation AI to monitor the amount and type of garbage.

[0050] The monitoring unit can monitor the amount and type of garbage using a camera in addition to a sensor inside the trash can. The monitoring unit, for example, monitors the amount of garbage using a camera installed inside the trash can. For example, the monitoring unit monitors the amount of garbage using an RGB camera. The monitoring unit can also monitor the type of garbage using a camera and perform analysis in cooperation with an internal sensor. For example, the monitoring unit can monitor the type of garbage using an infrared camera and perform analysis in cooperation with an internal sensor. The monitoring unit can also monitor the situation inside the trash can in real time using a camera to improve the accuracy of monitoring. For example, the monitoring unit can monitor the situation inside the trash can in real time using an RGB camera to improve the accuracy of monitoring. In this way, the amount and type of garbage can be monitored more accurately by using a camera. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input camera data into a generation AI and cause the generation AI to monitor the amount and type of garbage.

[0051] When monitoring the amount and type of garbage, the monitoring unit can determine monitoring priorities taking into account the frequency of garbage disposal. The monitoring unit, for example, prioritizes monitoring of types of garbage that are frequently disposed of. For example, the monitoring unit analyzes the frequency of garbage disposal and quickly monitors the types of garbage that are frequently disposed of. The monitoring unit can also determine priorities and perform monitoring taking into account the frequency of garbage disposal. For example, the monitoring unit determines priorities and performs monitoring based on the frequency of garbage disposal. In this way, monitoring can be performed by determining priorities by taking into account the frequency of garbage disposal. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the frequency of garbage disposal into a generation AI and have the generation AI determine the monitoring priorities.

[0052] When monitoring the amount and type of garbage, the monitoring unit can perform monitoring taking into account attribute information of the garbage putter. For example, if the garbage putter produces a lot of household garbage, the monitoring unit performs monitoring based on that attribute information. For example, the monitoring unit prioritizes monitoring household garbage based on the attribute information of the garbage putter. Furthermore, if the garbage putter is a company, the monitoring unit can also perform monitoring taking into account the type of garbage specific to the company. For example, the monitoring unit monitors the type of garbage specific to the company based on the attribute information of the garbage putter. Furthermore, the monitoring unit can prioritize monitoring specific types of garbage based on the attribute information of the garbage putter. For example, the monitoring unit prioritizes monitoring specific types of garbage based on the attribute information of the garbage putter. In this way, by taking into account the attribute information of the garbage putter, the amount and type of garbage can be monitored more accurately. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input attribute information of the garbage putter into the generation AI and cause the generation AI to monitor the amount and type of garbage.

[0053] The notification unit can use different notification methods for each type of garbage before the garbage bin becomes full. For example, the notification unit sends a notification to a smartphone before the plastic garbage bin becomes full. For example, the notification unit sends a notification to a smartphone before the plastic garbage bin becomes full. The notification unit can also provide an audio notification before the paper garbage bin becomes full. For example, the notification unit can provide an audio notification before the paper garbage bin becomes full. The notification unit can also provide an LED light notification before the metal garbage bin becomes full. For example, the notification unit can provide an LED light notification before the metal garbage bin becomes full. This allows for more appropriate notification by using different notification methods for each type of garbage. Some or all of the above-described processing by the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input data on the type of garbage into the generation AI and have the generation AI select the notification method.

[0054] The notification unit can improve the accuracy of notifications by referring to past notification data before the trash can becomes full. The notification unit, for example, predicts when the same type of trash will become full based on the past notification data. For example, the notification unit stores the past notification data in a database and predicts when the same type of trash will become full. The notification unit can also predict when similar types of trash will become full by referring to the past notification data. For example, the notification unit predicts when similar types of trash will become full based on the past notification data. The notification unit can also accurately notify the trash can before it becomes full by using the past notification data. For example, the notification unit accurately notifies the trash can before it becomes full based on the past notification data. As a result, the accuracy of notifications is improved by referring to the past notification data. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past notification data into a generation AI and cause the generation AI to improve the accuracy of notifications.

[0055] The notification unit can analyze the sound made when garbage is thrown into the trash can and provide a notification before the trash can is full. For example, the notification unit detects the sound made when garbage is thrown into the trash can with a sensor and predicts when the trash can will be full. For example, the notification unit can analyze the sound made when garbage is thrown into the trash can using voice recognition technology and predict when the trash can will be full. The notification unit can also analyze the sound made when garbage is thrown into the trash can and provide a notification before the trash can is full. For example, the notification unit can analyze the sound made when garbage is thrown into the trash can using voice recognition technology and provide a notification before the trash can is full. The notification unit can also analyze the sound made when garbage is thrown into the trash can and accurately predict when the trash can will be full and provide a notification. For example, the notification unit can analyze the sound made when garbage is thrown into the trash can using voice recognition technology and accurately predict when the trash can will be full and provide a notification. In this way, by analyzing the sound made when garbage is thrown into the trash can, it is possible to more accurately predict and provide a notification when the trash can will be full. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input data on the sound made when garbage is thrown into the generation AI and have the generation AI predict the timing of the notification.

[0056] The notification unit can use a different notification sound for each type of trash before the trash can is full. For example, the notification unit sets a specific notification sound before the trash can is full for plastic trash. For example, the notification unit sets a specific notification sound before the trash can is full for plastic trash. The notification unit can also set a different notification sound before the trash can is full for paper trash. For example, the notification unit can set a different notification sound before the trash can is full for paper trash. The notification unit can also set an even different notification sound before the trash can is full for metal trash. For example, the notification unit can set an even different notification sound before the trash can is full for metal trash. This allows for more appropriate notification by using different notification sounds for each type of trash. Some or all of the above-described processing by the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input data on the type of trash into a generation AI and cause the generation AI to select a notification sound.

[0057] The notification unit can determine the priority of notifications taking into account the frequency of garbage disposal before the trash can becomes full. The notification unit, for example, prioritizes notifications of types of garbage that are frequently disposed of. For example, the notification unit analyzes the frequency of garbage disposal and quickly notifies of types of garbage that are frequently disposed of. The notification unit can also determine the priority and provide notifications taking into account the frequency of garbage disposal. For example, the notification unit determines the priority and provides notifications based on the frequency of garbage disposal. In this way, the priority can be determined and notifications can be provided by taking the frequency of garbage disposal into consideration. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input data on the frequency of garbage disposal into a generation AI and have the generation AI determine the priority of notifications.

[0058] The notification unit can provide a notification before the trash can becomes full, taking into account the attribute information of the person who puts the trash. For example, if the trash person puts out a lot of household trash, the notification unit provides a notification based on that attribute information. For example, the notification unit provides a notification before the household trash can becomes full, based on the attribute information of the person who puts the trash. Furthermore, if the trash person puts out is a company, the notification unit can also provide a notification taking into account the type of trash specific to the company. For example, the notification unit provides a notification before the type of trash specific to the company becomes full, based on the attribute information of the person who puts the trash. Furthermore, the notification unit can prioritize notifications for specific types of trash based on the attribute information of the person who puts the trash. For example, the notification unit provides a notification before the type of trash specific to the company becomes full, based on the attribute information of the person who puts the trash. In this way, by taking into account the attribute information of the person who puts the trash, it is possible to more accurately predict and provide a notification when the trash can will become full. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input attribute information of the person who puts the garbage into the generation AI and have the generation AI predict the timing of the notification.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When determining the type of garbage, the analysis unit can analyze the vibrations generated when garbage is inserted. For example, the analysis unit can use an acceleration sensor to detect the vibrations generated when garbage is inserted and determine the type of garbage. The analysis unit can also use a vibration analysis algorithm to analyze the vibrations generated when garbage is inserted and determine the type of garbage. Furthermore, the analysis unit can combine and analyze the vibrations generated when garbage is inserted with data on the shape, material, and color of the garbage to more accurately determine the type of garbage. In this way, the analysis of the vibrations generated when garbage is inserted can more accurately determine the type of garbage.

[0061] When determining the type of garbage, the analysis unit can improve the accuracy of the determination by referring to past analysis data. For example, the analysis unit can more quickly determine the same type of garbage based on data analyzed in the past. The analysis unit can also store past analysis data in a database and quickly determine the type of garbage when the same type of garbage is thrown in. Furthermore, the analysis unit can also refer to past analysis data to determine similar types of garbage. In this way, by referring to past analysis data, the accuracy of the determination is improved.

[0062] The adjustment unit can fine-tune the opening and closing angle of the partition depending on the type of trash. For example, for plastic trash, the opening and closing angle of the partition can be set wider. For paper trash, the opening and closing angle of the partition can also be set narrower. Furthermore, for metal trash, the opening and closing angle of the partition can be set to a medium level. This allows for more accurate trash sorting by fine-tuning the opening and closing angle of the partition.

[0063] The monitoring unit can monitor the amount and type of garbage using external sensors in addition to sensors inside the trash can. For example, the amount of garbage can be monitored using a sensor installed outside the trash can. The type of garbage can also be monitored using an external sensor, and analyzed in conjunction with the internal sensor. Furthermore, the external sensor can be used to collect environmental information around the trash can, improving the accuracy of monitoring. As a result, the amount and type of garbage can be monitored more accurately by using an external sensor.

[0064] The notification unit can improve the accuracy of notifications by referring to past notification data before the trash can becomes full. For example, the notification unit can predict when the same type of trash will become full based on past notification data. The notification unit can also predict when a similar type of trash will become full by referring to past notification data. Furthermore, the notification unit can use past notification data to accurately notify the user before the trash can becomes full. In this way, by referring to past notification data, the accuracy of notifications is improved.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The analysis unit determines the type of dust. For example, the analysis unit analyzes the shape, material, and color of the dust to determine the type of dust. The analysis unit can analyze the shape of the dust using image analysis technology, the material of the dust using an optical sensor, and the color of the dust using an RGB sensor. Step 2: The adjustment unit adjusts the partitions based on the determined type of trash. The adjustment unit adjusts the position of the partitions using, for example, a motor, and if plastic trash is determined, opens the plastic partition. If paper trash is determined, opens the paper partition. If metal trash is determined, opens the metal partition. Step 3: The monitoring unit monitors the amount of garbage. The monitoring unit measures the amount of garbage using, for example, a weight sensor, and monitors the amount of garbage in real time using a sensor inside the garbage can. Step 4: The notification module will notify you when the trash can reaches 80% capacity or more. For example, the notification module will send a notification to your smartphone. It can also sound an alarm before the trash can becomes full, or turn on an LED light.

[0067] (Example 2) An automatic trash sorting bin according to an embodiment of the present invention is a system that automatically identifies the type of trash and properly separates it. When trash is placed in the automatic trash sorting bin, AI scans the trash and determines its type. Then, depending on the determined type, the dividers inside the bin automatically adjust to separate the trash into the appropriate locations. Furthermore, multiple sensors are installed inside the bin to monitor the amount and type of trash in real time. This allows a user to be notified before the bin is full. For example, when trash is placed in the automatic trash sorting bin, AI scans the trash and analyzes its shape, material, color, etc. to determine its type. Then, the dividers inside the bin automatically adjust according to the determined type of trash. For example, when plastic trash is placed in the bin, the divider for plastic opens and adjusts so that the trash falls into the appropriate location. Furthermore, sensors inside the bin monitor the amount and type of trash in real time and can send a notification to a smartphone before the bin is full. This allows users to simply place their trash in the bin and the system automatically separates it, eliminating the need for manual intervention. In addition, accurate garbage sorting improves recycling efficiency and contributes to environmental protection. As a result, automatic garbage sorting bins automatically separate garbage when users simply put it in, saving them time and effort. In addition, accurate garbage sorting improves recycling efficiency and contributes to environmental protection. For example, proper sorting of plastic garbage makes it easier to process it at recycling plants, improving the recycling rate.

[0068] The automatic trash sorting bin according to the embodiment includes an analysis unit, an adjustment unit, a monitoring unit, and a notification unit. The analysis unit determines the type of trash. For example, the analysis unit analyzes the shape, material, and color of the trash to determine the type of trash. For example, the analysis unit analyzes the shape of the trash using image analysis technology. The analysis unit can also analyze the material of the trash using an optical sensor. The analysis unit can also analyze the color of the trash using an RGB sensor. For example, the analysis unit 3D scans the shape of the trash using image analysis technology and determines the type of trash based on the shape data. The analysis unit can also identify the material of the trash using an optical sensor and determine the type of trash based on the material data. The analysis unit can also detect the color of the trash using an RGB sensor and determine the type of trash based on the color data. The adjustment unit adjusts the dividers based on the determined type of trash. For example, the adjustment unit adjusts the position of the dividers using a motor. For example, if plastic trash is determined, the adjustment unit opens the plastic divider. If paper trash is determined, the adjustment unit can open the paper divider. The adjustment unit can also open the metal partition when metal waste is determined. For example, the adjustment unit adjusts the position of the partition using a motor, and opens the plastic partition when plastic waste is determined. The adjustment unit can also open the paper partition when paper waste is determined. The adjustment unit can also open the metal partition when metal waste is determined. The monitoring unit monitors the amount of waste. The monitoring unit measures the amount of waste using, for example, a weight sensor. The monitoring unit monitors the amount of waste in real time using, for example, a sensor inside the trash can. The monitoring unit can also monitor the type of waste. For example, the monitoring unit measures the amount of waste using a weight sensor and notifies the user before the trash can is full. The monitoring unit can also monitor the type of waste using a sensor inside the trash can and notifies the user before the trash can is full. The notification unit notifies the user when the capacity of the trash can reaches 80% or more. The notification unit sends a notification to, for example, a smartphone. The notification unit can also sound an alarm before the trash can is full. The notification section can also turn on an LED light before the trash can is full.For example, the notification unit sends a notification to the smartphone when the trash can's capacity reaches 80% or more. The notification unit can also sound an alarm before the trash can is full. The notification unit can also turn on an LED light before the trash can is full. In this way, the automatic trash sorting bin according to the embodiment can automatically determine the type of trash and properly separate it. For example, the analysis unit analyzes the shape, material, and color of the trash to determine the type of trash. The adjustment unit adjusts the dividers based on the determined type of trash. The monitoring unit monitors the amount of trash and sends a notification before the trash can is full. The notification unit sends a notification when the trash can's capacity reaches 80% or more. In this way, the automatic trash sorting bin can automatically determine the type of trash and properly separate it.

[0069] The analysis unit can analyze the shape, material, and color of the garbage and determine the type of garbage. The analysis unit can analyze the shape of the garbage using, for example, image analysis technology. For example, the analysis unit can 3D scan the shape of the garbage using image analysis technology and determine the type of garbage based on the shape data. The analysis unit can also analyze the material of the garbage using an optical sensor. For example, the analysis unit can identify the material of the garbage using an optical sensor and determine the type of garbage based on the material data. The analysis unit can also analyze the color of the garbage using an RGB sensor. For example, the analysis unit can detect the color of the garbage using an RGB sensor and determine the type of garbage based on the color data. This allows for more accurate determination of the type of garbage by analyzing the shape, material, and color of the garbage. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the shape, material, and color of the garbage to a generation AI and have the generation AI determine the type of garbage.

[0070] The adjustment unit can adjust the dividers inside the trash can according to the determined type of trash. The adjustment unit adjusts the position of the dividers using, for example, a motor. For example, when plastic trash is determined, the adjustment unit opens the plastic divider. Furthermore, when paper trash is determined, the adjustment unit can also open the paper divider. Furthermore, when metal trash is determined, the adjustment unit can also open the metal divider. For example, the adjustment unit adjusts the position of the dividers using a motor, and when plastic trash is determined, the adjustment unit opens the plastic divider. Furthermore, when paper trash is determined, the adjustment unit can also open the paper divider. Furthermore, when metal trash is determined, the adjustment unit can also open the metal divider. In this way, by adjusting the dividers according to the type of trash, appropriate sorting is possible. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit inputs data on the determined type of trash into the generation AI and causes the generation AI to adjust the dividers.

[0071] The monitoring unit can monitor the amount and type of garbage every second using a sensor inside the trash can. The monitoring unit can measure the amount of garbage using, for example, a weight sensor. For example, the monitoring unit can monitor the amount of garbage in real time using a sensor inside the trash can. The monitoring unit can also monitor the type of garbage. For example, the monitoring unit can measure the amount of garbage using a weight sensor and issue a notification before the trash can becomes full. The monitoring unit can also monitor the type of garbage using a sensor inside the trash can and issue a notification before the trash can becomes full. In this way, by monitoring the amount and type of garbage in real time, notifications can be issued at the appropriate time. Some or all of the above-mentioned processing in the monitoring unit can be performed using, for example, AI, or can be performed without using AI. For example, the monitoring unit can input data on the amount and type of garbage into a generation AI to improve the accuracy of monitoring.

[0072] The notification unit can notify the user before the trash can becomes full. The notification unit, for example, sends a notification to a smartphone. For example, the notification unit can sound an alarm before the trash can becomes full. The notification unit can also turn on an LED light before the trash can becomes full. For example, the notification unit sends a notification to the smartphone when the capacity of the trash can reaches 80% or more. The notification unit can also sound an alarm before the trash can becomes full. The notification unit can also turn on an LED light before the trash can becomes full. In this way, by notifying the user before the trash can becomes full, it is possible to encourage the user to collect the trash. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input trash can capacity data to a generation AI and have the generation AI execute the timing of the notification.

[0073] The notification unit can send a notification to the smartphone. The notification unit, for example, sends a notification to the smartphone. For example, the notification unit can sound an alarm before the trash can is full. The notification unit can also turn on an LED light before the trash can is full. For example, the notification unit sends a notification to the smartphone when the trash can's capacity reaches 80% or more. The notification unit can sound an alarm before the trash can is full. The notification unit can also turn on an LED light before the trash can is full. By sending a notification to the smartphone, the user can take immediate action. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input trash can capacity data to a generation AI and have the generation AI execute the notification timing.

[0074] The analysis unit can estimate the emotions of the user who threw the trash and change the analysis parameters based on the estimated user emotions. For example, if the user who threw the trash is stressed, the AI ​​can increase the accuracy of the analysis and quickly determine the type of trash. Furthermore, if the user is relaxed, the AI ​​can determine the type of trash with normal analysis accuracy. Furthermore, if the user is in a hurry, the AI ​​can prioritize analysis speed and quickly determine the type of trash. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more accurate analysis by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to change analysis parameters.

[0075] The analysis unit can analyze the shape, material, and color of the garbage, as well as the odor and temperature of the garbage to determine its type. For example, the analysis unit may detect the odor of the garbage using a sensor and determine whether it is food waste. For example, the analysis unit may detect the odor of the garbage using a gas sensor and determine whether it is food waste. The analysis unit may also measure the temperature of the garbage using a sensor and determine whether warm garbage is food waste. For example, the analysis unit may measure the temperature of the garbage using an infrared sensor and determine whether warm garbage is food waste. The analysis unit may also analyze the odor and temperature of the garbage in combination to more accurately determine the type of garbage. For example, the analysis unit may analyze the odor and temperature of the garbage using a gas sensor and an infrared sensor in combination to determine whether it is food waste. In this way, analyzing the odor and temperature of the garbage allows for more accurate determination of the type of garbage. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input data on the odor and temperature of the garbage into the generation AI and have the generation AI determine the type of garbage.

[0076] When determining the type of garbage, the analysis unit can improve the accuracy of the determination by referring to past analysis data. The analysis unit, for example, can more quickly determine the same type of garbage based on data analyzed in the past. For example, the analysis unit stores past analysis data in a database and quickly determines the type of garbage when the same type of garbage is thrown in. The analysis unit can also refer to past analysis data to determine similar types of garbage. For example, the analysis unit can determine similar types of garbage based on past analysis data. The analysis unit can also reduce errors when determining the type of garbage by using past analysis data. For example, the analysis unit can reduce errors when determining the type of garbage based on past analysis data. As a result, referring to past analysis data improves the accuracy of the determination. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis data into the generation AI and cause the generation AI to improve the accuracy of the determination.

[0077] When determining the type of garbage, the analysis unit can analyze the sound made when the garbage is thrown in and determine the type. For example, the analysis unit detects the sound made when the garbage is thrown in using a sensor, and if a hard sound is heard, it determines that the garbage is metal garbage. For example, the analysis unit can analyze the sound made when the garbage is thrown in using voice recognition technology, and if a hard sound is heard, it determines that the garbage is metal garbage. The analysis unit can also analyze the sound made when the garbage is thrown in and if a soft sound like paper is heard, it determines that the garbage is paper garbage. For example, the analysis unit can analyze the sound made when the garbage is thrown in using voice recognition technology, and if a soft sound like paper is heard, it determines that the garbage is paper garbage. The analysis unit can also analyze the sound made when the garbage is thrown in and if a medium hard sound like plastic is heard, it determines that the garbage is plastic garbage. For example, the analysis unit can analyze the sound made when the garbage is thrown in using voice recognition technology, and if a medium hard sound like plastic is heard, it determines that the garbage is plastic garbage. In this way, by analyzing the sound made when the garbage is thrown in, the type of garbage can be determined more accurately. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the sound made when garbage is thrown into the generation AI and have the generation AI determine the type of garbage.

[0078] The analysis unit can estimate the emotions of the user who threw out the trash and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. For example, the analysis unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and provide a simple, highly visible display method. The analysis unit can also display detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the emotion using voice analysis technology, and display detailed analysis results. The analysis unit can also display concise analysis results that focus on the main points if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and display concise analysis results that focus on the main points. This allows the display method of the analysis results to be adjusted according to the user's emotions, resulting in a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results.

[0079] The analysis unit can analyze the weight of the garbage in addition to the shape, material, and color of the garbage to determine its type. For example, the analysis unit measures the weight of the garbage using a sensor and determines whether heavy garbage is metal. For example, the analysis unit can measure the weight of the garbage using a weight sensor and determine whether heavy garbage is metal. The analysis unit can also analyze the weight of the garbage and determine whether light garbage is paper. For example, the analysis unit can measure the weight of the garbage using a weight sensor and determine whether light garbage is paper. The analysis unit can also analyze the weight of the garbage in combination with the shape, material, and color of the garbage to more accurately determine the type of garbage. For example, the analysis unit measures the weight of the garbage using a weight sensor and determines the type of garbage by combining the weight, material, and color data. In this way, analyzing the weight of the garbage allows for more accurate determination of the type of garbage. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input garbage weight data into the generation AI and have the generation AI determine the type of garbage.

[0080] When determining the type of garbage, the analysis unit can determine the priority of the determination taking into account the frequency of garbage disposal. The analysis unit, for example, prioritizes the determination of types of garbage that are frequently disposed of. For example, the analysis unit analyzes the frequency of garbage disposal and quickly determines the types of garbage that are frequently disposed of. The analysis unit can also determine the priority and perform the analysis taking into account the frequency of garbage disposal. For example, the analysis unit determines the priority and performs the analysis based on the frequency of garbage disposal. In this way, the priority can be determined and the analysis can be performed by taking the frequency of garbage disposal into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency of garbage disposal into the generation AI and have the generation AI determine the priority of the determination.

[0081] When determining the type of garbage, the analysis unit can make the determination taking into account attribute information of the garbage putter. For example, if the garbage putter produces a lot of household garbage, the analysis unit makes the determination based on that attribute information. For example, the analysis unit prioritizes household garbage based on the attribute information of the garbage putter. Furthermore, if the garbage putter is a company, the analysis unit can also make the determination taking into account the type of garbage specific to the company. For example, the analysis unit determines the type of garbage specific to the company based on the attribute information of the garbage putter. Furthermore, the analysis unit can prioritize specific types of garbage based on the attribute information of the garbage putter. For example, the analysis unit prioritizes specific types of garbage based on the attribute information of the garbage putter. This allows for more accurate determination of the type of garbage by taking into account the attribute information of the garbage putter. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input attribute information of the garbage putter into the generation AI and cause the generation AI to determine the type of garbage.

[0082] The adjustment unit can estimate the emotion of the user who deposited the garbage and change the speed at which the dividers are adjusted based on the estimated emotion of the user. For example, if the user is feeling stressed, the adjustment unit can increase the speed at which the dividers are adjusted to quickly separate the garbage. For example, the adjustment unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and increase the speed at which the dividers are adjusted to quickly separate the garbage. The adjustment unit can also adjust the dividers at a normal speed when the user is relaxed. For example, the adjustment unit can record the user's voice, estimate the emotion using voice analysis technology, and adjust the dividers at a normal speed. The adjustment unit can also maximize the speed at which the dividers are adjusted to quickly separate the garbage when the user is in a hurry. For example, the adjustment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and maximize the speed at which the dividers are adjusted to quickly separate the garbage. This allows for more appropriate garbage separation by changing the speed at which the dividers are adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit may input user emotion data into the generation AI and cause the generation AI to change the adjustment speed of the divider.

[0083] The adjustment unit can fine-tune the opening and closing angle of the partition depending on the determined type of trash. For example, in the case of plastic trash, the adjustment unit sets the opening and closing angle of the partition wider. For example, when plastic trash is determined, the adjustment unit sets the opening and closing angle of the partition wider. The adjustment unit can also set the opening and closing angle of the partition narrower when paper trash is determined. For example, when paper trash is determined, the adjustment unit can also set the opening and closing angle of the partition narrower. The adjustment unit can also set the opening and closing angle of the partition to a medium level when metal trash is determined. For example, when metal trash is determined, the adjustment unit can also set the opening and closing angle of the partition to a medium level. This allows for more accurate trash sorting by fine-tuning the opening and closing angle of the partition. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to fine-tune the opening and closing angle of the partition.

[0084] The adjustment unit can automatically change the material and shape of the partitions depending on the type of trash. For example, in the case of plastic trash, the adjustment unit changes the material of the partitions to plastic. For example, if the adjustment unit determines that the trash is plastic, the adjustment unit changes the material of the partitions to plastic. The adjustment unit can also change the material of the partitions to paper in the case of paper trash. For example, if the adjustment unit determines that the trash is paper, the adjustment unit can also change the material of the partitions to paper. The adjustment unit can also change the material of the partitions to metal in the case of metal trash. For example, if the adjustment unit determines that the trash is metal, the adjustment unit can also change the material of the partitions to metal. This allows for more appropriate trash sorting by changing the material and shape of the partitions. Some or all of the above-described processing in the adjustment unit may be performed using, or without, AI. For example, the adjustment unit can input data on the determined type of trash into the generation AI and cause the generation AI to change the material and shape of the partitions.

[0085] The adjustment unit can adjust the timing of opening and closing the partitions depending on the type of waste. For example, in the case of plastic waste, the adjustment unit sets the timing of opening and closing the partitions earlier. For example, if the adjustment unit determines that the waste is plastic, the adjustment unit sets the timing of opening and closing the partitions earlier. The adjustment unit can also set the timing of opening and closing the partitions later in the case of paper waste. For example, if the adjustment unit determines that the waste is paper, the adjustment unit can also set the timing of opening and closing the partitions later. The adjustment unit can also set the timing of opening and closing the partitions to a medium level in the case of metal waste. For example, if the adjustment unit determines that the waste is metal, the adjustment unit can also set the timing of opening and closing the partitions to a medium level. This allows for more appropriate waste sorting by adjusting the timing of opening and closing the partitions. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of waste to the generation AI and cause the generation AI to adjust the timing of opening and closing the partitions.

[0086] The adjustment unit can estimate the emotion of the user who deposited the garbage and adjust the sound of the partitions opening and closing based on the estimated emotion of the user. For example, if the user is feeling stressed, the adjustment unit can quiet down the sound of the partitions opening and closing. For example, the adjustment unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and quiet down the sound of the partitions opening and closing. The adjustment unit can also set a normal sound of the partitions opening and closing when the user is relaxed. For example, the adjustment unit can record the user's voice, estimate the emotion using voice analysis technology, and set a normal sound of the partitions opening and closing. The adjustment unit can also minimize the sound of the partitions opening and closing to quickly separate the garbage when the user is in a hurry. For example, the adjustment unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and minimize the sound of the partitions opening and closing to quickly separate the garbage. This allows for more comfortable use by adjusting the sound of the partitions opening and closing based on the user's emotion. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the adjustment unit may be performed using AI, or may be performed without using AI. For example, the adjustment unit may input user emotion data into the generation AI and cause the generation AI to adjust the sound of a partition opening and closing.

[0087] The adjustment unit can change the opening / closing direction of the partition depending on the determined type of trash. For example, in the case of plastic trash, the adjustment unit sets the partition to open to the left. For example, if plastic trash is determined, the adjustment unit sets the partition to open to the left. Furthermore, the adjustment unit can also set the partition to open to the right in the case of paper trash. For example, if paper trash is determined, the adjustment unit can also set the partition to open to the right. Furthermore, the adjustment unit can also set the partition to open up and down in the case of metal trash. For example, if metal trash is determined, the adjustment unit can also set the partition to open up and down. In this way, by changing the opening / closing direction of the partition, trash can be more appropriately separated. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to change the opening / closing direction of the partition.

[0088] The adjustment unit can adjust the speed at which the partitions open and close depending on the type of trash. For example, in the case of plastic trash, the adjustment unit sets the speed at which the partitions open and close faster. For example, when plastic trash is determined, the adjustment unit sets the speed at which the partitions open and close faster. The adjustment unit can also set the speed at which the partitions open and close slower in the case of paper trash. For example, when paper trash is determined, the adjustment unit can also set the speed at which the partitions open and close slower. The adjustment unit can also set the speed at which the partitions open and close medium in the case of metal trash. For example, when metal trash is determined, the adjustment unit can also set the speed at which the partitions open and close medium. This allows for more appropriate trash sorting by adjusting the speed at which the partitions open and close. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to adjust the speed at which the partitions open and close.

[0089] The adjustment unit can adjust the number of times the partition is opened and closed depending on the type of trash. For example, in the case of plastic trash, the adjustment unit sets the number of times the partition is opened and closed to one. For example, if plastic trash is determined, the adjustment unit sets the number of times the partition is opened and closed to one. Furthermore, the adjustment unit can set the number of times the partition is opened and closed to two for paper trash. For example, if paper trash is determined, the adjustment unit can set the number of times the partition is opened and closed to two. Furthermore, the adjustment unit can set the number of times the partition is opened and closed to three for metal trash. For example, if metal trash is determined, the adjustment unit can set the number of times the partition is opened and closed to three. In this way, by adjusting the number of times the partition is opened and closed, trash can be more appropriately sorted. Some or all of the above-described processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input data on the determined type of trash to the generation AI and cause the generation AI to adjust the number of times the partition is opened and closed.

[0090] The monitoring unit can estimate the emotions of the user who threw out the trash and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit can increase the monitoring frequency and respond quickly. For example, the monitoring unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and increase the monitoring frequency to respond quickly. The monitoring unit can also set a normal monitoring frequency when the user is relaxed. For example, the monitoring unit can record the user's voice, estimate the emotion using voice analysis technology, and set a normal monitoring frequency. The monitoring unit can also maximize the monitoring frequency and respond quickly when the user is in a hurry. For example, the monitoring unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and maximize the monitoring frequency to respond quickly. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data into the generation AI and cause the generation AI to adjust the monitoring frequency.

[0091] The monitoring unit can monitor the amount and type of garbage using external sensors in addition to sensors inside the trash can. The monitoring unit, for example, monitors the amount of garbage using sensors installed outside the trash can. For example, the monitoring unit monitors the amount of garbage using an infrared sensor. The monitoring unit can also monitor the type of garbage using an external sensor and perform analysis in cooperation with the internal sensor. For example, the monitoring unit can monitor the type of garbage using an ultrasonic sensor and perform analysis in cooperation with the internal sensor. The monitoring unit can also collect environmental information around the trash can using an external sensor to improve the accuracy of monitoring. For example, the monitoring unit can collect temperature information around the trash can using a temperature sensor to improve the accuracy of monitoring. In this way, the use of external sensors can more accurately monitor the amount and type of garbage. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data from an external sensor into the generation AI and cause the generation AI to monitor the amount and type of garbage.

[0092] When monitoring the amount and type of garbage, the monitoring unit can improve the accuracy of the monitoring by referring to past monitoring data. The monitoring unit, for example, predicts the amount of the same type of garbage based on past monitoring data. For example, the monitoring unit stores past monitoring data in a database and predicts the amount of the same type of garbage. The monitoring unit can also refer to past monitoring data to monitor similar types of garbage. For example, the monitoring unit monitors similar types of garbage based on past monitoring data. The monitoring unit can also reduce errors when monitoring the amount and type of garbage by using past monitoring data. For example, the monitoring unit reduces errors when monitoring the amount and type of garbage based on past monitoring data. As a result, the accuracy of the monitoring is improved by referring to past monitoring data. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input past monitoring data into a generation AI and cause the generation AI to improve the accuracy of the monitoring.

[0093] The monitoring unit can monitor the amount and type of garbage by analyzing vibrations generated when garbage is inserted. For example, the monitoring unit may use a sensor to detect vibrations generated when garbage is inserted and monitor the amount of garbage. For example, the monitoring unit may use an acceleration sensor to detect vibrations generated when garbage is inserted and monitor the amount of garbage. The monitoring unit can also analyze the vibrations generated when garbage is inserted and monitor the type of garbage. For example, the monitoring unit can use a vibration analysis algorithm to analyze the vibrations generated when garbage is inserted and monitor the type of garbage. The monitoring unit can also analyze the vibrations generated when garbage is inserted and accurately monitor the amount and type of garbage. For example, the monitoring unit may combine an acceleration sensor and a vibration analysis algorithm to analyze the vibrations generated when garbage is inserted and accurately monitor the amount and type of garbage. This allows for more accurate monitoring of the amount and type of garbage by analyzing the vibrations generated when garbage is inserted. Some or all of the above-described processing performed by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit may input vibration data generated when garbage is inserted to a generation AI and cause the generation AI to monitor the amount and type of garbage.

[0094] The monitoring unit can estimate the emotions of the user who deposited the trash and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring unit provides a simple, highly visible display method. For example, the monitoring unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and provide a simple, highly visible display method. The monitoring unit can also display detailed monitoring results if the user is relaxed. For example, the monitoring unit can record the user's voice, estimate the emotion using voice analysis technology, and display detailed monitoring results. The monitoring unit can also display concise monitoring results that focus on the key points if the user is in a hurry. For example, the monitoring unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and display concise monitoring results that focus on the key points. This allows the display method of the monitoring results to be adjusted according to the user's emotions, resulting in a more appropriate display. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit may input user emotion data into the generation AI and cause the generation AI to adjust the display method of the monitoring results.

[0095] The monitoring unit can monitor the amount and type of garbage using a camera in addition to a sensor inside the trash can. The monitoring unit, for example, monitors the amount of garbage using a camera installed inside the trash can. For example, the monitoring unit monitors the amount of garbage using an RGB camera. The monitoring unit can also monitor the type of garbage using a camera and perform analysis in cooperation with an internal sensor. For example, the monitoring unit can monitor the type of garbage using an infrared camera and perform analysis in cooperation with an internal sensor. The monitoring unit can also monitor the situation inside the trash can in real time using a camera to improve the accuracy of monitoring. For example, the monitoring unit can monitor the situation inside the trash can in real time using an RGB camera to improve the accuracy of monitoring. In this way, the amount and type of garbage can be monitored more accurately by using a camera. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input camera data into a generation AI and cause the generation AI to monitor the amount and type of garbage.

[0096] When monitoring the amount and type of garbage, the monitoring unit can determine monitoring priorities taking into account the frequency of garbage disposal. The monitoring unit, for example, prioritizes monitoring of types of garbage that are frequently disposed of. For example, the monitoring unit analyzes the frequency of garbage disposal and quickly monitors the types of garbage that are frequently disposed of. The monitoring unit can also determine priorities and perform monitoring taking into account the frequency of garbage disposal. For example, the monitoring unit determines priorities and performs monitoring based on the frequency of garbage disposal. In this way, monitoring can be performed by determining priorities by taking into account the frequency of garbage disposal. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input data on the frequency of garbage disposal into a generation AI and have the generation AI determine the monitoring priorities.

[0097] When monitoring the amount and type of garbage, the monitoring unit can perform monitoring taking into account attribute information of the garbage putter. For example, if the garbage putter produces a lot of household garbage, the monitoring unit performs monitoring based on that attribute information. For example, the monitoring unit prioritizes monitoring household garbage based on the attribute information of the garbage putter. Furthermore, if the garbage putter is a company, the monitoring unit can also perform monitoring taking into account the type of garbage specific to the company. For example, the monitoring unit monitors the type of garbage specific to the company based on the attribute information of the garbage putter. Furthermore, the monitoring unit can prioritize monitoring specific types of garbage based on the attribute information of the garbage putter. For example, the monitoring unit prioritizes monitoring specific types of garbage based on the attribute information of the garbage putter. In this way, by taking into account the attribute information of the garbage putter, the amount and type of garbage can be monitored more accurately. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can input attribute information of the garbage putter into the generation AI and cause the generation AI to monitor the amount and type of garbage.

[0098] The notification unit can estimate the emotion of the user who threw out the trash and adjust the timing of the notification based on the estimated emotion of the user. For example, the notification unit can provide an earlier notification if the user is feeling stressed. For example, the notification unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and provide an earlier notification. The notification unit can also provide a notification at a normal timing if the user is relaxed. For example, the notification unit can record the user's voice, estimate the emotion using voice analysis technology, and provide a notification at a normal timing. The notification unit can also provide a prompt notification if the user is in a hurry. For example, the notification unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and provide a prompt notification. This allows for more appropriate notification by adjusting the timing of the notification according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input user emotion data to the generation AI and have the generation AI adjust the timing of notifications.

[0099] The notification unit can use different notification methods for each type of garbage before the garbage bin becomes full. For example, the notification unit sends a notification to a smartphone before the plastic garbage bin becomes full. For example, the notification unit sends a notification to a smartphone before the plastic garbage bin becomes full. The notification unit can also provide an audio notification before the paper garbage bin becomes full. For example, the notification unit can provide an audio notification before the paper garbage bin becomes full. The notification unit can also provide an LED light notification before the metal garbage bin becomes full. For example, the notification unit can provide an LED light notification before the metal garbage bin becomes full. This allows for more appropriate notification by using different notification methods for each type of garbage. Some or all of the above-described processing by the notification unit can be performed using AI, for example, or without AI. For example, the notification unit can input data on the type of garbage into the generation AI and have the generation AI select the notification method.

[0100] The notification unit can improve the accuracy of notifications by referring to past notification data before the trash can becomes full. The notification unit, for example, predicts when the same type of trash will become full based on the past notification data. For example, the notification unit stores the past notification data in a database and predicts when the same type of trash will become full. The notification unit can also predict when similar types of trash will become full by referring to the past notification data. For example, the notification unit predicts when similar types of trash will become full based on the past notification data. The notification unit can also accurately notify the trash can before it becomes full by using the past notification data. For example, the notification unit accurately notifies the trash can before it becomes full based on the past notification data. As a result, the accuracy of notifications is improved by referring to the past notification data. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past notification data into a generation AI and cause the generation AI to improve the accuracy of notifications.

[0101] The notification unit can analyze the sound made when garbage is thrown into the trash can and provide a notification before the trash can is full. For example, the notification unit detects the sound made when garbage is thrown into the trash can with a sensor and predicts when the trash can will be full. For example, the notification unit can analyze the sound made when garbage is thrown into the trash can using voice recognition technology and predict when the trash can will be full. The notification unit can also analyze the sound made when garbage is thrown into the trash can and provide a notification before the trash can is full. For example, the notification unit can analyze the sound made when garbage is thrown into the trash can using voice recognition technology and provide a notification before the trash can is full. The notification unit can also analyze the sound made when garbage is thrown into the trash can and accurately predict when the trash can will be full and provide a notification. For example, the notification unit can analyze the sound made when garbage is thrown into the trash can using voice recognition technology and accurately predict when the trash can will be full and provide a notification. In this way, by analyzing the sound made when garbage is thrown into the trash can, it is possible to more accurately predict and provide a notification when the trash can will be full. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input data on the sound made when garbage is thrown into the generation AI and have the generation AI predict the timing of the notification.

[0102] The notification unit can estimate the emotion of the user who threw out the trash and adjust the content of the notification based on the estimated emotion. For example, if the user is feeling stressed, the notification unit can provide concise and easy-to-understand notification content. For example, the notification unit can capture the user's facial expression with a camera, estimate the emotion using an emotion estimation algorithm, and provide concise and easy-to-understand notification content. The notification unit can also provide detailed notification content if the user is relaxed. For example, the notification unit can record the user's voice, estimate the emotion using voice analysis technology, and provide detailed notification content. The notification unit can also provide concise notification content that focuses on the main points if the user is in a hurry. For example, the notification unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the emotion using an emotion estimation algorithm, and provide concise notification content that focuses on the main points. This allows the notification content to be adjusted according to the user's emotion, resulting in more appropriate notifications. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, or without, an AI. For example, the notification unit may input user emotion data into the generation AI and cause the generation AI to adjust the content of the notification.

[0103] The notification unit can use a different notification sound for each type of trash before the trash can is full. For example, the notification unit sets a specific notification sound before the trash can is full for plastic trash. For example, the notification unit sets a specific notification sound before the trash can is full for plastic trash. The notification unit can also set a different notification sound before the trash can is full for paper trash. For example, the notification unit can set a different notification sound before the trash can is full for paper trash. The notification unit can also set an even different notification sound before the trash can is full for metal trash. For example, the notification unit can set an even different notification sound before the trash can is full for metal trash. This allows for more appropriate notification by using different notification sounds for each type of trash. Some or all of the above-described processing by the notification unit can be performed using, for example, AI, or can be performed without using AI. For example, the notification unit can input data on the type of trash into a generation AI and cause the generation AI to select a notification sound.

[0104] The notification unit can determine the priority of notifications taking into account the frequency of garbage disposal before the trash can becomes full. The notification unit, for example, prioritizes notifications of types of garbage that are frequently disposed of. For example, the notification unit analyzes the frequency of garbage disposal and quickly notifies of types of garbage that are frequently disposed of. The notification unit can also determine the priority and provide notifications taking into account the frequency of garbage disposal. For example, the notification unit determines the priority and provides notifications based on the frequency of garbage disposal. In this way, the priority can be determined and notifications can be provided by taking the frequency of garbage disposal into consideration. Some or all of the above-mentioned processing in the notification unit may be performed using AI, for example, or may be performed without using AI. For example, the notification unit can input data on the frequency of garbage disposal into a generation AI and have the generation AI determine the priority of notifications.

[0105] The notification unit can provide a notification before the trash can becomes full, taking into account the attribute information of the person who puts the trash. For example, if the trash person puts out a lot of household trash, the notification unit provides a notification based on that attribute information. For example, the notification unit provides a notification before the household trash can becomes full, based on the attribute information of the person who puts the trash. Furthermore, if the trash person puts out is a company, the notification unit can also provide a notification taking into account the type of trash specific to the company. For example, the notification unit provides a notification before the type of trash specific to the company becomes full, based on the attribute information of the person who puts the trash. Furthermore, the notification unit can prioritize notifications for specific types of trash based on the attribute information of the person who puts the trash. For example, the notification unit provides a notification before the type of trash specific to the company becomes full, based on the attribute information of the person who puts the trash. In this way, by taking into account the attribute information of the person who puts the trash, it is possible to more accurately predict and provide a notification when the trash can will become full. Some or all of the above-described processing by the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input attribute information of the person who puts the garbage into the generation AI and have the generation AI predict the timing of the notification. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, adjustment unit, monitoring unit, and notification unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit analyzes the shape, material, and color of the garbage using the camera 42 or optical sensor of the smart device 14, and determines the type of garbage using the identification processing unit 290 of the data processing device 12. The adjustment unit adjusts the position of the partition using, for example, the control unit 46A of the smart device 14. The monitoring unit monitors the amount of garbage in real time using, for example, a weight sensor of the smart device 14, and monitors the type of garbage using the identification processing unit 290 of the data processing device 12. The notification unit sends a notification to a smartphone via, for example, the communication I / F 44 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described analysis unit, adjustment unit, monitoring unit, and notification unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes the shape, material, and color of the dust using the camera 42 or optical sensor of the smart glasses 214, and determines the type of dust using the identification processing unit 290 of the data processing device 12. The adjustment unit adjusts the position of the partition using, for example, the control unit 46A of the smart glasses 214. The monitoring unit monitors the amount of dust in real time using, for example, a weight sensor of the smart glasses 214, and monitors the type of dust using the identification processing unit 290 of the data processing device 12. The notification unit sends a notification to a smartphone via, for example, the communication I / F 44 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, adjustment unit, monitoring unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit analyzes the shape, material, and color of the garbage using the camera 42 or optical sensor of the headset type terminal 314, and determines the type of garbage using the identification processing unit 290 of the data processing device 12. The adjustment unit adjusts the position of the partition using, for example, the control unit 46A of the headset type terminal 314. The monitoring unit monitors the amount of garbage in real time using, for example, a weight sensor of the headset type terminal 314, and monitors the type of garbage using the identification processing unit 290 of the data processing device 12. The notification unit sends a notification to a smartphone via, for example, the communication I / F 44 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, adjustment unit, monitoring unit, and notification unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes the shape, material, and color of the garbage using the camera 42 or optical sensor of the robot 414, and determines the type of garbage using the identification processing unit 290 of the data processing device 12. The adjustment unit adjusts the position of the partition using, for example, the control unit 46A of the robot 414. The monitoring unit monitors the amount of garbage in real time using, for example, a weight sensor of the robot 414, and monitors the type of garbage using the identification processing unit 290 of the data processing device 12. The notification unit sends a notification to a smartphone via, for example, the communication I / F 44 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] When determining the type of garbage, the analysis unit can analyze the vibrations generated when garbage is inserted. For example, the analysis unit can use an acceleration sensor to detect the vibrations generated when garbage is inserted and determine the type of garbage. The analysis unit can also use a vibration analysis algorithm to analyze the vibrations generated when garbage is inserted and determine the type of garbage. Furthermore, the analysis unit can combine and analyze the vibrations generated when garbage is inserted with data on the shape, material, and color of the garbage to more accurately determine the type of garbage. In this way, the analysis of the vibrations generated when garbage is inserted can more accurately determine the type of garbage.

[0108] When determining the type of garbage, the analysis unit can improve the accuracy of the determination by referring to past analysis data. For example, the analysis unit can more quickly determine the same type of garbage based on data analyzed in the past. The analysis unit can also store past analysis data in a database and quickly determine the type of garbage when the same type of garbage is thrown in. Furthermore, the analysis unit can also refer to past analysis data to determine similar types of garbage. In this way, by referring to past analysis data, the accuracy of the determination is improved.

[0109] The adjustment unit can fine-tune the opening and closing angle of the partition depending on the type of trash. For example, for plastic trash, the opening and closing angle of the partition can be set wider. For paper trash, the opening and closing angle of the partition can also be set narrower. Furthermore, for metal trash, the opening and closing angle of the partition can be set to a medium level. This allows for more accurate trash sorting by fine-tuning the opening and closing angle of the partition.

[0110] The monitoring unit can monitor the amount and type of garbage using external sensors in addition to sensors inside the trash can. For example, the amount of garbage can be monitored using a sensor installed outside the trash can. The type of garbage can also be monitored using an external sensor, and analyzed in conjunction with the internal sensor. Furthermore, the external sensor can be used to collect environmental information around the trash can, improving the accuracy of monitoring. As a result, the amount and type of garbage can be monitored more accurately by using an external sensor.

[0111] The notification unit can improve the accuracy of notifications by referring to past notification data before the trash can becomes full. For example, the notification unit can predict when the same type of trash will become full based on past notification data. The notification unit can also predict when a similar type of trash will become full by referring to past notification data. Furthermore, the notification unit can use past notification data to accurately notify the user before the trash can becomes full. In this way, by referring to past notification data, the accuracy of notifications is improved.

[0112] The analysis unit can estimate the emotions of the user who deposited the garbage and change the analysis parameters based on the estimated user emotions. For example, if the user is feeling stressed, the AI ​​will increase the accuracy of the analysis and quickly determine the type of garbage. Alternatively, if the user is relaxed, the AI ​​can determine the type of garbage with normal analysis accuracy. Furthermore, if the user is in a hurry, the AI ​​can prioritize analysis speed and quickly determine the type of garbage. This allows for more appropriate analysis by adjusting the accuracy of the analysis according to the user's emotions.

[0113] The adjustment unit can estimate the emotions of the user who deposited the garbage and change the speed at which the dividers are adjusted based on the estimated emotions of the user. For example, if the user is feeling stressed, the adjustment speed of the dividers can be increased to quickly separate the garbage. Alternatively, if the user is relaxed, the adjustment speed of the dividers can be adjusted at a normal speed. Furthermore, if the user is in a hurry, the adjustment speed of the dividers can be maximized to quickly separate the garbage. In this way, by changing the adjustment speed of the dividers according to the user's emotions, more appropriate separation can be performed.

[0114] The monitoring unit can estimate the emotions of the user who deposited the garbage and adjust the monitoring frequency based on the estimated emotions of the user. For example, if the user is feeling stressed, the monitoring frequency can be increased to respond quickly. Alternatively, if the user is relaxed, the monitoring frequency can be set to a normal frequency. Furthermore, if the user is in a hurry, the monitoring frequency can be maximized to respond quickly. In this way, more appropriate monitoring can be performed by adjusting the monitoring frequency according to the user's emotions.

[0115] The notification unit can estimate the emotion of the user who threw out the trash and adjust the timing of the notification based on the estimated emotion of the user. For example, if the user is feeling stressed, the notification can be sent earlier. If the user is relaxed, the notification can be sent at a normal timing. Furthermore, if the user is in a hurry, the notification can be sent quickly. In this way, by adjusting the timing of the notification according to the emotion of the user, more appropriate notifications can be sent.

[0116] The notification unit can estimate the emotion of the user who threw out the trash and adjust the content of the notification based on the estimated emotion of the user. For example, if the user is feeling stressed, the notification unit can provide simple and easy-to-understand content. If the user is relaxed, the notification unit can provide detailed content. Furthermore, if the user is in a hurry, the notification unit can provide concise content that focuses on the main points. In this way, by adjusting the content of the notification according to the user's emotion, more appropriate notifications can be provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The analysis unit determines the type of dust. For example, the analysis unit analyzes the shape, material, and color of the dust to determine the type of dust. The analysis unit can analyze the shape of the dust using image analysis technology, the material of the dust using an optical sensor, and the color of the dust using an RGB sensor. Step 2: The adjustment unit adjusts the partitions based on the determined type of trash. The adjustment unit adjusts the position of the partitions using, for example, a motor, and if plastic trash is determined, opens the plastic partition. If paper trash is determined, opens the paper partition. If metal trash is determined, opens the metal partition. Step 3: The monitoring unit monitors the amount of garbage. The monitoring unit measures the amount of garbage using, for example, a weight sensor, and monitors the amount of garbage in real time using a sensor inside the garbage can. Step 4: The notification module will notify you when the trash can reaches 80% capacity or more. For example, the notification module will send a notification to your smartphone. It can also sound an alarm before the trash can becomes full, or turn on an LED light.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit for determining the type of garbage; an adjustment unit that adjusts the partition based on the type of garbage determined by the analysis unit; a monitoring unit that monitors the amount of garbage; A notification section that notifies you when the trash can capacity reaches 80% or more. Equipped with A system characterized by:

2. The analysis unit Analyze the shape, material, and color of the garbage to determine the type of garbage 2. The system of claim 1.

3. The adjustment unit Adjust the partitions inside the trash can according to the type of trash detected 2. The system of claim 1.

4. The monitoring unit The amount and type of garbage is monitored every second using sensors inside the trash can.

2. The system of claim 1.

5. The notification unit Notify the user before the trash is full 2. The system of claim 1.

6. The notification unit Send notifications to your smartphone 2. The system of claim 1.

7. The analysis unit Estimate the emotions of the user who puts the garbage in and change the analysis parameters based on the estimated emotions of the user.

2. The system of claim 1.

8. The analysis unit In addition to the shape, material, and color of the garbage, the odor and temperature are analyzed to determine the type.

2. The system of claim 1.

9. The analysis unit When determining the type of garbage, refer to past analysis data to improve the accuracy of the determination.

2. The system of claim 1.

10. The analysis unit When determining the type of garbage, the sound made when the garbage is put in is analyzed to determine the type.

2. The system of claim 1.

Images