system

Abstract

The system according to this embodiment aims to sort waste accurately and efficiently. [Solution] The system according to the embodiment comprises a determination unit, a presentation unit, a control unit, and an authentication unit. The determination unit determines the state of the waste. The presentation unit presents the correct method of sorting the waste based on the determination by the determination unit. The control unit controls the opening of the public dustbin based on the information presented by the presentation unit. The authentication unit takes a picture of the sorted bag with a smartphone and issues a sorting completion authentication stamp.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the separation of garbage depends on human senses, and there is a risk that accurate separation cannot be performed.

[0005] The system according to the embodiment aims to accurately and efficiently separate garbage.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a determination unit, a presentation unit, a control unit, and an authentication unit. The determination unit determines the state of the waste. The presentation unit presents the correct method of sorting the waste based on the determination made by the determination unit. The control unit controls the opening of the public dustbin based on the information presented by the presentation unit. The authentication unit takes a picture of the sorted bag with a smartphone and issues a sorting completion authentication stamp. [Effects of the Invention]

[0007] The system according to this embodiment can sort waste accurately and efficiently. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The waste sorting support system according to an embodiment of the present invention is a system that promotes the recycling of household waste using generating AI. In this waste sorting support system, the generating AI determines the state of the waste so that the user does not need to determine the type of waste. Next, based on the results determined by the generating AI, the correct method of sorting the waste is presented. Furthermore, by setting a reading app on a public dustbin, the system ensures that only the opening of the box that matches the information read is opened. This promotes correct sorting and prevents the loss of opportunities for resource recycling and damage to facilities. The system also provides a mechanism to reward users who have sorted their waste correctly by taking a picture of the sorted bags with a smartphone and receiving a sorting completion authentication stamp. First, when a user disposes of waste, they launch the app and select their municipality. Next, they read the object with the app. At this time, the generating AI determines the state of the waste and presents the correct sorting method. For example, it may indicate that if the contents are discarded, rinsed, and dried, it becomes a plastic resource, or if only the contents have been discarded, it becomes non-burnable waste. Furthermore, a reading app is installed in public trash cans, and only the slot of the trash can that matches the scanned information will open. This allows users to sort their waste correctly. In addition, a system is provided that rewards users who sort their waste correctly by allowing them to take a picture of the sorted bags with their smartphones and receive a sorting completion stamp. For example, points are awarded for putting in the correct items or based on the number of sorting completion stamps collected. This system allows users to sort their waste correctly in a game-like manner, promoting resource recovery and preventing damage to facilities. Moreover, the point reward system increases user motivation and further promotes correct sorting. For example, by collecting sorting completion stamps, users can accumulate points that can be used in an electronic payment system. In this way, by using generation AI, a system is provided that promotes resource recovery of household waste and promotes correct sorting. As a result, the waste sorting support system promotes resource recovery of household waste and promotes correct sorting.

[0029] The waste sorting support system according to this embodiment comprises a determination unit, a presentation unit, a control unit, and an authentication unit. The determination unit determines the state of the waste. The determination unit determines the state of the waste by analyzing the material, shape, and degree of contamination of the waste, for example, using a generation AI. The generation AI can analyze images of the waste using deep learning technology and identify the material and shape. The determination unit can also measure the weight and density of the waste using sensors in order to determine the state of the waste. For example, the determination unit measures the weight of the waste and estimates the material based on the result. The determination unit can also measure the density of the waste and estimate the shape based on the result. The presentation unit presents the correct waste sorting method based on the results determined by the determination unit. The presentation unit presents the waste sorting method using, for example, a generation AI. The generation AI can present the waste sorting method to the user in an easy-to-understand manner using natural language processing technology. The presentation unit can also use a display or an audio output device to present the waste sorting method. For example, the display unit shows the waste sorting method on a screen, providing a visual presentation to the user. The display unit can also provide the waste sorting method verbally using an audio output device. The control unit controls the opening of the public dustbin based on the information presented by the display unit. The control unit controls the opening and closing of the opening using, for example, a generating AI. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. The control unit can also use a motor or actuator to control the opening and closing of the opening. For example, the control unit can use a motor to open and close the opening and select the appropriate opening according to the type of waste. The control unit can also use an actuator to open and close the opening and adjust the opening and closing speed according to the type of waste. The authentication unit takes a picture of the sorted bag with a smartphone and issues a sorting completion authentication stamp. The authentication unit issues a sorting completion authentication stamp using, for example, a generating AI. The generating AI can use image recognition technology to analyze the state of the sorted bag and determine whether the sorting was done correctly. Furthermore, the authentication unit can also use a smartphone camera or QR code (registered trademark) reader to issue a sorting completion authentication stamp.For example, the authentication unit uses a smartphone camera to photograph the sorted bags, analyzes the image, and issues a sorting completion authentication stamp. The authentication unit can also issue a sorting completion authentication stamp using a QR code reader. As a result, the waste sorting support system according to this embodiment promotes correct sorting by determining the state of the waste, suggesting the correct sorting method, controlling the opening of the public dustbin, and issuing a sorting completion authentication stamp.

[0030] The identification unit determines the state of the waste. For example, it uses a generative AI to analyze the material, shape, and degree of soiling of the waste to determine its state. The generative AI uses deep learning technology to analyze images of waste and identify their material and shape. Specifically, the generative AI is trained using a large dataset of waste images and can identify materials such as plastic, metal, paper, and glass with high accuracy. It also recognizes common shapes such as bottles, cans, and boxes, and further analyzes the color tone and texture of the images to evaluate the degree of soiling. Based on these analysis results, the identification unit makes a comprehensive judgment about the material, shape, and degree of soiling of the waste. Furthermore, the identification unit can also measure the weight and density of the waste using sensors to determine its state. For example, it can measure the weight of the waste using a weight sensor and estimate the material based on the result. Since plastic and paper are relatively lightweight, while metal and glass tend to be heavy, weight data is useful for material estimation. It can also measure the density of the waste using a density sensor and estimate the shape based on the result. High-density waste is more likely to be solid, while low-density waste is likely to have many cavities. This allows the identification unit to analyze the material, shape, and degree of contamination of the waste from multiple angles, enabling accurate identification.

[0031] The display unit presents the correct method of sorting waste based on the results determined by the identification unit. The display unit can, for example, use a generative AI to present waste sorting methods. The generative AI uses natural language processing technology to present waste sorting methods to the user in an easy-to-understand manner. Specifically, the generative AI generates appropriate sorting methods based on information about the material, shape, and degree of soiling of the waste provided by the identification unit. For example, for plastic bottles, it would instruct the user to remove the cap and peel off the label before placing them in the recycling box. The display unit can also use a display or audio output device to present waste sorting methods. The display shows the sorting method in text along with an image of the waste, making it visually easy for the user to understand. Furthermore, the display unit can also present waste sorting methods audibly using an audio output device. This makes it possible to accommodate users who have difficulty reading text on a display, such as the visually impaired and the elderly. The display unit can also use animations and videos to show sorting methods to aid user understanding. For example, the waste sorting procedure can be shown using animation, allowing users to visually understand how to actually sort their waste. This enables the display to provide users with accurate and easy-to-understand sorting methods, promoting proper waste sorting.

[0032] The control unit controls the opening of the public dustbin based on the information presented by the presentation unit. The control unit controls the opening and closing of the opening using, for example, a generating AI. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. Specifically, the generating AI selects the appropriate opening and controls its opening and closing based on information about the material and shape of the waste provided by the identification unit. For example, it opens the opening for plastic waste and the opening for metal waste. The control unit can also use motors or actuators to control the opening and closing of the opening. It uses a motor to open and close the opening and selects the appropriate opening according to the type of waste. Furthermore, it can use actuators to adjust the opening and closing speed. For example, it can open and close slowly for heavy waste and quickly for light waste to ensure smooth waste disposal. In addition to opening and closing the opening, the control unit can also monitor the waste disposal status and issue a warning when it becomes full. For example, it can use a sensor to measure the amount of waste in the opening and, if it exceeds a certain amount, display a warning to the user and instruct them to use another opening. This allows the control unit to efficiently control the opening of public waste bins and assist in the proper sorting of waste.

[0033] The authentication unit takes a picture of the sorted bags with a smartphone and issues a sorting completion authentication stamp. The authentication unit can issue the sorting completion authentication stamp using, for example, a generating AI. The generating AI can analyze the condition of the sorted bags using image recognition technology and determine whether the sorting was done correctly. Specifically, the generating AI analyzes the image of the sorted bags taken with the smartphone camera and checks the contents of the bags, the presence or absence of labels, and the condition of the bags. For example, for plastic waste bags, it checks whether the caps have been removed and whether the labels have been peeled off. The authentication unit can also use the smartphone camera or a QR code reader to issue the sorting completion authentication stamp. The user takes a picture of the sorted bags using the smartphone camera, analyzes the image, and issues the sorting completion authentication stamp. Furthermore, it can also issue the sorting completion authentication stamp using a QR code reader. For example, it can read the QR code attached to the sorted bags and verify the accuracy of the sorting based on that information. In addition to issuing the sorting completion authentication stamp, the authentication unit can also record the sorting history and have a function to provide the user with feedback on the degree of sorting achievement and areas for improvement. For example, the system can record how users have sorted their waste in the past and evaluate their sorting achievement. It can also provide feedback to users on points that are often overlooked during sorting and areas for improvement, helping them with future sorting. This allows the authentication unit to guarantee the accuracy of sorting and promote good sorting habits.

[0034] The rewards department will provide point rewards. For example, the rewards department will reward points to users who sort their waste correctly. The rewards department can use a generation AI to calculate points based on the number of sorting completion authentication stamps. For example, the rewards department will reward 10 points for every 10 sorting completion authentication stamps collected. The rewards department can also adjust the point multiplier based on the number of sorting completion authentication stamps. For example, the rewards department will double the point reward multiplier for every 20 sorting completion authentication stamps collected. Furthermore, the rewards department can use a smartphone app or email to notify users of the point reward results. For example, the rewards department will notify users of the point reward results via a smartphone app. The rewards department can also notify users of the point reward results via email. By rewarding users who sort their waste correctly with points, user motivation will increase, and correct sorting will be further promoted.

[0035] The selection function allows users to select a municipality after launching the app. For example, a smartphone app can be used to select a municipality. The selection function uses generative AI to suggest the most suitable municipality based on the user's location and past selection history. For example, it suggests the nearest municipality based on the user's current location. It can also prioritize displaying frequently used municipalities based on the user's past selection history. Furthermore, when a user selects a municipality, the selection function can display the municipality's sorting rules and garbage collection schedule. For example, it can display the sorting rules of the selected municipality, providing users with information to ensure correct sorting. It can also display the municipality's garbage collection schedule, allowing users to understand when to put out their garbage. This allows users to apply region-specific sorting methods simply by launching the app and selecting a municipality.

[0036] The reading unit reads the object. The reading unit can read the object using, for example, barcode scanning or image recognition technology. The reading unit can use generative AI to analyze the material and shape of the object and suggest the appropriate sorting method. For example, the reading unit can use barcode scanning to read the barcode of a product and obtain product information. The reading unit can also use image recognition technology to analyze an image of the object and identify its material and shape. Furthermore, the reading unit can use a display or audio output device to present the object reading results to the user. For example, the reading unit can display the object reading results on a display to present them visually to the user. The reading unit can also use an audio output device to present the object reading results audibly. This allows for accurate determination of the waste condition by reading the object.

[0037] The identification unit uses a generation AI to determine the state of the waste. For example, the identification unit uses the generation AI to analyze the material, shape, and degree of contamination of the waste to determine its state. The generation AI can analyze images of waste using deep learning technology to identify the material and shape. In addition, the identification unit can also measure the weight and density of the waste using sensors to determine its state. For example, the identification unit measures the weight of the waste and estimates the material based on the result. It can also measure the density of the waste and estimate the shape based on the result. In this way, the state of the waste can be accurately determined by using the generation AI.

[0038] The display unit uses generative AI to present the correct way to sort garbage. For example, the display unit uses generative AI to present garbage sorting methods. The generative AI uses natural language processing technology to present garbage sorting methods to the user in an easy-to-understand manner. The display unit can also use a display or audio output device to present garbage sorting methods. For example, the display unit can display garbage sorting methods on a screen to present them visually to the user. The display unit can also use an audio output device to present garbage sorting methods audibly. In this way, by using generative AI, the correct way to sort garbage can be accurately presented.

[0039] The control unit controls the opening of the public waste bin using a generating AI. For example, the control unit uses the generating AI to control the opening and closing of the opening. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. The control unit can also use motors or actuators to control the opening and closing of the opening. For example, the control unit can use a motor to open and close the opening and select the appropriate opening according to the type of waste. Alternatively, the control unit can use actuators to open and close the opening and closing and adjust the opening and closing speed according to the type of waste. In this way, the opening of the public waste bin can be accurately controlled using a generating AI.

[0040] The authentication unit issues a sorting completion authentication stamp using a generating AI. For example, the authentication unit uses a generating AI to issue the sorting completion authentication stamp. The generating AI can analyze the state of the sorted bags using image recognition technology to determine whether the sorting was done correctly. The authentication unit can also use a smartphone camera or QR code reader to issue the sorting completion authentication stamp. For example, the authentication unit can take a picture of the sorted bags using a smartphone camera, analyze the image, and issue the sorting completion authentication stamp. Alternatively, the authentication unit can issue the sorting completion authentication stamp using a QR code reader. This allows for accurate issuance of the sorting completion authentication stamp by using a generating AI.

[0041] The identification unit customizes its identification method based on the material and shape of the waste when determining its condition. For example, the identification unit uses a generating AI to analyze the material and shape of the waste and customize the identification method. The generating AI can analyze images of waste using deep learning technology and identify the material and shape. For example, in the case of plastic products, the identification unit identifies the type of material and suggests the appropriate sorting method. In the case of metal products, the identification unit can also adjust the identification method considering the shape and size. Furthermore, in the case of paper products, the identification unit can customize the identification method based on the thickness and texture. As a result, the identification accuracy is improved by customizing the identification method based on the material and shape of the waste.

[0042] The identification unit optimizes its identification algorithm by referring to past identification data when determining the state of waste. For example, the identification unit analyzes past identification data using generative AI to optimize the identification algorithm. Generative AI can learn from past identification data using machine learning techniques to improve identification accuracy. For example, the identification unit optimizes identification patterns based on past identification data. The identification unit can also introduce algorithms to reduce identification errors based on past identification data. Furthermore, the identification unit can continuously improve its identification algorithm based on past identification data. In this way, by referring to past identification data, the identification algorithm can be optimized and identification accuracy can be improved.

[0043] The identification unit adjusts its identification method based on the location and time of waste generation when determining the state of the waste. For example, the identification unit uses a generation AI to analyze the location and time of waste generation and adjust the identification method. The generation AI can identify the location and time of waste generation using location information and time data. For example, if the waste is generated in a home, the identification unit applies the identification method for household waste. The identification unit can also apply the identification method for office waste if the waste is generated in an office. Furthermore, if the waste is generated at night, the identification unit can apply the identification method specific to nighttime waste. By adjusting the identification method based on the location and time of waste generation, the accuracy of the identification is improved.

[0044] The identification unit improves its identification accuracy by referring to relevant information about the waste when determining its condition. For example, the identification unit uses a generating AI to analyze relevant information about the waste and improve its identification accuracy. The generating AI can identify the material and shape of the waste by referring to product barcodes and product information databases. For example, the identification unit scans product barcodes and improves identification accuracy based on product information. The identification unit can also obtain material information from product barcodes and suggest appropriate sorting methods. Furthermore, the identification unit can optimize its identification algorithm by referring to product barcodes. In this way, it can improve identification accuracy by referring to relevant information about the waste.

[0045] The display unit applies different display methods depending on the type and condition of the waste when presenting sorting methods. For example, the display unit uses a generating AI to analyze the type and condition of the waste and present the appropriate sorting method. The generating AI can analyze images of waste using deep learning technology and identify the material and shape. For example, in the case of plastic products, the display unit presents sorting methods according to the material. In the case of metal products, the display unit can also present sorting methods according to the shape and size. Furthermore, in the case of paper products, the display unit can also present sorting methods according to the thickness and texture. By applying different display methods according to the type and condition of the waste, the accuracy of the sorting methods is improved.

[0046] The presentation unit optimizes its presentation algorithm by referring to past presentation data when presenting sorting methods. For example, the presentation unit analyzes past presentation data using generative AI to optimize the presentation algorithm. Generative AI can learn from past presentation data using machine learning techniques to improve the accuracy of sorting methods. For example, the presentation unit applies an algorithm that presents the optimal sorting method based on past presentation data. The presentation unit can also learn from past presentation data and optimize sorting method patterns. Furthermore, the presentation unit can introduce an algorithm that reduces errors in sorting methods based on past presentation data. In this way, by referring to past presentation data, the presentation algorithm can be optimized and the accuracy of sorting methods can be improved.

[0047] The display unit, when presenting sorting methods, considers the user's geographical location information to present the most suitable sorting method. For example, the display unit uses a generating AI to analyze the user's geographical location information and present the most suitable sorting method. The generating AI can identify the user's current location using GPS data and location information services. For example, the display unit presents region-specific sorting methods based on the user's current location. The display unit can also present information on the nearest sorting facility based on the user's geographical location information. Furthermore, the display unit can present the most suitable sorting method considering the user's geographical location information. This allows the display unit to present region-specific sorting methods by considering the user's geographical location information.

[0048] The presentation unit, when presenting sorting methods, refers to the user's past sorting history to present customized sorting methods. For example, the presentation unit uses generative AI to analyze the user's past sorting history and present customized sorting methods. Generative AI can learn from the user's past sorting history using machine learning techniques and present the optimal sorting method. For example, the presentation unit presents the optimal sorting method based on the user's past sorting history. The presentation unit can also learn sorting patterns from the user's past sorting history and present customized sorting methods. Furthermore, the presentation unit can introduce algorithms that reduce the error in sorting methods by referring to the user's past sorting history. This allows it to present customized sorting methods by referring to the user's past sorting history.

[0049] The control unit applies different control methods to the dustbin's opening depending on the type and condition of the waste. For example, the control unit uses a generating AI to analyze the type and condition of the waste and apply the appropriate control method. The generating AI uses deep learning technology to analyze images of the waste and identify its material and shape. For example, in the case of plastic products, the control unit applies a control method to the opening that is appropriate for the material. In the case of metal products, the control unit can also apply a control method to the opening that is appropriate for the shape and size. Furthermore, in the case of paper products, the control unit can also apply a control method to the opening that is appropriate for the thickness and texture. By applying a control method according to the type and condition of the waste, the accuracy of opening control is improved.

[0050] The control unit optimizes the control algorithm by referring to past control data when controlling the dustbin's inlet. For example, the control unit analyzes past control data using generative AI and optimizes the control algorithm. Generative AI can learn from past control data using machine learning techniques to improve the accuracy of inlet control. For example, the control unit applies the optimal inlet control method based on past control data. The control unit can also optimize the inlet control pattern by learning from past control data. Furthermore, the control unit can introduce an algorithm to reduce the control error of the inlet based on past control data. In this way, by referring to past control data, the control algorithm can be optimized and the accuracy of inlet control can be improved.

[0051] The control unit adjusts the control method for the dustbin's opening based on the location and time of waste generation. For example, the control unit uses a generation AI to analyze the location and time of waste generation and apply an appropriate control method. The generation AI can identify the location and time of waste generation using location information and time data. For example, if the waste is generated in a home, the control unit applies a control method for household waste openings. The control unit can also apply an office waste opening control method if the waste is generated in an office. Furthermore, if the waste is generated at night, the control unit can apply a night-specific opening control method. By adjusting the control method based on the location and time of waste generation, the accuracy of opening control is improved.

[0052] The control unit improves control accuracy by referring to relevant information about the waste when controlling the dustbin's input port. For example, the control unit analyzes the relevant information about the waste using a generating AI to improve control accuracy. The generating AI can identify the material and shape of the waste by referring to the product's barcode or product information database. For example, the control unit scans the product's barcode and improves the control accuracy of the input port based on the product information. The control unit can also obtain material information from the product's barcode and apply an appropriate input port control method. Furthermore, the control unit can optimize the input port control algorithm by referring to the product's barcode. In this way, control accuracy can be improved by referring to relevant information about the waste.

[0053] The authentication unit applies different authentication methods depending on the type and condition of the waste when issuing a sorting completion authentication stamp. For example, the authentication unit uses a generating AI to analyze the type and condition of the waste and apply the appropriate authentication method. The generating AI can analyze images of the waste using deep learning technology and identify the material and shape. For example, in the case of plastic products, the authentication unit applies an authentication method according to the material. In the case of metal products, the authentication unit can also apply an authentication method according to the shape and size. Furthermore, in the case of paper products, the authentication unit can also apply an authentication method according to the thickness and texture. By applying an authentication method according to the type and condition of the waste, the authentication accuracy is improved.

[0054] The authentication unit optimizes its authentication algorithm by referring to past authentication data when assigning a sorting completion authentication stamp. For example, the authentication unit analyzes past authentication data using generative AI and optimizes the authentication algorithm. Generative AI can learn from past authentication data using machine learning techniques to improve authentication accuracy. For example, the authentication unit introduces an algorithm that applies the optimal authentication method based on past authentication data. The authentication unit can also learn from past authentication data and optimize authentication patterns. Furthermore, the authentication unit can introduce an algorithm that reduces authentication errors based on past authentication data. In this way, by referring to past authentication data, the authentication algorithm can be optimized and authentication accuracy can be improved.

[0055] The authentication unit applies the most suitable authentication method when issuing a sorting completion authentication stamp, taking into account the user's geographical location information. For example, the authentication unit analyzes the user's geographical location information using a generating AI and applies the most suitable authentication method. The generating AI can determine the user's current location using GPS data and location information services. For example, the authentication unit applies a region-specific authentication method based on the user's current location. The authentication unit can also provide information on the nearest authentication facility based on the user's geographical location information. Furthermore, the authentication unit can apply the most suitable authentication method by taking into account the user's geographical location information. This allows for the application of region-specific authentication methods by considering the user's geographical location information.

[0056] The authentication unit applies a customized authentication method by referring to the user's past sorting history when granting a sorting completion authentication stamp. For example, the authentication unit can analyze the user's past sorting history using generative AI and apply a customized authentication method. Generative AI can learn from the user's past sorting history using machine learning techniques and apply the optimal authentication method. For example, the authentication unit applies the optimal authentication method based on the user's past sorting history. The authentication unit can also learn authentication patterns from the user's past sorting history and apply a customized authentication method. Furthermore, the authentication unit can introduce an algorithm that reduces the error of the authentication method by referring to the user's past sorting history. This allows the application of a customized authentication method by referring to the user's past sorting history.

[0057] The rewards unit customizes the reward points based on the user's transaction history when redeeming points. For example, the rewards unit analyzes the user's transaction history using a generation AI and customizes the reward points. The generation AI can learn from the user's transaction history using machine learning techniques and provide the optimal reward points. For example, the rewards unit customizes the reward points based on the user's past transaction history. The rewards unit can also learn point redemption patterns from the user's transaction history and provide customized reward points. Furthermore, the rewards unit can introduce an algorithm that reduces errors in reward points by referring to the user's transaction history. This improves the accuracy of rewards by customizing reward points based on the user's transaction history.

[0058] The rewards unit applies the optimal reward method when redeeming points, taking into account the user's geographical location information. For example, the rewards unit uses a generating AI to analyze the user's geographical location information and apply the optimal reward method. The generating AI can identify the user's current location using GPS data and location information services. For example, the rewards unit applies a region-specific reward method based on the user's current location. The rewards unit can also provide information on the nearest reward facilities based on the user's geographical location information. Furthermore, the rewards unit can apply the optimal reward method by taking the user's geographical location information into consideration. This allows for the application of region-specific reward methods by considering the user's geographical location information.

[0059] The selection function, when selecting a municipality, refers to the user's past selection history to suggest the optimal selection method. For example, the selection function uses generative AI to analyze the user's past selection history and suggest the optimal selection method. Generative AI can learn from the user's past selection history using machine learning techniques and provide the optimal selection method. For example, the selection function suggests the optimal municipality based on the user's past selection history. The selection function can also learn selection patterns from the user's past selection history and provide the optimal selection method. Furthermore, the selection function can introduce an algorithm that reduces the error in the selection method by referring to the user's past selection history. This allows the system to suggest the optimal selection method by referring to the user's past selection history.

[0060] The selection unit presents the optimal selection method when users choose a municipality, taking into account the user's geographical location information. For example, the selection unit uses a generative AI to analyze the user's geographical location information and present the optimal selection method. The generative AI can determine the user's current location using GPS data and location services. For instance, the selection unit presents region-specific municipalities based on the user's current location. Furthermore, the selection unit can provide information on the nearest municipality based on the user's geographical location information. In addition, the selection unit can present the optimal municipality by considering the user's geographical location information. This allows for the presentation of region-specific municipalities by considering the user's geographical location information.

[0061] The reading unit applies different reading methods depending on the type and condition of the waste when reading an object. For example, the reading unit uses a generative AI to analyze the type and condition of the waste and apply the appropriate reading method. The generative AI can analyze images of waste using deep learning technology and identify their material and shape. For example, in the case of plastic products, the reading unit applies a reading method appropriate to the material. In the case of metal products, the reading unit can also apply a reading method appropriate to the shape and size. Furthermore, in the case of paper products, the reading unit can apply a reading method appropriate to the thickness and texture. By applying a reading method according to the type and condition of the waste, the reading accuracy is improved.

[0062] The reading unit applies the optimal reading method when reading an object, taking into account the user's geographical location information. For example, the reading unit analyzes the user's geographical location information using a generation AI and applies the optimal reading method. The generation AI can determine the user's current location using GPS data or location information services. For example, the reading unit applies a region-specific reading method based on the user's current location. The reading unit can also provide information on the nearest reading facility based on the user's geographical location information. Furthermore, the reading unit can apply the optimal reading method by taking the user's geographical location information into consideration. This allows for the application of region-specific reading methods by considering the user's geographical location information.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The identification unit can adjust its identification method based on the location and time of day the waste was generated. For example, if the waste is generated in a home, the unit can apply a method for identifying household waste. If the waste is generated in an office, it can apply a method for identifying office waste. Furthermore, if the waste is generated at night, it can apply a method for identifying waste specific to nighttime. By adjusting the identification method based on the location and time of day the waste was generated, the accuracy of the identification is improved.

[0065] The control unit can also apply different control methods to the dustbin's opening depending on the type and condition of the waste. For example, for plastic products, a control method appropriate to the material of the opening can be applied. For metal products, a control method appropriate to the shape and size of the opening can be applied. Furthermore, for paper products, a control method appropriate to the thickness and texture of the opening can be applied. By applying a control method according to the type and condition of the waste, the accuracy of opening control can be improved.

[0066] The rewards unit can also customize reward points based on the user's transaction history when redeeming points. For example, it can customize reward points based on the user's past transaction history. It can also learn point redemption patterns from the user's transaction history and provide customized reward points. Furthermore, it can introduce an algorithm that reduces errors in reward points by referring to the user's transaction history. As a result, the accuracy of rewards is improved by customizing reward points based on the user's transaction history.

[0067] The reading unit can apply different reading methods depending on the type and condition of the object being read. For example, for plastic products, a reading method appropriate to the material is applied. For metal products, a reading method appropriate to the shape and size can be applied. Furthermore, for paper products, a reading method appropriate to the thickness and texture can be applied. By applying a reading method according to the type and condition of the waste, the reading accuracy is improved.

[0068] The identification unit can optimize its identification algorithm by referring to past identification data when determining the state of waste. For example, it can optimize the identification pattern based on past identification data. It can also introduce an algorithm to reduce the error in identification based on past identification data. Furthermore, it can continuously improve the identification algorithm based on past identification data. In this way, by referring to past identification data, the identification algorithm can be optimized and the identification accuracy can be improved.

[0069] The control unit can also optimize the control algorithm by referring to past control data when controlling the dustbin's inlet. For example, it can apply the optimal inlet control method based on past control data. It can also optimize the inlet control pattern by learning from past control data. Furthermore, it can introduce an algorithm to reduce the control error of the inlet based on past control data. In this way, by referring to past control data, the control algorithm can be optimized and the accuracy of inlet control can be improved.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The identification unit assesses the condition of the waste. The identification unit uses a generating AI to analyze the material, shape, and degree of contamination of the waste, thereby determining its condition. The generating AI uses deep learning technology to analyze images of the waste and identify its material and shape. The identification unit can also measure the weight and density of the waste using sensors. For example, it can measure the weight of the waste and estimate its material based on the result. Furthermore, it can measure the density of the waste and estimate its shape based on the result. Step 2: The presentation unit presents the correct waste sorting method based on the results determined by the identification unit. The presentation unit uses generative AI to present the waste sorting method. The generative AI uses natural language processing technology to present the waste sorting method to the user in an easy-to-understand manner. The presentation unit also uses a display and an audio output device to present the waste sorting method. For example, the waste sorting method can be displayed on the screen to present it visually to the user. Alternatively, the waste sorting method can be presented audibly using an audio output device. Step 3: The control unit controls the opening of the public waste bin based on the information presented by the presentation unit. The control unit controls the opening and closing of the opening using a generating AI. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. The control unit can also open and close the opening using a motor or actuator. For example, it can use a motor to open and close the opening and select the appropriate opening according to the type of waste. It can also use an actuator to adjust the opening and closing speed of the opening. Step 4: The authentication unit takes a picture of the sorted bags with a smartphone and issues a sorting completion authentication stamp. The authentication unit issues the sorting completion authentication stamp using a generating AI. The generating AI can analyze the state of the sorted bags using image recognition technology and determine whether the sorting was done correctly. The authentication unit can also issue the sorting completion authentication stamp using a smartphone camera or QR code reader. For example, it can take a picture of the sorted bags with a smartphone camera, analyze the image, and issue a sorting completion authentication stamp. It can also issue a sorting completion authentication stamp using a QR code reader.

[0072] (Example of form 2) The waste sorting support system according to an embodiment of the present invention is a system that promotes the recycling of household waste using generating AI. In this waste sorting support system, the generating AI determines the state of the waste so that the user does not need to determine the type of waste. Next, based on the results determined by the generating AI, the correct method of sorting the waste is presented. Furthermore, by setting a reading app on a public dustbin, the system ensures that only the opening of the box that matches the information read is opened. This promotes correct sorting and prevents the loss of opportunities for resource recycling and damage to facilities. The system also provides a mechanism to reward users who have sorted their waste correctly by taking a picture of the sorted bags with a smartphone and receiving a sorting completion authentication stamp. First, when a user disposes of waste, they launch the app and select their municipality. Next, they read the object with the app. At this time, the generating AI determines the state of the waste and presents the correct sorting method. For example, it may indicate that if the contents are discarded, rinsed, and dried, it becomes a plastic resource, or if only the contents have been discarded, it becomes non-burnable waste. Furthermore, a reading app is installed in public trash cans, and only the slot of the trash can that matches the scanned information will open. This allows users to sort their waste correctly. In addition, a system is provided that rewards users who sort their waste correctly by allowing them to take a picture of the sorted bags with their smartphones and receive a sorting completion stamp. For example, points are awarded for putting in the correct items or based on the number of sorting completion stamps collected. This system allows users to sort their waste correctly in a game-like manner, promoting resource recovery and preventing damage to facilities. Moreover, the point reward system increases user motivation and further promotes correct sorting. For example, by collecting sorting completion stamps, users can accumulate points that can be used in an electronic payment system. In this way, by using generation AI, a system is provided that promotes resource recovery of household waste and promotes correct sorting. As a result, the waste sorting support system promotes resource recovery of household waste and promotes correct sorting.

[0073] The waste sorting support system according to this embodiment comprises a determination unit, a presentation unit, a control unit, and an authentication unit. The determination unit determines the state of the waste. The determination unit determines the state of the waste by analyzing the material, shape, and degree of contamination of the waste, for example, using a generation AI. The generation AI can analyze images of the waste using deep learning technology and identify the material and shape. The determination unit can also measure the weight and density of the waste using sensors in order to determine the state of the waste. For example, the determination unit measures the weight of the waste and estimates the material based on the result. The determination unit can also measure the density of the waste and estimate the shape based on the result. The presentation unit presents the correct waste sorting method based on the results determined by the determination unit. The presentation unit presents the waste sorting method using, for example, a generation AI. The generation AI can present the waste sorting method to the user in an easy-to-understand manner using natural language processing technology. The presentation unit can also use a display or an audio output device to present the waste sorting method. For example, the display unit shows the waste sorting method on a screen, providing a visual presentation to the user. The display unit can also provide the waste sorting method verbally using an audio output device. The control unit controls the opening of the public dustbin based on the information presented by the display unit. The control unit controls the opening and closing of the opening using, for example, a generating AI. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. The control unit can also use a motor or actuator to control the opening and closing of the opening. For example, the control unit can use a motor to open and close the opening and select the appropriate opening according to the type of waste. The control unit can also use an actuator to open and close the opening and adjust the opening and closing speed according to the type of waste. The authentication unit takes a picture of the sorted bag with a smartphone and issues a sorting completion authentication stamp. The authentication unit issues a sorting completion authentication stamp using, for example, a generating AI. The generating AI can use image recognition technology to analyze the state of the sorted bag and determine whether the sorting was done correctly. Furthermore, the authentication unit can also use a smartphone camera or QR code reader to issue a sorting completion authentication stamp.For example, the authentication unit uses a smartphone camera to photograph the sorted bags, analyzes the image, and issues a sorting completion authentication stamp. The authentication unit can also issue a sorting completion authentication stamp using a QR code reader. As a result, the waste sorting support system according to this embodiment promotes correct sorting by determining the state of the waste, suggesting the correct sorting method, controlling the opening of the public dustbin, and issuing a sorting completion authentication stamp.

[0074] The identification unit determines the state of the waste. For example, it uses a generative AI to analyze the material, shape, and degree of soiling of the waste to determine its state. The generative AI uses deep learning technology to analyze images of waste and identify their material and shape. Specifically, the generative AI is trained using a large dataset of waste images and can identify materials such as plastic, metal, paper, and glass with high accuracy. It also recognizes common shapes such as bottles, cans, and boxes, and further analyzes the color tone and texture of the images to evaluate the degree of soiling. Based on these analysis results, the identification unit makes a comprehensive judgment about the material, shape, and degree of soiling of the waste. Furthermore, the identification unit can also measure the weight and density of the waste using sensors to determine its state. For example, it can measure the weight of the waste using a weight sensor and estimate the material based on the result. Since plastic and paper are relatively lightweight, while metal and glass tend to be heavy, weight data is useful for material estimation. It can also measure the density of the waste using a density sensor and estimate the shape based on the result. High-density waste is more likely to be solid, while low-density waste is likely to have many cavities. This allows the identification unit to analyze the material, shape, and degree of contamination of the waste from multiple angles, enabling accurate identification.

[0075] The display unit presents the correct method of sorting waste based on the results determined by the identification unit. The display unit can, for example, use a generative AI to present waste sorting methods. The generative AI uses natural language processing technology to present waste sorting methods to the user in an easy-to-understand manner. Specifically, the generative AI generates appropriate sorting methods based on information about the material, shape, and degree of soiling of the waste provided by the identification unit. For example, for plastic bottles, it would instruct the user to remove the cap and peel off the label before placing them in the recycling box. The display unit can also use a display or audio output device to present waste sorting methods. The display shows the sorting method in text along with an image of the waste, making it visually easy for the user to understand. Furthermore, the display unit can also present waste sorting methods audibly using an audio output device. This makes it possible to accommodate users who have difficulty reading text on a display, such as the visually impaired and the elderly. The display unit can also use animations and videos to show sorting methods to aid user understanding. For example, the waste sorting procedure can be shown using animation, allowing users to visually understand how to actually sort their waste. This enables the display to provide users with accurate and easy-to-understand sorting methods, promoting proper waste sorting.

[0076] The control unit controls the opening of the public dustbin based on the information presented by the presentation unit. The control unit controls the opening and closing of the opening using, for example, a generating AI. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. Specifically, the generating AI selects the appropriate opening and controls its opening and closing based on information about the material and shape of the waste provided by the identification unit. For example, it opens the opening for plastic waste and the opening for metal waste. The control unit can also use motors or actuators to control the opening and closing of the opening. It uses a motor to open and close the opening and selects the appropriate opening according to the type of waste. Furthermore, it can use actuators to adjust the opening and closing speed. For example, it can open and close slowly for heavy waste and quickly for light waste to ensure smooth waste disposal. In addition to opening and closing the opening, the control unit can also monitor the waste disposal status and issue a warning when it becomes full. For example, it can use a sensor to measure the amount of waste in the opening and, if it exceeds a certain amount, display a warning to the user and instruct them to use another opening. This allows the control unit to efficiently control the opening of public waste bins and assist in the proper sorting of waste.

[0077] The authentication unit takes a picture of the sorted bags with a smartphone and issues a sorting completion authentication stamp. The authentication unit can issue the sorting completion authentication stamp using, for example, a generating AI. The generating AI can analyze the condition of the sorted bags using image recognition technology and determine whether the sorting was done correctly. Specifically, the generating AI analyzes the image of the sorted bags taken with the smartphone camera and checks the contents of the bags, the presence or absence of labels, and the condition of the bags. For example, for plastic waste bags, it checks whether the caps have been removed and whether the labels have been peeled off. The authentication unit can also use the smartphone camera or a QR code reader to issue the sorting completion authentication stamp. The user takes a picture of the sorted bags using the smartphone camera, analyzes the image, and issues the sorting completion authentication stamp. Furthermore, it can also issue the sorting completion authentication stamp using a QR code reader. For example, it can read the QR code attached to the sorted bags and verify the accuracy of the sorting based on that information. In addition to issuing the sorting completion authentication stamp, the authentication unit can also record the sorting history and have a function to provide the user with feedback on the degree of sorting achievement and areas for improvement. For example, the system can record how users have sorted their waste in the past and evaluate their sorting achievement. It can also provide feedback to users on points that are often overlooked during sorting and areas for improvement, helping them with future sorting. This allows the authentication unit to guarantee the accuracy of sorting and promote good sorting habits.

[0078] The rewards department will provide point rewards. For example, the rewards department will reward points to users who sort their waste correctly. The rewards department can use a generation AI to calculate points based on the number of sorting completion authentication stamps. For example, the rewards department will reward 10 points for every 10 sorting completion authentication stamps collected. The rewards department can also adjust the point multiplier based on the number of sorting completion authentication stamps. For example, the rewards department will double the point reward multiplier for every 20 sorting completion authentication stamps collected. Furthermore, the rewards department can use a smartphone app or email to notify users of the point reward results. For example, the rewards department will notify users of the point reward results via a smartphone app. The rewards department can also notify users of the point reward results via email. By rewarding users who sort their waste correctly with points, user motivation will increase, and correct sorting will be further promoted.

[0079] The selection function allows users to select a municipality after launching the app. For example, a smartphone app can be used to select a municipality. The selection function uses generative AI to suggest the most suitable municipality based on the user's location and past selection history. For example, it suggests the nearest municipality based on the user's current location. It can also prioritize displaying frequently used municipalities based on the user's past selection history. Furthermore, when a user selects a municipality, the selection function can display the municipality's sorting rules and garbage collection schedule. For example, it can display the sorting rules of the selected municipality, providing users with information to ensure correct sorting. It can also display the municipality's garbage collection schedule, allowing users to understand when to put out their garbage. This allows users to apply region-specific sorting methods simply by launching the app and selecting a municipality.

[0080] The reading unit reads the object. The reading unit can read the object using, for example, barcode scanning or image recognition technology. The reading unit can use generative AI to analyze the material and shape of the object and suggest the appropriate sorting method. For example, the reading unit can use barcode scanning to read the barcode of a product and obtain product information. The reading unit can also use image recognition technology to analyze an image of the object and identify its material and shape. Furthermore, the reading unit can use a display or audio output device to present the object reading results to the user. For example, the reading unit can display the object reading results on a display to present them visually to the user. The reading unit can also use an audio output device to present the object reading results audibly. This allows for accurate determination of the waste condition by reading the object.

[0081] The identification unit uses a generation AI to determine the state of the waste. For example, the identification unit uses the generation AI to analyze the material, shape, and degree of contamination of the waste to determine its state. The generation AI can analyze images of waste using deep learning technology to identify the material and shape. In addition, the identification unit can also measure the weight and density of the waste using sensors to determine its state. For example, the identification unit measures the weight of the waste and estimates the material based on the result. It can also measure the density of the waste and estimate the shape based on the result. In this way, the state of the waste can be accurately determined by using the generation AI.

[0082] The display unit uses generative AI to present the correct way to sort garbage. For example, the display unit uses generative AI to present garbage sorting methods. The generative AI uses natural language processing technology to present garbage sorting methods to the user in an easy-to-understand manner. The display unit can also use a display or audio output device to present garbage sorting methods. For example, the display unit can display garbage sorting methods on a screen to present them visually to the user. The display unit can also use an audio output device to present garbage sorting methods audibly. In this way, by using generative AI, the correct way to sort garbage can be accurately presented.

[0083] The control unit controls the opening of the public waste bin using a generating AI. For example, the control unit uses the generating AI to control the opening and closing of the opening. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. The control unit can also use motors or actuators to control the opening and closing of the opening. For example, the control unit can use a motor to open and close the opening and select the appropriate opening according to the type of waste. Alternatively, the control unit can use actuators to open and close the opening and closing and adjust the opening and closing speed according to the type of waste. In this way, the opening of the public waste bin can be accurately controlled using a generating AI.

[0084] The authentication unit issues a sorting completion authentication stamp using a generating AI. For example, the authentication unit uses a generating AI to issue the sorting completion authentication stamp. The generating AI can analyze the state of the sorted bags using image recognition technology to determine whether the sorting was done correctly. The authentication unit can also use a smartphone camera or QR code reader to issue the sorting completion authentication stamp. For example, the authentication unit can take a picture of the sorted bags using a smartphone camera, analyze the image, and issue the sorting completion authentication stamp. Alternatively, the authentication unit can issue the sorting completion authentication stamp using a QR code reader. This allows for accurate issuance of the sorting completion authentication stamp by using a generating AI.

[0085] The assessment unit estimates the user's emotions and adjusts the accuracy of its garbage condition assessment based on the estimated emotions. For example, the assessment unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the assessment unit can use a camera to capture the user's facial expression, and the generative AI can analyze that expression to estimate the emotion. Alternatively, the assessment unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotion. Furthermore, the assessment unit adjusts the accuracy of its garbage condition assessment based on the estimated emotions of the user. For example, if the user is stressed, the assessment accuracy is increased to reduce the user's burden. Conversely, if the user is relaxed, the assessment accuracy can be set to normal, and standard assessment can be performed. In this way, the user's burden can be reduced by adjusting the assessment accuracy based on the user's emotions.

[0086] The identification unit customizes its identification method based on the material and shape of the waste when determining its condition. For example, the identification unit uses a generating AI to analyze the material and shape of the waste and customize the identification method. The generating AI can analyze images of waste using deep learning technology and identify the material and shape. For example, in the case of plastic products, the identification unit identifies the type of material and suggests the appropriate sorting method. In the case of metal products, the identification unit can also adjust the identification method considering the shape and size. Furthermore, in the case of paper products, the identification unit can customize the identification method based on the thickness and texture. As a result, the identification accuracy is improved by customizing the identification method based on the material and shape of the waste.

[0087] The identification unit optimizes its identification algorithm by referring to past identification data when determining the state of waste. For example, the identification unit analyzes past identification data using generative AI to optimize the identification algorithm. Generative AI can learn from past identification data using machine learning techniques to improve identification accuracy. For example, the identification unit optimizes identification patterns based on past identification data. The identification unit can also introduce algorithms to reduce identification errors based on past identification data. Furthermore, the identification unit can continuously improve its identification algorithm based on past identification data. In this way, by referring to past identification data, the identification algorithm can be optimized and identification accuracy can be improved.

[0088] The assessment unit estimates the user's emotions and adjusts the display method of the assessment results based on the estimated emotions. For example, the assessment unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology and voice analysis technology. For example, the assessment unit can use a camera to capture the user's facial expression, and the generative AI will analyze that expression to estimate the emotion. Alternatively, the assessment unit can use a microphone to record the user's voice, and the generative AI will analyze that voice to estimate the emotion. Furthermore, the assessment unit adjusts the display method of the assessment results based on the estimated emotions of the user. For example, if the user is tense, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information can also be provided. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0089] The identification unit adjusts its identification method based on the location and time of waste generation when determining the state of the waste. For example, the identification unit uses a generation AI to analyze the location and time of waste generation and adjust the identification method. The generation AI can identify the location and time of waste generation using location information and time data. For example, if the waste is generated in a home, the identification unit applies the identification method for household waste. The identification unit can also apply the identification method for office waste if the waste is generated in an office. Furthermore, if the waste is generated at night, the identification unit can apply the identification method specific to nighttime waste. By adjusting the identification method based on the location and time of waste generation, the accuracy of the identification is improved.

[0090] The identification unit improves its identification accuracy by referring to relevant information about the waste when determining its condition. For example, the identification unit uses a generating AI to analyze relevant information about the waste and improve its identification accuracy. The generating AI can identify the material and shape of the waste by referring to product barcodes and product information databases. For example, the identification unit scans product barcodes and improves identification accuracy based on product information. The identification unit can also obtain material information from product barcodes and suggest appropriate sorting methods. Furthermore, the identification unit can optimize its identification algorithm by referring to product barcodes. In this way, it can improve identification accuracy by referring to relevant information about the waste.

[0091] The presentation unit estimates the user's emotions and adjusts the content of the classification method presentation based on the estimated emotions. For example, the presentation unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the presentation unit can use a camera to capture the user's facial expression, and the generative AI will analyze that expression to estimate the emotion. Alternatively, the presentation unit can use a microphone to record the user's voice, and the generative AI will analyze that voice to estimate the emotion. Furthermore, the presentation unit adjusts the content of the classification method presentation based on the estimated emotions of the user. For example, if the user is feeling stressed, a simple classification method will be presented. If the user is relaxed, a more detailed classification method may be presented. In this way, by adjusting the presentation content based on the user's emotions, classification methods that are easy for the user to understand can be presented.

[0092] The display unit applies different display methods depending on the type and condition of the waste when presenting sorting methods. For example, the display unit uses a generating AI to analyze the type and condition of the waste and present the appropriate sorting method. The generating AI can analyze images of waste using deep learning technology and identify the material and shape. For example, in the case of plastic products, the display unit presents sorting methods according to the material. In the case of metal products, the display unit can also present sorting methods according to the shape and size. Furthermore, in the case of paper products, the display unit can also present sorting methods according to the thickness and texture. By applying different display methods according to the type and condition of the waste, the accuracy of the sorting methods is improved.

[0093] The presentation unit optimizes its presentation algorithm by referring to past presentation data when presenting sorting methods. For example, the presentation unit analyzes past presentation data using generative AI to optimize the presentation algorithm. Generative AI can learn from past presentation data using machine learning techniques to improve the accuracy of sorting methods. For example, the presentation unit applies an algorithm that presents the optimal sorting method based on past presentation data. The presentation unit can also learn from past presentation data and optimize sorting method patterns. Furthermore, the presentation unit can introduce an algorithm that reduces errors in sorting methods based on past presentation data. In this way, by referring to past presentation data, the presentation algorithm can be optimized and the accuracy of sorting methods can be improved.

[0094] The presentation unit estimates the user's emotions and determines the priority of the presented content based on the estimated emotions. For example, the presentation unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the presentation unit can use a camera to capture the user's facial expressions, and the generative AI can analyze those expressions to estimate the emotions. Alternatively, the presentation unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotions. Furthermore, the presentation unit determines the priority of the presented content based on the estimated emotions of the user. For example, if the user is nervous, important information will be presented first. Conversely, if the user is relaxed, detailed information may be presented first. In this way, by determining the priority of the presented content based on the user's emotions, information that is important to the user can be presented first.

[0095] The display unit, when presenting sorting methods, considers the user's geographical location information to present the most suitable sorting method. For example, the display unit uses a generating AI to analyze the user's geographical location information and present the most suitable sorting method. The generating AI can identify the user's current location using GPS data and location information services. For example, the display unit presents region-specific sorting methods based on the user's current location. The display unit can also present information on the nearest sorting facility based on the user's geographical location information. Furthermore, the display unit can present the most suitable sorting method considering the user's geographical location information. This allows the display unit to present region-specific sorting methods by considering the user's geographical location information.

[0096] The presentation unit, when presenting sorting methods, refers to the user's past sorting history to present customized sorting methods. For example, the presentation unit uses generative AI to analyze the user's past sorting history and present customized sorting methods. Generative AI can learn from the user's past sorting history using machine learning techniques and present the optimal sorting method. For example, the presentation unit presents the optimal sorting method based on the user's past sorting history. The presentation unit can also learn sorting patterns from the user's past sorting history and present customized sorting methods. Furthermore, the presentation unit can introduce algorithms that reduce the error in sorting methods by referring to the user's past sorting history. This allows it to present customized sorting methods by referring to the user's past sorting history.

[0097] The control unit estimates the user's emotions and adjusts the control of the dustbin's opening based on the estimated emotions. The control unit estimates the user's emotions using, for example, generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the control unit can use a camera to capture the user's facial expression, and the generative AI will analyze that expression to estimate the emotion. The control unit can also use a microphone to record the user's voice, and the generative AI will analyze that voice to estimate the emotion. Furthermore, the control unit adjusts the control of the dustbin's opening based on the estimated emotions. For example, if the user is feeling stressed, the opening will be opened and closed quickly. Conversely, if the user is relaxed, the opening will be opened and closed at a normal speed. In this way, the burden on the user can be reduced by adjusting the opening control based on the user's emotions.

[0098] The control unit applies different control methods to the dustbin's opening depending on the type and condition of the waste. For example, the control unit uses a generating AI to analyze the type and condition of the waste and apply the appropriate control method. The generating AI uses deep learning technology to analyze images of the waste and identify its material and shape. For example, in the case of plastic products, the control unit applies a control method to the opening that is appropriate for the material. In the case of metal products, the control unit can also apply a control method to the opening that is appropriate for the shape and size. Furthermore, in the case of paper products, the control unit can also apply a control method to the opening that is appropriate for the thickness and texture. By applying a control method according to the type and condition of the waste, the accuracy of opening control is improved.

[0099] The control unit optimizes the control algorithm by referring to past control data when controlling the dustbin's inlet. For example, the control unit analyzes past control data using generative AI and optimizes the control algorithm. Generative AI can learn from past control data using machine learning techniques to improve the accuracy of inlet control. For example, the control unit applies the optimal inlet control method based on past control data. The control unit can also optimize the inlet control pattern by learning from past control data. Furthermore, the control unit can introduce an algorithm to reduce the control error of the inlet based on past control data. In this way, by referring to past control data, the control algorithm can be optimized and the accuracy of inlet control can be improved.

[0100] The control unit estimates the user's emotions and adjusts the opening and closing timing of the slot based on the estimated emotions. The control unit estimates the user's emotions using, for example, generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the control unit can use a camera to capture the user's facial expression, and the generative AI will analyze that expression to estimate the emotion. The control unit can also use a microphone to record the user's voice, and the generative AI will analyze that voice to estimate the emotion. Furthermore, the control unit adjusts the opening and closing timing of the slot based on the estimated emotions of the user. For example, if the user is tense, the slot will open and close quickly. If the user is relaxed, the slot will open and close at the normal timing. In this way, the user's burden can be reduced by adjusting the opening and closing timing based on the user's emotions.

[0101] The control unit adjusts the control method for the dustbin's opening based on the location and time of waste generation. For example, the control unit uses a generation AI to analyze the location and time of waste generation and apply an appropriate control method. The generation AI can identify the location and time of waste generation using location information and time data. For example, if the waste is generated in a home, the control unit applies a control method for household waste openings. The control unit can also apply an office waste opening control method if the waste is generated in an office. Furthermore, if the waste is generated at night, the control unit can apply a night-specific opening control method. By adjusting the control method based on the location and time of waste generation, the accuracy of opening control is improved.

[0102] The control unit improves control accuracy by referring to relevant information about the waste when controlling the dustbin's input port. For example, the control unit analyzes the relevant information about the waste using a generating AI to improve control accuracy. The generating AI can identify the material and shape of the waste by referring to the product's barcode or product information database. For example, the control unit scans the product's barcode and improves the control accuracy of the input port based on the product information. The control unit can also obtain material information from the product's barcode and apply an appropriate input port control method. Furthermore, the control unit can optimize the input port control algorithm by referring to the product's barcode. In this way, control accuracy can be improved by referring to relevant information about the waste.

[0103] The authentication unit estimates the user's emotions and adjusts the method of issuing authentication stamps based on the estimated emotions. For example, the authentication unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the authentication unit can use a camera to capture the user's facial expressions, and the generative AI can analyze those expressions to estimate the emotions. Alternatively, the authentication unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotions. Furthermore, the authentication unit adjusts the method of issuing authentication stamps based on the estimated emotions of the user. For example, if the user is feeling stressed, the authentication stamp will be issued quickly. Conversely, if the user is relaxed, the authentication stamp can be issued at a normal speed. In this way, the burden on the user can be reduced by adjusting the method of issuing authentication stamps based on the user's emotions.

[0104] The authentication unit applies different authentication methods depending on the type and condition of the waste when issuing a sorting completion authentication stamp. For example, the authentication unit uses a generating AI to analyze the type and condition of the waste and apply the appropriate authentication method. The generating AI can analyze images of the waste using deep learning technology and identify the material and shape. For example, in the case of plastic products, the authentication unit applies an authentication method according to the material. In the case of metal products, the authentication unit can also apply an authentication method according to the shape and size. Furthermore, in the case of paper products, the authentication unit can also apply an authentication method according to the thickness and texture. By applying an authentication method according to the type and condition of the waste, the authentication accuracy is improved.

[0105] The authentication unit optimizes its authentication algorithm by referring to past authentication data when assigning a sorting completion authentication stamp. For example, the authentication unit analyzes past authentication data using generative AI and optimizes the authentication algorithm. Generative AI can learn from past authentication data using machine learning techniques to improve authentication accuracy. For example, the authentication unit introduces an algorithm that applies the optimal authentication method based on past authentication data. The authentication unit can also learn from past authentication data and optimize authentication patterns. Furthermore, the authentication unit can introduce an algorithm that reduces authentication errors based on past authentication data. In this way, by referring to past authentication data, the authentication algorithm can be optimized and authentication accuracy can be improved.

[0106] The authentication unit estimates the user's emotions and adjusts the display method of the authentication stamp based on the estimated emotions. The authentication unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the authentication unit can use a camera to capture the user's facial expression, and the generative AI can analyze that expression to estimate the emotion. Alternatively, the authentication unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotion. Furthermore, the authentication unit adjusts the display method of the authentication stamp based on the estimated emotions of the user. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information can be provided. In this way, by adjusting the display method based on the user's emotions, a display that is easy for the user to see is possible.

[0107] The authentication unit applies the most suitable authentication method when issuing a sorting completion authentication stamp, taking into account the user's geographical location information. For example, the authentication unit analyzes the user's geographical location information using a generating AI and applies the most suitable authentication method. The generating AI can determine the user's current location using GPS data and location information services. For example, the authentication unit applies a region-specific authentication method based on the user's current location. The authentication unit can also provide information on the nearest authentication facility based on the user's geographical location information. Furthermore, the authentication unit can apply the most suitable authentication method by taking into account the user's geographical location information. This allows for the application of region-specific authentication methods by considering the user's geographical location information.

[0108] The authentication unit applies a customized authentication method by referring to the user's past sorting history when granting a sorting completion authentication stamp. For example, the authentication unit can analyze the user's past sorting history using generative AI and apply a customized authentication method. Generative AI can learn from the user's past sorting history using machine learning techniques and apply the optimal authentication method. For example, the authentication unit applies the optimal authentication method based on the user's past sorting history. The authentication unit can also learn authentication patterns from the user's past sorting history and apply a customized authentication method. Furthermore, the authentication unit can introduce an algorithm that reduces the error of the authentication method by referring to the user's past sorting history. This allows the application of a customized authentication method by referring to the user's past sorting history.

[0109] The reward unit estimates the user's emotions and adjusts the point reward method based on the estimated emotions. For example, the reward unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the reward unit can use a camera to capture the user's facial expressions, and the generative AI analyzes those expressions to estimate the emotions. The reward unit can also use a microphone to record the user's voice, and the generative AI analyzes that voice to estimate the emotions. Furthermore, the reward unit adjusts the point reward method based on the estimated emotions of the user. For example, if the user is feeling stressed, points will be awarded quickly. Conversely, if the user is relaxed, points will be awarded at a normal speed. In this way, the burden on the user can be reduced by adjusting the point reward method based on the user's emotions.

[0110] The rewards unit customizes the reward points based on the user's transaction history when redeeming points. For example, the rewards unit analyzes the user's transaction history using a generation AI and customizes the reward points. The generation AI can learn from the user's transaction history using machine learning techniques and provide the optimal reward points. For example, the rewards unit customizes the reward points based on the user's past transaction history. The rewards unit can also learn point redemption patterns from the user's transaction history and provide customized reward points. Furthermore, the rewards unit can introduce an algorithm that reduces errors in reward points by referring to the user's transaction history. This improves the accuracy of rewards by customizing reward points based on the user's transaction history.

[0111] The reward unit estimates the user's emotions and adjusts the display method of reward points based on the estimated emotions. The reward unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the reward unit can use a camera to capture the user's facial expressions, and the generative AI analyzes those expressions to estimate the emotions. The reward unit can also use a microphone to record the user's voice, and the generative AI analyzes that voice to estimate the emotions. Furthermore, the reward unit adjusts the display method of reward points based on the estimated emotions of the user. For example, if the user is tense, it provides a simple and highly visible display method. If the user is relaxed, it can also provide a display method that includes detailed information. By adjusting the display method based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand.

[0112] The rewards unit applies the optimal reward method when redeeming points, taking into account the user's geographical location information. For example, the rewards unit uses a generating AI to analyze the user's geographical location information and apply the optimal reward method. The generating AI can identify the user's current location using GPS data and location information services. For example, the rewards unit applies a region-specific reward method based on the user's current location. The rewards unit can also provide information on the nearest reward facilities based on the user's geographical location information. Furthermore, the rewards unit can apply the optimal reward method by taking the user's geographical location information into consideration. This allows for the application of region-specific reward methods by considering the user's geographical location information.

[0113] The selection unit estimates the user's emotions and adjusts the method of selecting a municipality based on the estimated emotions. The selection unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the selection unit can use a camera to capture the user's facial expressions, and the generative AI can analyze those expressions to estimate the emotions. Alternatively, the selection unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotions. Furthermore, the selection unit adjusts the method of selecting a municipality based on the estimated emotions of the user. For example, if the user is feeling stressed, a simple selection method can be provided. Conversely, if the user is relaxed, a more detailed selection method can be provided. By adjusting the selection method based on the user's emotions, the burden on the user can be reduced.

[0114] The selection function, when selecting a municipality, refers to the user's past selection history to suggest the optimal selection method. For example, the selection function uses generative AI to analyze the user's past selection history and suggest the optimal selection method. Generative AI can learn from the user's past selection history using machine learning techniques and provide the optimal selection method. For example, the selection function suggests the optimal municipality based on the user's past selection history. The selection function can also learn selection patterns from the user's past selection history and provide the optimal selection method. Furthermore, the selection function can introduce an algorithm that reduces the error in the selection method by referring to the user's past selection history. This allows the system to suggest the optimal selection method by referring to the user's past selection history.

[0115] The selection unit estimates the user's emotions and determines the priority of municipality selection based on the estimated emotions. The selection unit estimates the user's emotions, for example, using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the selection unit can use a camera to capture the user's facial expressions, and the generative AI analyzes those expressions to estimate the emotions. Alternatively, the selection unit can use a microphone to record the user's voice, and the generative AI analyzes that voice to estimate the emotions. Furthermore, the selection unit determines the priority of municipality selection based on the estimated emotions. For example, if the user is tense, important municipalities will be presented first. Conversely, if the user is relaxed, municipalities containing detailed information may be presented first. In this way, by determining priorities based on the user's emotions, municipalities that are important to the user can be presented first.

[0116] The selection unit presents the optimal selection method when users choose a municipality, taking into account the user's geographical location information. For example, the selection unit uses a generative AI to analyze the user's geographical location information and present the optimal selection method. The generative AI can determine the user's current location using GPS data and location services. For instance, the selection unit presents region-specific municipalities based on the user's current location. Furthermore, the selection unit can provide information on the nearest municipality based on the user's geographical location information. In addition, the selection unit can present the optimal municipality by considering the user's geographical location information. This allows for the presentation of region-specific municipalities by considering the user's geographical location information.

[0117] The reading unit estimates the user's emotions and adjusts the object reading method based on the estimated emotions. For example, the reading unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the reading unit can use a camera to capture the user's facial expressions, and the generative AI can analyze those expressions to estimate the emotions. Alternatively, the reading unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotions. Furthermore, the reading unit adjusts the object reading method based on the estimated emotions of the user. For example, if the user is feeling stressed, it can provide a method to read objects quickly. Conversely, if the user is relaxed, it can provide a method to read objects at a normal speed. By adjusting the reading method based on the user's emotions, the burden on the user can be reduced.

[0118] The reading unit applies different reading methods depending on the type and condition of the waste when reading an object. For example, the reading unit uses a generative AI to analyze the type and condition of the waste and apply the appropriate reading method. The generative AI can analyze images of waste using deep learning technology and identify their material and shape. For example, in the case of plastic products, the reading unit applies a reading method appropriate to the material. In the case of metal products, the reading unit can also apply a reading method appropriate to the shape and size. Furthermore, in the case of paper products, the reading unit can apply a reading method appropriate to the thickness and texture. By applying a reading method according to the type and condition of the waste, the reading accuracy is improved.

[0119] The reading unit estimates the user's emotions and adjusts the display method of the reading results based on the estimated emotions. For example, the reading unit estimates the user's emotions using generative AI. Generative AI can estimate the user's emotions using facial recognition technology or voice analysis technology. For example, the reading unit can use a camera to capture the user's facial expression, and the generative AI can analyze that expression to estimate the emotion. Alternatively, the reading unit can use a microphone to record the user's voice, and the generative AI can analyze that voice to estimate the emotion. Furthermore, the reading unit adjusts the display method of the reading results based on the estimated emotions of the user. For example, if the user is tense, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information can also be provided. In this way, by adjusting the display method based on the user's emotions, a display that is easy for the user to understand becomes possible.

[0120] The reading unit applies the optimal reading method when reading an object, taking into account the user's geographical location information. For example, the reading unit analyzes the user's geographical location information using a generation AI and applies the optimal reading method. The generation AI can determine the user's current location using GPS data or location information services. For example, the reading unit applies a region-specific reading method based on the user's current location. The reading unit can also provide information on the nearest reading facility based on the user's geographical location information. Furthermore, the reading unit can apply the optimal reading method by taking the user's geographical location information into consideration. This allows for the application of region-specific reading methods by considering the user's geographical location information.

[0121] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0122] The identification unit can adjust its identification method based on the location and time of day the waste was generated. For example, if the waste is generated in a home, the unit can apply a method for identifying household waste. If the waste is generated in an office, it can apply a method for identifying office waste. Furthermore, if the waste is generated at night, it can apply a method for identifying waste specific to nighttime. By adjusting the identification method based on the location and time of day the waste was generated, the accuracy of the identification is improved.

[0123] The presentation unit can also estimate the user's emotions and adjust the content of the classification method based on the estimated emotions. For example, if the user is feeling stressed, a simple classification method may be presented. Conversely, if the user is relaxed, a more detailed classification method may be presented. By adjusting the presentation content based on the user's emotions, a classification method that is easy for the user to understand can be presented.

[0124] The control unit can also apply different control methods to the dustbin's opening depending on the type and condition of the waste. For example, for plastic products, a control method appropriate to the material of the opening can be applied. For metal products, a control method appropriate to the shape and size of the opening can be applied. Furthermore, for paper products, a control method appropriate to the thickness and texture of the opening can be applied. By applying a control method according to the type and condition of the waste, the accuracy of opening control can be improved.

[0125] The authentication unit can also estimate the user's emotions and adjust the method of issuing authentication stamps based on those emotions. For example, if the user is feeling stressed, it can issue authentication stamps quickly. Conversely, if the user is relaxed, it can issue authentication stamps at a normal rate. By adjusting the method of issuing authentication stamps based on the user's emotions, the burden on the user can be reduced.

[0126] The rewards unit can also customize reward points based on the user's transaction history when redeeming points. For example, it can customize reward points based on the user's past transaction history. It can also learn point redemption patterns from the user's transaction history and provide customized reward points. Furthermore, it can introduce an algorithm that reduces errors in reward points by referring to the user's transaction history. As a result, the accuracy of rewards is improved by customizing reward points based on the user's transaction history.

[0127] The selection function can also estimate the user's emotions and adjust the method of selecting a municipality based on those emotions. For example, if the user is feeling stressed, a simple selection method can be provided. Conversely, if the user is relaxed, a more detailed selection method can be provided. This reduces the user's burden by adjusting the selection method based on their emotions.

[0128] The reading unit can apply different reading methods depending on the type and condition of the object being read. For example, for plastic products, a reading method appropriate to the material is applied. For metal products, a reading method appropriate to the shape and size can be applied. Furthermore, for paper products, a reading method appropriate to the thickness and texture can be applied. By applying a reading method according to the type and condition of the waste, the reading accuracy is improved.

[0129] The identification unit can optimize its identification algorithm by referring to past identification data when determining the state of waste. For example, it can optimize the identification pattern based on past identification data. It can also introduce an algorithm to reduce the error in identification based on past identification data. Furthermore, it can continuously improve the identification algorithm based on past identification data. In this way, by referring to past identification data, the identification algorithm can be optimized and the identification accuracy can be improved.

[0130] The presentation unit can also estimate the user's emotions and prioritize the content presented based on those emotions. For example, if the user is nervous, important information can be presented first. Conversely, if the user is relaxed, detailed information can be presented first. By prioritizing the content presented based on the user's emotions, information that is important to the user can be presented preferentially.

[0131] The control unit can also optimize the control algorithm by referring to past control data when controlling the dustbin's inlet. For example, it can apply the optimal inlet control method based on past control data. It can also optimize the inlet control pattern by learning from past control data. Furthermore, it can introduce an algorithm to reduce the control error of the inlet based on past control data. In this way, by referring to past control data, the control algorithm can be optimized and the accuracy of inlet control can be improved.

[0132] The following briefly describes the processing flow for example form 2.

[0133] Step 1: The identification unit assesses the condition of the waste. The identification unit uses a generating AI to analyze the material, shape, and degree of contamination of the waste, thereby determining its condition. The generating AI uses deep learning technology to analyze images of the waste and identify its material and shape. The identification unit can also measure the weight and density of the waste using sensors. For example, it can measure the weight of the waste and estimate its material based on the result. Furthermore, it can measure the density of the waste and estimate its shape based on the result. Step 2: The presentation unit presents the correct waste sorting method based on the results determined by the identification unit. The presentation unit uses generative AI to present the waste sorting method. The generative AI uses natural language processing technology to present the waste sorting method to the user in an easy-to-understand manner. The presentation unit also uses a display and an audio output device to present the waste sorting method. For example, the waste sorting method can be displayed on the screen to present it visually to the user. Alternatively, the waste sorting method can be presented audibly using an audio output device. Step 3: The control unit controls the opening of the public waste bin based on the information presented by the presentation unit. The control unit controls the opening and closing of the opening using a generating AI. The generating AI can optimize the timing of opening and closing the opening according to the type of waste. The control unit can also open and close the opening using a motor or actuator. For example, it can use a motor to open and close the opening and select the appropriate opening according to the type of waste. It can also use an actuator to adjust the opening and closing speed of the opening. Step 4: The authentication unit takes a picture of the sorted bags with a smartphone and issues a sorting completion authentication stamp. The authentication unit issues the sorting completion authentication stamp using a generating AI. The generating AI can analyze the state of the sorted bags using image recognition technology and determine whether the sorting was done correctly. The authentication unit can also issue the sorting completion authentication stamp using a smartphone camera or QR code reader. For example, it can take a picture of the sorted bags with a smartphone camera, analyze the image, and issue a sorting completion authentication stamp. It can also issue a sorting completion authentication stamp using a QR code reader.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0136] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] Each of the multiple elements described above, including the identification unit, presentation unit, control unit, authentication unit, reward unit, selection unit, and reading unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the identification unit uses the camera 42 and sensors of the smart device 14 to determine the state of the waste and analyzes it using generating AI. The presentation unit uses the display 40A and speaker 40B of the smart device 14 to present the waste sorting method. The control unit controls the opening of the public dustbin by the identification processing unit 290 of the data processing unit 12. The authentication unit uses the camera 42 of the smart device 14 to photograph the sorted bag and uses generating AI to issue a sorting completion authentication stamp. The reward unit uses the identification processing unit 290 of the data processing unit 12 to provide point rewards. The selection unit allows the user to select a municipality using an app on the smart device 14. The reading unit reads the target object using the camera 42 and barcode scanning of the smart device 14. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0138] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0139] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0144] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0145] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the identification unit, presentation unit, control unit, authentication unit, reward unit, selection unit, and reading unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the identification unit uses the camera 42 and sensors of the smart glasses 214 to determine the state of the waste and analyzes it using generating AI. The presentation unit uses the display and speaker 240 of the smart glasses 214 to present the waste sorting method. The control unit controls the opening of the public dustbin using the identification processing unit 290 of the data processing unit 12. The authentication unit uses the camera 42 of the smart glasses 214 to photograph the sorted bag and uses generating AI to give a sorting completion authentication stamp. The reward unit uses the identification processing unit 290 of the data processing unit 12 to provide point rewards. The selection unit allows the user to select a municipality using the app on the smart glasses 214. The reading unit reads the target object using the camera 42 and barcode scanning of the smart glasses 214. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0155] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the identification unit, presentation unit, control unit, authentication unit, reward unit, selection unit, and reading unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the identification unit uses the camera 42 and sensors of the headset terminal 314 to determine the state of the waste and analyzes it using generating AI. The presentation unit uses the display 343 and speaker 240 of the headset terminal 314 to present the waste sorting method. The control unit controls the opening of the public dustbin by the identification processing unit 290 of the data processing unit 12. The authentication unit uses the camera 42 of the headset terminal 314 to photograph the sorted bag and uses generating AI to give a sorting completion authentication stamp. The reward unit performs point rewards by the identification processing unit 290 of the data processing unit 12. The selection unit allows the user to select a municipality using the app on the headset terminal 314. The reading unit reads the target object using the camera 42 and barcode scanning of the headset terminal 314. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0170] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0171] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0178] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0179] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0180] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0181] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0182] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0186] Each of the multiple elements described above, including the identification unit, presentation unit, control unit, authentication unit, reward unit, selection unit, and reading unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the identification unit uses the camera 42 and sensors of the robot 414 to determine the state of the waste and analyzes it using a generating AI. The presentation unit uses the display and speaker 240 of the robot 414 to present the waste sorting method. The control unit controls the opening of the public dustbin by the identification processing unit 290 of the data processing unit 12. The authentication unit uses the camera 42 of the robot 414 to photograph the sorted bags and uses a generating AI to give a sorting completion authentication stamp. The reward unit uses the identification processing unit 290 of the data processing unit 12 to provide point rewards. The selection unit allows the user to select a municipality using the app on the robot 414. The reading unit reads the target object using the camera 42 and barcode scanning of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0187] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0189] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0190] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0191] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0195] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0196] 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.

[0197] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0198] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0199] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0200] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0202] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0203] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0204] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0205] (Note 1) A section for determining the condition of the waste, A display unit that presents the correct method of sorting waste based on the results determined by the aforementioned determination unit, A control unit that controls the opening of a public dustbin based on the information presented by the aforementioned display unit, It includes an authentication unit that takes a picture of the sorted bags with a smartphone and issues a stamp to verify that sorting is complete. A system characterized by the following features. (Note 2) It is equipped with a rewards section that provides point rewards. The system described in Appendix 1, characterized by the features described herein. (Note 3) The app includes a selection section where the user launches the app and chooses a local government. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a reading unit that reads the target object. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned determination unit is, The generation AI determines the state of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, The AI ​​generates information to show the correct way to sort waste. The system described in Appendix 1, characterized by the features described herein. (Note 7) The control unit, AI-generated to control the opening of public trash cans. The system described in Appendix 1, characterized by the features described herein. (Note 8) The authentication unit, The AI ​​generates a stamp to confirm that sorting is complete. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned determination unit is, The system estimates the user's emotions and adjusts the accuracy of its waste condition assessment based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned determination unit is, When assessing the condition of waste, the assessment method is customized based on the material and shape of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned determination unit is, When determining the condition of waste, the assessment algorithm is optimized by referring to past assessment data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned determination unit is, It estimates the user's emotions and adjusts how the results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned determination unit is, When assessing the condition of waste, the assessment method is adjusted based on the location and time of day the waste is generated. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned determination unit is, When assessing the condition of waste, we refer to related information about the waste to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned display unit is, The system estimates the user's emotions and adjusts the suggested categorization methods based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned display unit is, When presenting sorting methods, different methods should be applied depending on the type and condition of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned display unit is, When presenting sorting methods, we optimize the presentation algorithm by referring to past presentation data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, It estimates the user's emotions and prioritizes the content presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting sorting methods, the system should consider the user's geographical location to suggest the most suitable sorting method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting sorting methods, the system will refer to the user's past sorting history to suggest customized sorting methods. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, The system estimates the user's emotions and adjusts the control of the dustbin's opening based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, When controlling the opening of the dustbin, different control methods are applied depending on the type and condition of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, When controlling the dustbin's input port, the control algorithm is optimized by referring to past control data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, The system estimates the user's emotions and adjusts the opening and closing timing of the input slot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, When controlling the dustbin's opening, the control method is adjusted based on the location and time of waste generation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, When controlling the dustbin's opening, the system improves control accuracy by referencing information related to the waste. The system described in Appendix 1, characterized by the features described herein. (Note 27) The authentication unit, The system estimates the user's emotions and adjusts the method of awarding authentication stamps based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The authentication unit, When issuing a sorting completion stamp, different authentication methods will be applied depending on the type and condition of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 29) The authentication unit, When awarding a sorting completion authentication stamp, the authentication algorithm is optimized by referring to past authentication data. The system described in Appendix 1, characterized by the features described herein. (Note 30) The authentication unit, The system estimates the user's emotions and adjusts how authentication stamps are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The authentication unit, When awarding a sorting completion authentication stamp, the most suitable authentication method will be applied, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The authentication unit, When granting a sorting completion authentication stamp, a customized authentication method is applied by referencing the user's past sorting history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The reduction unit is The system estimates the user's emotions and adjusts the point reward method based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The reduction unit is When awarding points, the reward points are customized based on the user's transaction history. The system described in Appendix 2, characterized by the features described herein. (Note 35) The reduction unit is The system estimates the user's emotions and adjusts how reward points are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The reduction unit is When providing point rewards, the optimal reward method will be applied considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned selection unit is We estimate user sentiment and adjust the method of selecting a local government based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned selection unit is When selecting a municipality, the system will refer to the user's past selection history to suggest the most suitable selection method. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned selection unit is The system estimates user sentiment and determines the priority of municipal selection based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned selection unit is When selecting a municipality, the system will consider the user's geographical location to suggest the most suitable selection method. The system described in Appendix 3, characterized by the features described herein. (Note 41) The reading unit is It estimates the user's emotions and adjusts the object's reading method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The reading unit is When reading an object, different reading methods are applied depending on the type and condition of the waste. The system described in Appendix 4, characterized by the features described herein. (Note 43) The reading unit is It estimates the user's emotions and adjusts how the reading results are displayed based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 44) The reading unit is When reading an object, the system applies the optimal reading method, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A section for determining the condition of the waste, A display unit that presents the correct method of sorting waste based on the results determined by the aforementioned determination unit, A control unit that controls the opening of a public dustbin based on the information presented by the aforementioned display unit, It includes an authentication unit that takes a picture of the sorted bags with a smartphone and issues a stamp to verify that sorting is complete. A system characterized by the following features.

2. It is equipped with a rewards section that provides point rewards. The system according to feature 1.

3. The app includes a selection section where the user launches the app and chooses a local government. The system according to feature 1.

4. It is equipped with a reading unit that reads the target object. The system according to feature 1.

5. The aforementioned determination unit is, The AI ​​generates the waste to determine its condition. The system according to feature 1.

6. The aforementioned display unit is, The AI ​​generates information to suggest the correct way to sort waste. The system according to feature 1.

7. The control unit, AI-generated to control the opening of public trash cans. The system according to feature 1.

8. The authentication unit, The AI ​​generates a stamp to confirm that sorting is complete. The system according to feature 1.

9. The aforementioned determination unit is, The system estimates the user's emotions and adjusts the accuracy of its waste condition assessment based on those emotions. The system according to feature 1.

10. The aforementioned determination unit is, When assessing the condition of waste, the assessment method is customized based on the material and shape of the waste. The system according to feature 1.

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