system

Abstract

The system according to this embodiment aims to provide a procedure for easily disposing of bulky waste. [Solution] The system according to this embodiment comprises a shooting unit, an analysis unit, an acquisition unit, a guidance unit, and an auction unit. The shooting unit takes a photograph of the bulky waste to be disposed of. The analysis unit analyzes the photograph taken by the shooting unit and identifies the type of waste. The acquisition unit acquires the quantity and fees based on the type of waste identified by the analysis unit. The guidance unit provides guidance on how to dispose of the waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. The auction unit automatically puts the waste up for auction if the type of waste identified by the analysis unit is valuable.

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, there is a problem that the procedure for disposing of bulky waste is complicated and time-consuming.

[0005] The system according to the embodiment aims to provide a simple procedure for disposing of bulky waste.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a shooting unit, an analysis unit, an acquisition unit, a guidance unit, and an auction unit. The shooting unit takes a photograph of the bulky waste to be disposed of. The analysis unit analyzes the photograph taken by the shooting unit and identifies the type of waste. The acquisition unit obtains the quantity and fees based on the type of waste identified by the analysis unit. The guidance unit provides guidance on how to dispose of the waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. The auction unit automatically puts the waste up for auction if the type of waste identified by the analysis unit is valuable. [Effects of the Invention]

[0007] The system according to this embodiment can provide a procedure for easily disposing of bulky waste. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between 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) An embodiment of the present invention provides a bulky waste disposal system that simplifies the procedure for disposing of bulky waste and automatically puts valuable items up for auction. The bulky waste disposal system works by having the user take photos of the bulky waste they wish to dispose of. An AI identifies the type of waste from the photos and retrieves the quantity and fees in real time. Furthermore, the AI ​​determines the type of waste and, if it is not bulky waste, guides the user on how to dispose of it as non-burnable waste. Finally, the AI ​​determines the value of the waste and automatically puts it up for auction. For example, the bulky waste disposal system works by having the user take photos of the bulky waste they wish to dispose of. The user uses a smartphone or camera to take photos of the waste, such as old furniture or home appliances. These photos are input into the AI. Next, the AI ​​analyzes the input photos and identifies the type of waste. The AI ​​uses image recognition technology to identify the type of waste from the photos with high accuracy. For example, the AI ​​analyzes a photo of furniture and identifies it as bulky waste. Based on the identified type of waste, the quantity and fees are retrieved in real time. Furthermore, the AI ​​determines the type of waste and, if it's not bulky waste, guides the user on how to dispose of it as non-burnable waste. For example, if the AI ​​analyzes a photo of furniture and determines it's non-burnable waste, it will guide the user on how to dispose of it that way. This eliminates the need to go through the bulky waste disposal procedure if it doesn't require an application to the local government. Finally, the AI ​​identifies valuable waste and automatically lists it for auction. For example, if the AI ​​analyzes a photo of furniture and determines it's valuable, it automatically lists it on an auction site. In this process, the AI ​​smoothly guides the user through the product description and price setting. This system dramatically simplifies the bulky waste disposal process and supports the promotion of recycling and reuse. Users can easily dispose of bulky waste without complicated procedures and, at times, even list valuable items for auction. In this way, the bulky waste disposal system simplifies the bulky waste disposal process and automatically lists valuable items for auction.

[0029] The bulky waste disposal system according to this embodiment comprises a shooting unit, an analysis unit, an acquisition unit, a guidance unit, and a listing unit. The shooting unit takes a photograph of the bulky waste to be disposed of. The shooting unit takes a photograph of the waste to be disposed of, for example, using a smartphone or camera. For example, the shooting unit can take a photograph of old furniture or home appliances. The shooting unit can also take a high-resolution photograph using a digital camera. Furthermore, the shooting unit can also take a photograph using a smartphone camera app. The analysis unit analyzes the photograph taken by the shooting unit and identifies the type of waste. The analysis unit identifies the type of waste from the photograph, for example, using image recognition technology. For example, the analysis unit uses AI to analyze a photograph of furniture and identify that it is bulky waste. Furthermore, the analysis unit can also use deep learning technology to analyze the photograph and identify the type of waste with high accuracy. Furthermore, the analysis unit can also use computer vision technology to analyze the photograph and identify the type of waste. The acquisition unit acquires the quantity and fees based on the type of waste identified by the analysis unit. The acquisition unit acquires the quantity and fees in real time based on the identified type of waste, for example. For example, the acquisition unit uses AI to calculate the quantity of waste and determine the fee. The acquisition unit can also acquire regional fee information and calculate the fee. Furthermore, the acquisition unit can calculate the weight and volume based on the identified waste type and calculate the fee. The guidance unit guides the user on how to dispose of waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. For example, the guidance unit uses AI to determine the type of waste and, if it is not bulky waste, guides the user on how to dispose of it as non-burnable waste. For example, the guidance unit analyzes a photo of furniture and, if it determines that it is non-burnable waste, guides the user on how to dispose of it as non-burnable waste. Furthermore, the guidance unit can acquire regional waste sorting methods and guide the user. Furthermore, the guidance unit can acquire information on collection days and collection locations and guide the user. The listing unit automatically lists waste on auction if the type of waste identified by the analysis unit is valuable. For example, the listing unit uses AI to determine the value of waste and, if it determines that it is valuable, automatically lists it on an auction site.For example, the listing unit analyzes photos of furniture and, if it determines that the item is valuable, automatically lists it on an auction site. The listing unit can also use AI to smoothly guide the user through the process of writing product descriptions and setting prices. Furthermore, the listing unit can acquire information from auction sites and automatically perform the listing procedures. As a result, the bulky waste disposal system according to this embodiment simplifies the procedure for disposing of bulky waste and enables the automatic listing of valuable items on auction sites.

[0030] The photography unit takes pictures of bulky waste that the user wants to dispose of. The photography unit uses, for example, a smartphone or camera to take pictures of the waste. Specifically, it uses a smartphone camera app to allow users to easily take pictures of the waste. The smartphone camera app automatically selects the optimal settings during shooting, enabling high-resolution photos. When using a digital camera, users can take high-resolution photos and provide detailed information. For example, when photographing old furniture or appliances, the camera's zoom function can be used to capture details clearly. Furthermore, the photography unit provides the ability to take photos from multiple angles, allowing the analysis unit to more accurately identify the type of waste. For example, photos of the front, sides, and back of furniture can be taken to understand the overall shape and condition. This allows the photography unit to enable users to easily take high-quality photos and provide them to the system.

[0031] The analysis unit analyzes the photographs taken by the photography unit to identify the type of waste. For example, the analysis unit uses image recognition technology to identify the type of waste from the photograph. Specifically, it uses AI to analyze a photograph of furniture and identify that it is bulky waste. The AI ​​uses deep learning technology to learn from a vast amount of image data and can identify the type of waste with high accuracy. For example, it can analyze the shape and characteristics of furniture to identify it as a specific type of furniture such as a chair, table, or sofa. It can also use computer vision technology to detect objects in a photograph and determine whether or not they are waste. Furthermore, the analysis unit can analyze the background and surrounding information of the photograph to understand the condition and usage of the waste. For example, it can determine the degree of wear and damage from a photograph of furniture and evaluate whether or not it is reusable. As a result, the analysis unit can analyze the photographs taken with high accuracy and quickly and accurately identify the type of waste.

[0032] The acquisition unit retrieves quantities and fees based on the types of waste identified by the analysis unit. For example, the acquisition unit retrieves quantities and fees in real time based on the identified types of waste. Specifically, it uses AI to calculate the quantity of waste and the fees. The AI ​​can calculate appropriate fees based on information such as the type, size, and weight of the identified waste. The acquisition unit can also retrieve regional fee information and calculate fees. For example, it can refer to a database of regional waste disposal fees and collection methods to calculate accurate fees. Furthermore, the acquisition unit can calculate weight and volume based on the identified types of waste and calculate fees. For example, it can calculate the weight and volume of large furniture and home appliances and calculate the corresponding fees. As a result, the acquisition unit can quickly and accurately retrieve quantities and fees based on the types of waste identified by the analysis unit.

[0033] The guidance unit will guide users on how to dispose of waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. For example, the guidance unit can use AI to determine the type of waste and, if it is not bulky waste, guide users on how to dispose of it as non-burnable waste. Specifically, if the AI ​​analyzes the type of waste and determines that it is non-burnable waste, it will guide the user on how to dispose of it as non-burnable waste. For example, if it analyzes a photo of furniture and determines that it is non-burnable waste, it will guide the user on how to dispose of it as non-burnable waste. The guidance unit can also acquire and provide information on waste sorting methods for each region. For example, it can provide information on waste sorting rules, collection days, and collection locations for each region, enabling users to dispose of their waste correctly. Furthermore, the guidance unit can acquire and provide information on collection days and collection locations. For example, it can provide information on collection days and collection locations in the user's area, enabling users to dispose of their waste at the appropriate time. In this way, the guidance unit supports users in accurately sorting and properly disposing of their waste.

[0034] The listing unit automatically puts items up for auction if the type of waste identified by the analysis unit is valuable. For example, the listing unit uses AI to determine the value of waste, and if it determines that the waste is valuable, it automatically lists it on an auction site. Specifically, the AI ​​analyzes photos of furniture, and if it determines that it is valuable, it automatically lists it on an auction site. The AI ​​can evaluate the value of an item based on past auction data and market prices and set an appropriate listing price. The listing unit can also smoothly guide the user through the process of describing the item and setting the price. For example, it can provide a detailed description of the item's features and condition and set an appropriate price. Furthermore, the listing unit can obtain information from auction sites and automate the listing process. For example, it can use the auction site's API to automatically register the item's information and complete the listing process. In addition, the listing unit can monitor the progress of the auction and adjust the price or relist the item as needed. In this way, the listing unit efficiently puts valuable waste up for auction, enabling users to make effective use of unwanted items.

[0035] The shooting unit can take pictures of the waste to be disposed of using a smartphone or camera. For example, the shooting unit can take pictures of the waste to be disposed of using a smartphone. For example, the shooting unit can take pictures using a smartphone camera app. The shooting unit can also take high-resolution pictures using a digital camera. For example, the shooting unit can take pictures of furniture and home appliances using a digital camera. Furthermore, when the shooting unit takes pictures of the waste to be disposed of using a smartphone or camera, it can use AI to adjust the timing and angle of the shot. This makes it easy for the user to take pictures of the waste to be disposed of. Smartphones and cameras include, but are not limited to, the latest models and high-resolution cameras. Some or all of the above processing in the shooting unit may be performed using, for example, AI, or not using AI. For example, when the shooting unit takes pictures using a smartphone camera app, it can use AI to automatically adjust the optimal shooting settings.

[0036] The analysis unit can identify the type of waste from a photograph using image recognition technology. For example, the analysis unit can use AI to analyze a photograph of furniture and identify that it is bulky waste. The analysis unit can also use deep learning technology to analyze a photograph and identify the type of waste with high accuracy. For example, the analysis unit can use deep learning technology to analyze a photograph of furniture and identify that it is bulky waste. The analysis unit can also use computer vision technology to analyze a photograph and identify the type of waste. For example, the analysis unit can use computer vision technology to analyze a photograph of furniture and identify that it is bulky waste. This allows for high-precision identification of the type of waste. Image recognition technology includes, but is not limited to, deep learning and computer vision. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input a photograph of furniture into a generative AI, which can analyze the photograph and identify the type of waste.

[0037] The acquisition unit can acquire quantities and fees in real time based on the identified waste type. For example, the acquisition unit can acquire quantities and fees in real time based on the identified waste type. For example, the acquisition unit can use AI to calculate the quantity of waste and the fee. The acquisition unit can also acquire regional fee information and calculate the fee. For example, the acquisition unit can acquire regional fee information and calculate the fee. The acquisition unit can also calculate weight and volume based on the identified waste type and calculate the fee. For example, the acquisition unit can calculate weight and volume based on the identified waste type and calculate the fee. This allows for the acquisition of waste quantities and fees in real time. Real time includes, but is not limited to, processing time and update frequency. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input quantities and fees based on the identified waste type into AI, and the AI ​​can calculate quantities and fees in real time.

[0038] The guidance unit can guide users on how to dispose of items as non-burnable waste if they are not bulky waste. For example, the guidance unit can use AI to determine the type of waste and, if it is not bulky waste, guide users on how to dispose of it as non-burnable waste. For example, the guidance unit can analyze a photo of furniture and, if it determines that it is non-burnable waste, guide the user on how to dispose of it as non-burnable waste. The guidance unit can also obtain and provide users with information on waste sorting methods specific to each region. For example, the guidance unit can obtain and provide users with information on waste sorting methods specific to each region. The guidance unit can also obtain and provide users with information on collection dates and collection locations. For example, the guidance unit can obtain and provide users with information on collection dates and collection locations. This allows the guidance unit to guide users on how to dispose of items that are not bulky waste. How to dispose of items as non-burnable waste includes, but is not limited to, sorting methods and collection dates. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the information desk can input a photo of furniture into an AI, which can then provide guidance on how to dispose of it as non-combustible waste.

[0039] The listing unit can automatically list valuable waste items on auction sites. For example, the listing unit can use AI to determine the value of waste items, and if it determines that an item is valuable, it will automatically list it on an auction site. For example, the listing unit can analyze photos of furniture, and if it determines that an item is valuable, it will automatically list it on an auction site. The listing unit can also use AI to smoothly guide the user through tasks such as writing product descriptions and setting prices. For example, the listing unit can input product descriptions and price settings into the AI, which will then automatically set them. The listing unit can also obtain information from auction sites and automatically perform the listing process. For example, the listing unit can obtain information from auction sites and automatically perform the listing process. This allows valuable waste items to be automatically listed on auction sites. Automatic listing on auction sites includes, but is not limited to, specific auction sites and listing methods. Some or all of the above-described processes in the listing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the listing unit can input photos of furniture into a generative AI, which can determine if the waste is valuable and automatically list it on auction.

[0040] The camera unit can automatically take photos from multiple angles to improve analysis accuracy. For example, the camera unit can have AI automatically rotate the camera and take multiple photos from different angles. The camera unit can also have AI instruct the user to take photos from different angles and acquire multiple photos. The camera unit can also have AI use a drone to take photos from multiple angles, including aerial shots. This improves analysis accuracy by taking photos from multiple angles. Multiple angles include, but are not limited to, 30 degrees, 45 degrees, 90 degrees, etc. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can connect the camera to AI, and the AI ​​can automatically rotate the camera and take photos from multiple angles.

[0041] The camera unit can automatically remove the background and highlight only the subject. For example, the camera unit can use AI image processing techniques to automatically remove the background and highlight only the subject. Alternatively, the camera unit can use AI to provide the user with guidelines for background removal, allowing for manual background removal. The camera unit can also use AI to remove the background in real time and capture a photograph with only the subject highlighted. This allows for the capture of a photograph with only the subject highlighted by removing the background. Automatic background removal includes, but is not limited to, chroma key technology and image processing algorithms. Some or all of the above processing in the camera unit may be performed using, for example, generative AI, or without generative AI. For example, the camera unit can input a photograph into a generative AI, which can automatically remove the background and highlight only the subject.

[0042] The camera unit can automatically adjust the optimal shooting settings, taking into account the user's geographical location information. For example, the camera unit can use AI to acquire weather information for the user's current location and automatically adjust the optimal shooting settings. The camera unit can also use AI to automatically adjust the optimal shooting settings, taking into account the light conditions for the user's current location. The camera unit can also use AI to automatically adjust the optimal shooting settings, taking into account the light conditions for the user's current location. The camera unit can also use AI to acquire background information for the user's current location and automatically adjust the optimal shooting settings. This allows the camera unit to automatically adjust the optimal shooting settings, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above-described processes in the camera unit may be performed using, for example, AI, or without AI. For example, the shooting unit can input the user's geographical location information into the AI, which can then automatically adjust the optimal shooting settings.

[0043] The shooting unit can suggest the optimal shooting method by referring to the user's past shooting history. For example, the shooting unit can use AI to analyze the user's past shooting history and suggest the optimal shooting method. The shooting unit can also use AI to suggest the most successful shooting method from the user's past shooting history. The shooting unit can also use AI to automatically adjust the optimal shooting settings based on the user's past shooting history. For example, the shooting unit can use AI to automatically adjust the optimal shooting settings based on the user's past shooting history. This allows the shooting unit to suggest the optimal shooting method by referring to the user's past shooting history. Past shooting history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the shooting unit may be performed using, for example, AI, or not using AI. For example, the shooting unit can input the user's past shooting history into AI, and the AI ​​can suggest the optimal shooting method.

[0044] The analysis unit can perform a detailed analysis of the material and condition of an object to perform a more accurate identification. For example, the analysis unit can use AI to perform a detailed analysis of the material of an object and perform an accurate identification. The analysis unit can also use AI to perform a detailed analysis of the condition of an object and perform an accurate identification. For example, the analysis unit can use AI to perform a detailed analysis of the condition of an object and perform an accurate identification. The analysis unit can also use AI to analyze a combination of the material and condition of an object and perform an accurate identification. For example, the analysis unit can use AI to analyze a combination of the material and condition of an object and perform an accurate identification. This improves the identification accuracy by analyzing the material and condition of the object in detail. The material and condition include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs a photograph of the object into a generating AI, which then analyzes the material and condition in detail to perform accurate identification.

[0045] The analysis unit can improve identification accuracy by considering the object's usage history and year of manufacture. For example, the analysis unit can use AI to analyze the object's usage history and improve identification accuracy. The analysis unit can also use AI to analyze the object's year of manufacture and improve identification accuracy. The analysis unit can also use AI to analyze the object's usage history and year of manufacture in combination and improve identification accuracy. For example, the analysis unit can use AI to analyze the object's usage history and year of manufacture in combination and improve identification accuracy. This improves identification accuracy by considering the object's usage history and year of manufacture. Usage history and year of manufacture include, but are not limited to, serial numbers and purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input data on the object's usage history and year of manufacture into a generating AI, which can then improve identification accuracy.

[0046] The analysis unit can evaluate the market value of an object in real time and reflect it in the results. For example, the analysis unit can use AI to evaluate the market value of an object in real time and reflect it in the analysis results. For example, the analysis unit can use AI to evaluate the market value of an object in real time and reflect it in the analysis results. The analysis unit can also evaluate the market value of an object based on market data and reflect it in the analysis results. For example, the analysis unit can evaluate the market value of an object based on market data and reflect it in the analysis results. The analysis unit can also evaluate the market value of an object considering real-time market trends and reflect it in the analysis results. For example, the analysis unit can evaluate the market value of an object considering real-time market trends and reflect it in the analysis results. This allows the market value of an object to be evaluated in real time and reflected in the analysis results. Market value includes, but is not limited to, auction prices and resale values. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input market data of the target object into a generating AI, which can then evaluate its market value in real time and reflect this in the results.

[0047] The analysis unit can automatically acquire relevant information about the object (e.g., manufacturer, model number) to improve identification accuracy. For example, the analysis unit can use AI to automatically acquire manufacturer information about the object to improve identification accuracy. The analysis unit can also use AI to automatically acquire the model number of the object to improve identification accuracy. For example, the analysis unit can use AI to automatically acquire the model number of the object to improve identification accuracy. The analysis unit can also use AI to acquire a combination of manufacturer information and model number to improve identification accuracy. For example, the analysis unit can use AI to acquire a combination of manufacturer information and model number to improve identification accuracy. This improves identification accuracy by automatically acquiring relevant information about the object. Relevant information includes, but is not limited to, manufacturer and model number. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input manufacturer information and model number of the target object into the generating AI, which can then automatically acquire related information and improve identification accuracy.

[0048] The acquisition unit can dynamically adjust fees and quantities considering market trends for the target object. For example, the acquisition unit can use AI to analyze market trends in real time and dynamically adjust fees. The acquisition unit can also use AI to dynamically adjust the optimal quantity based on market trends. The acquisition unit can also use AI to dynamically adjust fees and quantities simultaneously, taking market trends into consideration. Market trends include, but are not limited to, price trends and demand forecasts. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input market trend data into AI, which can dynamically adjust fees and quantities.

[0049] The acquisition unit can calculate the optimal fee by referring to the past transaction history of the target object. The acquisition unit can, for example, use AI to analyze the past transaction history of the target object and calculate the optimal fee. The acquisition unit can also calculate the optimal fee based on past transaction data. The acquisition unit can also dynamically calculate the optimal fee by considering the transaction history of the target object. The acquisition unit can dynamically calculate the optimal fee by considering the transaction history of the target object. This allows the optimal fee to be calculated by referring to the past transaction history of the target object. Past transaction history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input past transaction history data into AI, and the AI ​​can calculate the optimal fee.

[0050] The acquisition unit can calculate the optimal fee by considering the user's geographical location information. For example, the acquisition unit can use AI to calculate the optimal fee by considering market trends in the user's current location. The acquisition unit can also use AI to calculate the optimal fee based on the user's geographical location information. The acquisition unit can also use AI to calculate the optimal fee by considering the economic conditions in the user's current location. This allows the acquisition unit to calculate the optimal fee by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's geographical location information into AI, which can then calculate the optimal fee.

[0051] The acquisition unit can suggest the optimal quantity by referring to the user's past transaction history. For example, the acquisition unit can use AI to analyze the user's past transaction history and suggest the optimal quantity. The acquisition unit can also suggest the optimal quantity based on past transaction data. For example, the acquisition unit can suggest the optimal quantity based on past transaction data. The acquisition unit can also dynamically suggest the optimal quantity by considering the user's transaction history. For example, the acquisition unit can dynamically suggest the optimal quantity by considering the user's transaction history. This allows the acquisition unit to suggest the optimal quantity by referring to the user's past transaction history. The optimal quantity includes, but is not limited to, demand forecasting and inventory management. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's past transaction history data into AI, and the AI ​​can suggest the optimal quantity.

[0052] The guidance unit can propose the optimal disposal method considering the material and condition of the object. For example, the guidance unit can use AI to analyze the material of the object and propose the optimal disposal method. The guidance unit can also use AI to analyze the condition of the object and propose the optimal disposal method. The guidance unit can also use AI to analyze the material and condition of the object in combination and propose the optimal disposal method. This allows the guidance unit to propose the optimal disposal method considering the material and condition of the object. Materials and conditions include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input data on the material and condition of the object into the AI, which can then propose the optimal disposal method.

[0053] The guidance unit can suggest the optimal disposal method considering the size and weight of the object. For example, the guidance unit can use AI to analyze the size of the object and suggest the optimal disposal method. The guidance unit can also use AI to analyze the weight of the object and suggest the optimal disposal method. The guidance unit can also use AI to analyze the size and weight of the object in combination and suggest the optimal disposal method. This allows the guidance unit to suggest the optimal disposal method considering the size and weight of the object. Size and weight include, but are not limited to, dimensions and weight measurements. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input data on the size and weight of the object into the AI, which can then suggest the optimal disposal method.

[0054] The guidance unit can suggest the optimal disposal method considering the user's geographical location information. For example, the guidance unit can use AI to obtain garbage collection information for the user's current location and suggest the optimal disposal method. The guidance unit can also use AI to suggest the optimal disposal method based on the user's geographical location information. The guidance unit can also use AI to suggest the optimal disposal method considering the rules of the user's current municipality. This allows the guidance unit to suggest the optimal disposal method considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input the user's geographical location information into the AI, which can then suggest the optimal disposal method.

[0055] The guidance unit can suggest the optimal disposal method by referring to the user's past disposal history. The guidance unit can, for example, use AI to analyze the user's past disposal history and suggest the optimal disposal method. The guidance unit can also suggest the optimal disposal method based on past disposal data. The guidance unit can also dynamically suggest the optimal disposal method by considering the user's disposal history. The guidance unit can dynamically suggest the optimal disposal method by considering the user's disposal history. Past disposal history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input the user's past disposal history data into AI, and the AI ​​can suggest the optimal disposal method.

[0056] The listing unit can evaluate the market value of an item in real time and set the optimal listing price. For example, the listing unit can use AI to evaluate the market value of an item in real time and set the optimal listing price. For example, the listing unit can use AI to evaluate the market value of an item in real time and set the optimal listing price. The listing unit can also evaluate the market value of an item and set the optimal listing price based on market data. For example, the listing unit can evaluate the market value of an item and set the optimal listing price based on market data. The listing unit can also evaluate the market value of an item and set the optimal listing price considering real-time market trends. For example, the listing unit can evaluate the market value of an item and set the optimal listing price considering real-time market trends. This allows for the setting of the optimal listing price by evaluating the market value of an item in real time. Market value includes, but is not limited to, auction prices and resale values. Some or all of the above processing in the listing unit may be performed using, for example, generative AI, or without generative AI. For example, the listing unit can input market data for the item into a generating AI, which can then evaluate its market value in real time and set the optimal listing price.

[0057] The listing unit can automatically acquire detailed information about the object (e.g., manufacturer, model number) and reflect it in the listing information. For example, the listing unit can use AI to automatically acquire the manufacturer information of the object and reflect it in the listing information. The listing unit can also use AI to automatically acquire the model number of the object and reflect it in the listing information. For example, the listing unit can use AI to automatically acquire the model number of the object and reflect it in the listing information. The listing unit can also use AI to acquire a combination of manufacturer information and model number of the object and reflect it in the listing information. For example, the listing unit can use AI to acquire a combination of manufacturer information and model number of the object and reflect it in the listing information. This allows for the automatic acquisition of detailed information about the object and its reflection in the listing information. Detailed information includes, but is not limited to, the manufacturer and model number. Some or all of the above processing in the listing unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the listing department can input the manufacturer information and model number of the item into a generating AI, which can then automatically retrieve the detailed information and reflect it in the listing information.

[0058] The listing unit can propose the optimal listing method by considering the user's geographical location information. For example, the listing unit can propose the optimal listing method by considering the market trends in the user's current location using AI. The listing unit can also propose the optimal listing method by considering the market trends in the user's current location using AI. For example, the listing unit can propose the optimal listing method by considering the economic conditions in the user's current location using AI. This allows the listing unit to propose the optimal listing method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the listing unit may be performed using, for example, AI, or without AI. For example, the listing unit can input the user's geographical location information into AI, and the AI ​​can propose the optimal listing method.

[0059] The listing unit can suggest the optimal listing method by referring to the user's past listing history. For example, the listing unit can use AI to analyze the user's past listing history and suggest the optimal listing method. The listing unit can also suggest the optimal listing method based on past listing data. For example, the listing unit can suggest the optimal listing method based on past listing data. The listing unit can also dynamically suggest the optimal listing method by considering the user's listing history. For example, the listing unit can dynamically suggest the optimal listing method by considering the user's listing history. This allows the optimal listing method to be suggested by referring to the user's past listing history. Past listing history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the listing unit may be performed using, for example, AI, or not using AI. For example, the listing unit can input the user's past listing history data into AI, and the AI ​​can suggest the optimal listing method.

[0060] The listing unit can suggest the optimal listing timing by referring to the user's calendar information. For example, the listing unit can use AI to refer to the user's calendar information and suggest the optimal listing timing. The listing unit can also use AI to consider the user's schedule and suggest the optimal listing timing. The listing unit can also use AI to dynamically suggest the optimal listing timing based on the user's calendar information. For example, the listing unit can use AI to dynamically suggest the optimal listing timing based on the user's calendar information. This allows the optimal listing timing to be suggested by referring to the user's calendar information. Some or all of the above processing in the listing unit may be performed using AI, or not using AI. For example, the listing unit can input the user's calendar information into AI, and AI can suggest the optimal listing timing.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The camera unit can automatically take photos from multiple angles to improve analysis accuracy. For example, the AI ​​can automatically rotate the camera and take multiple photos from different angles. The AI ​​can also instruct the user to take photos from different angles and acquire multiple photos. Furthermore, the AI ​​can use a drone to take photos from multiple angles, including aerial shots. This improves analysis accuracy by taking photos from multiple angles. Multiple angles include, but are not limited to, 30 degrees, 45 degrees, 90 degrees, etc. Some or all of the above processing in the camera unit may be performed using, for example, the AI, or not. For example, the camera can be connected to the AI, and the AI ​​can automatically rotate the camera and take photos from multiple angles.

[0063] The analysis unit can perform a detailed analysis of the material and condition of an object to perform more accurate identification. For example, AI can perform a detailed analysis of the material of an object to perform accurate identification. AI can also perform a detailed analysis of the condition of an object to perform accurate identification. Furthermore, AI can perform an analysis that combines the material and condition of an object to perform accurate identification. As a result, the accuracy of identification is improved by analyzing the material and condition of the object in detail. Materials and conditions include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, a photograph of an object can be input into a generating AI, which can then perform a detailed analysis of its material and condition to perform accurate identification.

[0064] The acquisition unit can dynamically adjust fees and quantities considering market trends for the target object. For example, AI can analyze market trends in real time and dynamically adjust fees. AI can also dynamically adjust the optimal quantity based on market trends. Furthermore, AI can dynamically adjust both fees and quantities simultaneously, taking market trends into consideration. This allows for dynamic adjustment of fees and quantities considering market trends for the target object. Market trends include, but are not limited to, price trends and demand forecasts. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, market trend data can be input into AI, which can then dynamically adjust fees and quantities.

[0065] The guidance unit can suggest the optimal disposal method considering the material and condition of the object. For example, AI can analyze the material of the object and suggest the optimal disposal method. AI can also analyze the condition of the object and suggest the optimal disposal method. Furthermore, AI can analyze a combination of the material and condition of the object and suggest the optimal disposal method. This allows for the suggestion of the optimal disposal method considering the material and condition of the object. Materials and conditions include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above-described processes in the guidance unit may be performed using AI, or without AI. For example, data on the material and condition of the object can be input into the AI, which then suggests the optimal disposal method.

[0066] The listing unit can evaluate the market value of an item in real time and set the optimal listing price. For example, an AI can evaluate the market value of an item in real time and set the optimal listing price. It can also evaluate the market value of an item and set the optimal listing price based on market data. Furthermore, it can evaluate the market value of an item and set the optimal listing price considering real-time market trends. In this way, the optimal listing price can be set by evaluating the market value of an item in real time. Market value includes, but is not limited to, auction prices and resale values. Some or all of the above processing in the listing unit may be performed using, for example, a generating AI, or without a generating AI. For example, market data of an item can be input into a generating AI, which can evaluate the market value in real time and set the optimal listing price.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The photographer takes pictures of the bulky waste they want to dispose of. The photographer can take pictures of the waste using a smartphone or camera, for example. For example, the photographer can take pictures of old furniture or home appliances. The photographer can also take high-resolution photos using a digital camera. Furthermore, the photographer can take pictures using a smartphone camera app. Step 2: The analysis unit analyzes the photographs taken by the photography unit and identifies the type of waste. The analysis unit identifies the type of waste from the photograph using, for example, image recognition technology. For example, the analysis unit uses AI to analyze a photograph of furniture and identify that it is bulky waste. The analysis unit can also analyze the photograph using deep learning technology to identify the type of waste with high accuracy. Furthermore, the analysis unit can also analyze the photograph using computer vision technology to identify the type of waste. Step 3: The acquisition unit retrieves the quantity and fees based on the type of waste identified by the analysis unit. For example, the acquisition unit retrieves the quantity and fees in real time based on the identified type of waste. For example, the acquisition unit uses AI to calculate the quantity of waste and then calculates the fees. The acquisition unit can also retrieve regional fee information and then calculate the fees. Furthermore, the acquisition unit can calculate the weight and volume based on the identified type of waste and then calculate the fees. Step 4: The guidance unit will guide the user on how to dispose of the waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. For example, the guidance unit can use AI to determine the type of waste and, if it is not bulky waste, guide the user on how to dispose of it as non-burnable waste. For example, the guidance unit can analyze a photo of furniture and, if it determines that it is non-burnable waste, guide the user on how to dispose of it as non-burnable waste. The guidance unit can also obtain and guide the user on waste sorting methods specific to the region. Furthermore, the guidance unit can obtain and guide the user on collection days and collection locations. Step 5: The listing unit automatically puts items up for auction if the type of waste identified by the analysis unit is valuable. For example, the listing unit uses AI to determine the value of waste, and if it determines that it is valuable, it automatically lists it on an auction site. For example, the listing unit analyzes photos of furniture, and if it determines that it is valuable, it automatically lists it on an auction site. The listing unit can also use AI to smoothly guide the user through product descriptions and price setting. Furthermore, the listing unit can obtain information from auction sites and automatically perform the listing process.

[0069] (Example of form 2) An embodiment of the present invention provides a bulky waste disposal system that simplifies the procedure for disposing of bulky waste and automatically puts valuable items up for auction. The bulky waste disposal system works by having the user take photos of the bulky waste they wish to dispose of. An AI identifies the type of waste from the photos and retrieves the quantity and fees in real time. Furthermore, the AI ​​determines the type of waste and, if it is not bulky waste, guides the user on how to dispose of it as non-burnable waste. Finally, the AI ​​determines the value of the waste and automatically puts it up for auction. For example, the bulky waste disposal system works by having the user take photos of the bulky waste they wish to dispose of. The user uses a smartphone or camera to take photos of the waste, such as old furniture or home appliances. These photos are input into the AI. Next, the AI ​​analyzes the input photos and identifies the type of waste. The AI ​​uses image recognition technology to identify the type of waste from the photos with high accuracy. For example, the AI ​​analyzes a photo of furniture and identifies it as bulky waste. Based on the identified type of waste, the quantity and fees are retrieved in real time. Furthermore, the AI ​​determines the type of waste and, if it's not bulky waste, guides the user on how to dispose of it as non-burnable waste. For example, if the AI ​​analyzes a photo of furniture and determines it's non-burnable waste, it will guide the user on how to dispose of it that way. This eliminates the need to go through the bulky waste disposal procedure if it doesn't require an application to the local government. Finally, the AI ​​identifies valuable waste and automatically lists it for auction. For example, if the AI ​​analyzes a photo of furniture and determines it's valuable, it automatically lists it on an auction site. In this process, the AI ​​smoothly guides the user through the product description and price setting. This system dramatically simplifies the bulky waste disposal process and supports the promotion of recycling and reuse. Users can easily dispose of bulky waste without complicated procedures and, at times, even list valuable items for auction. In this way, the bulky waste disposal system simplifies the bulky waste disposal process and automatically lists valuable items for auction.

[0070] The bulky waste disposal system according to this embodiment comprises a shooting unit, an analysis unit, an acquisition unit, a guidance unit, and a listing unit. The shooting unit takes a photograph of the bulky waste to be disposed of. The shooting unit takes a photograph of the waste to be disposed of, for example, using a smartphone or camera. For example, the shooting unit can take a photograph of old furniture or home appliances. The shooting unit can also take a high-resolution photograph using a digital camera. Furthermore, the shooting unit can also take a photograph using a smartphone camera app. The analysis unit analyzes the photograph taken by the shooting unit and identifies the type of waste. The analysis unit identifies the type of waste from the photograph, for example, using image recognition technology. For example, the analysis unit uses AI to analyze a photograph of furniture and identify that it is bulky waste. Furthermore, the analysis unit can also use deep learning technology to analyze the photograph and identify the type of waste with high accuracy. Furthermore, the analysis unit can also use computer vision technology to analyze the photograph and identify the type of waste. The acquisition unit acquires the quantity and fees based on the type of waste identified by the analysis unit. The acquisition unit acquires the quantity and fees in real time based on the identified type of waste, for example. For example, the acquisition unit uses AI to calculate the quantity of waste and determine the fee. The acquisition unit can also acquire regional fee information and calculate the fee. Furthermore, the acquisition unit can calculate the weight and volume based on the identified waste type and calculate the fee. The guidance unit guides the user on how to dispose of waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. For example, the guidance unit uses AI to determine the type of waste and, if it is not bulky waste, guides the user on how to dispose of it as non-burnable waste. For example, the guidance unit analyzes a photo of furniture and, if it determines that it is non-burnable waste, guides the user on how to dispose of it as non-burnable waste. Furthermore, the guidance unit can acquire regional waste sorting methods and guide the user. Furthermore, the guidance unit can acquire information on collection days and collection locations and guide the user. The listing unit automatically lists waste on auction if the type of waste identified by the analysis unit is valuable. For example, the listing unit uses AI to determine the value of waste and, if it determines that it is valuable, automatically lists it on an auction site.For example, the listing unit analyzes photos of furniture and, if it determines that the item is valuable, automatically lists it on an auction site. The listing unit can also use AI to smoothly guide the user through the process of writing product descriptions and setting prices. Furthermore, the listing unit can acquire information from auction sites and automatically perform the listing procedures. As a result, the bulky waste disposal system according to this embodiment simplifies the procedure for disposing of bulky waste and enables the automatic listing of valuable items on auction sites.

[0071] The photography unit takes pictures of bulky waste that the user wants to dispose of. The photography unit uses, for example, a smartphone or camera to take pictures of the waste. Specifically, it uses a smartphone camera app to allow users to easily take pictures of the waste. The smartphone camera app automatically selects the optimal settings during shooting, enabling high-resolution photos. When using a digital camera, users can take high-resolution photos and provide detailed information. For example, when photographing old furniture or appliances, the camera's zoom function can be used to capture details clearly. Furthermore, the photography unit provides the ability to take photos from multiple angles, allowing the analysis unit to more accurately identify the type of waste. For example, photos of the front, sides, and back of furniture can be taken to understand the overall shape and condition. This allows the photography unit to enable users to easily take high-quality photos and provide them to the system.

[0072] The analysis unit analyzes the photographs taken by the photography unit to identify the type of waste. For example, the analysis unit uses image recognition technology to identify the type of waste from the photograph. Specifically, it uses AI to analyze a photograph of furniture and identify that it is bulky waste. The AI ​​uses deep learning technology to learn from a vast amount of image data and can identify the type of waste with high accuracy. For example, it can analyze the shape and characteristics of furniture to identify it as a specific type of furniture such as a chair, table, or sofa. It can also use computer vision technology to detect objects in a photograph and determine whether or not they are waste. Furthermore, the analysis unit can analyze the background and surrounding information of the photograph to understand the condition and usage of the waste. For example, it can determine the degree of wear and damage from a photograph of furniture and evaluate whether or not it is reusable. As a result, the analysis unit can analyze the photographs taken with high accuracy and quickly and accurately identify the type of waste.

[0073] The acquisition unit retrieves quantities and fees based on the types of waste identified by the analysis unit. For example, the acquisition unit retrieves quantities and fees in real time based on the identified types of waste. Specifically, it uses AI to calculate the quantity of waste and the fees. The AI ​​can calculate appropriate fees based on information such as the type, size, and weight of the identified waste. The acquisition unit can also retrieve regional fee information and calculate fees. For example, it can refer to a database of regional waste disposal fees and collection methods to calculate accurate fees. Furthermore, the acquisition unit can calculate weight and volume based on the identified types of waste and calculate fees. For example, it can calculate the weight and volume of large furniture and home appliances and calculate the corresponding fees. As a result, the acquisition unit can quickly and accurately retrieve quantities and fees based on the types of waste identified by the analysis unit.

[0074] The guidance unit will guide users on how to dispose of waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. For example, the guidance unit can use AI to determine the type of waste and, if it is not bulky waste, guide users on how to dispose of it as non-burnable waste. Specifically, if the AI ​​analyzes the type of waste and determines that it is non-burnable waste, it will guide the user on how to dispose of it as non-burnable waste. For example, if it analyzes a photo of furniture and determines that it is non-burnable waste, it will guide the user on how to dispose of it as non-burnable waste. The guidance unit can also acquire and provide information on waste sorting methods for each region. For example, it can provide information on waste sorting rules, collection days, and collection locations for each region, enabling users to dispose of their waste correctly. Furthermore, the guidance unit can acquire and provide information on collection days and collection locations. For example, it can provide information on collection days and collection locations in the user's area, enabling users to dispose of their waste at the appropriate time. In this way, the guidance unit supports users in accurately sorting and properly disposing of their waste.

[0075] The listing unit automatically puts items up for auction if the type of waste identified by the analysis unit is valuable. For example, the listing unit uses AI to determine the value of waste, and if it determines that the waste is valuable, it automatically lists it on an auction site. Specifically, the AI ​​analyzes photos of furniture, and if it determines that it is valuable, it automatically lists it on an auction site. The AI ​​can evaluate the value of an item based on past auction data and market prices and set an appropriate listing price. The listing unit can also smoothly guide the user through the process of describing the item and setting the price. For example, it can provide a detailed description of the item's features and condition and set an appropriate price. Furthermore, the listing unit can obtain information from auction sites and automate the listing process. For example, it can use the auction site's API to automatically register the item's information and complete the listing process. In addition, the listing unit can monitor the progress of the auction and adjust the price or relist the item as needed. In this way, the listing unit efficiently puts valuable waste up for auction, enabling users to make effective use of unwanted items.

[0076] The shooting unit can take pictures of the waste to be disposed of using a smartphone or camera. For example, the shooting unit can take pictures of the waste to be disposed of using a smartphone. For example, the shooting unit can take pictures using a smartphone camera app. The shooting unit can also take high-resolution pictures using a digital camera. For example, the shooting unit can take pictures of furniture and home appliances using a digital camera. Furthermore, when the shooting unit takes pictures of the waste to be disposed of using a smartphone or camera, it can use AI to adjust the timing and angle of the shot. This makes it easy for the user to take pictures of the waste to be disposed of. Smartphones and cameras include, but are not limited to, the latest models and high-resolution cameras. Some or all of the above processing in the shooting unit may be performed using, for example, AI, or not using AI. For example, when the shooting unit takes pictures using a smartphone camera app, it can use AI to automatically adjust the optimal shooting settings.

[0077] The analysis unit can identify the type of waste from a photograph using image recognition technology. For example, the analysis unit can use AI to analyze a photograph of furniture and identify that it is bulky waste. The analysis unit can also use deep learning technology to analyze a photograph and identify the type of waste with high accuracy. For example, the analysis unit can use deep learning technology to analyze a photograph of furniture and identify that it is bulky waste. The analysis unit can also use computer vision technology to analyze a photograph and identify the type of waste. For example, the analysis unit can use computer vision technology to analyze a photograph of furniture and identify that it is bulky waste. This allows for high-precision identification of the type of waste. Image recognition technology includes, but is not limited to, deep learning and computer vision. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input a photograph of furniture into a generative AI, which can analyze the photograph and identify the type of waste.

[0078] The acquisition unit can acquire quantities and fees in real time based on the identified waste type. For example, the acquisition unit can acquire quantities and fees in real time based on the identified waste type. For example, the acquisition unit can use AI to calculate the quantity of waste and the fee. The acquisition unit can also acquire regional fee information and calculate the fee. For example, the acquisition unit can acquire regional fee information and calculate the fee. The acquisition unit can also calculate weight and volume based on the identified waste type and calculate the fee. For example, the acquisition unit can calculate weight and volume based on the identified waste type and calculate the fee. This allows for the acquisition of waste quantities and fees in real time. Real time includes, but is not limited to, processing time and update frequency. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input quantities and fees based on the identified waste type into AI, and the AI ​​can calculate quantities and fees in real time.

[0079] The guidance unit can guide users on how to dispose of items as non-burnable waste if they are not bulky waste. For example, the guidance unit can use AI to determine the type of waste and, if it is not bulky waste, guide users on how to dispose of it as non-burnable waste. For example, the guidance unit can analyze a photo of furniture and, if it determines that it is non-burnable waste, guide the user on how to dispose of it as non-burnable waste. The guidance unit can also obtain and provide users with information on waste sorting methods specific to each region. For example, the guidance unit can obtain and provide users with information on waste sorting methods specific to each region. The guidance unit can also obtain and provide users with information on collection dates and collection locations. For example, the guidance unit can obtain and provide users with information on collection dates and collection locations. This allows the guidance unit to guide users on how to dispose of items that are not bulky waste. How to dispose of items as non-burnable waste includes, but is not limited to, sorting methods and collection dates. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the information desk can input a photo of furniture into an AI, which can then provide guidance on how to dispose of it as non-combustible waste.

[0080] The listing unit can automatically list valuable waste items on auction sites. For example, the listing unit can use AI to determine the value of waste items, and if it determines that an item is valuable, it will automatically list it on an auction site. For example, the listing unit can analyze photos of furniture, and if it determines that an item is valuable, it will automatically list it on an auction site. The listing unit can also use AI to smoothly guide the user through tasks such as writing product descriptions and setting prices. For example, the listing unit can input product descriptions and price settings into the AI, which will then automatically set them. The listing unit can also obtain information from auction sites and automatically perform the listing process. For example, the listing unit can obtain information from auction sites and automatically perform the listing process. This allows valuable waste items to be automatically listed on auction sites. Automatic listing on auction sites includes, but is not limited to, specific auction sites and listing methods. Some or all of the above-described processes in the listing unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the listing unit can input photos of furniture into a generative AI, which can determine if the waste is valuable and automatically list it on auction.

[0081] The shooting unit can estimate the user's emotions and adjust the timing of shooting based on the estimated emotions. For example, if the user is stressed, the AI ​​can quickly take a picture, minimizing the user's effort. The shooting unit can also wait for the optimal angle and lighting conditions before taking a picture if the user is relaxed. The shooting unit can also take a picture immediately if the user is in a hurry, allowing the user to quickly move on to the next step. This allows for shooting at the optimal timing according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the shooting unit may be performed using AI, or not using AI. For example, the shooting unit can input user facial expression data into the generation AI, which can then estimate the user's emotions and adjust the timing of the shoot.

[0082] The camera unit can automatically take photos from multiple angles to improve analysis accuracy. For example, the camera unit can have AI automatically rotate the camera and take multiple photos from different angles. The camera unit can also have AI instruct the user to take photos from different angles and acquire multiple photos. The camera unit can also have AI use a drone to take photos from multiple angles, including aerial shots. This improves analysis accuracy by taking photos from multiple angles. Multiple angles include, but are not limited to, 30 degrees, 45 degrees, 90 degrees, etc. Some or all of the above processing in the camera unit may be performed using AI, for example, or without AI. For example, the camera unit can connect the camera to AI, and the AI ​​can automatically rotate the camera and take photos from multiple angles.

[0083] The camera unit can automatically remove the background and highlight only the subject. For example, the camera unit can use AI image processing techniques to automatically remove the background and highlight only the subject. Alternatively, the camera unit can use AI to provide the user with guidelines for background removal, allowing for manual background removal. The camera unit can also use AI to remove the background in real time and capture a photograph with only the subject highlighted. This allows for the capture of a photograph with only the subject highlighted by removing the background. Automatic background removal includes, but is not limited to, chroma key technology and image processing algorithms. Some or all of the above processing in the camera unit may be performed using, for example, generative AI, or without generative AI. For example, the camera unit can input a photograph into a generative AI, which can automatically remove the background and highlight only the subject.

[0084] The camera unit can estimate the user's emotions and determine the priority of objects to photograph based on the estimated emotions. For example, if the user is stressed, the AI ​​can prioritize photographing important objects. If the user is relaxed, the AI ​​can also photograph all objects equally. If the user is in a hurry, the AI ​​can prioritize photographing the most valuable objects. This allows the camera unit to determine the priority of objects to photograph according to the user's emotions. The priority of objects to photograph may include, but is not limited to, importance and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority of objects to be photographed.

[0085] The camera unit can automatically adjust the optimal shooting settings, taking into account the user's geographical location information. For example, the camera unit can use AI to acquire weather information for the user's current location and automatically adjust the optimal shooting settings. The camera unit can also use AI to automatically adjust the optimal shooting settings, taking into account the light conditions for the user's current location. The camera unit can also use AI to automatically adjust the optimal shooting settings, taking into account the light conditions for the user's current location. The camera unit can also use AI to acquire background information for the user's current location and automatically adjust the optimal shooting settings. This allows the camera unit to automatically adjust the optimal shooting settings, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above-described processes in the camera unit may be performed using, for example, AI, or without AI. For example, the shooting unit can input the user's geographical location information into the AI, which can then automatically adjust the optimal shooting settings.

[0086] The shooting unit can suggest the optimal shooting method by referring to the user's past shooting history. For example, the shooting unit can use AI to analyze the user's past shooting history and suggest the optimal shooting method. The shooting unit can also use AI to suggest the most successful shooting method from the user's past shooting history. The shooting unit can also use AI to automatically adjust the optimal shooting settings based on the user's past shooting history. For example, the shooting unit can use AI to automatically adjust the optimal shooting settings based on the user's past shooting history. This allows the shooting unit to suggest the optimal shooting method by referring to the user's past shooting history. Past shooting history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the shooting unit may be performed using, for example, AI, or not using AI. For example, the shooting unit can input the user's past shooting history into AI, and the AI ​​can suggest the optimal shooting method.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. This allows the display method of the analysis results to be adjusted according to the user's emotions. Display methods of analysis results include, but are not limited to, graph displays and text displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, the generating AI can estimate the user's emotions, and the method of displaying the analysis results can be adjusted.

[0088] The analysis unit can perform a detailed analysis of the material and condition of an object to perform a more accurate identification. For example, the analysis unit can use AI to perform a detailed analysis of the material of an object and perform an accurate identification. The analysis unit can also use AI to perform a detailed analysis of the condition of an object and perform an accurate identification. For example, the analysis unit can use AI to perform a detailed analysis of the condition of an object and perform an accurate identification. The analysis unit can also use AI to analyze a combination of the material and condition of an object and perform an accurate identification. For example, the analysis unit can use AI to analyze a combination of the material and condition of an object and perform an accurate identification. This improves the identification accuracy by analyzing the material and condition of the object in detail. The material and condition include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit inputs a photograph of the object into a generating AI, which then analyzes the material and condition in detail to perform accurate identification.

[0089] The analysis unit can improve identification accuracy by considering the object's usage history and year of manufacture. For example, the analysis unit can use AI to analyze the object's usage history and improve identification accuracy. The analysis unit can also use AI to analyze the object's year of manufacture and improve identification accuracy. The analysis unit can also use AI to analyze the object's usage history and year of manufacture in combination and improve identification accuracy. For example, the analysis unit can use AI to analyze the object's usage history and year of manufacture in combination and improve identification accuracy. This improves identification accuracy by considering the object's usage history and year of manufacture. Usage history and year of manufacture include, but are not limited to, serial numbers and purchase history. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input data on the object's usage history and year of manufacture into a generating AI, which can then improve identification accuracy.

[0090] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying important analysis results. For example, if the user is relaxed, the analysis unit can prioritize displaying all analysis results equally. For example, if the user is relaxed, the analysis unit can prioritize displaying all analysis results equally. For example, if the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results. For example, if the user is in a hurry, the analysis unit can prioritize displaying the most important analysis results. In this way, the priority of analysis results can be determined according to the user's emotions. The priority of analysis results includes, but is not limited to, importance and urgency. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority of the analysis results.

[0091] The analysis unit can evaluate the market value of an object in real time and reflect it in the results. For example, the analysis unit can use AI to evaluate the market value of an object in real time and reflect it in the analysis results. For example, the analysis unit can use AI to evaluate the market value of an object in real time and reflect it in the analysis results. The analysis unit can also evaluate the market value of an object based on market data and reflect it in the analysis results. For example, the analysis unit can evaluate the market value of an object based on market data and reflect it in the analysis results. The analysis unit can also evaluate the market value of an object considering real-time market trends and reflect it in the analysis results. For example, the analysis unit can evaluate the market value of an object considering real-time market trends and reflect it in the analysis results. This allows the market value of an object to be evaluated in real time and reflected in the analysis results. Market value includes, but is not limited to, auction prices and resale values. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input market data of the target object into a generating AI, which can then evaluate its market value in real time and reflect this in the results.

[0092] The analysis unit can automatically acquire relevant information about the object (e.g., manufacturer, model number) to improve identification accuracy. For example, the analysis unit can use AI to automatically acquire manufacturer information about the object to improve identification accuracy. The analysis unit can also use AI to automatically acquire the model number of the object to improve identification accuracy. For example, the analysis unit can use AI to automatically acquire the model number of the object to improve identification accuracy. The analysis unit can also use AI to acquire a combination of manufacturer information and model number to improve identification accuracy. For example, the analysis unit can use AI to acquire a combination of manufacturer information and model number to improve identification accuracy. This improves identification accuracy by automatically acquiring relevant information about the object. Relevant information includes, but is not limited to, manufacturer and model number. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input manufacturer information and model number of the target object into the generating AI, which can then automatically acquire related information and improve identification accuracy.

[0093] The information acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated user emotions. For example, if the user is stressed, the information acquisition unit can prioritize acquiring important information. For example, if the user is relaxed, the information acquisition unit can acquire all information equally. For example, if the user is relaxed, the information acquisition unit can acquire all information equally. For example, if the user is in a hurry, the information acquisition unit can prioritize acquiring the most important information. For example, if the user is in a hurry, the information acquisition unit can prioritize acquiring the most important information. This allows the priority of information to be acquired to be determined according to the user's emotions. The priority of information to be acquired includes, but is not limited to, importance and urgency. Emotion estimation is implemented using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, for example, but is not limited to. Some or all of the above processing in the information acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority of the information to be acquired.

[0094] The acquisition unit can dynamically adjust fees and quantities considering market trends for the target object. For example, the acquisition unit can use AI to analyze market trends in real time and dynamically adjust fees. The acquisition unit can also use AI to dynamically adjust the optimal quantity based on market trends. The acquisition unit can also use AI to dynamically adjust fees and quantities simultaneously, taking market trends into consideration. Market trends include, but are not limited to, price trends and demand forecasts. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input market trend data into AI, which can dynamically adjust fees and quantities.

[0095] The acquisition unit can calculate the optimal fee by referring to the past transaction history of the target object. The acquisition unit can, for example, use AI to analyze the past transaction history of the target object and calculate the optimal fee. The acquisition unit can also calculate the optimal fee based on past transaction data. The acquisition unit can also dynamically calculate the optimal fee by considering the transaction history of the target object. The acquisition unit can dynamically calculate the optimal fee by considering the transaction history of the target object. This allows the optimal fee to be calculated by referring to the past transaction history of the target object. Past transaction history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input past transaction history data into AI, and the AI ​​can calculate the optimal fee.

[0096] The information acquisition unit can estimate the user's emotions and adjust the display method of the acquired information based on the estimated user emotions. For example, if the user is tense, the information acquisition unit can provide a simple and highly visible display method. For example, if the user is tense, the information acquisition unit can provide a simple and highly visible display method. For example, if the user is relaxed, the information acquisition unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the information acquisition unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the information acquisition unit can provide a display method that gets straight to the point. This allows the display method of acquired information to be adjusted according to the user's emotions. Display methods of information include, but are not limited to, graph displays and text displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's facial expression data into a generating AI, which can then estimate the user's emotions and adjust how the acquired information is displayed.

[0097] The acquisition unit can calculate the optimal fee by considering the user's geographical location information. For example, the acquisition unit can use AI to calculate the optimal fee by considering market trends in the user's current location. The acquisition unit can also use AI to calculate the optimal fee based on the user's geographical location information. The acquisition unit can also use AI to calculate the optimal fee by considering the economic conditions in the user's current location. This allows the acquisition unit to calculate the optimal fee by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's geographical location information into AI, which can then calculate the optimal fee.

[0098] The acquisition unit can suggest the optimal quantity by referring to the user's past transaction history. For example, the acquisition unit can use AI to analyze the user's past transaction history and suggest the optimal quantity. The acquisition unit can also suggest the optimal quantity based on past transaction data. For example, the acquisition unit can suggest the optimal quantity based on past transaction data. The acquisition unit can also dynamically suggest the optimal quantity by considering the user's transaction history. For example, the acquisition unit can dynamically suggest the optimal quantity by considering the user's transaction history. This allows the acquisition unit to suggest the optimal quantity by referring to the user's past transaction history. The optimal quantity includes, but is not limited to, demand forecasting and inventory management. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's past transaction history data into AI, and the AI ​​can suggest the optimal quantity.

[0099] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated emotions. For example, if the user is nervous, the guidance unit can provide guidance in a calm voice. For example, if the user is nervous, the guidance unit can provide guidance in a calm voice. For example, if the user is relaxed, the guidance unit can provide guidance in a cheerful voice. For example, if the user is relaxed, the guidance unit can provide guidance in a cheerful voice. For example, if the user is in a hurry, the guidance unit can provide guidance quickly and concisely. For example, if the user is in a hurry, the guidance unit can provide guidance quickly and concisely. In this way, the way the guidance is presented can be adjusted according to the user's emotions. The way the guidance is presented includes, but is not limited to, text, voice, and visuals. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and adjust the way the guidance is presented.

[0100] The guidance unit can propose the optimal disposal method considering the material and condition of the object. For example, the guidance unit can use AI to analyze the material of the object and propose the optimal disposal method. The guidance unit can also use AI to analyze the condition of the object and propose the optimal disposal method. The guidance unit can also use AI to analyze the material and condition of the object in combination and propose the optimal disposal method. This allows the guidance unit to propose the optimal disposal method considering the material and condition of the object. Materials and conditions include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input data on the material and condition of the object into the AI, which can then propose the optimal disposal method.

[0101] The guidance unit can suggest the optimal disposal method considering the size and weight of the object. For example, the guidance unit can use AI to analyze the size of the object and suggest the optimal disposal method. The guidance unit can also use AI to analyze the weight of the object and suggest the optimal disposal method. The guidance unit can also use AI to analyze the size and weight of the object in combination and suggest the optimal disposal method. This allows the guidance unit to suggest the optimal disposal method considering the size and weight of the object. Size and weight include, but are not limited to, dimensions and weight measurements. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input data on the size and weight of the object into the AI, which can then suggest the optimal disposal method.

[0102] The guidance unit can estimate the user's emotions and determine the priority of guidance based on the estimated emotions. For example, if the user is stressed, the guidance unit can prioritize important guidance. For example, if the user is stressed, the guidance unit can prioritize important guidance. For example, if the user is relaxed, the guidance unit can prioritize all guidance equally. For example, if the user is in a hurry, the guidance unit can prioritize the most important guidance. For example, if the user is in a hurry, the guidance unit can prioritize the most important guidance. In this way, the priority of guidance can be determined according to the user's emotions. Guidance priority includes, but is not limited to, importance and urgency. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority of guidance.

[0103] The guidance unit can suggest the optimal disposal method considering the user's geographical location information. For example, the guidance unit can use AI to obtain garbage collection information for the user's current location and suggest the optimal disposal method. The guidance unit can also use AI to suggest the optimal disposal method based on the user's geographical location information. The guidance unit can also use AI to suggest the optimal disposal method considering the rules of the user's current municipality. This allows the guidance unit to suggest the optimal disposal method considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or without AI. For example, the guidance unit can input the user's geographical location information into the AI, which can then suggest the optimal disposal method.

[0104] The guidance unit can suggest the optimal disposal method by referring to the user's past disposal history. The guidance unit can, for example, use AI to analyze the user's past disposal history and suggest the optimal disposal method. The guidance unit can also suggest the optimal disposal method based on past disposal data. The guidance unit can also dynamically suggest the optimal disposal method by considering the user's disposal history. The guidance unit can dynamically suggest the optimal disposal method by considering the user's disposal history. Past disposal history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the guidance unit may be performed using, for example, AI, or not using AI. For example, the guidance unit can input the user's past disposal history data into AI, and the AI ​​can suggest the optimal disposal method.

[0105] The listing function can estimate the user's emotions and adjust the way the listing is presented based on those emotions. For example, if the user is nervous, the listing function can provide a simple and highly visible listing. For example, if the user is nervous, the listing function can provide a simple and highly visible listing. For example, if the user is relaxed, the listing function can provide a listing that includes detailed information. For example, if the user is relaxed, the listing function can provide a listing that includes detailed information. For example, if the user is in a hurry, the listing function can provide a listing that gets straight to the point. For example, if the user is in a hurry, the listing function can provide a listing that gets straight to the point. This allows the listing to be presented according to the user's emotions. The presentation of the listing includes, but is not limited to, text, images, and videos. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the listing section may be performed using AI, for example, or without AI. For example, the listing section can input user facial expression data into a generating AI, which can then estimate the user's emotions and adjust the way the listing is presented.

[0106] The listing unit can evaluate the market value of an item in real time and set the optimal listing price. For example, the listing unit can use AI to evaluate the market value of an item in real time and set the optimal listing price. For example, the listing unit can use AI to evaluate the market value of an item in real time and set the optimal listing price. The listing unit can also evaluate the market value of an item and set the optimal listing price based on market data. For example, the listing unit can evaluate the market value of an item and set the optimal listing price based on market data. The listing unit can also evaluate the market value of an item and set the optimal listing price considering real-time market trends. For example, the listing unit can evaluate the market value of an item and set the optimal listing price considering real-time market trends. This allows for the setting of the optimal listing price by evaluating the market value of an item in real time. Market value includes, but is not limited to, auction prices and resale values. Some or all of the above processing in the listing unit may be performed using, for example, generative AI, or without generative AI. For example, the listing unit can input market data for the item into a generating AI, which can then evaluate its market value in real time and set the optimal listing price.

[0107] The listing unit can automatically acquire detailed information about the object (e.g., manufacturer, model number) and reflect it in the listing information. For example, the listing unit can use AI to automatically acquire the manufacturer information of the object and reflect it in the listing information. The listing unit can also use AI to automatically acquire the model number of the object and reflect it in the listing information. For example, the listing unit can use AI to automatically acquire the model number of the object and reflect it in the listing information. The listing unit can also use AI to acquire a combination of manufacturer information and model number of the object and reflect it in the listing information. For example, the listing unit can use AI to acquire a combination of manufacturer information and model number of the object and reflect it in the listing information. This allows for the automatic acquisition of detailed information about the object and its reflection in the listing information. Detailed information includes, but is not limited to, the manufacturer and model number. Some or all of the above processing in the listing unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the listing department can input the manufacturer information and model number of the item into a generating AI, which can then automatically retrieve the detailed information and reflect it in the listing information.

[0108] The listing unit can estimate the user's emotions and determine the priority of listings based on the estimated emotions. For example, if the user is stressed, the listing unit can prioritize important listings. For example, if the user is relaxed, the listing unit can prioritize all listings equally. For example, if the user is relaxed, the listing unit can prioritize all listings equally. For example, if the user is in a hurry, the listing unit can prioritize the most important listings. For example, if the user is in a hurry, the listing unit can prioritize the most important listings. This allows listing priorities to be determined according to the user's emotions. Listing priorities include, but are not limited to, importance and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the listing unit may be performed using, for example, AI, or not using AI. For example, the listing unit can input user facial expression data into a generating AI, which can then estimate the user's emotions and determine the priority of listings.

[0109] The listing unit can propose the optimal listing method by considering the user's geographical location information. For example, the listing unit can propose the optimal listing method by considering the market trends in the user's current location using AI. The listing unit can also propose the optimal listing method by considering the market trends in the user's current location using AI. For example, the listing unit can propose the optimal listing method by considering the economic conditions in the user's current location using AI. This allows the listing unit to propose the optimal listing method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the listing unit may be performed using, for example, AI, or without AI. For example, the listing unit can input the user's geographical location information into AI, and the AI ​​can propose the optimal listing method.

[0110] The listing unit can suggest the optimal listing method by referring to the user's past listing history. For example, the listing unit can use AI to analyze the user's past listing history and suggest the optimal listing method. The listing unit can also suggest the optimal listing method based on past listing data. For example, the listing unit can suggest the optimal listing method based on past listing data. The listing unit can also dynamically suggest the optimal listing method by considering the user's listing history. For example, the listing unit can dynamically suggest the optimal listing method by considering the user's listing history. This allows the optimal listing method to be suggested by referring to the user's past listing history. Past listing history includes, but is not limited to, databases and cloud storage. Some or all of the above processing in the listing unit may be performed using, for example, AI, or not using AI. For example, the listing unit can input the user's past listing history data into AI, and the AI ​​can suggest the optimal listing method.

[0111] The listing unit can suggest the optimal listing timing by referring to the user's calendar information. For example, the listing unit can use AI to refer to the user's calendar information and suggest the optimal listing timing. The listing unit can also use AI to consider the user's schedule and suggest the optimal listing timing. The listing unit can also use AI to dynamically suggest the optimal listing timing based on the user's calendar information. For example, the listing unit can use AI to dynamically suggest the optimal listing timing based on the user's calendar information. This allows the optimal listing timing to be suggested by referring to the user's calendar information. Some or all of the above processing in the listing unit may be performed using AI, or not using AI. For example, the listing unit can input the user's calendar information into AI, and AI can suggest the optimal listing timing.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The shooting unit can estimate the user's emotions and adjust the timing of shooting based on the estimated emotions. For example, if the user is stressed, the AI ​​can quickly take a picture, minimizing the user's effort. If the user is relaxed, the AI ​​can wait for the optimal angle and lighting conditions before taking a picture. Furthermore, if the user is in a hurry, the AI ​​can take a picture immediately, allowing the user to quickly move on to the next step. This allows for shooting at the optimal timing according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the shooting unit may be performed using, for example, AI, or not using AI. For example, the shooting unit can input user facial data into a generative AI, which can estimate the user's emotions and adjust the timing of shooting.

[0114] The camera unit can automatically take photos from multiple angles to improve analysis accuracy. For example, the AI ​​can automatically rotate the camera and take multiple photos from different angles. The AI ​​can also instruct the user to take photos from different angles and acquire multiple photos. Furthermore, the AI ​​can use a drone to take photos from multiple angles, including aerial shots. This improves analysis accuracy by taking photos from multiple angles. Multiple angles include, but are not limited to, 30 degrees, 45 degrees, 90 degrees, etc. Some or all of the above processing in the camera unit may be performed using, for example, the AI, or not. For example, the camera can be connected to the AI, and the AI ​​can automatically rotate the camera and take photos from multiple angles.

[0115] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. This allows the display method of the analysis results to be adjusted according to the user's emotions. Display methods of the analysis results include, but are not limited to, graph displays and text displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, user facial expression data can be input to a generative AI, which can estimate the user's emotions and adjust the display method of the analysis results.

[0116] The analysis unit can perform a detailed analysis of the material and condition of an object to perform more accurate identification. For example, AI can perform a detailed analysis of the material of an object to perform accurate identification. AI can also perform a detailed analysis of the condition of an object to perform accurate identification. Furthermore, AI can perform an analysis that combines the material and condition of an object to perform accurate identification. As a result, the accuracy of identification is improved by analyzing the material and condition of the object in detail. Materials and conditions include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, a photograph of an object can be input into a generating AI, which can then perform a detailed analysis of its material and condition to perform accurate identification.

[0117] The acquisition unit can estimate the user's emotions and determine the priority of information to acquire based on the estimated user emotions. For example, if the user is stressed, important information can be prioritized for acquisition. If the user is relaxed, all information can be acquired equally. Furthermore, if the user is in a hurry, the most important information can be prioritized for acquisition. This allows the priority of information to be acquired to be determined according to the user's emotions. The priority of information to be acquired includes, but is not limited to, importance and urgency. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, user facial expression data can be input to a generative AI, which can estimate the user's emotions and determine the priority of information to acquire.

[0118] The acquisition unit can dynamically adjust fees and quantities considering market trends for the target object. For example, AI can analyze market trends in real time and dynamically adjust fees. AI can also dynamically adjust the optimal quantity based on market trends. Furthermore, AI can dynamically adjust both fees and quantities simultaneously, taking market trends into consideration. This allows for dynamic adjustment of fees and quantities considering market trends for the target object. Market trends include, but are not limited to, price trends and demand forecasts. Some or all of the above-described processes in the acquisition unit may be performed using AI, for example, or without AI. For example, market trend data can be input into AI, which can then dynamically adjust fees and quantities.

[0119] The guidance unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated emotions. For example, if the user is nervous, the guidance can be given in a calm voice. If the user is relaxed, the guidance can be given in a cheerful voice. Furthermore, if the user is in a hurry, the guidance can be given quickly and concisely. In this way, the way the guidance is presented can be adjusted according to the user's emotions. The way the guidance is presented includes, but is not limited to, text, voice, and visuals. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the guidance unit may be performed using AI, or not using AI. For example, user facial expression data can be input to a generative AI, which can estimate the user's emotions and adjust the way the guidance is presented.

[0120] The guidance unit can suggest the optimal disposal method considering the material and condition of the object. For example, AI can analyze the material of the object and suggest the optimal disposal method. AI can also analyze the condition of the object and suggest the optimal disposal method. Furthermore, AI can analyze a combination of the material and condition of the object and suggest the optimal disposal method. This allows for the suggestion of the optimal disposal method considering the material and condition of the object. Materials and conditions include, but are not limited to, metals, plastics, and deteriorated states. Some or all of the above-described processes in the guidance unit may be performed using AI, or without AI. For example, data on the material and condition of the object can be input into the AI, which then suggests the optimal disposal method.

[0121] The listing section can estimate the user's emotions and adjust the way the listing is presented based on those emotions. For example, if the user is nervous, a simple and highly visible listing can be provided. If the user is relaxed, a listing with detailed information can be provided. Furthermore, if the user is in a hurry, a listing that gets straight to the point can be provided. This allows the listing to be presented according to the user's emotions. The listing presentation includes, but is not limited to, text, images, and videos. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to those examples. Some or all of the above processing in the listing section may be performed using AI, or not using AI. For example, user facial expression data can be input into a generative AI, which can estimate the user's emotions and adjust the way the listing is presented.

[0122] The listing unit can evaluate the market value of an item in real time and set the optimal listing price. For example, an AI can evaluate the market value of an item in real time and set the optimal listing price. It can also evaluate the market value of an item and set the optimal listing price based on market data. Furthermore, it can evaluate the market value of an item and set the optimal listing price considering real-time market trends. In this way, the optimal listing price can be set by evaluating the market value of an item in real time. Market value includes, but is not limited to, auction prices and resale values. Some or all of the above processing in the listing unit may be performed using, for example, a generating AI, or without a generating AI. For example, market data of an item can be input into a generating AI, which can evaluate the market value in real time and set the optimal listing price.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The photographer takes pictures of the bulky waste they want to dispose of. The photographer can take pictures of the waste using a smartphone or camera, for example. For example, the photographer can take pictures of old furniture or home appliances. The photographer can also take high-resolution photos using a digital camera. Furthermore, the photographer can take pictures using a smartphone camera app. Step 2: The analysis unit analyzes the photographs taken by the photography unit and identifies the type of waste. The analysis unit identifies the type of waste from the photograph using, for example, image recognition technology. For example, the analysis unit uses AI to analyze a photograph of furniture and identify that it is bulky waste. The analysis unit can also analyze the photograph using deep learning technology to identify the type of waste with high accuracy. Furthermore, the analysis unit can also analyze the photograph using computer vision technology to identify the type of waste. Step 3: The acquisition unit retrieves the quantity and fees based on the type of waste identified by the analysis unit. For example, the acquisition unit retrieves the quantity and fees in real time based on the identified type of waste. For example, the acquisition unit uses AI to calculate the quantity of waste and then calculates the fees. The acquisition unit can also retrieve regional fee information and then calculate the fees. Furthermore, the acquisition unit can calculate the weight and volume based on the identified type of waste and then calculate the fees. Step 4: The guidance unit will guide the user on how to dispose of the waste as non-burnable waste if the type of waste identified by the analysis unit is not bulky waste. For example, the guidance unit can use AI to determine the type of waste and, if it is not bulky waste, guide the user on how to dispose of it as non-burnable waste. For example, the guidance unit can analyze a photo of furniture and, if it determines that it is non-burnable waste, guide the user on how to dispose of it as non-burnable waste. The guidance unit can also obtain and guide the user on waste sorting methods specific to the region. Furthermore, the guidance unit can obtain and guide the user on collection days and collection locations. Step 5: The listing unit automatically puts items up for auction if the type of waste identified by the analysis unit is valuable. For example, the listing unit uses AI to determine the value of waste, and if it determines that it is valuable, it automatically lists it on an auction site. For example, the listing unit analyzes photos of furniture, and if it determines that it is valuable, it automatically lists it on an auction site. The listing unit can also use AI to smoothly guide the user through product descriptions and price setting. Furthermore, the listing unit can obtain information from auction sites and automatically perform the listing process.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the shooting unit, analysis unit, acquisition unit, guidance unit, and listing unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart device 14 to take a picture of the waste to be disposed of. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the picture and identify the type of waste. The acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12, which acquires the quantity and fees in real time based on the identified type of waste. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides guidance on how to dispose of waste as non-burnable waste if it is not bulky waste. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically lists valuable waste for auction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the shooting unit, analysis unit, acquisition unit, guidance unit, and listing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart glasses 214 to take a picture of the waste to be disposed of. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the picture and identify the type of waste. The acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12, which acquires the quantity and fees in real time based on the identified type of waste. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides guidance on how to dispose of waste as non-burnable waste if it is not bulky waste. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically lists valuable waste for auction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the shooting unit, analysis unit, acquisition unit, guidance unit, and listing unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the headset terminal 314 to take a picture of the waste to be disposed of. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the picture and identify the type of waste. The acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12, which acquires the quantity and fees in real time based on the identified type of waste. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides guidance on how to dispose of waste as non-burnable waste if it is not bulky waste. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically lists valuable waste for auction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the shooting unit, analysis unit, acquisition unit, guidance unit, and listing unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the robot 414 to take a picture of the waste to be disposed of. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the picture and identify the type of waste. The acquisition unit is implemented by the identification processing unit 290 of the data processing unit 12, which acquires the quantity and fees in real time based on the identified type of waste. The guidance unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides guidance on how to dispose of waste as non-burnable waste if it is not bulky waste. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically lists valuable waste for auction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The photography team takes pictures of bulky waste that people want to throw away, An analysis unit analyzes the photograph taken by the aforementioned imaging unit and identifies the type of waste, An acquisition unit that acquires the quantity and fee based on the type of waste identified by the analysis unit, A guide unit provides instructions on how to dispose of waste as non-combustible waste if the type of waste identified by the analysis unit is not bulky waste. The system includes an auction unit that automatically puts items up for auction if the type of waste identified by the analysis unit is valuable. A system characterized by the following features. (Note 2) The aforementioned imaging unit is Use your smartphone or camera to take a picture of the trash you want to throw away. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Identifying the type of waste from a photograph using image recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, Get real-time information on quantities and fees based on the identified waste type. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned guide section is This guide explains how to dispose of items that are not considered bulky waste as non-burnable waste. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned exhibit section is, Automatically put valuable junk up for auction. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is Automatically captures photos from multiple angles to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is Automatically removes the background and highlights only the subject. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of objects to photograph based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is The system automatically adjusts the optimal shooting settings, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is The system suggests the optimal shooting method by referring to the user's past shooting history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Detailed analysis of the object's material and condition enables more accurate identification. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Improve identification accuracy by considering the usage history and manufacturing year of the object. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The market value of the object is evaluated in real time and reflected in the results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Automatically acquires relevant information about the target object and improves identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The acquisition unit is, It estimates the user's emotions and determines the priority of information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The acquisition unit is, The commission and quantity are dynamically adjusted based on market trends for the target product. The system described in Appendix 1, characterized by the features described herein. (Note 21) The acquisition unit is, The optimal fee is calculated by referring to the past transaction history of the subject. The system described in Appendix 1, characterized by the features described herein. (Note 22) The acquisition unit is, It estimates the user's emotions and adjusts how the information obtained is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The acquisition unit is, The system calculates the optimal fee considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The acquisition unit is, We suggest the optimal quantity by referring to the user's past transaction history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned guide section is The system estimates the user's emotions and adjusts the way guidance is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned guide section is We will propose the most suitable disposal method considering the material and condition of the object. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned guide section is We will suggest the most suitable disposal method considering the size and weight of the object. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned guide section is The system estimates the user's emotions and determines the priority of guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned guide section is We suggest the optimal disposal method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned guide section is It suggests the optimal disposal method by referring to the user's past disposal history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned exhibit section is, The system estimates the user's emotions and adjusts the way the listing is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned exhibit section is, The system evaluates the market value of the item in real time and sets the optimal listing price. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned exhibit section is, The system automatically retrieves detailed information about the item and reflects it in the listing information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned exhibit section is, It estimates user sentiment and determines the priority of listings based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned exhibit section is, We suggest the optimal listing method considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned exhibit section is, We suggest the optimal listing method by referring to the user's past listing history. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned exhibit section is, We suggest the optimal listing time by referencing the user's calendar information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 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. The photography team takes pictures of bulky waste that people want to throw away, An analysis unit analyzes the photograph taken by the aforementioned imaging unit and identifies the type of waste, An acquisition unit that acquires the quantity and fee based on the type of waste identified by the analysis unit, A guide unit provides instructions on how to dispose of waste as non-combustible waste if the type of waste identified by the analysis unit is not bulky waste. The system includes an auction unit that automatically puts items up for auction if the type of waste identified by the analysis unit is valuable. A system characterized by the following features.

2. The aforementioned imaging unit is Use your smartphone or camera to take a picture of the trash you want to throw away. The system according to feature 1.

3. The aforementioned analysis unit, Identifying the type of waste from a photograph using image recognition technology The system according to feature 1.

4. The acquisition unit is, Get real-time information on quantities and fees based on the identified waste type. The system according to feature 1.

5. The aforementioned guide section is This guide explains how to dispose of items that are not considered bulky waste as non-burnable waste. The system according to feature 1.

6. The aforementioned exhibit section is, Automatically put valuable junk up for auction. The system according to feature 1.

7. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

8. The aforementioned imaging unit is Automatically captures photos from multiple angles to improve analysis accuracy. The system according to feature 1.

Images