system

Abstract

We provide the system. [Solution] A means of identifying waste that has been put in using image recognition technology, A means for automatically sorting identified waste into appropriate containers, A means of reading wireless identification tags attached to waste, A means for transmitting waste management data to a central computer system, A means of analyzing the transmitted data and generating suggestions for product improvement, A means of managing points based on the user's waste disposal activity, A system that includes this.

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, 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 as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Waste management in households is complicated by the sorting process, and the mixing of incompatible waste causes a decrease in recycling efficiency. Also, there is a lack of a feedback mechanism to convey to companies the need to improve the recyclability of products, making continuous improvement towards reducing environmental impact difficult. Furthermore, the lack of incentives for consumers to actively participate in recycling is also a factor hindering the solution of this problem. 【Means for Solving the Problems】 【0005】 This invention provides a system comprising a device that automatically identifies and classifies waste using image recognition technology, and a device that reads wireless identification tags attached to the waste. The system transmits the collected waste management data to a central computer system, where it generates product improvement suggestions based on the analyzed data. Furthermore, it encourages consumer recycling behavior and promotes involvement in waste management by enabling redemption or verification through a point management function based on the user's waste disposal. This makes it possible to reduce the environmental impact through efficient waste management and product improvement in conjunction with it. 【0006】 "Image recognition technology" is a technology that analyzes images captured by cameras or sensors to identify specific objects. 【0007】 A "wireless identification tag" is a small device that uses a technology called RFID to wirelessly transmit and receive information about an object. 【0008】 A "central computing system" is a centralized computer system used to analyze collected data and generate, manage, and provide necessary information. 【0009】 "Product improvement suggestions" are pieces of information that, based on analyzed data, recommend changes to the manufacturer to improve the recycling efficiency of their products and reduce their environmental impact. 【0010】 The "point management function" is a system function that calculates, awards, and manages points based on user actions, and allows users to check and exchange those points. 【0011】 An "automatic identification and classification device" is a device that uses sensors and AI technology to classify the waste that is fed into it and to process it appropriately. [Brief explanation of the drawing] 【0012】 [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0013】 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. 【0014】 First, the terms used in the following description will be explained. 【0015】 In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like. 【0016】 In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0017】 In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0018】 In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc. 【0019】 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0020】 [First Embodiment] 【0021】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0022】 As shown in Figure 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. 【0023】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0024】 The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0025】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0026】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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. 【0027】 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. 【0028】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0029】 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. 【0030】 The 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. 【0031】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0032】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0033】 This invention is a waste management system that utilizes AI and RFID technologies to enable efficient waste sorting and promote recycling in homes and commercial facilities. The embodiments are described in detail below. 【0034】 First, the smart trash cans, which are installed as terminals, are equipped with multiple sensors and cameras that automatically photograph waste when it is placed inside, generating image data. AI-based image recognition technology analyzes this data to identify the type of waste. For example, if a user puts in a plastic bottle, the terminal immediately classifies it as plastic. Furthermore, an RFID reader built into the terminal reads the RFID tags attached to the waste and collects lifecycle data. 【0035】 Next, the terminal packages this data and transmits it to the server via wireless communication. The server analyzes the received data and calculates waste recycling trends and recycling rates for specific products based on it. Furthermore, the server automatically generates improvement suggestions as feedback to the company, encouraging them to improve recyclability. 【0036】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The system uses a server to tally points, allowing users to visually track their progress. These points can be exchanged for services and goods offered by partner communities and businesses, thereby encouraging more active recycling efforts from users. 【0037】 As a concrete example, consider a household use scenario. When a user places everyday plastic containers and paper waste into a smart trash can, the device not only accurately sorts them, but the collected data is also analyzed on a server, and suggestions for improving the products that make up the majority of the waste are sent to the manufacturers. This entire process is automated, allowing users to contribute to efficient recycling simply by putting in their waste. In this way, the invention contributes to more efficient waste management and the realization of a sustainable society. 【0038】 The following describes the processing flow. 【0039】 Step 1: 【0040】 The device uses sensors to detect when a user places trash into the smart trash can. After detection, the camera automatically activates and takes a picture of the trash that has been placed inside. 【0041】 Step 2: 【0042】 AI image recognition technology, running inside the device, analyzes the captured image data. This analysis identifies the material and type of waste (e.g., plastic, metal, paper, etc.). 【0043】 Step 3: 【0044】 The terminal uses an RFID reader to read the RFID tags attached to the waste that is thrown in. The lifecycle data obtained through this process is used to track the waste. 【0045】 Step 4: 【0046】 Identified waste is automatically sorted into the appropriate container by a selection mechanism within the terminal. This sorting is based on the identification results. 【0047】 Step 5: 【0048】 The terminal generates a data package containing information about the identified waste and data obtained from the RFID, and sends it to the server. 【0049】 Step 6: 【0050】 The server analyzes the received data package and creates statistical information on the collected waste data. This process generates data for recycling rates for each product and for suggesting improvements. 【0051】 Step 7: 【0052】 Based on the analysis results, the server automatically generates product improvement suggestions for manufacturing companies. These suggestions aim to reduce environmental impact and improve recycling efficiency. 【0053】 Step 8: 【0054】 The server adds eco-points to the user's account. Points are calculated based on the user's recycling activities. The server also updates information so that users can check and use their points through a dedicated app. 【0055】 (Example 1) 【0056】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0057】 In modern society, proper waste management and recycling are crucial challenges in environmental protection. However, waste sorting operations in homes and businesses are not always efficient, limiting the progress of recycling. In addition, there is a lack of methods to utilize waste information for product improvement, hindering the realization of a sustainable society. 【0058】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0059】 In this invention, the server includes means for identifying waste using image recognition technology, means for reading wireless identification tags attached to the waste, and means for analyzing data to evaluate recycling trends. This enables efficient identification and classification of waste, automatic generation of feedback for product improvement, and promotion of proactive recycling activities by users. 【0060】 "Image recognition technology" is a technology that uses computer vision algorithms to analyze visual information from digital images or videos and automatically identify objects and shapes. 【0061】 "Identified waste" refers to waste whose properties and materials have been identified using image recognition technology or wireless identification technology. 【0062】 A "wireless identification tag" is a small electronic device that uses radio waves to read information about an object without contact and track it. 【0063】 A "central computing system" is a central information processing device used to receive, analyze, and manage various types of data via a network. 【0064】 "Eco-points" are points that are quantified based on an evaluation of a user's recycling activities and environmentally conscious behavior, and are usually provided in the form of exchangeable services or goods. 【0065】 To implement this invention, a waste management system utilizing AI technology and wireless identification technology is required. The smart trash can, which is installed as a terminal, is equipped with multiple sensors and cameras for identifying waste. As a result, waste placed in by the user is immediately photographed, and image data is generated. This image data is analyzed by a terminal running image recognition software using a generation AI model, and the type of waste is identified. 【0066】 For example, when a user discards a plastic bottle, the terminal instantly classifies it as plastic. Additionally, an RFID reader built into the terminal reads the wireless identification tag attached to the waste, collecting product information and lifecycle data. This data is compiled into a single package and transmitted to a server via wireless communication. 【0067】 The server uses advanced data analysis software to analyze the received data. This analysis includes evaluating recycling trends and generating improvement suggestions for specific products. The generated suggestions are automatically fed back to manufacturers to encourage improvements in recyclability. 【0068】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The server visualizes the accumulated points, making them easy for users to track. Eco-points can be exchanged for goods and services offered by partner communities and companies, providing users with an incentive to actively engage in environmentally conscious behavior. 【0069】 As a concrete example, a possible prompt to be input into a generating AI model might be: "Please describe in detail how to implement a waste management system using AI and wireless identification technology. Please also specify what hardware and software will be used." 【0070】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0071】 Step 1: 【0072】 The device detects the placement of waste. When a user places waste into the smart trash can, the sensor detects this action and triggers the generation of image data using the placement signal. In this case, the input is the waste placed by the user, and the output is an image of the waste. 【0073】 Step 2: 【0074】 Based on image data generated by the device, an AI model is used to identify waste. Image recognition technology is utilized to determine the material and category of the captured waste from the image. The input is image data of the captured waste, and the output is data regarding the identified type of waste. 【0075】 Step 3: 【0076】 The terminal reads the wireless identification tag attached to the waste and obtains lifecycle information. An RFID reader is used to collect information contactlessly from the tag attached to the waste. The input is the signal from the RFID tag, and the output is the individual waste information. 【0077】 Step 4: 【0078】 The terminal packages the data it collects and sends it to the server. Image analysis results and information obtained from RFID tags are integrated and formatted into a single data package. The input is identified waste type data and lifecycle information, and the output is the data package sent to the server. 【0079】 Step 5: 【0080】 The server analyzes the received data packages to generate recycling trends and product improvement suggestions. Using data analysis software, it calculates recycling rates and trends by comparing them with past data, and creates feedback for the product. The input is the data package sent from the terminal, and the output is the analysis results and improvement suggestions. 【0081】 Step 6: 【0082】 The server provides feedback on the analysis results to relevant parties and compiles user eco-points. Improvement suggestions are sent via email and the company portal, and eco-points are updated in the database. The inputs are the analysis results and user recycling activity data, and the outputs are the submitted feedback and updated eco-points. 【0083】 Step 7: 【0084】 Users check their eco-points using a dedicated application and select redeemable rewards. The application displays data from the server and presents options based on the points. The input is the eco-point data provided by the server, and the output is the reward selected by the user. 【0085】 (Application Example 1) 【0086】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0087】 While waste management in homes and cities is crucial from an environmental protection standpoint, there is a lack of systems that allow users to accurately and efficiently classify waste and receive rewards for their efforts. Furthermore, there is a need for methods that utilize waste lifecycle information to drive product improvement. 【0088】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0089】 In this invention, the server includes means for identifying waste using image recognition means, means for managing rewards based on the user's waste disposal actions, and means for outputting and displaying waste classification information to the user via a virtual environment. This allows the user to easily classify waste and convert the received rewards into other forms of value. Furthermore, it enables the feedback of waste information that can lead to product improvements. 【0090】 "Image recognition means" refers to a technology that uses cameras and sensors to recognize the waste being introduced and to identify its type. 【0091】 A "storage device" is equipment used to properly store identified waste materials by type. 【0092】 "Wireless identification information" refers to data read through tags attached to waste, and includes information about the waste's lifecycle. 【0093】 A "centralized information processing system" is a computer system that analyzes collected waste management information and generates suggestions for product improvement. 【0094】 "Rewards" refer to points or other incentives that users receive when they properly dispose of waste. 【0095】 A "virtual environment" is a digital platform that displays waste classification information in a way that allows users to intuitively understand it. 【0096】 The system implementing this invention operates around three main components: a terminal, a server, and a user. 【0097】 The terminal is equipped with image sensors, such as a camera, to recognize the waste being deposited, and uses a Python program to classify the waste using machine learning libraries such as TENSORFLOW®. Furthermore, the terminal has a built-in RFID reader to acquire wireless identification information and collect lifecycle data. The waste information collected by the terminal is transmitted wirelessly to a centralized information processing system (server). 【0098】 The server analyzes received data, generates product improvement suggestions using AI models, and provides feedback to users through a digital platform. It also operates a reward system based on users' waste disposal behavior. This server utilizes cloud computing technology to process large amounts of data quickly. Through this system, users can actively participate in waste management by reviewing waste classification results and receiving rewards. 【0099】 One concrete example is a scenario where a user takes a picture of waste to be disposed of using their smartphone and receives the classification result in real time. When a user puts in a plastic bottle, the device photographs it, recognizes it as "plastic," and visually presents the optimal disposal method in a virtual environment. An example of a prompt message might be, "Your first piece of waste disposed of today has been correctly classified. Next, share this digital feedback with your friends and let's all participate. Small daily eco-activities can make a big difference." 【0100】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0101】 Step 1: 【0102】 When a user places waste into the terminal, the terminal uses its camera sensor to capture an image of the waste. The input is the physical state of the waste, and the output is its image data. The terminal then supplies this data to an AI image recognition model. 【0103】 Step 2: 【0104】 The device uses TensorFlow to generate an AI model that analyzes images of waste and identifies its category. In this process, the input is the image data obtained in step 1, and the output is the category information of the identified waste. 【0105】 Step 3: 【0106】 The RFID reader built into the terminal reads the wireless identification information attached to the waste. The input is the RFID tag information attached to the waste, and the output is the waste's lifecycle data. 【0107】 Step 4: 【0108】 The terminal integrates the image recognition results and information obtained via RFID, packages them, and transmits them to a centralized information processing unit (server). The input is the recognition results and lifecycle data, and the output is the integrated data transmitted to the server. 【0109】 Step 5: 【0110】 The server analyzes the received data and generates environmental impact assessments and product improvement suggestions. Input is data from the terminal, and output is the assessment results and suggestions. 【0111】 Step 6: 【0112】 The server calculates rewards based on the user's waste disposal actions and reflects the reward information in the user's virtual environment. The input is the user's activity data, and the output is the updated reward information. 【0113】 Step 7: 【0114】 The system receives updated information from the user's smartphone or smart glasses and visually displays the waste classification results and reward information in a virtual environment. The input is updated information from the server, and the output is the display to the user. 【0115】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0116】 This invention combines an emotion engine with a waste management system to recognize user emotions and more effectively promote recycling activities. This system integrates AI-based image recognition, RFID technology, and emotion recognition technology. The following describes embodiments of the present invention in detail. 【0117】 First, the smart trash can itself is equipped with an image recognition camera and an emotion engine. When a user puts in trash, the device takes an image, and the AI ​​identifies the type of waste. Simultaneously, the built-in RFID reader reads the wireless identification tag attached to the waste. This data is integrated and stored as accurate waste management information. 【0118】 Furthermore, the emotion engine analyzes the user's facial expressions and actions while disposing of waste, recognizing the user's emotions. For example, if a user disposes of trash with a smile, the emotion engine incorporates positive feedback into the system. 【0119】 The terminal transmits all this data to a central computing system (server) in real time. The server analyzes the received data and generates feedback and product improvement suggestions for the user based on the identified waste and sentiment data. For example, to reinforce the user's positive sentiment, the server adds eco-points for successful recycling activities and provides messages of praise through the application. 【0120】 As a concrete example, consider a scenario where a user recycles at home. Suppose the user places a plastic bottle into a smart trash can and smiles. At this point, the device correctly sorts the bottle, and the emotion engine detects a positive emotion. The server processes the data, increasing the user's eco-points, and simultaneously sends a message to the smartphone app saying, "Great recycling! Keep it up!" This motivates the user to engage in further recycling activities. 【0121】 In this way, the embodiment of the present invention can simultaneously achieve rationalization of waste management and improvement of user motivation, thereby contributing to the realization of a more sustainable society. 【0122】 The following describes the processing flow. 【0123】 Step 1: 【0124】 The user places their trash into the smart trash can. A sensor detects this and confirms that the waste has been placed inside. 【0125】 Step 2: 【0126】 The device's camera automatically activates and takes a picture of the waste. This image is immediately analyzed by the device's image recognition AI, which identifies the type of waste. For example, it might be identified as a plastic bottle. 【0127】 Step 3: 【0128】 The terminal uses a wireless identification tag reader to read the RFID tags attached to the waste. This allows the product's lifecycle data to be obtained. 【0129】 Step 4: 【0130】 The emotion engine built into the device detects the user's face and analyzes their emotions. For example, it can detect if the user is smiling. 【0131】 Step 5: 【0132】 The terminal integrates identified waste information, RFID data, and emotional data to generate a data package, which is then sent to the server. This data package includes waste type, emotional state, and lifecycle information. 【0133】 Step 6: 【0134】 The server analyzes the received data. Based on the waste recycling rate and the user's emotional tendencies, it generates personalized feedback. 【0135】 Step 7: 【0136】 The server uses emotion data to award eco-points to users with positive emotions. This information is then reflected in the user's application. 【0137】 Step 8: 【0138】 Users can use a smartphone application to check their eco-points and view feedback messages they have received. This can motivate them to engage in further recycling activities. 【0139】 (Example 2) 【0140】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0141】 Conventional waste management systems have challenges in accurately identifying and managing waste, and lack sufficient motivation to improve users' recycling awareness. This hinders the promotion of recycling activities that reduce environmental impact. 【0142】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0143】 In this invention, the server includes means for classifying input waste using visual recognition technology, means for analyzing acquired waste information and user sentiment information to generate integrated data, and means for calculating and providing rewards based on the user's waste disposal behavior through an application. This promotes accurate waste management and active user participation, enabling a reduction in environmental impact and the realization of sustainable recycling activities. 【0144】 "Visual recognition technology" refers to technologies used to identify and classify specific objects or patterns from image data. 【0145】 "Wireless identification technology" is a technology that uses radio waves to acquire identification information attached to an object without physical contact. 【0146】 "Waste" is a general term for substances, products, and materials that are no longer needed and are disposed of. 【0147】 "Emotional information" refers to data that indicates an emotional state, analyzed from a person's facial expressions and behavior. 【0148】 "Integrated data" refers to data that combines different types of data into a single, unified dataset. 【0149】 A "processing device" is a computer or system used to receive, analyze, and process data. 【0150】 "Feedback" refers to information that conveys reactions and evaluations of user actions. 【0151】 "Behavioral improvement suggestions" are specific proposals and advice for improving user behavior. 【0152】 A "reward" is a point or perk that provides an incentive for a particular action. 【0153】 This invention is an integrated system for enhancing waste management and promoting user recycling activities. This system integrates image recognition technology, wireless identification technology, and emotion recognition technology to enable a series of waste management and user feedback processes. Embodiments of this invention will be described in detail below. 【0154】 First, the smart trash can, which is the terminal itself, is equipped with an image recognition camera, an RFID reader, and an emotion engine. When a user puts in trash, the terminal takes an image of it with its built-in camera. The captured image is analyzed using AI technology to identify the type of waste. Software such as TensorFlow and OpenCV is used for this process. In addition, the RFID tag attached to the waste is automatically read by the RFID reader, and detailed information about the waste is obtained. This information includes product ID and material information. 【0155】 Furthermore, the device's built-in emotion engine captures the user's facial expressions and analyzes their emotional state. For example, using the emotion recognition API and SDK provided by Microsoft®, if the system detects that the user is smiling, it is recorded as a positive emotion. 【0156】 This acquired data is sent in real time to a server, which acts as a processing unit. The server analyzes the integrated data and generates feedback on the user's recycling activities. Using a generative AI model, action plans and reward messages can be created. For example, the server calculates recycling points and provides them as rewards through the application for successful recycling activities. 【0157】 As a concrete example, consider a scenario where a user throws a plastic bottle into a smart trash can at home. If the user places the bottle in with a smile, the device classifies the bottle using image recognition, and the emotion engine records the positive emotion. The server processes this data, and an eco-point is displayed in the app along with a message such as, "Great recycling! Keep it up!" 【0158】 An example of a prompt would be: "When a user throws a plastic bottle into a smart trash can and smiles, what kind of feedback should be provided to increase their motivation for recycling?" This prompt would then be input into the generating AI model as text. 【0159】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0160】 Step 1: 【0161】 The device captures an image with its built-in camera when the user places waste into it. This image data is input to an AI image recognition module. The image recognition algorithm (e.g., TensorFlow or OpenCV) identifies the object from the image and outputs the type of waste (e.g., plastic, metal, paper). Through this process, the waste is visually classified. 【0162】 Step 2: 【0163】 The terminal uses an RFID reader to read the RFID tags attached to the waste. This tag information is entered into an identification database, and detailed information about the waste (e.g., product ID, material information) is output. The RFID data is integrated with image identification results to enhance waste management data. 【0164】 Step 3: 【0165】 The device captures the user's face with its camera and inputs the image into the emotion recognition engine. The emotion recognition system (e.g., emotion recognition API or SDK) analyzes the user's facial expressions and outputs an emotional state (e.g., positive, negative). This information is recorded as the user's emotion data. 【0166】 Step 4: 【0167】 The device transmits integrated waste management data and sentiment data to the server in real time. The server analyzes the received data as input and uses a generative AI model to determine the content of the feedback to the user. The analyzed data outputs action plans for feedback and messages of praise. 【0168】 Step 5: 【0169】 Based on the data analysis results, the server sends feedback to the user's smartphone app. The message includes incentives such as points added to the user's account and a message of appreciation. This feedback can help users increase their motivation for recycling. 【0170】 (Application Example 2) 【0171】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0172】 Waste management in modern cities is crucial from an environmental protection standpoint, but maintaining and improving residents' motivation for recycling remains a challenge. While conventional systems allow for proper waste classification and processing, they lack mechanisms to provide feedback that fully considers user emotions and motivations. Therefore, a sustainable approach is needed to prevent recycling activities from being merely a one-off effort. 【0173】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0174】 In this invention, the server includes means for identifying the waste being submitted using image recognition technology, means for analyzing the user's emotional state and providing positive feedback, and means for managing points based on the user's waste disposal activities. This makes it possible to provide positive feedback on the user's actions, in addition to the proper classification and disposal of waste, thereby sustainably promoting recycling activities among individual residents. 【0175】 "Image recognition technology" is a technology used to automatically identify the type of waste that has been put into the system. 【0176】 A "wireless identification tag" is a device that electronically stores detailed information about waste and transmits that information via wireless communication. 【0177】 A "central computing system" is a computer system for centrally collecting and analyzing waste data. 【0178】 "Methods for analyzing the user's emotional state" refers to technology that analyzes the user's facial expressions and actions when disposing of waste and recognizes their emotions. 【0179】 "Means of providing positive feedback" refers to a function that sends users messages of praise and encouragement based on sentiment analysis results in order to increase their motivation for recycling activities. 【0180】 "A means of managing points based on users' waste disposal activities" refers to a system that awards eco-points to users in accordance with their recycling activities and records and manages their history. 【0181】 This invention is a system designed to streamline waste management in smart cities and promote recycling activities among residents. The system utilizes smart trash cans as terminals and integrates image recognition technology, wireless identification tags, and sentiment analysis technology. 【0182】 First, a camera on the terminal photographs the waste submitted by the user, and image recognition technology is used to identify its type. Furthermore, the terminal reads a wireless identification tag to obtain detailed information about the waste and transmits it to the central computer system. 【0183】 This central computing system utilizes image processing libraries such as OpenCV to analyze acquired data. Using emotion analysis technology, it analyzes the user's facial expressions and actions when disposing of waste, recognizing the user's emotional state. Based on the user's emotions, it generates positive feedback and notifies the user via communication technology. 【0184】 As an example, if the system detects a smile when a user places a plastic bottle into a smart trash can, the central computer system recognizes this positive emotion and sends a message to the smartphone application saying, "Great recycling! Keep it up!" This feedback is expected to further encourage the user's recycling activities. 【0185】 A concrete example of a prompt statement is, "In Tokyo's smart city, install smart trash cans for waste management and utilize emotion recognition technology to promote citizens' recycling activities." In this way, the present invention combines technology and emotion analysis to enhance residents' motivation and realize sustainable waste management. 【0186】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0187】 Step 1: 【0188】 When a user places waste into the terminal, it takes a picture of the waste with its camera. The captured image is acquired by the terminal. The input at this time is the physical waste, and the output is digital image data. The terminal analyzes this data using image recognition technology to identify the type of waste. For example, it classifies it into categories such as plastic bottles, paper, and metal. 【0189】 Step 2: 【0190】 The terminal reads the wireless identification tag attached to the waste using a reader. The information obtained from the tag is the input, and detailed information about the waste is acquired using the RFID reader. This information is matched with the identified waste type and stored as waste management data. The output is organized waste management data. 【0191】 Step 3: 【0192】 The device uses captured images of the user to analyze their facial expressions using emotion analysis technology. The input is an image of the user's face. The output generates emotional states such as smiles or unhappiness. For example, a smile would be classified as "positive." The specific operation of emotion analysis involves extracting facial features as numerical data and classifying them using a generative AI model. 【0193】 Step 4: 【0194】 The terminal transmits identified waste data and sentiment analysis data to the central computer system in real time. The input consists of waste type data and user sentiment data, while the output is the central computer system receiving the data. The terminal uses a communication module to perform the specific operation of transmitting data to the central system. 【0195】 Step 5: 【0196】 The server analyzes the received data in a central computing system. The input consists of waste data and sentiment data. The server uses a generative AI model to analyze the input data and evaluate the user's recycling activities. The output includes positive feedback messages and calculated eco-points. 【0197】 Step 6: 【0198】 The server generates and notifies the user of positive feedback based on the analysis results. The input is the analyzed evaluation data, and the output is a message displayed on the user's smartphone or PC. For example, a message such as "Great recycling! Keep it up!" is generated. The server then uses a notification system to send the feedback to the user. 【0199】 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. 【0200】 Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0201】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0202】 [Second Embodiment] 【0203】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0204】 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. 【0205】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0206】 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. 【0207】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0208】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0209】 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. 【0210】 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 using the processor 28. The storage 32 stores the specific processing program 56. 【0211】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0212】 The 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. 【0213】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0214】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0215】 This invention is a waste management system that utilizes AI and RFID technologies to enable efficient waste sorting and promote recycling in homes and commercial facilities. The embodiments are described in detail below. 【0216】 First, the smart trash cans, which are installed as terminals, are equipped with multiple sensors and cameras that automatically photograph waste when it is placed inside, generating image data. AI-based image recognition technology analyzes this data to identify the type of waste. For example, if a user puts in a plastic bottle, the terminal immediately classifies it as plastic. Furthermore, an RFID reader built into the terminal reads the RFID tags attached to the waste and collects lifecycle data. 【0217】 Next, the terminal packages this data and transmits it to the server via wireless communication. The server analyzes the received data and calculates waste recycling trends and recycling rates for specific products based on it. Furthermore, the server automatically generates improvement suggestions as feedback to the company, encouraging them to improve recyclability. 【0218】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The system uses a server to tally points, allowing users to visually track their progress. These points can be exchanged for services and goods offered by partner communities and businesses, thereby encouraging more active recycling efforts from users. 【0219】 As a concrete example, consider a household use scenario. When a user places everyday plastic containers and paper waste into a smart trash can, the device not only accurately sorts them, but the collected data is also analyzed on a server, and suggestions for improving the products that make up the majority of the waste are sent to the manufacturers. This entire process is automated, allowing users to contribute to efficient recycling simply by putting in their waste. In this way, the invention contributes to more efficient waste management and the realization of a sustainable society. 【0220】 The following describes the processing flow. 【0221】 Step 1: 【0222】 The device uses sensors to detect when a user places trash into the smart trash can. After detection, the camera automatically activates and takes a picture of the trash that has been placed inside. 【0223】 Step 2: 【0224】 AI image recognition technology, running inside the device, analyzes the captured image data. This analysis identifies the material and type of waste (e.g., plastic, metal, paper, etc.). 【0225】 Step 3: 【0226】 The terminal uses an RFID reader to read the RFID tags attached to the waste that is thrown in. The lifecycle data obtained through this process is used to track the waste. 【0227】 Step 4: 【0228】 Identified waste is automatically sorted into the appropriate container by a selection mechanism within the terminal. This sorting is based on the identification results. 【0229】 Step 5: 【0230】 The terminal generates a data package containing information about the identified waste and data obtained from the RFID, and sends it to the server. 【0231】 Step 6: 【0232】 The server analyzes the received data package and creates statistical information on the collected waste data. This process generates data for recycling rates for each product and for suggesting improvements. 【0233】 Step 7: 【0234】 Based on the analysis results, the server automatically generates product improvement suggestions for manufacturing companies. These suggestions aim to reduce environmental impact and improve recycling efficiency. 【0235】 Step 8: 【0236】 The server adds eco-points to the user's account. Points are calculated based on the user's recycling activities. The server also updates information so that users can check and use their points through a dedicated app. 【0237】 (Example 1) 【0238】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0239】 In modern society, proper waste management and recycling are crucial challenges in environmental protection. However, waste sorting operations in homes and businesses are not always efficient, limiting the progress of recycling. In addition, there is a lack of methods to utilize waste information for product improvement, hindering the realization of a sustainable society. 【0240】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0241】 In this invention, the server includes means for identifying waste using image recognition technology, means for reading wireless identification tags attached to the waste, and means for analyzing data to evaluate recycling trends. This enables efficient identification and classification of waste, automatic generation of feedback for product improvement, and promotion of proactive recycling activities by users. 【0242】 "Image recognition technology" is a technology that uses computer vision algorithms to analyze visual information from digital images or videos and automatically identify objects and shapes. 【0243】 "Identified waste" refers to waste whose properties and materials have been identified using image recognition technology or wireless identification technology. 【0244】 A "wireless identification tag" is a small electronic device that uses radio waves to read information about an object without contact and track it. 【0245】 A "central computing system" is a central information processing device used to receive, analyze, and manage various types of data via a network. 【0246】 "Eco-points" are points that are quantified based on an evaluation of a user's recycling activities and environmentally conscious behavior, and are usually provided in the form of exchangeable services or goods. 【0247】 To implement this invention, a waste management system utilizing AI technology and wireless identification technology is required. The smart trash can, which is installed as a terminal, is equipped with multiple sensors and cameras for identifying waste. As a result, waste placed in by the user is immediately photographed, and image data is generated. This image data is analyzed by a terminal running image recognition software using a generation AI model, and the type of waste is identified. 【0248】 For example, when a user discards a plastic bottle, the terminal instantly classifies it as plastic. Additionally, an RFID reader built into the terminal reads the wireless identification tag attached to the waste, collecting product information and lifecycle data. This data is compiled into a single package and transmitted to a server via wireless communication. 【0249】 The server uses advanced data analysis software to analyze the received data. This analysis includes evaluating recycling trends and generating improvement suggestions for specific products. The generated suggestions are automatically fed back to manufacturers to encourage improvements in recyclability. 【0250】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The server visualizes the accumulated points, making them easy for users to track. Eco-points can be exchanged for goods and services offered by partner communities and companies, providing users with an incentive to actively engage in environmentally conscious behavior. 【0251】 As a concrete example, a possible prompt to be input into a generating AI model might be: "Please describe in detail how to implement a waste management system using AI and wireless identification technology. Please also specify what hardware and software will be used." 【0252】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0253】 Step 1: 【0254】 The device detects the placement of waste. When a user places waste into the smart trash can, the sensor detects this action and triggers the generation of image data using the placement signal. In this case, the input is the waste placed by the user, and the output is an image of the waste. 【0255】 Step 2: 【0256】 Based on image data generated by the device, an AI model is used to identify waste. Image recognition technology is utilized to determine the material and category of the captured waste from the image. The input is image data of the captured waste, and the output is data regarding the identified type of waste. 【0257】 Step 3: 【0258】 The terminal reads the wireless identification tag attached to the waste and obtains lifecycle information. An RFID reader is used to collect information contactlessly from the tag attached to the waste. The input is the signal from the RFID tag, and the output is the individual waste information. 【0259】 Step 4: 【0260】 The terminal packages the data it collects and sends it to the server. Image analysis results and information obtained from RFID tags are integrated and formatted into a single data package. The input is identified waste type data and lifecycle information, and the output is the data package sent to the server. 【0261】 Step 5: 【0262】 The server analyzes the received data packages to generate recycling trends and product improvement suggestions. Using data analysis software, it calculates recycling rates and trends by comparing them with past data, and creates feedback for the product. The input is the data package sent from the terminal, and the output is the analysis results and improvement suggestions. 【0263】 Step 6: 【0264】 The server provides feedback on the analysis results to relevant parties and compiles user eco-points. Improvement suggestions are sent via email and the company portal, and eco-points are updated in the database. The inputs are the analysis results and user recycling activity data, and the outputs are the submitted feedback and updated eco-points. 【0265】 Step 7: 【0266】 Users check their eco-points using a dedicated application and select redeemable rewards. The application displays data from the server and presents options based on the points. The input is the eco-point data provided by the server, and the output is the reward selected by the user. 【0267】 (Application Example 1) 【0268】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0269】 While waste management in homes and cities is crucial from an environmental protection standpoint, there is a lack of systems that allow users to accurately and efficiently classify waste and receive rewards for their efforts. Furthermore, there is a need for methods that utilize waste lifecycle information to drive product improvement. 【0270】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0271】 In this invention, the server includes means for identifying waste using image recognition means, means for managing rewards based on the user's waste disposal actions, and means for outputting and displaying waste classification information to the user via a virtual environment. This allows the user to easily classify waste and convert the received rewards into other forms of value. Furthermore, it enables the feedback of waste information that can lead to product improvements. 【0272】 "Image recognition means" refers to a technology that uses cameras and sensors to recognize the waste being introduced and to identify its type. 【0273】 A "storage device" is equipment used to properly store identified waste materials by type. 【0274】 "Wireless identification information" refers to data read through tags attached to waste, and includes information about the waste's lifecycle. 【0275】 A "centralized information processing system" is a computer system that analyzes collected waste management information and generates suggestions for product improvement. 【0276】 "Rewards" refer to points or other incentives that users receive when they properly dispose of waste. 【0277】 A "virtual environment" is a digital platform that displays waste classification information in a way that allows users to intuitively understand it. 【0278】 The system implementing this invention operates around three main components: a terminal, a server, and a user. 【0279】 The terminal is equipped with image sensors, such as a camera, to recognize the waste being deposited, and uses a Python program to classify the waste using machine learning libraries such as TensorFlow. Furthermore, the terminal has a built-in RFID reader to acquire wireless identification information and collect lifecycle data. The waste information collected by the terminal is transmitted wirelessly to a centralized information processing unit (server). 【0280】 The server analyzes received data, generates product improvement suggestions using AI models, and provides feedback to users through a digital platform. It also operates a reward system based on users' waste disposal behavior. This server utilizes cloud computing technology to process large amounts of data quickly. Through this system, users can actively participate in waste management by reviewing waste classification results and receiving rewards. 【0281】 One concrete example is a scenario where a user takes a picture of waste to be disposed of using their smartphone and receives the classification result in real time. When a user puts in a plastic bottle, the device photographs it, recognizes it as "plastic," and visually presents the optimal disposal method in a virtual environment. An example of a prompt message might be, "Your first piece of waste disposed of today has been correctly classified. Next, share this digital feedback with your friends and let's all participate. Small daily eco-activities can make a big difference." 【0282】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0283】 Step 1: 【0284】 When the user inputs waste into the terminal, the terminal uses the camera sensor to obtain an image of the waste. The input is the physical state of the waste, and the output is the image data. The terminal supplies this data to the AI image recognition model. 【0285】 Step 2: 【0286】 The terminal analyzes the waste image using the AI model generated with TensorFlow to identify its category. At this time, the input is the image data obtained in Step 1, and the output is the category information of the identified waste. 【0287】 Step 3: 【0288】 The RFID reader built into the terminal reads the wireless identification information attached to the waste. The input is the RFID tag information attached to the waste, and the output is the life cycle data of the waste. 【0289】 Step 4: 【0290】 The terminal integrates the image recognition results and the information obtained by RFID, packages them, and sends them to a centralized information processing device (server). The input is the recognition results and the life cycle data, and the output is the integrated data sent to the server. 【0291】 Step 5: 【0292】 The server analyzes the received data and generates proposals for environmental impact assessment and product improvement. The input is the data from the terminal, and the output is the assessment results and the content of the proposals. 【0293】 Step 6: 【0294】 The server calculates the reward based on the user's waste disposal behavior and reflects the reward information in the user's virtual environment. The input is the user's activity data, and the output is the updated reward information. 【0295】 Step 7: 【0296】 The system receives updated information from the user's smartphone or smart glasses and visually displays the waste classification results and reward information in a virtual environment. The input is updated information from the server, and the output is the display to the user. 【0297】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0298】 This invention combines an emotion engine with a waste management system to recognize user emotions and more effectively promote recycling activities. This system integrates AI-based image recognition, RFID technology, and emotion recognition technology. The following describes embodiments of the present invention in detail. 【0299】 First, the smart trash can itself is equipped with an image recognition camera and an emotion engine. When a user puts in trash, the device takes an image, and the AI ​​identifies the type of waste. Simultaneously, the built-in RFID reader reads the wireless identification tag attached to the waste. This data is integrated and stored as accurate waste management information. 【0300】 Furthermore, the emotion engine analyzes the user's facial expressions and actions while disposing of waste, recognizing the user's emotions. For example, if a user disposes of trash with a smile, the emotion engine incorporates positive feedback into the system. 【0301】 The terminal sends all this data in real time to the central computer system (server). The server analyzes the received data and generates feedback for the user and product improvement suggestions based on the identified waste and emotion data. For example, the server adds eco-points for successful recycling activities to strengthen the user's positive emotions and provides a praise message through the application. 【0302】 As a specific example, consider the scenario where a user conducts recycling at home. Suppose the user puts a PET bottle into the smart trash bin and shows a smiling face. At this time, the terminal properly classifies the PET bottle, and the emotion engine detects positive emotions. The server processes the data, increases the user's eco-points, and at the same time sends a message "Great recycling! Please continue!" to the smartphone application. The user can thus be motivated to engage in further recycling activities. 【0303】 In this way, the embodiment of the present invention can simultaneously achieve the rationalization of waste management and the improvement of user motivation, contributing to the realization of a more sustainable society. 【0304】 The processing flow will be described below. 【0305】 Step 1: 【0306】 The user puts garbage into the smart trash bin. The sensor detects this and confirms that the waste has been put in. 【0307】 Step 2: 【0308】 The camera of the terminal automatically activates and takes a picture of the garbage. This image is immediately analyzed by the image recognition AI in the terminal, and the type of waste is identified. For example, it is identified as a plastic bottle. 【0309】 Step 3: 【0310】 The terminal uses a wireless identification tag reader to read the RFID tags attached to the waste. This allows the product's lifecycle data to be obtained. 【0311】 Step 4: 【0312】 The emotion engine built into the device detects the user's face and analyzes their emotions. For example, it can detect if the user is smiling. 【0313】 Step 5: 【0314】 The terminal integrates identified waste information, RFID data, and emotional data to generate a data package, which is then sent to the server. This data package includes waste type, emotional state, and lifecycle information. 【0315】 Step 6: 【0316】 The server analyzes the received data. Based on the waste recycling rate and the user's emotional tendencies, it generates personalized feedback. 【0317】 Step 7: 【0318】 The server uses emotion data to award eco-points to users with positive emotions. This information is then reflected in the user's application. 【0319】 Step 8: 【0320】 Users can use a smartphone application to check their eco-points and view feedback messages they have received. This can motivate them to engage in further recycling activities. 【0321】 (Example 2) 【0322】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0323】 Conventional waste management systems have challenges in accurately identifying and managing waste, and lack sufficient motivation to improve users' recycling awareness. This hinders the promotion of recycling activities that reduce environmental impact. 【0324】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0325】 In this invention, the server includes means for classifying input waste using visual recognition technology, means for analyzing acquired waste information and user sentiment information to generate integrated data, and means for calculating and providing rewards based on the user's waste disposal behavior through an application. This promotes accurate waste management and active user participation, enabling a reduction in environmental impact and the realization of sustainable recycling activities. 【0326】 "Visual recognition technology" refers to technologies used to identify and classify specific objects or patterns from image data. 【0327】 "Wireless identification technology" is a technology that uses radio waves to acquire identification information attached to an object without physical contact. 【0328】 "Waste" is a general term for substances, products, and materials that are no longer needed and are disposed of. 【0329】 "Emotional information" refers to data that indicates an emotional state, analyzed from a person's facial expressions and behavior. 【0330】 "Integrated data" refers to data that combines different types of data into a single, unified dataset. 【0331】 A "processing device" is a computer or system used to receive, analyze, and process data. 【0332】 "Feedback" refers to information that conveys reactions and evaluations of user actions. 【0333】 "Behavioral improvement suggestions" are specific proposals and advice for improving user behavior. 【0334】 A "reward" is a point or perk that provides an incentive for a particular action. 【0335】 This invention is an integrated system for enhancing waste management and promoting user recycling activities. This system integrates image recognition technology, wireless identification technology, and emotion recognition technology to enable a series of waste management and user feedback processes. Embodiments of this invention will be described in detail below. 【0336】 First, the smart trash can, which is the terminal itself, is equipped with an image recognition camera, an RFID reader, and an emotion engine. When a user puts in trash, the terminal takes an image of it with its built-in camera. The captured image is analyzed using AI technology to identify the type of waste. Software such as TensorFlow and OpenCV is used for this process. In addition, the RFID tag attached to the waste is automatically read by the RFID reader, and detailed information about the waste is obtained. This information includes product ID and material information. 【0337】 Furthermore, the device's built-in emotion engine captures the user's facial expressions and analyzes their emotional state. For example, using Microsoft's emotion recognition API and SDK, if the system detects that the user is smiling, it records this as a positive emotion. 【0338】 This acquired data is sent in real time to a server, which acts as a processing unit. The server analyzes the integrated data and generates feedback on the user's recycling activities. Using a generative AI model, action plans and reward messages can be created. For example, the server calculates recycling points and provides them as rewards through the application for successful recycling activities. 【0339】 As a concrete example, consider a scenario where a user throws a plastic bottle into a smart trash can at home. If the user places the bottle in with a smile, the device classifies the bottle using image recognition, and the emotion engine records the positive emotion. The server processes this data, and an eco-point is displayed in the app along with a message such as, "Great recycling! Keep it up!" 【0340】 An example of a prompt would be: "When a user throws a plastic bottle into a smart trash can and smiles, what kind of feedback should be provided to increase their motivation for recycling?" This prompt would then be input into the generating AI model as text. 【0341】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0342】 Step 1: 【0343】 The device captures an image with its built-in camera when the user places waste into it. This image data is input to an AI image recognition module. The image recognition algorithm (e.g., TensorFlow or OpenCV) identifies the object from the image and outputs the type of waste (e.g., plastic, metal, paper). Through this process, the waste is visually classified. 【0344】 Step 2: 【0345】 The terminal uses an RFID reader to read the RFID tags attached to the waste. This tag information is entered into an identification database, and detailed information about the waste (e.g., product ID, material information) is output. The RFID data is integrated with image identification results to enhance waste management data. 【0346】 Step 3: 【0347】 The device captures the user's face with its camera and inputs the image into the emotion recognition engine. The emotion recognition system (e.g., emotion recognition API or SDK) analyzes the user's facial expressions and outputs an emotional state (e.g., positive, negative). This information is recorded as the user's emotion data. 【0348】 Step 4: 【0349】 The device transmits integrated waste management data and sentiment data to the server in real time. The server analyzes the received data as input and uses a generative AI model to determine the content of the feedback to the user. The analyzed data outputs action plans for feedback and messages of praise. 【0350】 Step 5: 【0351】 Based on the data analysis results, the server sends feedback to the user's smartphone app. The message includes incentives such as points added to the user's account and a message of appreciation. This feedback can help users increase their motivation for recycling. 【0352】 (Application Example 2) 【0353】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0354】 Waste management in modern cities is crucial from an environmental protection standpoint, but maintaining and improving residents' motivation for recycling remains a challenge. While conventional systems allow for proper waste classification and processing, they lack mechanisms to provide feedback that fully considers user emotions and motivations. Therefore, a sustainable approach is needed to prevent recycling activities from being merely a one-off effort. 【0355】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0356】 In this invention, the server includes means for identifying the waste being submitted using image recognition technology, means for analyzing the user's emotional state and providing positive feedback, and means for managing points based on the user's waste disposal activities. This makes it possible to provide positive feedback on the user's actions, in addition to the proper classification and disposal of waste, thereby sustainably promoting recycling activities among individual residents. 【0357】 "Image recognition technology" is a technology used to automatically identify the type of waste that has been put into the system. 【0358】 A "wireless identification tag" is a device that electronically stores detailed information about waste and transmits that information via wireless communication. 【0359】 A "central computing system" is a computer system for centrally collecting and analyzing waste data. 【0360】 "Methods for analyzing the user's emotional state" refers to technology that analyzes the user's facial expressions and actions when disposing of waste and recognizes their emotions. 【0361】 "Means of providing positive feedback" refers to a function that sends users messages of praise and encouragement based on sentiment analysis results in order to increase their motivation for recycling activities. 【0362】 "A means of managing points based on users' waste disposal activities" refers to a system that awards eco-points to users in accordance with their recycling activities and records and manages their history. 【0363】 This invention is a system designed to streamline waste management in smart cities and promote recycling activities among residents. The system utilizes smart trash cans as terminals and integrates image recognition technology, wireless identification tags, and sentiment analysis technology. 【0364】 First, a camera on the terminal photographs the waste submitted by the user, and image recognition technology is used to identify its type. Furthermore, the terminal reads a wireless identification tag to obtain detailed information about the waste and transmits it to the central computer system. 【0365】 This central computing system utilizes image processing libraries such as OpenCV to analyze acquired data. Using emotion analysis technology, it analyzes the user's facial expressions and actions when disposing of waste, recognizing the user's emotional state. Based on the user's emotions, it generates positive feedback and notifies the user via communication technology. 【0366】 As an example, if the system detects a smile when a user places a plastic bottle into a smart trash can, the central computer system recognizes this positive emotion and sends a message to the smartphone application saying, "Great recycling! Keep it up!" This feedback is expected to further encourage the user's recycling activities. 【0367】 A concrete example of a prompt statement is, "In Tokyo's smart city, install smart trash cans for waste management and utilize emotion recognition technology to promote citizens' recycling activities." In this way, the present invention combines technology and emotion analysis to enhance residents' motivation and realize sustainable waste management. 【0368】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0369】 Step 1: 【0370】 When a user places waste into the terminal, it takes a picture of the waste with its camera. The captured image is acquired by the terminal. The input at this time is the physical waste, and the output is digital image data. The terminal analyzes this data using image recognition technology to identify the type of waste. For example, it classifies it into categories such as plastic bottles, paper, and metal. 【0371】 Step 2: 【0372】 The terminal reads the wireless identification tag attached to the waste using a reader. The information obtained from the tag is the input, and detailed information about the waste is acquired using the RFID reader. This information is matched with the identified waste type and stored as waste management data. The output is organized waste management data. 【0373】 Step 3: 【0374】 The device uses captured images of the user to analyze their facial expressions using emotion analysis technology. The input is an image of the user's face. The output generates emotional states such as smiles or unhappiness. For example, a smile would be classified as "positive." The specific operation of emotion analysis involves extracting facial features as numerical data and classifying them using a generative AI model. 【0375】 Step 4: 【0376】 The terminal transmits identified waste data and sentiment analysis data to the central computer system in real time. The input consists of waste type data and user sentiment data, while the output is the central computer system receiving the data. The terminal uses a communication module to perform the specific operation of transmitting data to the central system. 【0377】 Step 5: 【0378】 The server analyzes the received data in a central computing system. The input consists of waste data and sentiment data. The server uses a generative AI model to analyze the input data and evaluate the user's recycling activities. The output includes positive feedback messages and calculated eco-points. 【0379】 Step 6: 【0380】 The server generates and notifies the user of positive feedback based on the analysis results. The input is the analyzed evaluation data, and the output is a message displayed on the user's smartphone or PC. For example, a message such as "Great recycling! Keep it up!" is generated. The server then uses a notification system to send the feedback to the user. 【0381】 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. 【0382】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0383】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0384】 [Third Embodiment] 【0385】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0386】 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. 【0387】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0388】 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. 【0389】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0390】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0391】 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. 【0392】 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. 【0393】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0394】 The 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. 【0395】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0396】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0397】 This invention is a waste management system that utilizes AI and RFID technologies to enable efficient waste sorting and promote recycling in homes and commercial facilities. The embodiments are described in detail below. 【0398】 First, the smart trash cans, which are installed as terminals, are equipped with multiple sensors and cameras that automatically photograph waste when it is placed inside, generating image data. AI-based image recognition technology analyzes this data to identify the type of waste. For example, if a user puts in a plastic bottle, the terminal immediately classifies it as plastic. Furthermore, an RFID reader built into the terminal reads the RFID tags attached to the waste and collects lifecycle data. 【0399】 Next, the terminal packages this data and transmits it to the server via wireless communication. The server analyzes the received data and calculates waste recycling trends and recycling rates for specific products based on it. Furthermore, the server automatically generates improvement suggestions as feedback to the company, encouraging them to improve recyclability. 【0400】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The system uses a server to tally points, allowing users to visually track their progress. These points can be exchanged for services and goods offered by partner communities and businesses, thereby encouraging more active recycling efforts from users. 【0401】 As a concrete example, consider a household use scenario. When a user places everyday plastic containers and paper waste into a smart trash can, the device not only accurately sorts them, but the collected data is also analyzed on a server, and suggestions for improving the products that make up the majority of the waste are sent to the manufacturers. This entire process is automated, allowing users to contribute to efficient recycling simply by putting in their waste. In this way, the invention contributes to more efficient waste management and the realization of a sustainable society. 【0402】 The following describes the processing flow. 【0403】 Step 1: 【0404】 The device uses sensors to detect when a user places trash into the smart trash can. After detection, the camera automatically activates and takes a picture of the trash that has been placed inside. 【0405】 Step 2: 【0406】 AI image recognition technology, running inside the device, analyzes the captured image data. This analysis identifies the material and type of waste (e.g., plastic, metal, paper, etc.). 【0407】 Step 3: 【0408】 The terminal uses an RFID reader to read the RFID tags attached to the waste that is thrown in. The lifecycle data obtained through this process is used to track the waste. 【0409】 Step 4: 【0410】 Identified waste is automatically sorted into the appropriate container by a selection mechanism within the terminal. This sorting is based on the identification results. 【0411】 Step 5: 【0412】 The terminal generates a data package containing information about the identified waste and data obtained from the RFID, and sends it to the server. 【0413】 Step 6: 【0414】 The server analyzes the received data package and creates statistical information on the collected waste data. This process generates data for recycling rates for each product and for suggesting improvements. 【0415】 Step 7: 【0416】 Based on the analysis results, the server automatically generates product improvement suggestions for manufacturing companies. These suggestions aim to reduce environmental impact and improve recycling efficiency. 【0417】 Step 8: 【0418】 The server adds eco-points to the user's account. Points are calculated based on the user's recycling activities. The server also updates information so that users can check and use their points through a dedicated app. 【0419】 (Example 1) 【0420】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0421】 In modern society, proper waste management and recycling are crucial challenges in environmental protection. However, waste sorting operations in homes and businesses are not always efficient, limiting the progress of recycling. In addition, there is a lack of methods to utilize waste information for product improvement, hindering the realization of a sustainable society. 【0422】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0423】 In this invention, the server includes means for identifying waste using image recognition technology, means for reading wireless identification tags attached to the waste, and means for analyzing data to evaluate recycling trends. This enables efficient identification and classification of waste, automatic generation of feedback for product improvement, and promotion of proactive recycling activities by users. 【0424】 "Image recognition technology" is a technology that uses computer vision algorithms to analyze visual information from digital images or videos and automatically identify objects and shapes. 【0425】 "Identified waste" refers to waste whose properties and materials have been identified using image recognition technology or wireless identification technology. 【0426】 A "wireless identification tag" is a small electronic device that uses radio waves to read information about an object without contact and track it. 【0427】 A "central computing system" is a central information processing device used to receive, analyze, and manage various types of data via a network. 【0428】 "Eco-points" are points that are quantified based on an evaluation of a user's recycling activities and environmentally conscious behavior, and are usually provided in the form of exchangeable services or goods. 【0429】 To implement this invention, a waste management system utilizing AI technology and wireless identification technology is required. The smart trash can, which is installed as a terminal, is equipped with multiple sensors and cameras for identifying waste. As a result, waste placed in by the user is immediately photographed, and image data is generated. This image data is analyzed by a terminal running image recognition software using a generation AI model, and the type of waste is identified. 【0430】 For example, when a user discards a plastic bottle, the terminal instantly classifies it as plastic. Additionally, an RFID reader built into the terminal reads the wireless identification tag attached to the waste, collecting product information and lifecycle data. This data is compiled into a single package and transmitted to a server via wireless communication. 【0431】 The server uses advanced data analysis software to analyze the received data. This analysis includes evaluating recycling trends and generating improvement suggestions for specific products. The generated suggestions are automatically fed back to manufacturers to encourage improvements in recyclability. 【0432】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The server visualizes the accumulated points, making them easy for users to track. Eco-points can be exchanged for goods and services offered by partner communities and companies, providing users with an incentive to actively engage in environmentally conscious behavior. 【0433】 As a concrete example, a possible prompt to be input into a generating AI model might be: "Please describe in detail how to implement a waste management system using AI and wireless identification technology. Please also specify what hardware and software will be used." 【0434】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0435】 Step 1: 【0436】 The device detects the placement of waste. When a user places waste into the smart trash can, the sensor detects this action and triggers the generation of image data using the placement signal. In this case, the input is the waste placed by the user, and the output is an image of the waste. 【0437】 Step 2: 【0438】 Based on image data generated by the device, an AI model is used to identify waste. Image recognition technology is utilized to determine the material and category of the captured waste from the image. The input is image data of the captured waste, and the output is data regarding the identified type of waste. 【0439】 Step 3: 【0440】 The terminal reads the wireless identification tag attached to the waste and obtains lifecycle information. An RFID reader is used to collect information contactlessly from the tag attached to the waste. The input is the signal from the RFID tag, and the output is the individual waste information. 【0441】 Step 4: 【0442】 The terminal packages the data it collects and sends it to the server. Image analysis results and information obtained from RFID tags are integrated and formatted into a single data package. The input is identified waste type data and lifecycle information, and the output is the data package sent to the server. 【0443】 Step 5: 【0444】 The server analyzes the received data packages to generate recycling trends and product improvement suggestions. Using data analysis software, it calculates recycling rates and trends by comparing them with past data, and creates feedback for the product. The input is the data package sent from the terminal, and the output is the analysis results and improvement suggestions. 【0445】 Step 6: 【0446】 The server provides feedback on the analysis results to relevant parties and compiles user eco-points. Improvement suggestions are sent via email and the company portal, and eco-points are updated in the database. The inputs are the analysis results and user recycling activity data, and the outputs are the submitted feedback and updated eco-points. 【0447】 Step 7: 【0448】 Users check their eco-points using a dedicated application and select redeemable rewards. The application displays data from the server and presents options based on the points. The input is the eco-point data provided by the server, and the output is the reward selected by the user. 【0449】 (Application Example 1) 【0450】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0451】 While waste management in homes and cities is crucial from an environmental protection standpoint, there is a lack of systems that allow users to accurately and efficiently classify waste and receive rewards for their efforts. Furthermore, there is a need for methods that utilize waste lifecycle information to drive product improvement. 【0452】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0453】 In this invention, the server includes means for identifying waste using image recognition means, means for managing rewards based on the user's waste disposal actions, and means for outputting and displaying waste classification information to the user via a virtual environment. This allows the user to easily classify waste and convert the received rewards into other forms of value. Furthermore, it enables the feedback of waste information that can lead to product improvements. 【0454】 "Image recognition means" refers to a technology that uses cameras and sensors to recognize the waste being introduced and to identify its type. 【0455】 A "storage device" is equipment used to properly store identified waste materials by type. 【0456】 "Wireless identification information" refers to data read through tags attached to waste, and includes information about the waste's lifecycle. 【0457】 A "centralized information processing system" is a computer system that analyzes collected waste management information and generates suggestions for product improvement. 【0458】 "Rewards" refer to points or other incentives that users receive when they properly dispose of waste. 【0459】 A "virtual environment" is a digital platform that displays waste classification information in a way that allows users to intuitively understand it. 【0460】 The system implementing this invention operates around three main components: a terminal, a server, and a user. 【0461】 The terminal is equipped with image sensors, such as a camera, to recognize the waste being deposited, and uses a Python program to classify the waste using machine learning libraries such as TensorFlow. Furthermore, the terminal has a built-in RFID reader to acquire wireless identification information and collect lifecycle data. The waste information collected by the terminal is transmitted wirelessly to a centralized information processing unit (server). 【0462】 The server analyzes received data, generates product improvement suggestions using AI models, and provides feedback to users through a digital platform. It also operates a reward system based on users' waste disposal behavior. This server utilizes cloud computing technology to process large amounts of data quickly. Through this system, users can actively participate in waste management by reviewing waste classification results and receiving rewards. 【0463】 One concrete example is a scenario where a user takes a picture of waste to be disposed of using their smartphone and receives the classification result in real time. When a user puts in a plastic bottle, the device photographs it, recognizes it as "plastic," and visually presents the optimal disposal method in a virtual environment. An example of a prompt message might be, "Your first piece of waste disposed of today has been correctly classified. Next, share this digital feedback with your friends and let's all participate. Small daily eco-activities can make a big difference." 【0464】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0465】 Step 1: 【0466】 When a user places waste into the terminal, the terminal uses its camera sensor to capture an image of the waste. The input is the physical state of the waste, and the output is its image data. The terminal then supplies this data to an AI image recognition model. 【0467】 Step 2: 【0468】 The device uses TensorFlow to generate an AI model that analyzes images of waste and identifies its category. In this process, the input is the image data obtained in step 1, and the output is the category information of the identified waste. 【0469】 Step 3: 【0470】 The RFID reader built into the terminal reads the wireless identification information attached to the waste. The input is the RFID tag information attached to the waste, and the output is the waste's lifecycle data. 【0471】 Step 4: 【0472】 The terminal integrates the image recognition results and information obtained via RFID, packages them, and transmits them to a centralized information processing unit (server). The input is the recognition results and lifecycle data, and the output is the integrated data transmitted to the server. 【0473】 Step 5: 【0474】 The server analyzes the received data and generates environmental impact assessments and product improvement suggestions. Input is data from the terminal, and output is the assessment results and suggestions. 【0475】 Step 6: 【0476】 The server calculates rewards based on the user's waste disposal actions and reflects the reward information in the user's virtual environment. The input is the user's activity data, and the output is the updated reward information. 【0477】 Step 7: 【0478】 The system receives updated information from the user's smartphone or smart glasses and visually displays the waste classification results and reward information in a virtual environment. The input is updated information from the server, and the output is the display to the user. 【0479】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0480】 This invention combines an emotion engine with a waste management system to recognize user emotions and more effectively promote recycling activities. This system integrates AI-based image recognition, RFID technology, and emotion recognition technology. The following describes embodiments of the present invention in detail. 【0481】 First, the smart trash can itself is equipped with an image recognition camera and an emotion engine. When a user puts in trash, the device takes an image, and the AI ​​identifies the type of waste. Simultaneously, the built-in RFID reader reads the wireless identification tag attached to the waste. This data is integrated and stored as accurate waste management information. 【0482】 Furthermore, the emotion engine analyzes the user's facial expressions and actions while disposing of waste, recognizing the user's emotions. For example, if a user disposes of trash with a smile, the emotion engine incorporates positive feedback into the system. 【0483】 The terminal transmits all this data to a central computing system (server) in real time. The server analyzes the received data and generates feedback and product improvement suggestions for the user based on the identified waste and sentiment data. For example, to reinforce the user's positive sentiment, the server adds eco-points for successful recycling activities and provides messages of praise through the application. 【0484】 As a concrete example, consider a scenario where a user recycles at home. Suppose the user places a plastic bottle into a smart trash can and smiles. At this point, the device correctly sorts the bottle, and the emotion engine detects a positive emotion. The server processes the data, increasing the user's eco-points, and simultaneously sends a message to the smartphone app saying, "Great recycling! Keep it up!" This motivates the user to engage in further recycling activities. 【0485】 In this way, the embodiment of the present invention can simultaneously achieve rationalization of waste management and improvement of user motivation, thereby contributing to the realization of a more sustainable society. 【0486】 The following describes the processing flow. 【0487】 Step 1: 【0488】 The user places their trash into the smart trash can. A sensor detects this and confirms that the waste has been placed inside. 【0489】 Step 2: 【0490】 The device's camera automatically activates and takes a picture of the waste. This image is immediately analyzed by the device's image recognition AI, which identifies the type of waste. For example, it might be identified as a plastic bottle. 【0491】 Step 3: 【0492】 The terminal uses a wireless identification tag reader to read the RFID tags attached to the waste. This allows the product's lifecycle data to be obtained. 【0493】 Step 4: 【0494】 The emotion engine built into the device detects the user's face and analyzes their emotions. For example, it can detect if the user is smiling. 【0495】 Step 5: 【0496】 The terminal integrates identified waste information, RFID data, and emotional data to generate a data package, which is then sent to the server. This data package includes waste type, emotional state, and lifecycle information. 【0497】 Step 6: 【0498】 The server analyzes the received data. Based on the waste recycling rate and the user's emotional tendencies, it generates personalized feedback. 【0499】 Step 7: 【0500】 The server uses emotion data to award eco-points to users with positive emotions. This information is then reflected in the user's application. 【0501】 Step 8: 【0502】 Users can use a smartphone application to check their eco-points and view feedback messages they have received. This can motivate them to engage in further recycling activities. 【0503】 (Example 2) 【0504】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0505】 Conventional waste management systems have challenges in accurately identifying and managing waste, and lack sufficient motivation to improve users' recycling awareness. This hinders the promotion of recycling activities that reduce environmental impact. 【0506】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0507】 In this invention, the server includes means for classifying input waste using visual recognition technology, means for analyzing acquired waste information and user sentiment information to generate integrated data, and means for calculating and providing rewards based on the user's waste disposal behavior through an application. This promotes accurate waste management and active user participation, enabling a reduction in environmental impact and the realization of sustainable recycling activities. 【0508】 "Visual recognition technology" refers to technologies used to identify and classify specific objects or patterns from image data. 【0509】 "Wireless identification technology" is a technology that uses radio waves to acquire identification information attached to an object without physical contact. 【0510】 "Waste" is a general term for substances, products, and materials that are no longer needed and are disposed of. 【0511】 "Emotional information" refers to data that indicates an emotional state, analyzed from a person's facial expressions and behavior. 【0512】 "Integrated data" refers to data that combines different types of data into a single, unified dataset. 【0513】 A "processing device" is a computer or system used to receive, analyze, and process data. 【0514】 "Feedback" refers to information that conveys reactions and evaluations of user actions. 【0515】 "Behavioral improvement suggestions" are specific proposals and advice for improving user behavior. 【0516】 A "reward" is a point or perk that provides an incentive for a particular action. 【0517】 This invention is an integrated system for enhancing waste management and promoting user recycling activities. This system integrates image recognition technology, wireless identification technology, and emotion recognition technology to enable a series of waste management and user feedback processes. Embodiments of this invention will be described in detail below. 【0518】 First, the smart trash can, which is the terminal itself, is equipped with an image recognition camera, an RFID reader, and an emotion engine. When a user puts in trash, the terminal takes an image of it with its built-in camera. The captured image is analyzed using AI technology to identify the type of waste. Software such as TensorFlow and OpenCV is used for this process. In addition, the RFID tag attached to the waste is automatically read by the RFID reader, and detailed information about the waste is obtained. This information includes product ID and material information. 【0519】 Furthermore, the device's built-in emotion engine captures the user's facial expressions and analyzes their emotional state. For example, using Microsoft's emotion recognition API and SDK, if the system detects that the user is smiling, it records this as a positive emotion. 【0520】 This acquired data is sent in real time to a server, which acts as a processing unit. The server analyzes the integrated data and generates feedback on the user's recycling activities. Using a generative AI model, action plans and reward messages can be created. For example, the server calculates recycling points and provides them as rewards through the application for successful recycling activities. 【0521】 As a concrete example, consider a scenario where a user throws a plastic bottle into a smart trash can at home. If the user places the bottle in with a smile, the device classifies the bottle using image recognition, and the emotion engine records the positive emotion. The server processes this data, and an eco-point is displayed in the app along with a message such as, "Great recycling! Keep it up!" 【0522】 An example of a prompt would be: "When a user throws a plastic bottle into a smart trash can and smiles, what kind of feedback should be provided to increase their motivation for recycling?" This prompt would then be input into the generating AI model as text. 【0523】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0524】 Step 1: 【0525】 The device captures an image with its built-in camera when the user places waste into it. This image data is input to an AI image recognition module. The image recognition algorithm (e.g., TensorFlow or OpenCV) identifies the object from the image and outputs the type of waste (e.g., plastic, metal, paper). Through this process, the waste is visually classified. 【0526】 Step 2: 【0527】 The terminal uses an RFID reader to read the RFID tags attached to the waste. This tag information is entered into an identification database, and detailed information about the waste (e.g., product ID, material information) is output. The RFID data is integrated with image identification results to enhance waste management data. 【0528】 Step 3: 【0529】 The device captures the user's face with its camera and inputs the image into the emotion recognition engine. The emotion recognition system (e.g., emotion recognition API or SDK) analyzes the user's facial expressions and outputs an emotional state (e.g., positive, negative). This information is recorded as the user's emotion data. 【0530】 Step 4: 【0531】 The device transmits integrated waste management data and sentiment data to the server in real time. The server analyzes the received data as input and uses a generative AI model to determine the content of the feedback to the user. The analyzed data outputs action plans for feedback and messages of praise. 【0532】 Step 5: 【0533】 Based on the data analysis results, the server sends feedback to the user's smartphone app. The message includes incentives such as points added to the user's account and a message of appreciation. This feedback can help users increase their motivation for recycling. 【0534】 (Application Example 2) 【0535】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0536】 Waste management in modern cities is crucial from an environmental protection standpoint, but maintaining and improving residents' motivation for recycling remains a challenge. While conventional systems allow for proper waste classification and processing, they lack mechanisms to provide feedback that fully considers user emotions and motivations. Therefore, a sustainable approach is needed to prevent recycling activities from being merely a one-off effort. 【0537】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0538】 In this invention, the server includes means for identifying the waste being submitted using image recognition technology, means for analyzing the user's emotional state and providing positive feedback, and means for managing points based on the user's waste disposal activities. This makes it possible to provide positive feedback on the user's actions, in addition to the proper classification and disposal of waste, thereby sustainably promoting recycling activities among individual residents. 【0539】 "Image recognition technology" is a technology used to automatically identify the type of waste that has been put into the system. 【0540】 A "wireless identification tag" is a device that electronically stores detailed information about waste and transmits that information via wireless communication. 【0541】 A "central computing system" is a computer system for centrally collecting and analyzing waste data. 【0542】 "Methods for analyzing the user's emotional state" refers to technology that analyzes the user's facial expressions and actions when disposing of waste and recognizes their emotions. 【0543】 "Means of providing positive feedback" refers to a function that sends users messages of praise and encouragement based on sentiment analysis results in order to increase their motivation for recycling activities. 【0544】 "A means of managing points based on users' waste disposal activities" refers to a system that awards eco-points to users in accordance with their recycling activities and records and manages their history. 【0545】 This invention is a system designed to streamline waste management in smart cities and promote recycling activities among residents. The system utilizes smart trash cans as terminals and integrates image recognition technology, wireless identification tags, and sentiment analysis technology. 【0546】 First, a camera on the terminal photographs the waste submitted by the user, and image recognition technology is used to identify its type. Furthermore, the terminal reads a wireless identification tag to obtain detailed information about the waste and transmits it to the central computer system. 【0547】 This central computing system utilizes image processing libraries such as OpenCV to analyze acquired data. Using emotion analysis technology, it analyzes the user's facial expressions and actions when disposing of waste, recognizing the user's emotional state. Based on the user's emotions, it generates positive feedback and notifies the user via communication technology. 【0548】 As an example, if the system detects a smile when a user places a plastic bottle into a smart trash can, the central computer system recognizes this positive emotion and sends a message to the smartphone application saying, "Great recycling! Keep it up!" This feedback is expected to further encourage the user's recycling activities. 【0549】 A concrete example of a prompt statement is, "In Tokyo's smart city, install smart trash cans for waste management and utilize emotion recognition technology to promote citizens' recycling activities." In this way, the present invention combines technology and emotion analysis to enhance residents' motivation and realize sustainable waste management. 【0550】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0551】 Step 1: 【0552】 When a user places waste into the terminal, it takes a picture of the waste with its camera. The captured image is acquired by the terminal. The input at this time is the physical waste, and the output is digital image data. The terminal analyzes this data using image recognition technology to identify the type of waste. For example, it classifies it into categories such as plastic bottles, paper, and metal. 【0553】 Step 2: 【0554】 The terminal reads the wireless identification tag attached to the waste using a reader. The information obtained from the tag is the input, and detailed information about the waste is acquired using the RFID reader. This information is matched with the identified waste type and stored as waste management data. The output is organized waste management data. 【0555】 Step 3: 【0556】 The device uses captured images of the user to analyze their facial expressions using emotion analysis technology. The input is an image of the user's face. The output generates emotional states such as smiles or unhappiness. For example, a smile would be classified as "positive." The specific operation of emotion analysis involves extracting facial features as numerical data and classifying them using a generative AI model. 【0557】 Step 4: 【0558】 The terminal transmits identified waste data and sentiment analysis data to the central computer system in real time. The input consists of waste type data and user sentiment data, while the output is the central computer system receiving the data. The terminal uses a communication module to perform the specific operation of transmitting data to the central system. 【0559】 Step 5: 【0560】 The server analyzes the received data in a central computing system. The input consists of waste data and sentiment data. The server uses a generative AI model to analyze the input data and evaluate the user's recycling activities. The output includes positive feedback messages and calculated eco-points. 【0561】 Step 6: 【0562】 The server generates and notifies the user of positive feedback based on the analysis results. The input is the analyzed evaluation data, and the output is a message displayed on the user's smartphone or PC. For example, a message such as "Great recycling! Keep it up!" is generated. The server then uses a notification system to send the feedback to the user. 【0563】 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. 【0564】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0565】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0566】 [Fourth Embodiment] 【0567】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0568】 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. 【0569】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network). 【0570】 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. 【0571】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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. 【0572】 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0573】 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. 【0574】 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0575】 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. 【0576】 The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30. 【0577】 The 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. 【0578】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0579】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0580】 This invention is a waste management system that utilizes AI and RFID technologies to enable efficient waste sorting and promote recycling in homes and commercial facilities. The embodiments are described in detail below. 【0581】 First, the smart trash cans, which are installed as terminals, are equipped with multiple sensors and cameras that automatically photograph waste when it is placed inside, generating image data. AI-based image recognition technology analyzes this data to identify the type of waste. For example, if a user puts in a plastic bottle, the terminal immediately classifies it as plastic. Furthermore, an RFID reader built into the terminal reads the RFID tags attached to the waste and collects lifecycle data. 【0582】 Next, the terminal packages this data and transmits it to the server via wireless communication. The server analyzes the received data and calculates waste recycling trends and recycling rates for specific products based on it. Furthermore, the server automatically generates improvement suggestions as feedback to the company, encouraging them to improve recyclability. 【0583】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The system uses a server to tally points, allowing users to visually track their progress. These points can be exchanged for services and goods offered by partner communities and businesses, thereby encouraging more active recycling efforts from users. 【0584】 As a concrete example, consider a household use scenario. When a user places everyday plastic containers and paper waste into a smart trash can, the device not only accurately sorts them, but the collected data is also analyzed on a server, and suggestions for improving the products that make up the majority of the waste are sent to the manufacturers. This entire process is automated, allowing users to contribute to efficient recycling simply by putting in their waste. In this way, the invention contributes to more efficient waste management and the realization of a sustainable society. 【0585】 The following describes the processing flow. 【0586】 Step 1: 【0587】 The device uses sensors to detect when a user places trash into the smart trash can. After detection, the camera automatically activates and takes a picture of the trash that has been placed inside. 【0588】 Step 2: 【0589】 AI image recognition technology, running inside the device, analyzes the captured image data. This analysis identifies the material and type of waste (e.g., plastic, metal, paper, etc.). 【0590】 Step 3: 【0591】 The terminal uses an RFID reader to read the RFID tags attached to the waste that is thrown in. The lifecycle data obtained through this process is used to track the waste. 【0592】 Step 4: 【0593】 Identified waste is automatically sorted into the appropriate container by a selection mechanism within the terminal. This sorting is based on the identification results. 【0594】 Step 5: 【0595】 The terminal generates a data package containing information about the identified waste and data obtained from the RFID, and sends it to the server. 【0596】 Step 6: 【0597】 The server analyzes the received data package and creates statistical information on the collected waste data. This process generates data for recycling rates for each product and for suggesting improvements. 【0598】 Step 7: 【0599】 Based on the analysis results, the server automatically generates product improvement suggestions for manufacturing companies. These suggestions aim to reduce environmental impact and improve recycling efficiency. 【0600】 Step 8: 【0601】 The server adds eco-points to the user's account. Points are calculated based on the user's recycling activities. The server also updates information so that users can check and use their points through a dedicated app. 【0602】 (Example 1) 【0603】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0604】 In modern society, proper waste management and recycling are crucial challenges in environmental protection. However, waste sorting operations in homes and businesses are not always efficient, limiting the progress of recycling. In addition, there is a lack of methods to utilize waste information for product improvement, hindering the realization of a sustainable society. 【0605】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0606】 In this invention, the server includes means for identifying waste using image recognition technology, means for reading wireless identification tags attached to the waste, and means for analyzing data to evaluate recycling trends. This enables efficient identification and classification of waste, automatic generation of feedback for product improvement, and promotion of proactive recycling activities by users. 【0607】 "Image recognition technology" is a technology that uses computer vision algorithms to analyze visual information from digital images or videos and automatically identify objects and shapes. 【0608】 "Identified waste" refers to waste whose properties and materials have been identified using image recognition technology or wireless identification technology. 【0609】 A "wireless identification tag" is a small electronic device that uses radio waves to read information about an object without contact and track it. 【0610】 A "central computing system" is a central information processing device used to receive, analyze, and manage various types of data via a network. 【0611】 "Eco-points" are points that are quantified based on an evaluation of a user's recycling activities and environmentally conscious behavior, and are usually provided in the form of exchangeable services or goods. 【0612】 To implement this invention, a waste management system utilizing AI technology and wireless identification technology is required. The smart trash can, which is installed as a terminal, is equipped with multiple sensors and cameras for identifying waste. As a result, waste placed in by the user is immediately photographed, and image data is generated. This image data is analyzed by a terminal running image recognition software using a generation AI model, and the type of waste is identified. 【0613】 For example, when a user discards a plastic bottle, the terminal instantly classifies it as plastic. Additionally, an RFID reader built into the terminal reads the wireless identification tag attached to the waste, collecting product information and lifecycle data. This data is compiled into a single package and transmitted to a server via wireless communication. 【0614】 The server uses advanced data analysis software to analyze the received data. This analysis includes evaluating recycling trends and generating improvement suggestions for specific products. The generated suggestions are automatically fed back to manufacturers to encourage improvements in recyclability. 【0615】 Users can check their eco-points earned through a dedicated application, based on their waste disposal activities. The server visualizes the accumulated points, making them easy for users to track. Eco-points can be exchanged for goods and services offered by partner communities and companies, providing users with an incentive to actively engage in environmentally conscious behavior. 【0616】 As a concrete example, a possible prompt to be input into a generating AI model might be: "Please describe in detail how to implement a waste management system using AI and wireless identification technology. Please also specify what hardware and software will be used." 【0617】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0618】 Step 1: 【0619】 The device detects the placement of waste. When a user places waste into the smart trash can, the sensor detects this action and triggers the generation of image data using the placement signal. In this case, the input is the waste placed by the user, and the output is an image of the waste. 【0620】 Step 2: 【0621】 Based on image data generated by the device, an AI model is used to identify waste. Image recognition technology is utilized to determine the material and category of the captured waste from the image. The input is image data of the captured waste, and the output is data regarding the identified type of waste. 【0622】 Step 3: 【0623】 The terminal reads the wireless identification tag attached to the waste and obtains lifecycle information. An RFID reader is used to collect information contactlessly from the tag attached to the waste. The input is the signal from the RFID tag, and the output is the individual waste information. 【0624】 Step 4: 【0625】 The terminal packages the data it collects and sends it to the server. Image analysis results and information obtained from RFID tags are integrated and formatted into a single data package. The input is identified waste type data and lifecycle information, and the output is the data package sent to the server. 【0626】 Step 5: 【0627】 The server analyzes the received data packages to generate recycling trends and product improvement suggestions. Using data analysis software, it calculates recycling rates and trends by comparing them with past data, and creates feedback for the product. The input is the data package sent from the terminal, and the output is the analysis results and improvement suggestions. 【0628】 Step 6: 【0629】 The server provides feedback on the analysis results to relevant parties and compiles user eco-points. Improvement suggestions are sent via email and the company portal, and eco-points are updated in the database. The inputs are the analysis results and user recycling activity data, and the outputs are the submitted feedback and updated eco-points. 【0630】 Step 7: 【0631】 Users check their eco-points using a dedicated application and select redeemable rewards. The application displays data from the server and presents options based on the points. The input is the eco-point data provided by the server, and the output is the reward selected by the user. 【0632】 (Application Example 1) 【0633】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0634】 While waste management in homes and cities is crucial from an environmental protection standpoint, there is a lack of systems that allow users to accurately and efficiently classify waste and receive rewards for their efforts. Furthermore, there is a need for methods that utilize waste lifecycle information to drive product improvement. 【0635】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0636】 In this invention, the server includes means for identifying waste using image recognition means, means for managing rewards based on the user's waste disposal actions, and means for outputting and displaying waste classification information to the user via a virtual environment. This allows the user to easily classify waste and convert the received rewards into other forms of value. Furthermore, it enables the feedback of waste information that can lead to product improvements. 【0637】 "Image recognition means" refers to a technology that uses cameras and sensors to recognize the waste being introduced and to identify its type. 【0638】 A "storage device" is equipment used to properly store identified waste materials by type. 【0639】 "Wireless identification information" refers to data read through tags attached to waste, and includes information about the waste's lifecycle. 【0640】 A "centralized information processing system" is a computer system that analyzes collected waste management information and generates suggestions for product improvement. 【0641】 "Rewards" refer to points or other incentives that users receive when they properly dispose of waste. 【0642】 A "virtual environment" is a digital platform that displays waste classification information in a way that allows users to intuitively understand it. 【0643】 The system implementing this invention operates around three main components: a terminal, a server, and a user. 【0644】 The terminal is equipped with image sensors, such as a camera, to recognize the waste being deposited, and uses a Python program to classify the waste using machine learning libraries such as TensorFlow. Furthermore, the terminal has a built-in RFID reader to acquire wireless identification information and collect lifecycle data. The waste information collected by the terminal is transmitted wirelessly to a centralized information processing unit (server). 【0645】 The server analyzes received data, generates product improvement suggestions using AI models, and provides feedback to users through a digital platform. It also operates a reward system based on users' waste disposal behavior. This server utilizes cloud computing technology to process large amounts of data quickly. Through this system, users can actively participate in waste management by reviewing waste classification results and receiving rewards. 【0646】 One concrete example is a scenario where a user takes a picture of waste to be disposed of using their smartphone and receives the classification result in real time. When a user puts in a plastic bottle, the device photographs it, recognizes it as "plastic," and visually presents the optimal disposal method in a virtual environment. An example of a prompt message might be, "Your first piece of waste disposed of today has been correctly classified. Next, share this digital feedback with your friends and let's all participate. Small daily eco-activities can make a big difference." 【0647】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0648】 Step 1: 【0649】 When a user places waste into the terminal, the terminal uses its camera sensor to capture an image of the waste. The input is the physical state of the waste, and the output is its image data. The terminal then supplies this data to an AI image recognition model. 【0650】 Step 2: 【0651】 The device uses TensorFlow to generate an AI model that analyzes images of waste and identifies its category. In this process, the input is the image data obtained in step 1, and the output is the category information of the identified waste. 【0652】 Step 3: 【0653】 The RFID reader built into the terminal reads the wireless identification information attached to the waste. The input is the RFID tag information attached to the waste, and the output is the waste's lifecycle data. 【0654】 Step 4: 【0655】 The terminal integrates the image recognition results and information obtained via RFID, packages them, and transmits them to a centralized information processing unit (server). The input is the recognition results and lifecycle data, and the output is the integrated data transmitted to the server. 【0656】 Step 5: 【0657】 The server analyzes the received data and generates environmental impact assessments and product improvement suggestions. Input is data from the terminal, and output is the assessment results and suggestions. 【0658】 Step 6: 【0659】 The server calculates rewards based on the user's waste disposal actions and reflects the reward information in the user's virtual environment. The input is the user's activity data, and the output is the updated reward information. 【0660】 Step 7: 【0661】 The system receives updated information from the user's smartphone or smart glasses and visually displays the waste classification results and reward information in a virtual environment. The input is updated information from the server, and the output is the display to the user. 【0662】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0663】 This invention combines an emotion engine with a waste management system to recognize user emotions and more effectively promote recycling activities. This system integrates AI-based image recognition, RFID technology, and emotion recognition technology. The following describes embodiments of the present invention in detail. 【0664】 First, the smart trash can itself is equipped with an image recognition camera and an emotion engine. When a user puts in trash, the device takes an image, and the AI ​​identifies the type of waste. Simultaneously, the built-in RFID reader reads the wireless identification tag attached to the waste. This data is integrated and stored as accurate waste management information. 【0665】 Furthermore, the emotion engine analyzes the user's facial expressions and actions while disposing of waste, recognizing the user's emotions. For example, if a user disposes of trash with a smile, the emotion engine incorporates positive feedback into the system. 【0666】 The terminal transmits all this data to a central computing system (server) in real time. The server analyzes the received data and generates feedback and product improvement suggestions for the user based on the identified waste and sentiment data. For example, to reinforce the user's positive sentiment, the server adds eco-points for successful recycling activities and provides messages of praise through the application. 【0667】 As a concrete example, consider a scenario where a user recycles at home. Suppose the user places a plastic bottle into a smart trash can and smiles. At this point, the device correctly sorts the bottle, and the emotion engine detects a positive emotion. The server processes the data, increasing the user's eco-points, and simultaneously sends a message to the smartphone app saying, "Great recycling! Keep it up!" This motivates the user to engage in further recycling activities. 【0668】 In this way, the embodiment of the present invention can simultaneously achieve rationalization of waste management and improvement of user motivation, thereby contributing to the realization of a more sustainable society. 【0669】 The following describes the processing flow. 【0670】 Step 1: 【0671】 The user places their trash into the smart trash can. A sensor detects this and confirms that the waste has been placed inside. 【0672】 Step 2: 【0673】 The device's camera automatically activates and takes a picture of the waste. This image is immediately analyzed by the device's image recognition AI, which identifies the type of waste. For example, it might be identified as a plastic bottle. 【0674】 Step 3: 【0675】 The terminal uses a wireless identification tag reader to read the RFID tags attached to the waste. This allows the product's lifecycle data to be obtained. 【0676】 Step 4: 【0677】 The emotion engine built into the device detects the user's face and analyzes their emotions. For example, it can detect if the user is smiling. 【0678】 Step 5: 【0679】 The terminal integrates identified waste information, RFID data, and emotional data to generate a data package, which is then sent to the server. This data package includes waste type, emotional state, and lifecycle information. 【0680】 Step 6: 【0681】 The server analyzes the received data. Based on the waste recycling rate and the user's emotional tendencies, it generates personalized feedback. 【0682】 Step 7: 【0683】 The server uses emotion data to award eco-points to users with positive emotions. This information is then reflected in the user's application. 【0684】 Step 8: 【0685】 Users can use a smartphone application to check their eco-points and view feedback messages they have received. This can motivate them to engage in further recycling activities. 【0686】 (Example 2) 【0687】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0688】 Conventional waste management systems have challenges in accurately identifying and managing waste, and lack sufficient motivation to improve users' recycling awareness. This hinders the promotion of recycling activities that reduce environmental impact. 【0689】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0690】 In this invention, the server includes means for classifying input waste using visual recognition technology, means for analyzing acquired waste information and user sentiment information to generate integrated data, and means for calculating and providing rewards based on the user's waste disposal behavior through an application. This promotes accurate waste management and active user participation, enabling a reduction in environmental impact and the realization of sustainable recycling activities. 【0691】 "Visual recognition technology" refers to technologies used to identify and classify specific objects or patterns from image data. 【0692】 "Wireless identification technology" is a technology that uses radio waves to acquire identification information attached to an object without physical contact. 【0693】 "Waste" is a general term for substances, products, and materials that are no longer needed and are disposed of. 【0694】 "Emotional information" refers to data that indicates an emotional state, analyzed from a person's facial expressions and behavior. 【0695】 "Integrated data" refers to data that combines different types of data into a single, unified dataset. 【0696】 A "processing device" is a computer or system used to receive, analyze, and process data. 【0697】 "Feedback" refers to information that conveys reactions and evaluations of user actions. 【0698】 "Behavioral improvement suggestions" are specific proposals and advice for improving user behavior. 【0699】 A "reward" is a point or perk that provides an incentive for a particular action. 【0700】 This invention is an integrated system for enhancing waste management and promoting user recycling activities. This system integrates image recognition technology, wireless identification technology, and emotion recognition technology to enable a series of waste management and user feedback processes. Embodiments of this invention will be described in detail below. 【0701】 First, the smart trash can, which is the terminal itself, is equipped with an image recognition camera, an RFID reader, and an emotion engine. When a user puts in trash, the terminal takes an image of it with its built-in camera. The captured image is analyzed using AI technology to identify the type of waste. Software such as TensorFlow and OpenCV is used for this process. In addition, the RFID tag attached to the waste is automatically read by the RFID reader, and detailed information about the waste is obtained. This information includes product ID and material information. 【0702】 Furthermore, the device's built-in emotion engine captures the user's facial expressions and analyzes their emotional state. For example, using Microsoft's emotion recognition API and SDK, if the system detects that the user is smiling, it records this as a positive emotion. 【0703】 This acquired data is sent in real time to a server, which acts as a processing unit. The server analyzes the integrated data and generates feedback on the user's recycling activities. Using a generative AI model, action plans and reward messages can be created. For example, the server calculates recycling points and provides them as rewards through the application for successful recycling activities. 【0704】 As a concrete example, consider a scenario where a user throws a plastic bottle into a smart trash can at home. If the user places the bottle in with a smile, the device classifies the bottle using image recognition, and the emotion engine records the positive emotion. The server processes this data, and an eco-point is displayed in the app along with a message such as, "Great recycling! Keep it up!" 【0705】 An example of a prompt would be: "When a user throws a plastic bottle into a smart trash can and smiles, what kind of feedback should be provided to increase their motivation for recycling?" This prompt would then be input into the generating AI model as text. 【0706】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0707】 Step 1: 【0708】 The device captures an image with its built-in camera when the user places waste into it. This image data is input to an AI image recognition module. The image recognition algorithm (e.g., TensorFlow or OpenCV) identifies the object from the image and outputs the type of waste (e.g., plastic, metal, paper). Through this process, the waste is visually classified. 【0709】 Step 2: 【0710】 The terminal uses an RFID reader to read the RFID tags attached to the waste. This tag information is entered into an identification database, and detailed information about the waste (e.g., product ID, material information) is output. The RFID data is integrated with image identification results to enhance waste management data. 【0711】 Step 3: 【0712】 The device captures the user's face with its camera and inputs the image into the emotion recognition engine. The emotion recognition system (e.g., emotion recognition API or SDK) analyzes the user's facial expressions and outputs an emotional state (e.g., positive, negative). This information is recorded as the user's emotion data. 【0713】 Step 4: 【0714】 The device transmits integrated waste management data and sentiment data to the server in real time. The server analyzes the received data as input and uses a generative AI model to determine the content of the feedback to the user. The analyzed data outputs action plans for feedback and messages of praise. 【0715】 Step 5: 【0716】 Based on the data analysis results, the server sends feedback to the user's smartphone app. The message includes incentives such as points added to the user's account and a message of appreciation. This feedback can help users increase their motivation for recycling. 【0717】 (Application Example 2) 【0718】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0719】 Waste management in modern cities is crucial from an environmental protection standpoint, but maintaining and improving residents' motivation for recycling remains a challenge. While conventional systems allow for proper waste classification and processing, they lack mechanisms to provide feedback that fully considers user emotions and motivations. Therefore, a sustainable approach is needed to prevent recycling activities from being merely a one-off effort. 【0720】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0721】 In this invention, the server includes means for identifying the waste being submitted using image recognition technology, means for analyzing the user's emotional state and providing positive feedback, and means for managing points based on the user's waste disposal activities. This makes it possible to provide positive feedback on the user's actions, in addition to the proper classification and disposal of waste, thereby sustainably promoting recycling activities among individual residents. 【0722】 "Image recognition technology" is a technology used to automatically identify the type of waste that has been put into the system. 【0723】 A "wireless identification tag" is a device that electronically stores detailed information about waste and transmits that information via wireless communication. 【0724】 A "central computing system" is a computer system for centrally collecting and analyzing waste data. 【0725】 "Methods for analyzing the user's emotional state" refers to technology that analyzes the user's facial expressions and actions when disposing of waste and recognizes their emotions. 【0726】 "Means of providing positive feedback" refers to a function that sends users messages of praise and encouragement based on sentiment analysis results in order to increase their motivation for recycling activities. 【0727】 "A means of managing points based on users' waste disposal activities" refers to a system that awards eco-points to users in accordance with their recycling activities and records and manages their history. 【0728】 This invention is a system designed to streamline waste management in smart cities and promote recycling activities among residents. The system utilizes smart trash cans as terminals and integrates image recognition technology, wireless identification tags, and sentiment analysis technology. 【0729】 First, a camera on the terminal photographs the waste submitted by the user, and image recognition technology is used to identify its type. Furthermore, the terminal reads a wireless identification tag to obtain detailed information about the waste and transmits it to the central computer system. 【0730】 This central computing system utilizes image processing libraries such as OpenCV to analyze acquired data. Using emotion analysis technology, it analyzes the user's facial expressions and actions when disposing of waste, recognizing the user's emotional state. Based on the user's emotions, it generates positive feedback and notifies the user via communication technology. 【0731】 As an example, if the system detects a smile when a user places a plastic bottle into a smart trash can, the central computer system recognizes this positive emotion and sends a message to the smartphone application saying, "Great recycling! Keep it up!" This feedback is expected to further encourage the user's recycling activities. 【0732】 A concrete example of a prompt statement is, "In Tokyo's smart city, install smart trash cans for waste management and utilize emotion recognition technology to promote citizens' recycling activities." In this way, the present invention combines technology and emotion analysis to enhance residents' motivation and realize sustainable waste management. 【0733】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0734】 Step 1: 【0735】 When a user places waste into the terminal, it takes a picture of the waste with its camera. The captured image is acquired by the terminal. The input at this time is the physical waste, and the output is digital image data. The terminal analyzes this data using image recognition technology to identify the type of waste. For example, it classifies it into categories such as plastic bottles, paper, and metal. 【0736】 Step 2: 【0737】 The terminal reads the wireless identification tag attached to the waste using a reader. The information obtained from the tag is the input, and detailed information about the waste is acquired using the RFID reader. This information is matched with the identified waste type and stored as waste management data. The output is organized waste management data. 【0738】 Step 3: 【0739】 The device uses captured images of the user to analyze their facial expressions using emotion analysis technology. The input is an image of the user's face. The output generates emotional states such as smiles or unhappiness. For example, a smile would be classified as "positive." The specific operation of emotion analysis involves extracting facial features as numerical data and classifying them using a generative AI model. 【0740】 Step 4: 【0741】 The terminal transmits identified waste data and sentiment analysis data to the central computer system in real time. The input consists of waste type data and user sentiment data, while the output is the central computer system receiving the data. The terminal uses a communication module to perform the specific operation of transmitting data to the central system. 【0742】 Step 5: 【0743】 The server analyzes the received data in a central computing system. The input consists of waste data and sentiment data. The server uses a generative AI model to analyze the input data and evaluate the user's recycling activities. The output includes positive feedback messages and calculated eco-points. 【0744】 Step 6: 【0745】 The server generates and notifies the user of positive feedback based on the analysis results. The input is the analyzed evaluation data, and the output is a message displayed on the user's smartphone or PC. For example, a message such as "Great recycling! Keep it up!" is generated. The server then uses a notification system to send the feedback to the user. 【0746】 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. 【0747】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0748】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0749】 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. 【0750】 Figure 9 shows an 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. 【0751】 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. 【0752】 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. 【0753】 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, motorcycles, etc., 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, for example, based 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. 【0754】 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." 【0755】 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. 【0756】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0757】 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0758】 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. 【0759】 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. 【0760】 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. 【0761】 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. 【0762】 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. 【0763】 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. 【0764】 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. 【0765】 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 the like 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. 【0766】 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 as being incorporated by reference. 【0767】 The following is further disclosed regarding the embodiments described above. 【0768】 (Claim 1) 【0769】 A means of identifying waste that has been put in using image recognition technology, 【0770】 A means for automatically sorting identified waste into appropriate containers, 【0771】 A means of reading wireless identification tags attached to waste, 【0772】 A means for transmitting waste management data to a central computer system, 【0773】 A means of analyzing the transmitted data and generating suggestions for product improvement, 【0774】 A means of managing points based on the user's waste disposal activity, 【0775】 A system that includes this. 【0776】 (Claim 2) 【0777】 The system according to claim 1, wherein a central computer system evaluates the environmental impact based on the waste identification results and proposes improvements to specific products. 【0778】 (Claim 3) 【0779】 The system according to claim 1, which allows a user to check points and redeem them for cash. 【0780】 "Example 1" 【0781】 (Claim 1) 【0782】 A means of identifying waste that has been put in using image recognition technology, 【0783】 A means for automatically sorting identified waste into appropriate containers, 【0784】 A means of reading wireless identification tags attached to waste, 【0785】 A means for transmitting waste management data to a central computer system, 【0786】 A means of analyzing transmitted data to analyze waste recycling trends and generate suggestions for product improvement, 【0787】 A means of aggregating and visualizing eco-points based on users' waste disposal activities, 【0788】 A system that includes this. 【0789】 (Claim 2) 【0790】 The system according to claim 1, wherein a central computer system analyzes waste trends and recycling rates, evaluates environmental impact, and proposes improvements to specific products. 【0791】 (Claim 3) 【0792】 The system according to claim 1, which allows users to check eco-points and exchange them for services or goods provided by local communities and businesses. 【0793】 "Application Example 1" 【0794】 (Claim 1) 【0795】 A means for identifying waste using image recognition means, 【0796】 A means for automatically classifying identified waste into appropriate storage equipment, 【0797】 A means for obtaining wireless identification information attached to waste, 【0798】 A means for transmitting waste management information to a centralized information processing device, 【0799】 A means of analyzing transmitted information to generate suggestions for product improvement, 【0800】 A means of managing rewards based on the user's waste disposal behavior, 【0801】 A means of outputting waste classification information via a virtual environment and displaying it to the user, 【0802】 A system that includes this. 【0803】 (Claim 2) 【0804】 The system according to claim 1, wherein a centralized information processing device evaluates the environmental impact based on the waste identification results and proposes improvements to specific products. 【0805】 (Claim 3) 【0806】 The system according to claim 1, which allows a user to check their rewards and convert them into other values. 【0807】 "Example 2 of combining an emotion engine" 【0808】 (Claim 1) 【0809】 A method for classifying waste that has been introduced using visual recognition technology, 【0810】 A means of acquiring identification information attached to waste using wireless identification technology, 【0811】 A means for analyzing acquired waste information and user sentiment information to generate integrated data, 【0812】 Means for transmitting integrated data to a processing unit, 【0813】 A means for generating user feedback and suggestions for behavioral improvement based on processed data, 【0814】 A means of calculating and providing rewards through an application based on the user's waste disposal behavior, 【0815】 A system that includes this. 【0816】 (Claim 2) 【0817】 The system according to claim 1, wherein the processing device analyzes waste and emotional data to generate suggestions for optimizing user behavior. 【0818】 (Claim 3) 【0819】 The system according to claim 1, which allows users to check rewards and use those rewards as incentives. 【0820】 "Application example 2 when combining with an emotional engine" 【0821】 (Claim 1) 【0822】 A means of identifying waste that has been put in using image recognition technology, 【0823】 A means for automatically sorting identified waste into appropriate containers, 【0824】 A means of reading wireless identification tags attached to waste, 【0825】 A means for transmitting waste management data to a central computer system, 【0826】 A means of analyzing the transmitted data and generating suggestions for product improvement, 【0827】 A means of managing points based on the user's waste disposal activity, 【0828】 A means of analyzing the user's emotional state and providing positive feedback, 【0829】 A system that includes this. 【0830】 (Claim 2) 【0831】 The system according to claim 1, wherein a central computer system evaluates the environmental impact based on the waste identification results and proposes improvements to specific products. 【0832】 (Claim 3) 【0833】 The system according to claim 1, which allows a user to check points and redeem them for cash. [Explanation of Symbols] 【0834】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A means of identifying waste that has been put in using image recognition technology, A means for automatically sorting identified waste into appropriate containers, A means of reading wireless identification tags attached to waste, A means for transmitting waste management data to a central computer system, A means of analyzing the transmitted data and generating suggestions for product improvement, A means of managing points based on the user's waste disposal activity, A system that includes this. [Claim 2] The system according to claim 1, wherein a central computer system evaluates the environmental impact based on the waste identification results and proposes improvements to specific products. [Claim 3] The system according to claim 1, which allows a user to check points and redeem them for cash.

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