system

Abstract

We provide the system. [Solution] A means for acquiring images of waste using an image acquisition means, A communication means for transmitting the acquired image data to a server, A means for analyzing the image using a generative artificial intelligence model operating within the server and determining the classification of waste and appropriate sorting methods, Means for transmitting the determined sorting method and related environmental information to a terminal, A system including a display means for displaying the transmitted sorting information to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, awareness of environmental issues has been increasing globally. However, it is difficult for ordinary consumers to correctly understand the complicated waste sorting methods and incorporate them into their daily lives. As a result, many people carry out incorrect sorting, leading to a decrease in the recycling efficiency of resources and an increase in environmental load. Therefore, it is required to enable users to easily and correctly sort waste and improve environmental awareness.

Means for Solving the Problems

[0005] This invention provides a system in which a user takes an image of waste, sends the image to a server, and a generating artificial intelligence model on the server analyzes the image. The system then determines the classification of the waste and the appropriate sorting method, and provides this information to the user's terminal. This system allows users to easily and accurately sort their waste. Furthermore, users can earn points by viewing environmental education content and participating in eco-activities, thereby promoting sustainable lifestyle habits.

[0006] "Image acquisition means" refers to a device or function that allows the user to take images of waste.

[0007] "Communication means" refers to the network connection function for sending acquired image data to the server.

[0008] A "generative artificial intelligence model" is an algorithm that learns from data and analyzes images to determine the classification and sorting methods of waste.

[0009] A "server" is a computing resource that receives image data and runs a generational artificial intelligence model.

[0010] "Display means" refers to display devices or interfaces that visually provide users with analysis results and classification information.

[0011] "Waste" refers to unwanted items and materials that require processing or sorting.

[0012] "Classification" is the act of organizing waste based on its type and characteristics and dividing it into different categories.

[0013] "Waste sorting methods" refer to the established methods for separating waste into different types in order to properly process or reuse it.

[0014] "Environmental education content" refers to information provision and programs designed to teach users about environmental protection and sustainable lifestyles.

[0015] "Eco activities" refer to actions and events aimed at environmental protection and effective use of resources, and users can participate in them.

[0016] "Points" are rewards or scores that users can obtain by participating in eco activities or using environmental education content.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained. ]>

[0020] In the following embodiments, the numbered 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. [[ID=2!]]

[0021] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the numbered 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.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system that helps users easily and properly sort their everyday waste. This system can be implemented by installing an application on the user's device and connecting to a server.

[0039] The application is used when users process waste generated in their daily lives. First, the device acquires an image of the waste through its camera. This image data is then transmitted to a server via the network.

[0040] The server inputs the received image data into a generating artificial intelligence model. This model is a sophisticated algorithm that performs image analysis based on a vast amount of training data. The server uses the model to identify the type of waste and determine the appropriate sorting method. For example, if a plastic container is included, it will instruct the server to separate the cap from the container body for disposal.

[0041] Analysis results and sorting advice are sent from the server to the terminal. The terminal receives the information and displays it to the user in a visually easy-to-understand format. Based on this display, the user can actually sort the waste correctly.

[0042] Furthermore, users can view environmental education content and participate in eco-friendly activities through this application. These activities earn points, which are then credited to their account. The point system is designed to encourage users to continuously improve their environmental awareness and participate in eco-friendly activities.

[0043] As a concrete example, suppose a user takes a picture of an empty plastic bottle. The device sends this picture to a server, where a generative artificial intelligence model analyzes the image. As a result, it determines that the bottle cap and the bottle body should be separated, and this information is fed back to the user. The user can then properly separate the waste according to these instructions. Through this entire process, the present invention significantly simplifies the user's daily sorting tasks and contributes to solving environmental problems.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user launches an application on their device and uses the camera to take pictures of the waste that needs to be sorted. The device then imports the image data taken by the user into the system.

[0047] Step 2:

[0048] The device compresses the acquired image data and converts it into a format that can be efficiently transmitted to the server. Next, the device transmits the converted image data to the server via the internet.

[0049] Step 3:

[0050] The server receives image data sent from the terminal. The server verifies the integrity of the data and performs preprocessing, such as noise reduction and optimizing the image quality.

[0051] Step 4:

[0052] A generational artificial intelligence model on the server analyzes the pre-processed images. The model detects objects in the images and identifies the waste category based on their characteristics.

[0053] Step 5:

[0054] The server determines the appropriate waste sorting method based on the analysis results of the generated artificial intelligence model. Next, the server generates sorting advice based on the determined information and compiles easy-to-understand instructions for the user.

[0055] Step 6:

[0056] The server sends the generated sorting advice and related information to the terminal as a data package. The data is optimized to be fed back to the user in real time.

[0057] Step 7:

[0058] The terminal receives and decodes sorting advice sent from the server. The terminal displays the instructions to the user in a visually easy-to-understand format.

[0059] Step 8:

[0060] Users sort their waste according to the advice displayed on their device. Once sorting is complete, users can check the information in the application.

[0061] Step 9:

[0062] The device sends point information to the server based on the user's selective actions and eco-friendly activities. The server records this information in the user's profile and stores it in a database as a history of the user's sustainable activities.

[0063] (Example 1)

[0064] 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."

[0065] The present invention aims to provide a system for easily and accurately sorting increasing amounts of waste. Conventional sorting methods were time-consuming for users and required accurate knowledge of waste classification. Furthermore, there was a lack of means to sustainably promote increased environmental awareness. To solve these problems, the present invention has developed a system that provides automated waste classification and sorting advice in real time, encouraging users to raise environmental awareness and participate in sustainable activities.

[0066] 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.

[0067] In this invention, the server includes means for acquiring image data of waste using an image data acquisition device, a communication device for transmitting the acquired image data to a data processing device, and means for analyzing the image data using an automatic analysis model operating within the data processing device to determine the classification of the waste and the appropriate sorting method. This makes it possible for users to sort waste easily and reliably without requiring specialized knowledge. Furthermore, by providing environmental awareness content and encouraging participation in sustainable activities, it is possible to continuously improve users' environmental awareness.

[0068] An "image data acquisition device" refers to a device used to capture and acquire image data of waste, such as a camera on a terminal.

[0069] A "communication device" is a device that has the function of transmitting acquired image data to a data processing device.

[0070] A "data processing device" is a device that has the processing capabilities to perform image data analysis and classification on a server.

[0071] An "automated analysis model" is a generative AI model used to classify waste from image data and determine the appropriate sorting method based on that classification.

[0072] A "display device" is a device that has the function of visually displaying sorting methods and related information to the user.

[0073] "User" refers to an individual or organization that uses this system to receive and implement waste sorting instructions.

[0074] "Incentives" refer to points or rewards that users can earn by using environmental awareness content or participating in sustainable activities.

[0075] "Activity history" refers to data that records users' participation in environmental awareness content and sustainable activities.

[0076] This invention is a system that supports the proper sorting of waste, and is realized by using an application installed on a terminal and an automated analysis model on a server.

[0077] The user installs a specific application on their device. This application has the functionality to acquire image data of waste using the device's camera. When the user takes a picture of the waste they wish to separate, the device sends the image data to the server via a communication device.

[0078] The server inputs the received image data into an automated analysis model, which is an AI model that generates data. This automated analysis model is trained on a vast amount of training data and uses algorithms specialized for image analysis to identify the type of waste and determine the appropriate sorting method. In this process, prompts such as "Identify the object in this image and recommend the appropriate disposal method" are used.

[0079] Once the analysis is complete, the server sends instructions and relevant environmental information to the terminal. This information is then visually displayed to the user on the terminal's display device. For example, a specific example might be an instruction such as, "Remove the cap from the plastic bottle and dispose of the bottle itself in a separate container."

[0080] Furthermore, users can view environmental awareness content and participate in various sustainable activities through the application. The incentives earned from these activities are reflected in the user's activity history. Through this process, the system encourages users to improve their environmental awareness and practice sustainable living.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] The user takes a picture of the waste using the device's camera. The input for this step is the waste itself that the user photographs. The device uses the camera to acquire image data of the waste and saves the image file.

[0084] Step 2:

[0085] The terminal sends the acquired image data to the server. The input for this step is the image data stored within the terminal, and the output is the image data sent to the server. The terminal securely and quickly uploads the image data to the server via the internet.

[0086] Step 3:

[0087] The server inputs the transmitted image data into the generative AI model. The input for this step is the image data received by the server, and the model is instructed using the prompt message "Identify the object in this image and recommend an appropriate disposal method." Based on this data, the generative AI model performs image analysis to identify the type of waste and determine the sorting method.

[0088] Step 4:

[0089] The server determines the waste classification and sorting method based on the analysis results and transmits this information to the terminal. The input for this step is the analysis results obtained from the generated AI model, and the output is the specific sorting instructions sent to the terminal. The server transmits the results to the terminal in real time.

[0090] Step 5:

[0091] The terminal receives information from the server and displays sorting instructions to the user. The input for this step is sorting information received from the server, and the output is a sorting guide that the user can visually confirm. The terminal conveys instructions to the user by displaying sorting methods in text and diagrams on its display.

[0092] (Application Example 1)

[0093] 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."

[0094] Currently, waste sorting and separation within factories heavily rely on manual labor, making efficient and consistent operation difficult. This problem could lead to decreased recycling efficiency and increased environmental burden, thus necessitating a more automated and integrated system.

[0095] 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.

[0096] In this invention, the server includes means for acquiring images of waste using image acquisition means, communication means for transmitting the acquired image data to the server, means for analyzing the images using a generative artificial intelligence model operating within the server to determine the classification of the waste and an appropriate sorting method, and means for an automated machine to move or process the waste in an appropriate storage device based on the analysis results. This automates waste processing within the factory, enabling efficient and consistent recycling operations.

[0097] "Image acquisition means" refers to a device or method for photographing and acquiring images of waste.

[0098] "Communication means" refers to the infrastructure or protocol used to transmit acquired image data to a server.

[0099] A "generative artificial intelligence model" is an algorithm or software that has the ability to learn from vast amounts of data, analyze images of waste, and derive classification and sorting methods.

[0100] "Separation information" refers to information about the classification and handling of waste derived from the analyzed images.

[0101] "Display means" refers to a device or method for visually conveying sorting information to the user.

[0102] An "automated machine" is a mechanical device that autonomously handles waste based on analysis results.

[0103] A "storage device" is an approach or device for storing or holding sorted waste.

[0104] In the system for implementing this invention, an automated waste treatment system is first introduced to efficiently classify and separate waste. This system is configured as follows:

[0105] The server acquires images of the waste using an image acquisition system equipped with a camera for photographing the waste. A possible camera for this system would be a Logitech C920. The acquired image data is transmitted to the server in real time via a communication system.

[0106] The server analyzes the received image data using a generative artificial intelligence model. This analysis utilizes machine learning frameworks such as TENSORFLOW® and PyTorch. The generative AI model has been trained on a large amount of image data related to waste, and determines the appropriate classification and sorting method based on the type and condition of the waste.

[0107] Next, the server controls the automated machinery based on this decision, moving or processing the waste in the appropriate storage device. This enables efficient and accurate waste disposal without human intervention.

[0108] A concrete example is an automated machine positioned on a factory line that scans incoming waste in real time, instantly identifying plastics, metals, and other materials, and sorting them into their respective recycling containers. An example of a prompt message used in this process would be, "Please tell me which category this waste should be sorted into."

[0109] The introduction of this system will improve the efficiency of waste disposal, reduce environmental impact, and maximize resource recovery.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device acquires images of waste using its camera. The input is the waste present at the location, and the output is digital image data. The camera photographs the waste and converts it into image data.

[0113] Step 2:

[0114] The terminal sends the acquired image data to the server. The input is digital image data, and the output is the transfer of image data to the server. A communication module is used to upload data to the server via a specific network protocol (e.g., HTTP, MQTT).

[0115] Step 3:

[0116] The server inputs the received image data into a generating AI model. In this step, the server uses the received image data as input and obtains the AI ​​analysis results as output. For data analysis, a TensorFlow or PyTorch-based image recognition model is used to analyze the features of each pixel data and identify the type of waste.

[0117] Step 4:

[0118] The server uses the analysis results from the generated AI model to determine the classification of waste and the appropriate sorting method. The input is the analysis results of the AI ​​model, and the output is specific classification instructions and sorting procedures. Based on the analysis results, it generates sorting guides such as "Plastic, remove caps" and "Metal, complete separation is required."

[0119] Step 5:

[0120] The server transmits the sorting information it has determined to the terminal. The input is the sorting information processed and generated by the server, and the output is the data displayed on the terminal. The data is transmitted using a communication method in a format that the terminal can receive.

[0121] Step 6:

[0122] The terminal displays the received sorting information to the user. The input is sorting information received from the server, and the output is a visual display for the user. A display device such as an LCD is used to present the sorting method in a way that the user can easily understand.

[0123] Step 7:

[0124] The automated machine moves or processes waste into appropriate storage devices based on analysis results, without waiting for user instructions. The input is the analyzed information and sorting guide, and the output is the physical movement or processing result of the waste. It controls robotic arms and transfer belts to perform the appropriate processing.

[0125] 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.

[0126] This invention provides a system that assists users in sorting waste in their daily lives, and further combines it with an emotion engine that recognizes the user's emotional state to provide more personalized guidance for eco-friendly activities. To implement this system, the user uses a smartphone or a dedicated terminal as an image acquisition device.

[0127] The user acquires images of waste through the camera. The device sends these images to a server, where the analysis begins. After receiving the images, the server analyzes them using a generative artificial intelligence model that has been previously trained on a large amount of data related to waste, and determines the classification and sorting method of the waste.

[0128] In addition, the present invention further includes an emotion engine. The terminal acquires the user's emotions through voice and facial expressions from the user's camera or microphone. This data is sent to a server, where the emotion engine analyzes the data. Based on this analysis, feedback is generated that corresponds to the user's current emotions. For example, if the user is feeling stressed, specific and concise instructions are provided so that they do not feel burdened by the process.

[0129] The server not only provides advice on sorting methods but also sends motivational messages tailored to the user's emotions to their device. Users receive this information on their device, review the emotionally sensitive instructions, and sort their waste accordingly. Furthermore, rewards and points are adjusted based on the user's emotional state when they engage in eco-friendly activities. For example, users who receive positive feedback may earn more points than usual.

[0130] As a concrete example, consider a scenario where a user tries to sort plastic bottles at the end of a busy day. If the emotion engine detects that the user is a little tired, the server provides concise instructions to help them sort the bottles quickly. These instructions may also include a calming message. With this support, the user can actively participate in eco-friendly activities and, as a result, develop more sustainable lifestyle habits.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user launches an application on their device and takes a picture of the waste with the camera. The device then captures this image data and prepares to send it to the server.

[0134] Step 2:

[0135] The device acquires emotional data through the user's facial expressions and voice. This data is collected using the camera or microphone and sent to a server.

[0136] Step 3:

[0137] The server receives image data and emotion data sent from the terminal. After verifying the integrity of the data, the analysis process begins.

[0138] Step 4:

[0139] The AI ​​model on the server analyzes the received image data to identify waste and determine the appropriate sorting method. The analysis results are then generated as specific instructions.

[0140] Step 5:

[0141] The server uses an emotion engine to analyze the user's emotional data. Based on the user's emotional state, it generates appropriate messages and motivational feedback to accompany the discriminatory advice.

[0142] Step 6:

[0143] The server integrates the image analysis results and emotion-based feedback, and sends it to the terminal as a single data package.

[0144] Step 7:

[0145] The terminal decodes the integrated data received from the server. It then displays discretionary advice and emotionally sensitive messages to the user on the screen.

[0146] Step 8:

[0147] Users check the instructions displayed on their device and sort their waste based on the analysis results and emotional feedback. This information supports users' eco-friendly activities and leads to sustainable behavior.

[0148] Step 9:

[0149] The device sends data to the server regarding the user's responsible behavior, eco-friendly activities, and their responses to feedback. The server analyzes this data and updates the points system as needed.

[0150] (Example 2)

[0151] 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 as the "terminal".

[0152] While the importance of waste sorting in environmental conservation is increasing, its effective implementation is challenging due to individual users' emotional states and understanding of sorting methods. Many people are reluctant to sort waste, and this problem hinders the establishment of sustainable lifestyle habits.

[0153] 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.

[0154] In this invention, the server includes means for analyzing images of items to determine a sorting method, means for analyzing user emotional data to generate motivational messages, and means for providing the generated information to the user. This enables appropriate waste sorting guidance and motivation according to the user's emotional state.

[0155] "Image acquisition means" refers to devices or methods for users to acquire images of objects.

[0156] "Communication means" refers to methods and protocols for transmitting acquired data to another device or system.

[0157] An "information processing device" refers to a computer or system used to analyze data and make decisions.

[0158] An "intelligent model" refers to an algorithm or system that learns from data on objects and analyzes their characteristics.

[0159] "Means for acquiring emotional data" refers to devices or methods for detecting a user's emotional state.

[0160] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and makes decisions based on the results.

[0161] A "motivational message" refers to a message designed to encourage a user to take a specific action.

[0162] "Sorting method" refers to the process or procedure of classifying items according to specific criteria.

[0163] Embodiments of this invention will be described.

[0164] First, the user acquires an image of the target object using a smartphone or dedicated terminal. This terminal has a camera function, allowing the user to photograph waste or other items. The terminal transmits the acquired image data to an information processing device using a communication method. Wireless communication technology is used for this communication, utilizing Wi-Fi or mobile data communication.

[0165] The information processing device analyzes received image data using an intelligent model. This model is pre-trained on a large amount of data and has the ability to determine how to classify and sort items. The information processing device also includes an emotion analysis engine that analyzes the user's emotional data. The user's terminal acquires facial expressions and voice through its camera and microphone and transmits them as emotional data.

[0166] The analysis results in the generation of motivational messages tailored to the user's emotions, along with methods for sorting items. For example, if the user is prone to stress, a message such as "You've worked hard! Please try this easy sorting method" will be generated.

[0167] The feedback generated by the information processing device is then transmitted back to the user's terminal via communication means. The user can then confirm these instructions via the terminal's display and proceed with sorting the items. This entire process allows the user to sort waste more effectively and contribute to environmental protection.

[0168] As a concrete example, when a user sorts plastic bottles, the information processing device acquires an image, analyzes the image, and determines that "this bottle is recyclable." Depending on the user's emotional state, a message encouraging them to take action is displayed, along with specific instructions such as "Please remove the bottle cap and place it in the recycling bin."

[0169] An example of a prompt to input into a generative AI model might be: "Identify the objects in this image and provide a message tailored to the user's emotional state, along with an appropriate sorting method."

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] The user acquires images of waste using a device. The input is a still image taken with the device's built-in camera. Ideally, this image should be high-resolution and clearly show the characteristics of the waste. At this stage, the image data is stored in the device's temporary memory.

[0173] Step 2:

[0174] The terminal transmits the acquired image data to the information processing device. The input is the image data acquired in step 1, and the output is the transmission of the image data to the server. A secure protocol (e.g., HTTPS) is used for communication, ensuring the reliability and security of the data.

[0175] Step 3:

[0176] The server inputs the received image data into a generating AI model for analysis. The input is image data, and the output is classification information of the items and the result of the sorting method determination. The AI ​​model is trained on a large dataset and achieves high-precision classification using image analysis algorithms.

[0177] Step 4:

[0178] The user's device uses its camera and microphone to acquire user emotion data in real time. Inputs include the user's facial expressions and voice, while output is quantified emotion data. This is done using an emotion recognition algorithm, which analyzes the data based on multiple emotion scales.

[0179] Step 5:

[0180] The server inputs emotional data into the emotion analysis engine for analysis. The input is the emotional data acquired in step 4, and the output is the judgment result regarding the user's emotional state. The engine can identify complex emotional patterns using machine learning techniques.

[0181] Step 6:

[0182] The server generates feedback for the user based on the waste sorting method and the analyzed emotional state. The input is the output of steps 3 and 5, and the output is the sorting procedure and motivational messages. This provides appropriate guidance that takes the user's emotions into consideration.

[0183] Step 7:

[0184] The server sends the generated feedback to the user's terminal. The input is the feedback information created in step 6, and the output is the feedback display on the user's terminal. This ensures that the feedback is accurately communicated to the user.

[0185] Step 8:

[0186] The user actually sorts the waste based on the feedback received on the device. The input is the instructions and messages received in step 7, and the output is the properly sorted waste. This encourages action toward environmental protection.

[0187] This series of steps allows users to receive personalized guidance that reflects their emotional state, enabling them to effectively sort their waste.

[0188] (Application Example 2)

[0189] 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".

[0190] Waste sorting is often a cumbersome and stressful process, leading to decreased motivation. This invention aims to improve the user experience by providing a system for efficiently sorting waste while considering the user's emotional state.

[0191] 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.

[0192] In this invention, the server includes means for acquiring images of objects using image acquisition means, means for acquiring the user's emotional state using emotion recognition means, and means for generating and sending appropriate motivational messages to the user based on the emotional state. This enables the user to efficiently sort objects while receiving instructions that take their emotions into consideration.

[0193] "Image acquisition means" refers to a function that uses a camera or similar device to capture an image of an object and acquire that data.

[0194] "Communication means" refers to the means for transmitting acquired data to other devices, and is a function that performs data transmission via a network.

[0195] An "information processing device" refers to a device that receives and analyzes data, and is generally called a server.

[0196] A "generative artificial intelligence model" refers to a software model that learns patterns from large amounts of data and performs image analysis and sentiment analysis.

[0197] "Means for determining classification and processing methods" refers to a function that uses a generative artificial intelligence model to analyze the characteristics of objects from analyzed images and determine the appropriate processing method.

[0198] "Emotion recognition means" refers to a method for acquiring and analyzing a user's emotions from voice and facial expression data.

[0199] "Means for generating and sending motivational messages" refers to a function that creates messages tailored to the user's emotional state and sends them to the user's device.

[0200] "Display means" refers to a method for visually presenting analysis results or messages to the user, and generally involves using a display.

[0201] The system for implementing this invention mainly consists of the following hardware and software. The hardware includes a smartphone or dedicated terminal equipped with a camera and microphone. The software consists of a generative artificial intelligence model and an emotion recognition engine running on a server.

[0202] When a user takes a picture of waste using their device's camera, the image data is transmitted to a server via a communication method. On the server side, a generative artificial intelligence model analyzes this image to determine the classification of the waste and the appropriate disposal method. Because this model has been pre-trained on a large amount of image data, it is capable of highly accurate analysis.

[0203] Simultaneously, the emotion recognition engine acquires the user's voice and facial expression data and analyzes their emotional state. If the user is feeling stressed, it generates motivational messages to help them sort waste in a more enjoyable way. These messages are sent from the server to the user's device and displayed.

[0204] As a concrete example, when a user attempts to use the application to put a plastic bottle into a smart trash can, the device receives instructions from the server to recycle the bottle and a message such as "Thank you for your eco-friendly efforts today!", which are then displayed on the user's screen.

[0205] Examples of prompt messages include the following:

[0206] "How would you give instructions if the user is holding a water bottle? Also, what special message would you provide if the user is a little tired?"

[0207] In this way, a system is provided that allows users to easily and correctly sort waste and support sustainable living.

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The user takes an image of the waste using the camera on their device. The input is the image of the waste acquired through the camera, and the output is the image data. This image data is transmitted to the server via a communication method.

[0211] Step 2:

[0212] The server takes the received image data as input and performs image analysis using a generative artificial intelligence model. Here, the image data is processed as digital information, and waste is identified based on an object recognition algorithm. The output is the classified type of waste and the recommended disposal method.

[0213] Step 3:

[0214] Simultaneously, emotional data is acquired when the user speaks to the device or shows their facial expressions to the camera. The input is this voice and facial data, which is analyzed by the emotion recognition engine. The output is digital information indicating the user's current emotional state.

[0215] Step 4:

[0216] The server generates appropriate motivational messages based on classified waste and the user's emotional state. The input is waste classification information and emotional state information, and the output is the generated message. A generative AI model is used to create emotionally appropriate text.

[0217] Step 5:

[0218] The server sends the analyzed waste disposal method and generated motivational message to the terminal. The input is the disposal method and message generated by the server, and the output is the data transfer to the user terminal.

[0219] Step 6:

[0220] The user's device displays the received information on its screen. The input is information received from the server, and the output is a visual presentation to the user. This operation allows the user to receive instructions and enjoy participating in eco-friendly activities.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is a system that helps users easily and properly sort their everyday waste. This system can be implemented by installing an application on the user's device and connecting to a server.

[0238] The application is used when users process waste generated in their daily lives. First, the device acquires an image of the waste through its camera. This image data is then transmitted to a server via the network.

[0239] The server inputs the received image data into a generating artificial intelligence model. This model is a sophisticated algorithm that performs image analysis based on a vast amount of training data. The server uses the model to identify the type of waste and determine the appropriate sorting method. For example, if a plastic container is included, it will instruct the server to separate the cap from the container body for disposal.

[0240] Analysis results and sorting advice are sent from the server to the terminal. The terminal receives the information and displays it to the user in a visually easy-to-understand format. Based on this display, the user can actually sort the waste correctly.

[0241] Furthermore, users can view environmental education content and participate in eco-friendly activities through this application. These activities earn points, which are then credited to their account. The point system is designed to encourage users to continuously improve their environmental awareness and participate in eco-friendly activities.

[0242] As a concrete example, suppose a user takes a picture of an empty plastic bottle. The device sends this picture to a server, where a generative artificial intelligence model analyzes the image. As a result, it determines that the bottle cap and the bottle body should be separated, and this information is fed back to the user. The user can then properly separate the waste according to these instructions. Through this entire process, the present invention significantly simplifies the user's daily sorting tasks and contributes to solving environmental problems.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user launches an application on their device and uses the camera to take pictures of the waste that needs to be sorted. The device then imports the image data taken by the user into the system.

[0246] Step 2:

[0247] The device compresses the acquired image data and converts it into a format that can be efficiently transmitted to the server. Next, the device transmits the converted image data to the server via the internet.

[0248] Step 3:

[0249] The server receives image data sent from the terminal. The server verifies the integrity of the data and performs preprocessing, such as noise reduction and optimizing the image quality.

[0250] Step 4:

[0251] A generational artificial intelligence model on the server analyzes the pre-processed images. The model detects objects in the images and identifies the waste category based on their characteristics.

[0252] Step 5:

[0253] The server determines the appropriate waste sorting method based on the analysis results of the generated artificial intelligence model. Next, the server generates sorting advice based on the determined information and compiles easy-to-understand instructions for the user.

[0254] Step 6:

[0255] The server sends the generated sorting advice and related information to the terminal as a data package. The data is optimized to be fed back to the user in real time.

[0256] Step 7:

[0257] The terminal receives and decodes sorting advice sent from the server. The terminal displays the instructions to the user in a visually easy-to-understand format.

[0258] Step 8:

[0259] Users sort their waste according to the advice displayed on their device. Once sorting is complete, users can check the information in the application.

[0260] Step 9:

[0261] The device sends point information to the server based on the user's selective actions and eco-friendly activities. The server records this information in the user's profile and stores it in a database as a history of the user's sustainable activities.

[0262] (Example 1)

[0263] 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."

[0264] The present invention aims to provide a system for easily and accurately sorting increasing amounts of waste. Conventional sorting methods were time-consuming for users and required accurate knowledge of waste classification. Furthermore, there was a lack of means to sustainably promote increased environmental awareness. To solve these problems, the present invention has developed a system that provides automated waste classification and sorting advice in real time, encouraging users to raise environmental awareness and participate in sustainable activities.

[0265] 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.

[0266] In this invention, the server includes means for acquiring image data of waste using an image data acquisition device, a communication device for transmitting the acquired image data to a data processing device, and means for analyzing the image data using an automatic analysis model operating within the data processing device to determine the classification of the waste and the appropriate sorting method. This makes it possible for users to sort waste easily and reliably without requiring specialized knowledge. Furthermore, by providing environmental awareness content and encouraging participation in sustainable activities, it is possible to continuously improve users' environmental awareness.

[0267] An "image data acquisition device" refers to a device used to capture and acquire image data of waste, such as a camera on a terminal.

[0268] A "communication device" is a device that has the function of transmitting acquired image data to a data processing device.

[0269] A "data processing device" is a device that has the processing capabilities to perform image data analysis and classification on a server.

[0270] An "automated analysis model" is a generative AI model used to classify waste from image data and determine the appropriate sorting method based on that classification.

[0271] A "display device" is a device that has the function of visually displaying sorting methods and related information to the user.

[0272] "User" refers to an individual or organization that uses this system to receive and implement waste sorting instructions.

[0273] "Incentives" refer to points or rewards that users can earn by using environmental awareness content or participating in sustainable activities.

[0274] "Activity history" refers to data that records users' participation in environmental awareness content and sustainable activities.

[0275] This invention is a system that supports the proper sorting of waste, and is realized by using an application installed on a terminal and an automated analysis model on a server.

[0276] The user installs a specific application on their device. This application has the functionality to acquire image data of waste using the device's camera. When the user takes a picture of the waste they wish to separate, the device sends the image data to the server via a communication device.

[0277] The server inputs the received image data into an automated analysis model, which is an AI model that generates data. This automated analysis model is trained on a vast amount of training data and uses algorithms specialized for image analysis to identify the type of waste and determine the appropriate sorting method. In this process, prompts such as "Identify the object in this image and recommend the appropriate disposal method" are used.

[0278] Once the analysis is complete, the server sends instructions and relevant environmental information to the terminal. This information is then visually displayed to the user on the terminal's display device. For example, a specific example might be an instruction such as, "Remove the cap from the plastic bottle and dispose of the bottle itself in a separate container."

[0279] Furthermore, users can view environmental awareness content and participate in various sustainable activities through the application. The incentives earned from these activities are reflected in the user's activity history. Through this process, the system encourages users to improve their environmental awareness and practice sustainable living.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The user takes a picture of the waste with the terminal's camera. The input for this step is the waste itself that the user photographs. The terminal uses the camera to obtain the image data of the waste and saves the image file.

[0283] Step 2:

[0284] The terminal sends the acquired image data to the server. The input for this step is the image data stored in the terminal, and the output is the image data sent to the server. The terminal uploads the image data to the server safely and quickly via the Internet.

[0285] Step 3:

[0286] The server inputs the received image data into the generative AI model. The input for this step is the image data received by the server, and the model is instructed using the prompt sentence "Please identify the object in this image and recommend an appropriate waste disposal method." Based on this data, the generative AI model performs image analysis, identifies the type of waste, and determines the sorting method.

[0287] Step 4:

[0288] The server determines the classification result and sorting method of the waste based on the analysis result and sends that information to the terminal. The input for this step is the analysis result obtained from the generative AI model, and the output is the specific sorting instruction sent to the terminal. The server transmits the result to the terminal in real time.

[0289] Step 5:

[0290] The terminal receives the information from the server and displays the sorting instruction to the user. The input for this step is the sorting information received from the server, and the output is the sorting guide that the user can visually confirm. The terminal conveys the instruction to the user by displaying the sorting method in text or illustration on the display.

[0291] (Application Example 1)

[0292] 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."

[0293] Currently, waste sorting and separation within factories heavily rely on manual labor, making efficient and consistent operation difficult. This problem could lead to decreased recycling efficiency and increased environmental burden, thus necessitating a more automated and integrated system.

[0294] 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.

[0295] In this invention, the server includes means for acquiring images of waste using image acquisition means, communication means for transmitting the acquired image data to the server, means for analyzing the images using a generative artificial intelligence model operating within the server to determine the classification of the waste and an appropriate sorting method, and means for an automated machine to move or process the waste in an appropriate storage device based on the analysis results. This automates waste processing within the factory, enabling efficient and consistent recycling operations.

[0296] "Image acquisition means" refers to a device or method for photographing and acquiring images of waste.

[0297] "Communication means" refers to the infrastructure or protocol used to transmit acquired image data to a server.

[0298] A "generative artificial intelligence model" is an algorithm or software that has the ability to learn from vast amounts of data, analyze images of waste, and derive classification and sorting methods.

[0299] "Separation information" refers to information about the classification and handling of waste derived from the analyzed images.

[0300] "Display means" refers to a device or method for visually conveying sorting information to the user.

[0301] An "automated machine" is a mechanical device that autonomously handles waste based on analysis results.

[0302] A "storage device" is an approach or device for storing or holding sorted waste.

[0303] In the system for implementing this invention, an automated waste treatment system is first introduced to efficiently classify and separate waste. This system is configured as follows:

[0304] The server acquires images of the waste using an image acquisition system equipped with a camera for photographing the waste. A possible camera for this system would be a Logitech C920. The acquired image data is transmitted to the server in real time via a communication system.

[0305] The server analyzes the received image data using a generative artificial intelligence model. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch. The generative AI model has been trained on a large amount of image data related to waste, and determines the appropriate classification and sorting method based on the type and condition of the waste.

[0306] Next, the server controls the automated machinery based on this decision, moving or processing the waste in the appropriate storage device. This enables efficient and accurate waste disposal without human intervention.

[0307] A concrete example is an automated machine positioned on a factory line that scans incoming waste in real time, instantly identifying plastics, metals, and other materials, and sorting them into their respective recycling containers. An example of a prompt message used in this process would be, "Please tell me which category this waste should be sorted into."

[0308] By introducing this system, the efficiency of waste treatment is improved, enabling the reduction of environmental impact and the maximum recovery of resources.

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The terminal acquires an image of the waste using a camera. The input is the waste present at the site, and the output is digital image data. The camera captures the waste and converts it into data as an image.

[0312] Step 2:

[0313] The terminal transmits the acquired image data to the server. The input is digital image data, and the output is the transfer of the image data to the server. Using a communication module, the data is uploaded to the server via a specific network protocol (e.g., HTTP, MQTT).

[0314] Step 3:

[0315] The server inputs the received image data into the generated AI model. In this step, the image data received by the server is used as the input, and the analysis result by the AI is obtained as the output. For the analysis of the data, an image recognition model based on TensorFlow or PyTorch is used to analyze the characteristics of each pixel data to identify the type of waste.

[0316] Step 4:

[0317] The server uses the analysis result from the generated AI model to determine the classification of the waste and the appropriate separation method. The input is the analysis result of the AI model, and the output is the specific classification instruction and separation procedure. Based on the analysis result, separation guides such as "plastic, remove the cap" and "metal, complete separation required" are generated.

[0318] Step 5:

[0319] The server transmits the sorting information it has determined to the terminal. The input is the sorting information processed and generated by the server, and the output is the data displayed on the terminal. The data is transmitted using a communication method in a format that the terminal can receive.

[0320] Step 6:

[0321] The terminal displays the received sorting information to the user. The input is sorting information received from the server, and the output is a visual display for the user. A display device such as an LCD is used to present the sorting method in a way that the user can easily understand.

[0322] Step 7:

[0323] The automated machine moves or processes waste into appropriate storage devices based on analysis results, without waiting for user instructions. The input is the analyzed information and sorting guide, and the output is the physical movement or processing result of the waste. It controls robotic arms and transfer belts to perform the appropriate processing.

[0324] 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.

[0325] This invention provides a system that assists users in sorting waste in their daily lives, and further combines it with an emotion engine that recognizes the user's emotional state to provide more personalized guidance for eco-friendly activities. To implement this system, the user uses a smartphone or a dedicated terminal as an image acquisition device.

[0326] The user acquires images of waste through the camera. The device sends these images to a server, where the analysis begins. After receiving the images, the server analyzes them using a generative artificial intelligence model that has been previously trained on a large amount of data related to waste, and determines the classification and sorting method of the waste.

[0327] In addition, the present invention further includes an emotion engine. The terminal acquires the user's emotions through voice and facial expressions from the user's camera or microphone. This data is sent to a server, where the emotion engine analyzes the data. Based on this analysis, feedback is generated that corresponds to the user's current emotions. For example, if the user is feeling stressed, specific and concise instructions are provided so that they do not feel burdened by the process.

[0328] The server not only provides advice on sorting methods but also sends motivational messages tailored to the user's emotions to their device. Users receive this information on their device, review the emotionally sensitive instructions, and sort their waste accordingly. Furthermore, rewards and points are adjusted based on the user's emotional state when they engage in eco-friendly activities. For example, users who receive positive feedback may earn more points than usual.

[0329] As a concrete example, consider a scenario where a user tries to sort plastic bottles at the end of a busy day. If the emotion engine detects that the user is a little tired, the server provides concise instructions to help them sort the bottles quickly. These instructions may also include a calming message. With this support, the user can actively participate in eco-friendly activities and, as a result, develop more sustainable lifestyle habits.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user launches an application on their device and takes a picture of the waste with the camera. The device then captures this image data and prepares to send it to the server.

[0333] Step 2:

[0334] The device acquires emotional data through the user's facial expressions and voice. This data is collected using the camera or microphone and sent to a server.

[0335] Step 3:

[0336] The server receives image data and emotion data sent from the terminal. After verifying the integrity of the data, the analysis process begins.

[0337] Step 4:

[0338] The AI ​​model on the server analyzes the received image data to identify waste and determine the appropriate sorting method. The analysis results are then generated as specific instructions.

[0339] Step 5:

[0340] The server uses an emotion engine to analyze the user's emotional data. Based on the user's emotional state, it generates appropriate messages and motivational feedback to accompany the discriminatory advice.

[0341] Step 6:

[0342] The server integrates the image analysis results and emotion-based feedback, and sends it to the terminal as a single data package.

[0343] Step 7:

[0344] The terminal decodes the integrated data received from the server. It then displays discretionary advice and emotionally sensitive messages to the user on the screen.

[0345] Step 8:

[0346] Users check the instructions displayed on their device and sort their waste based on the analysis results and emotional feedback. This information supports users' eco-friendly activities and leads to sustainable behavior.

[0347] Step 9:

[0348] The device sends data to the server regarding the user's responsible behavior, eco-friendly activities, and their responses to feedback. The server analyzes this data and updates the points system as needed.

[0349] (Example 2)

[0350] 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".

[0351] While the importance of waste sorting in environmental conservation is increasing, its effective implementation is challenging due to individual users' emotional states and understanding of sorting methods. Many people are reluctant to sort waste, and this problem hinders the establishment of sustainable lifestyle habits.

[0352] 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.

[0353] In this invention, the server includes means for analyzing images of items to determine a sorting method, means for analyzing user emotional data to generate motivational messages, and means for providing the generated information to the user. This enables appropriate waste sorting guidance and motivation according to the user's emotional state.

[0354] "Image acquisition means" refers to devices or methods for users to acquire images of objects.

[0355] "Communication means" refers to methods and protocols for transmitting acquired data to another device or system.

[0356] An "information processing device" refers to a computer or system used to analyze data and make decisions.

[0357] An "intelligent model" refers to an algorithm or system that learns from data on objects and analyzes their characteristics.

[0358] "Means for acquiring emotional data" refers to devices or methods for detecting a user's emotional state.

[0359] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and makes decisions based on the results.

[0360] A "motivational message" refers to a message designed to encourage a user to take a specific action.

[0361] "Sorting method" refers to the process or procedure of classifying items according to specific criteria.

[0362] Embodiments of this invention will be described.

[0363] First, the user acquires an image of the target object using a smartphone or dedicated terminal. This terminal has a camera function, allowing the user to photograph waste or other items. The terminal transmits the acquired image data to an information processing device using a communication method. Wireless communication technology is used for this communication, utilizing Wi-Fi or mobile data communication.

[0364] The information processing device analyzes received image data using an intelligent model. This model is pre-trained on a large amount of data and has the ability to determine how to classify and sort items. The information processing device also includes an emotion analysis engine that analyzes the user's emotional data. The user's terminal acquires facial expressions and voice through its camera and microphone and transmits them as emotional data.

[0365] The analysis results in the generation of motivational messages tailored to the user's emotions, along with methods for sorting items. For example, if the user is prone to stress, a message such as "You've worked hard! Please try this easy sorting method" will be generated.

[0366] The feedback generated by the information processing device is then transmitted back to the user's terminal via communication means. The user can then confirm these instructions via the terminal's display and proceed with sorting the items. This entire process allows the user to sort waste more effectively and contribute to environmental protection.

[0367] As a concrete example, when a user sorts plastic bottles, the information processing device acquires an image, analyzes the image, and determines that "this bottle is recyclable." Depending on the user's emotional state, a message encouraging them to take action is displayed, along with specific instructions such as "Please remove the bottle cap and place it in the recycling bin."

[0368] An example of a prompt to input into a generative AI model might be: "Identify the objects in this image and provide a message tailored to the user's emotional state, along with an appropriate sorting method."

[0369] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0370] Step 1:

[0371] The user acquires images of waste using a device. The input is a still image taken with the device's built-in camera. Ideally, this image should be high-resolution and clearly show the characteristics of the waste. At this stage, the image data is stored in the device's temporary memory.

[0372] Step 2:

[0373] The terminal transmits the acquired image data to the information processing device. The input is the image data acquired in step 1, and the output is the transmission of the image data to the server. A secure protocol (e.g., HTTPS) is used for communication, ensuring the reliability and security of the data.

[0374] Step 3:

[0375] The server inputs the received image data into a generating AI model for analysis. The input is image data, and the output is classification information of the items and the result of the sorting method determination. The AI ​​model is trained on a large dataset and achieves high-precision classification using image analysis algorithms.

[0376] Step 4:

[0377] The user's device uses its camera and microphone to acquire user emotion data in real time. Inputs include the user's facial expressions and voice, while output is quantified emotion data. This is done using an emotion recognition algorithm, which analyzes the data based on multiple emotion scales.

[0378] Step 5:

[0379] The server inputs emotional data into the emotion analysis engine for analysis. The input is the emotional data acquired in step 4, and the output is the judgment result regarding the user's emotional state. The engine can identify complex emotional patterns using machine learning techniques.

[0380] Step 6:

[0381] The server generates feedback for the user based on the waste sorting method and the analyzed emotional state. The input is the output of steps 3 and 5, and the output is the sorting procedure and motivational messages. This provides appropriate guidance that takes the user's emotions into consideration.

[0382] Step 7:

[0383] The server sends the generated feedback to the user's terminal. The input is the feedback information created in step 6, and the output is the feedback display on the user's terminal. This ensures that the feedback is accurately communicated to the user.

[0384] Step 8:

[0385] The user actually sorts the waste based on the feedback received on the device. The input is the instructions and messages received in step 7, and the output is the properly sorted waste. This encourages action toward environmental protection.

[0386] This series of steps allows users to receive personalized guidance that reflects their emotional state, enabling them to effectively sort their waste.

[0387] (Application Example 2)

[0388] 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."

[0389] Waste sorting is often a cumbersome and stressful process, leading to decreased motivation. This invention aims to improve the user experience by providing a system for efficiently sorting waste while considering the user's emotional state.

[0390] 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.

[0391] In this invention, the server includes means for acquiring images of objects using image acquisition means, means for acquiring the user's emotional state using emotion recognition means, and means for generating and sending appropriate motivational messages to the user based on the emotional state. This enables the user to efficiently sort objects while receiving instructions that take their emotions into consideration.

[0392] "Image acquisition means" refers to a function that uses a camera or similar device to capture an image of an object and acquire that data.

[0393] "Communication means" refers to the means for transmitting acquired data to other devices, and is a function that performs data transmission via a network.

[0394] An "information processing device" refers to a device that receives and analyzes data, and is generally called a server.

[0395] A "generative artificial intelligence model" refers to a software model that learns patterns from large amounts of data and performs image analysis and sentiment analysis.

[0396] "Means for determining classification and processing methods" refers to a function that uses a generative artificial intelligence model to analyze the characteristics of objects from analyzed images and determine the appropriate processing method.

[0397] "Emotion recognition means" refers to a method for acquiring and analyzing a user's emotions from voice and facial expression data.

[0398] "Means for generating and sending motivational messages" refers to a function that creates messages tailored to the user's emotional state and sends them to the user's device.

[0399] "Display means" refers to a method for visually presenting analysis results or messages to the user, and generally involves using a display.

[0400] The system for implementing this invention mainly consists of the following hardware and software. The hardware includes a smartphone or dedicated terminal equipped with a camera and microphone. The software consists of a generative artificial intelligence model and an emotion recognition engine running on a server.

[0401] When a user takes a picture of waste using their device's camera, the image data is transmitted to a server via a communication method. On the server side, a generative artificial intelligence model analyzes this image to determine the classification of the waste and the appropriate disposal method. Because this model has been pre-trained on a large amount of image data, it is capable of highly accurate analysis.

[0402] Simultaneously, the emotion recognition engine acquires the user's voice and facial expression data and analyzes their emotional state. If the user is feeling stressed, it generates motivational messages to help them sort waste in a more enjoyable way. These messages are sent from the server to the user's device and displayed.

[0403] As a concrete example, when a user attempts to use the application to put a plastic bottle into a smart trash can, the device receives instructions from the server to recycle the bottle and a message such as "Thank you for your eco-friendly efforts today!", which are then displayed on the user's screen.

[0404] Examples of prompt messages include the following:

[0405] "How would you give instructions if the user is holding a water bottle? Also, what special message would you provide if the user is a little tired?"

[0406] In this way, a system is provided that allows users to easily and correctly sort waste and support sustainable living.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] The user takes an image of the waste using the camera on their device. The input is the image of the waste acquired through the camera, and the output is the image data. This image data is transmitted to the server via a communication method.

[0410] Step 2:

[0411] The server takes the received image data as input and performs image analysis using a generative artificial intelligence model. Here, the image data is processed as digital information, and waste is identified based on an object recognition algorithm. The output is the classified type of waste and the recommended disposal method.

[0412] Step 3:

[0413] Simultaneously, emotional data is acquired when the user speaks to the device or shows their facial expressions to the camera. The input is this voice and facial data, which is analyzed by the emotion recognition engine. The output is digital information indicating the user's current emotional state.

[0414] Step 4:

[0415] The server generates appropriate motivational messages based on classified waste and the user's emotional state. The input is waste classification information and emotional state information, and the output is the generated message. A generative AI model is used to create emotionally appropriate text.

[0416] Step 5:

[0417] The server sends the analyzed waste disposal method and generated motivational message to the terminal. The input is the disposal method and message generated by the server, and the output is the data transfer to the user terminal.

[0418] Step 6:

[0419] The user's device displays the received information on its screen. The input is information received from the server, and the output is a visual presentation to the user. This operation allows the user to receive instructions and enjoy participating in eco-friendly activities.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Third Embodiment]

[0424] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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".

[0436] This invention is a system that helps users easily and properly sort their everyday waste. This system can be implemented by installing an application on the user's device and connecting to a server.

[0437] The application is used when users process waste generated in their daily lives. First, the device acquires an image of the waste through its camera. This image data is then transmitted to a server via the network.

[0438] The server inputs the received image data into a generating artificial intelligence model. This model is a sophisticated algorithm that performs image analysis based on a vast amount of training data. The server uses the model to identify the type of waste and determine the appropriate sorting method. For example, if a plastic container is included, it will instruct the server to separate the cap from the container body for disposal.

[0439] Analysis results and sorting advice are sent from the server to the terminal. The terminal receives the information and displays it to the user in a visually easy-to-understand format. Based on this display, the user can actually sort the waste correctly.

[0440] Furthermore, users can view environmental education content and participate in eco-friendly activities through this application. These activities earn points, which are then credited to their account. The point system is designed to encourage users to continuously improve their environmental awareness and participate in eco-friendly activities.

[0441] As a concrete example, suppose a user takes a picture of an empty plastic bottle. The device sends this picture to a server, where a generative artificial intelligence model analyzes the image. As a result, it determines that the bottle cap and the bottle body should be separated, and this information is fed back to the user. The user can then properly separate the waste according to these instructions. Through this entire process, the present invention significantly simplifies the user's daily sorting tasks and contributes to solving environmental problems.

[0442] The following describes the processing flow.

[0443] Step 1:

[0444] The user launches an application on their device and uses the camera to take pictures of the waste that needs to be sorted. The device then imports the image data taken by the user into the system.

[0445] Step 2:

[0446] The device compresses the acquired image data and converts it into a format that can be efficiently transmitted to the server. Next, the device transmits the converted image data to the server via the internet.

[0447] Step 3:

[0448] The server receives image data sent from the terminal. The server verifies the integrity of the data and performs preprocessing, such as noise reduction and optimizing the image quality.

[0449] Step 4:

[0450] A generational artificial intelligence model on the server analyzes the pre-processed images. The model detects objects in the images and identifies the waste category based on their characteristics.

[0451] Step 5:

[0452] The server determines the appropriate waste sorting method based on the analysis results of the generated artificial intelligence model. Next, the server generates sorting advice based on the determined information and compiles easy-to-understand instructions for the user.

[0453] Step 6:

[0454] The server sends the generated sorting advice and related information to the terminal as a data package. The data is optimized to be fed back to the user in real time.

[0455] Step 7:

[0456] The terminal receives and decodes sorting advice sent from the server. The terminal displays the instructions to the user in a visually easy-to-understand format.

[0457] Step 8:

[0458] Users sort their waste according to the advice displayed on their device. Once sorting is complete, users can check the information in the application.

[0459] Step 9:

[0460] The device sends point information to the server based on the user's selective actions and eco-friendly activities. The server records this information in the user's profile and stores it in a database as a history of the user's sustainable activities.

[0461] (Example 1)

[0462] 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."

[0463] The present invention aims to provide a system for easily and accurately sorting increasing amounts of waste. Conventional sorting methods were time-consuming for users and required accurate knowledge of waste classification. Furthermore, there was a lack of means to sustainably promote increased environmental awareness. To solve these problems, the present invention has developed a system that provides automated waste classification and sorting advice in real time, encouraging users to raise environmental awareness and participate in sustainable activities.

[0464] 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.

[0465] In this invention, the server includes means for acquiring image data of waste using an image data acquisition device, a communication device for transmitting the acquired image data to a data processing device, and means for analyzing the image data using an automatic analysis model operating within the data processing device to determine the classification of the waste and the appropriate sorting method. This makes it possible for users to sort waste easily and reliably without requiring specialized knowledge. Furthermore, by providing environmental awareness content and encouraging participation in sustainable activities, it is possible to continuously improve users' environmental awareness.

[0466] An "image data acquisition device" refers to a device used to capture and acquire image data of waste, such as a camera on a terminal.

[0467] A "communication device" is a device that has the function of transmitting acquired image data to a data processing device.

[0468] A "data processing device" is a device that has the processing capabilities to perform image data analysis and classification on a server.

[0469] An "automated analysis model" is a generative AI model used to classify waste from image data and determine the appropriate sorting method based on that classification.

[0470] A "display device" is a device that has the function of visually displaying sorting methods and related information to the user.

[0471] "User" refers to an individual or organization that uses this system to receive and implement waste sorting instructions.

[0472] "Incentives" refer to points or rewards that users can earn by using environmental awareness content or participating in sustainable activities.

[0473] "Activity history" refers to data that records users' participation in environmental awareness content and sustainable activities.

[0474] This invention is a system that supports the proper sorting of waste, and is realized by using an application installed on a terminal and an automated analysis model on a server.

[0475] The user installs a specific application on their device. This application has the functionality to acquire image data of waste using the device's camera. When the user takes a picture of the waste they wish to separate, the device sends the image data to the server via a communication device.

[0476] The server inputs the received image data into an automated analysis model, which is an AI model that generates data. This automated analysis model is trained on a vast amount of training data and uses algorithms specialized for image analysis to identify the type of waste and determine the appropriate sorting method. In this process, prompts such as "Identify the object in this image and recommend the appropriate disposal method" are used.

[0477] Once the analysis is complete, the server sends instructions and relevant environmental information to the terminal. This information is then visually displayed to the user on the terminal's display device. For example, a specific example might be an instruction such as, "Remove the cap from the plastic bottle and dispose of the bottle itself in a separate container."

[0478] Furthermore, users can view environmental awareness content and participate in various sustainable activities through the application. The incentives earned from these activities are reflected in the user's activity history. Through this process, the system encourages users to improve their environmental awareness and practice sustainable living.

[0479] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0480] Step 1:

[0481] The user takes a picture of the waste using the device's camera. The input for this step is the waste itself that the user photographs. The device uses the camera to acquire image data of the waste and saves the image file.

[0482] Step 2:

[0483] The terminal sends the acquired image data to the server. The input for this step is the image data stored within the terminal, and the output is the image data sent to the server. The terminal securely and quickly uploads the image data to the server via the internet.

[0484] Step 3:

[0485] The server inputs the transmitted image data into the generative AI model. The input for this step is the image data received by the server, and the model is instructed using the prompt message "Identify the object in this image and recommend an appropriate disposal method." Based on this data, the generative AI model performs image analysis to identify the type of waste and determine the sorting method.

[0486] Step 4:

[0487] The server determines the waste classification and sorting method based on the analysis results and transmits this information to the terminal. The input for this step is the analysis results obtained from the generated AI model, and the output is the specific sorting instructions sent to the terminal. The server transmits the results to the terminal in real time.

[0488] Step 5:

[0489] The terminal receives information from the server and displays sorting instructions to the user. The input for this step is sorting information received from the server, and the output is a sorting guide that the user can visually confirm. The terminal conveys instructions to the user by displaying sorting methods in text and diagrams on its display.

[0490] (Application Example 1)

[0491] 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."

[0492] Currently, waste sorting and separation within factories heavily rely on manual labor, making efficient and consistent operation difficult. This problem could lead to decreased recycling efficiency and increased environmental burden, thus necessitating a more automated and integrated system.

[0493] 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.

[0494] In this invention, the server includes means for acquiring images of waste using image acquisition means, communication means for transmitting the acquired image data to the server, means for analyzing the images using a generative artificial intelligence model operating within the server to determine the classification of the waste and an appropriate sorting method, and means for an automated machine to move or process the waste in an appropriate storage device based on the analysis results. This automates waste processing within the factory, enabling efficient and consistent recycling operations.

[0495] "Image acquisition means" refers to a device or method for photographing and acquiring images of waste.

[0496] "Communication means" refers to the infrastructure or protocol used to transmit acquired image data to a server.

[0497] A "generative artificial intelligence model" is an algorithm or software that has the ability to learn from vast amounts of data, analyze images of waste, and derive classification and sorting methods.

[0498] "Separation information" refers to information about the classification and handling of waste derived from the analyzed images.

[0499] "Display means" refers to a device or method for visually conveying sorting information to the user.

[0500] An "automated machine" is a mechanical device that autonomously handles waste based on analysis results.

[0501] A "storage device" is an approach or device for storing or holding sorted waste.

[0502] In the system for implementing this invention, an automated waste treatment system is first introduced to efficiently classify and separate waste. This system is configured as follows:

[0503] The server acquires images of the waste using an image acquisition system equipped with a camera for photographing the waste. A possible camera for this system would be a Logitech C920. The acquired image data is transmitted to the server in real time via a communication system.

[0504] The server analyzes the received image data using a generative artificial intelligence model. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch. The generative AI model has been trained on a large amount of image data related to waste, and determines the appropriate classification and sorting method based on the type and condition of the waste.

[0505] Next, the server controls the automated machinery based on this decision, moving or processing the waste in the appropriate storage device. This enables efficient and accurate waste disposal without human intervention.

[0506] A concrete example is an automated machine positioned on a factory line that scans incoming waste in real time, instantly identifying plastics, metals, and other materials, and sorting them into their respective recycling containers. An example of a prompt message used in this process would be, "Please tell me which category this waste should be sorted into."

[0507] The introduction of this system will improve the efficiency of waste disposal, reduce environmental impact, and maximize resource recovery.

[0508] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0509] Step 1:

[0510] The device acquires images of waste using its camera. The input is the waste present at the location, and the output is digital image data. The camera photographs the waste and converts it into image data.

[0511] Step 2:

[0512] The terminal sends the acquired image data to the server. The input is digital image data, and the output is the transfer of image data to the server. A communication module is used to upload data to the server via a specific network protocol (e.g., HTTP, MQTT).

[0513] Step 3:

[0514] The server inputs the received image data into a generating AI model. In this step, the server uses the received image data as input and obtains the AI ​​analysis results as output. For data analysis, a TensorFlow or PyTorch-based image recognition model is used to analyze the features of each pixel data and identify the type of waste.

[0515] Step 4:

[0516] The server uses the analysis results from the generated AI model to determine the classification of waste and the appropriate sorting method. The input is the analysis results of the AI ​​model, and the output is specific classification instructions and sorting procedures. Based on the analysis results, it generates sorting guides such as "Plastic, remove caps" and "Metal, complete separation is required."

[0517] Step 5:

[0518] The server transmits the sorting information it has determined to the terminal. The input is the sorting information processed and generated by the server, and the output is the data displayed on the terminal. The data is transmitted using a communication method in a format that the terminal can receive.

[0519] Step 6:

[0520] The terminal displays the received sorting information to the user. The input is sorting information received from the server, and the output is a visual display for the user. A display device such as an LCD is used to present the sorting method in a way that the user can easily understand.

[0521] Step 7:

[0522] The automated machine moves or processes waste into appropriate storage devices based on analysis results, without waiting for user instructions. The input is the analyzed information and sorting guide, and the output is the physical movement or processing result of the waste. It controls robotic arms and transfer belts to perform the appropriate processing.

[0523] 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.

[0524] This invention provides a system that assists users in sorting waste in their daily lives, and further combines it with an emotion engine that recognizes the user's emotional state to provide more personalized guidance for eco-friendly activities. To implement this system, the user uses a smartphone or a dedicated terminal as an image acquisition device.

[0525] The user acquires images of waste through the camera. The device sends these images to a server, where the analysis begins. After receiving the images, the server analyzes them using a generative artificial intelligence model that has been previously trained on a large amount of data related to waste, and determines the classification and sorting method of the waste.

[0526] In addition, the present invention further includes an emotion engine. The terminal acquires the user's emotions through voice and facial expressions from the user's camera or microphone. This data is sent to a server, where the emotion engine analyzes the data. Based on this analysis, feedback is generated that corresponds to the user's current emotions. For example, if the user is feeling stressed, specific and concise instructions are provided so that they do not feel burdened by the process.

[0527] The server not only provides advice on sorting methods but also sends motivational messages tailored to the user's emotions to their device. Users receive this information on their device, review the emotionally sensitive instructions, and sort their waste accordingly. Furthermore, rewards and points are adjusted based on the user's emotional state when they engage in eco-friendly activities. For example, users who receive positive feedback may earn more points than usual.

[0528] As a concrete example, consider a scenario where a user tries to sort plastic bottles at the end of a busy day. If the emotion engine detects that the user is a little tired, the server provides concise instructions to help them sort the bottles quickly. These instructions may also include a calming message. With this support, the user can actively participate in eco-friendly activities and, as a result, develop more sustainable lifestyle habits.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The user launches an application on their device and takes a picture of the waste with the camera. The device then captures this image data and prepares to send it to the server.

[0532] Step 2:

[0533] The device acquires emotional data through the user's facial expressions and voice. This data is collected using the camera or microphone and sent to a server.

[0534] Step 3:

[0535] The server receives image data and emotion data sent from the terminal. After verifying the integrity of the data, the analysis process begins.

[0536] Step 4:

[0537] The AI ​​model on the server analyzes the received image data to identify waste and determine the appropriate sorting method. The analysis results are then generated as specific instructions.

[0538] Step 5:

[0539] The server uses an emotion engine to analyze the user's emotional data. Based on the user's emotional state, it generates appropriate messages and motivational feedback to accompany the discriminatory advice.

[0540] Step 6:

[0541] The server integrates the image analysis results and emotion-based feedback, and sends it to the terminal as a single data package.

[0542] Step 7:

[0543] The terminal decodes the integrated data received from the server. It then displays discretionary advice and emotionally sensitive messages to the user on the screen.

[0544] Step 8:

[0545] Users check the instructions displayed on their device and sort their waste based on the analysis results and emotional feedback. This information supports users' eco-friendly activities and leads to sustainable behavior.

[0546] Step 9:

[0547] The device sends data to the server regarding the user's responsible behavior, eco-friendly activities, and their responses to feedback. The server analyzes this data and updates the points system as needed.

[0548] (Example 2)

[0549] 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."

[0550] While the importance of waste sorting in environmental conservation is increasing, its effective implementation is challenging due to individual users' emotional states and understanding of sorting methods. Many people are reluctant to sort waste, and this problem hinders the establishment of sustainable lifestyle habits.

[0551] 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.

[0552] In this invention, the server includes means for analyzing images of items to determine a sorting method, means for analyzing user emotional data to generate motivational messages, and means for providing the generated information to the user. This enables appropriate waste sorting guidance and motivation according to the user's emotional state.

[0553] "Image acquisition means" refers to devices or methods for users to acquire images of objects.

[0554] "Communication means" refers to methods and protocols for transmitting acquired data to another device or system.

[0555] An "information processing device" refers to a computer or system used to analyze data and make decisions.

[0556] An "intelligent model" refers to an algorithm or system that learns from data on objects and analyzes their characteristics.

[0557] "Means for acquiring emotional data" refers to devices or methods for detecting a user's emotional state.

[0558] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and makes decisions based on the results.

[0559] A "motivational message" refers to a message designed to encourage a user to take a specific action.

[0560] "Sorting method" refers to the process or procedure of classifying items according to specific criteria.

[0561] Embodiments of this invention will be described.

[0562] First, the user acquires an image of the target object using a smartphone or dedicated terminal. This terminal has a camera function, allowing the user to photograph waste or other items. The terminal transmits the acquired image data to an information processing device using a communication method. Wireless communication technology is used for this communication, utilizing Wi-Fi or mobile data communication.

[0563] The information processing device analyzes received image data using an intelligent model. This model is pre-trained on a large amount of data and has the ability to determine how to classify and sort items. The information processing device also includes an emotion analysis engine that analyzes the user's emotional data. The user's terminal acquires facial expressions and voice through its camera and microphone and transmits them as emotional data.

[0564] The analysis results in the generation of motivational messages tailored to the user's emotions, along with methods for sorting items. For example, if the user is prone to stress, a message such as "You've worked hard! Please try this easy sorting method" will be generated.

[0565] The feedback generated by the information processing device is then transmitted back to the user's terminal via communication means. The user can then confirm these instructions via the terminal's display and proceed with sorting the items. This entire process allows the user to sort waste more effectively and contribute to environmental protection.

[0566] As a concrete example, when a user sorts plastic bottles, the information processing device acquires an image, analyzes the image, and determines that "this bottle is recyclable." Depending on the user's emotional state, a message encouraging them to take action is displayed, along with specific instructions such as "Please remove the bottle cap and place it in the recycling bin."

[0567] An example of a prompt to input into a generative AI model might be: "Identify the objects in this image and provide a message tailored to the user's emotional state, along with an appropriate sorting method."

[0568] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0569] Step 1:

[0570] The user acquires images of waste using a device. The input is a still image taken with the device's built-in camera. Ideally, this image should be high-resolution and clearly show the characteristics of the waste. At this stage, the image data is stored in the device's temporary memory.

[0571] Step 2:

[0572] The terminal transmits the acquired image data to the information processing device. The input is the image data acquired in step 1, and the output is the transmission of the image data to the server. A secure protocol (e.g., HTTPS) is used for communication, ensuring the reliability and security of the data.

[0573] Step 3:

[0574] The server inputs the received image data into a generating AI model for analysis. The input is image data, and the output is classification information of the items and the result of the sorting method determination. The AI ​​model is trained on a large dataset and achieves high-precision classification using image analysis algorithms.

[0575] Step 4:

[0576] The user's device uses its camera and microphone to acquire user emotion data in real time. Inputs include the user's facial expressions and voice, while output is quantified emotion data. This is done using an emotion recognition algorithm, which analyzes the data based on multiple emotion scales.

[0577] Step 5:

[0578] The server inputs emotional data into the emotion analysis engine for analysis. The input is the emotional data acquired in step 4, and the output is the judgment result regarding the user's emotional state. The engine can identify complex emotional patterns using machine learning techniques.

[0579] Step 6:

[0580] The server generates feedback for the user based on the waste sorting method and the analyzed emotional state. The input is the output of steps 3 and 5, and the output is the sorting procedure and motivational messages. This provides appropriate guidance that takes the user's emotions into consideration.

[0581] Step 7:

[0582] The server sends the generated feedback to the user's terminal. The input is the feedback information created in step 6, and the output is the feedback display on the user's terminal. This ensures that the feedback is accurately communicated to the user.

[0583] Step 8:

[0584] The user actually sorts the waste based on the feedback received on the device. The input is the instructions and messages received in step 7, and the output is the properly sorted waste. This encourages action toward environmental protection.

[0585] This series of steps allows users to receive personalized guidance that reflects their emotional state, enabling them to effectively sort their waste.

[0586] (Application Example 2)

[0587] 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."

[0588] Waste sorting is often a cumbersome and stressful process, leading to decreased motivation. This invention aims to improve the user experience by providing a system for efficiently sorting waste while considering the user's emotional state.

[0589] 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.

[0590] In this invention, the server includes means for acquiring images of objects using image acquisition means, means for acquiring the user's emotional state using emotion recognition means, and means for generating and sending appropriate motivational messages to the user based on the emotional state. This enables the user to efficiently sort objects while receiving instructions that take their emotions into consideration.

[0591] "Image acquisition means" refers to a function that uses a camera or similar device to capture an image of an object and acquire that data.

[0592] "Communication means" refers to the means for transmitting acquired data to other devices, and is a function that performs data transmission via a network.

[0593] An "information processing device" refers to a device that receives and analyzes data, and is generally called a server.

[0594] A "generative artificial intelligence model" refers to a software model that learns patterns from large amounts of data and performs image analysis and sentiment analysis.

[0595] "Means for determining classification and processing methods" refers to a function that uses a generative artificial intelligence model to analyze the characteristics of objects from analyzed images and determine the appropriate processing method.

[0596] "Emotion recognition means" refers to a method for acquiring and analyzing a user's emotions from voice and facial expression data.

[0597] "Means for generating and sending motivational messages" refers to a function that creates messages tailored to the user's emotional state and sends them to the user's device.

[0598] "Display means" refers to a method for visually presenting analysis results or messages to the user, and generally involves using a display.

[0599] The system for implementing this invention mainly consists of the following hardware and software. The hardware includes a smartphone or dedicated terminal equipped with a camera and microphone. The software consists of a generative artificial intelligence model and an emotion recognition engine running on a server.

[0600] When a user takes a picture of waste using their device's camera, the image data is transmitted to a server via a communication method. On the server side, a generative artificial intelligence model analyzes this image to determine the classification of the waste and the appropriate disposal method. Because this model has been pre-trained on a large amount of image data, it is capable of highly accurate analysis.

[0601] Simultaneously, the emotion recognition engine acquires the user's voice and facial expression data and analyzes their emotional state. If the user is feeling stressed, it generates motivational messages to help them sort waste in a more enjoyable way. These messages are sent from the server to the user's device and displayed.

[0602] As a concrete example, when a user attempts to use the application to put a plastic bottle into a smart trash can, the device receives instructions from the server to recycle the bottle and a message such as "Thank you for your eco-friendly efforts today!", which are then displayed on the user's screen.

[0603] Examples of prompt messages include the following:

[0604] "How would you give instructions if the user is holding a water bottle? Also, what special message would you provide if the user is a little tired?"

[0605] In this way, a system is provided that allows users to easily and correctly sort waste and support sustainable living.

[0606] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0607] Step 1:

[0608] The user takes an image of the waste using the camera on their device. The input is the image of the waste acquired through the camera, and the output is the image data. This image data is transmitted to the server via a communication method.

[0609] Step 2:

[0610] The server takes the received image data as input and performs image analysis using a generative artificial intelligence model. Here, the image data is processed as digital information, and waste is identified based on an object recognition algorithm. The output is the classified type of waste and the recommended disposal method.

[0611] Step 3:

[0612] Simultaneously, emotional data is acquired when the user speaks to the device or shows their facial expressions to the camera. The input is this voice and facial data, which is analyzed by the emotion recognition engine. The output is digital information indicating the user's current emotional state.

[0613] Step 4:

[0614] The server generates appropriate motivational messages based on classified waste and the user's emotional state. The input is waste classification information and emotional state information, and the output is the generated message. A generative AI model is used to create emotionally appropriate text.

[0615] Step 5:

[0616] The server sends the analyzed waste disposal method and generated motivational message to the terminal. The input is the disposal method and message generated by the server, and the output is the data transfer to the user terminal.

[0617] Step 6:

[0618] The user's device displays the received information on its screen. The input is information received from the server, and the output is a visual presentation to the user. This operation allows the user to receive instructions and enjoy participating in eco-friendly activities.

[0619] 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.

[0620] 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.

[0621] 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.

[0622] [Fourth Embodiment]

[0623] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0624] 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.

[0625] 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).

[0626] 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.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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".

[0636] This invention is a system that helps users easily and properly sort their everyday waste. This system can be implemented by installing an application on the user's device and connecting to a server.

[0637] The application is used when users process waste generated in their daily lives. First, the device acquires an image of the waste through its camera. This image data is then transmitted to a server via the network.

[0638] The server inputs the received image data into a generating artificial intelligence model. This model is a sophisticated algorithm that performs image analysis based on a vast amount of training data. The server uses the model to identify the type of waste and determine the appropriate sorting method. For example, if a plastic container is included, it will instruct the server to separate the cap from the container body for disposal.

[0639] Analysis results and sorting advice are sent from the server to the terminal. The terminal receives the information and displays it to the user in a visually easy-to-understand format. Based on this display, the user can actually sort the waste correctly.

[0640] Furthermore, users can view environmental education content and participate in eco-friendly activities through this application. These activities earn points, which are then credited to their account. The point system is designed to encourage users to continuously improve their environmental awareness and participate in eco-friendly activities.

[0641] As a concrete example, suppose a user takes a picture of an empty plastic bottle. The device sends this picture to a server, where a generative artificial intelligence model analyzes the image. As a result, it determines that the bottle cap and the bottle body should be separated, and this information is fed back to the user. The user can then properly separate the waste according to these instructions. Through this entire process, the present invention significantly simplifies the user's daily sorting tasks and contributes to solving environmental problems.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The user launches an application on their device and uses the camera to take pictures of the waste that needs to be sorted. The device then imports the image data taken by the user into the system.

[0645] Step 2:

[0646] The device compresses the acquired image data and converts it into a format that can be efficiently transmitted to the server. Next, the device transmits the converted image data to the server via the internet.

[0647] Step 3:

[0648] The server receives image data sent from the terminal. The server verifies the integrity of the data and performs preprocessing, such as noise reduction and optimizing the image quality.

[0649] Step 4:

[0650] A generational artificial intelligence model on the server analyzes the pre-processed images. The model detects objects in the images and identifies the waste category based on their characteristics.

[0651] Step 5:

[0652] The server determines the appropriate waste sorting method based on the analysis results of the generated artificial intelligence model. Next, the server generates sorting advice based on the determined information and compiles easy-to-understand instructions for the user.

[0653] Step 6:

[0654] The server sends the generated sorting advice and related information to the terminal as a data package. The data is optimized to be fed back to the user in real time.

[0655] Step 7:

[0656] The terminal receives and decodes sorting advice sent from the server. The terminal displays the instructions to the user in a visually easy-to-understand format.

[0657] Step 8:

[0658] Users sort their waste according to the advice displayed on their device. Once sorting is complete, users can check the information in the application.

[0659] Step 9:

[0660] The device sends point information to the server based on the user's selective actions and eco-friendly activities. The server records this information in the user's profile and stores it in a database as a history of the user's sustainable activities.

[0661] (Example 1)

[0662] 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".

[0663] The present invention aims to provide a system for easily and accurately sorting increasing amounts of waste. Conventional sorting methods were time-consuming for users and required accurate knowledge of waste classification. Furthermore, there was a lack of means to sustainably promote increased environmental awareness. To solve these problems, the present invention has developed a system that provides automated waste classification and sorting advice in real time, encouraging users to raise environmental awareness and participate in sustainable activities.

[0664] 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.

[0665] In this invention, the server includes means for acquiring image data of waste using an image data acquisition device, a communication device for transmitting the acquired image data to a data processing device, and means for analyzing the image data using an automatic analysis model operating within the data processing device to determine the classification of the waste and the appropriate sorting method. This makes it possible for users to sort waste easily and reliably without requiring specialized knowledge. Furthermore, by providing environmental awareness content and encouraging participation in sustainable activities, it is possible to continuously improve users' environmental awareness.

[0666] An "image data acquisition device" refers to a device used to capture and acquire image data of waste, such as a camera on a terminal.

[0667] A "communication device" is a device that has the function of transmitting acquired image data to a data processing device.

[0668] A "data processing device" is a device that has the processing capabilities to perform image data analysis and classification on a server.

[0669] An "automated analysis model" is a generative AI model used to classify waste from image data and determine the appropriate sorting method based on that classification.

[0670] A "display device" is a device that has the function of visually displaying sorting methods and related information to the user.

[0671] "User" refers to an individual or organization that uses this system to receive and implement waste sorting instructions.

[0672] "Incentives" refer to points or rewards that users can earn by using environmental awareness content or participating in sustainable activities.

[0673] "Activity history" refers to data that records users' participation in environmental awareness content and sustainable activities.

[0674] This invention is a system that supports the proper sorting of waste, and is realized by using an application installed on a terminal and an automated analysis model on a server.

[0675] The user installs a specific application on their device. This application has the functionality to acquire image data of waste using the device's camera. When the user takes a picture of the waste they wish to separate, the device sends the image data to the server via a communication device.

[0676] The server inputs the received image data into an automated analysis model, which is an AI model that generates data. This automated analysis model is trained on a vast amount of training data and uses algorithms specialized for image analysis to identify the type of waste and determine the appropriate sorting method. In this process, prompts such as "Identify the object in this image and recommend the appropriate disposal method" are used.

[0677] Once the analysis is complete, the server sends instructions and relevant environmental information to the terminal. This information is then visually displayed to the user on the terminal's display device. For example, a specific example might be an instruction such as, "Remove the cap from the plastic bottle and dispose of the bottle itself in a separate container."

[0678] Furthermore, users can view environmental awareness content and participate in various sustainable activities through the application. The incentives earned from these activities are reflected in the user's activity history. Through this process, the system encourages users to improve their environmental awareness and practice sustainable living.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The user takes a picture of the waste using the device's camera. The input for this step is the waste itself that the user photographs. The device uses the camera to acquire image data of the waste and saves the image file.

[0682] Step 2:

[0683] The terminal sends the acquired image data to the server. The input for this step is the image data stored within the terminal, and the output is the image data sent to the server. The terminal securely and quickly uploads the image data to the server via the internet.

[0684] Step 3:

[0685] The server inputs the transmitted image data into the generative AI model. The input for this step is the image data received by the server, and the model is instructed using the prompt message "Identify the object in this image and recommend an appropriate disposal method." Based on this data, the generative AI model performs image analysis to identify the type of waste and determine the sorting method.

[0686] Step 4:

[0687] The server determines the waste classification and sorting method based on the analysis results and transmits this information to the terminal. The input for this step is the analysis results obtained from the generated AI model, and the output is the specific sorting instructions sent to the terminal. The server transmits the results to the terminal in real time.

[0688] Step 5:

[0689] The terminal receives information from the server and displays sorting instructions to the user. The input for this step is sorting information received from the server, and the output is a sorting guide that the user can visually confirm. The terminal conveys instructions to the user by displaying sorting methods in text and diagrams on its display.

[0690] (Application Example 1)

[0691] 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".

[0692] Currently, waste sorting and separation within factories heavily rely on manual labor, making efficient and consistent operation difficult. This problem could lead to decreased recycling efficiency and increased environmental burden, thus necessitating a more automated and integrated system.

[0693] 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.

[0694] In this invention, the server includes means for acquiring images of waste using image acquisition means, communication means for transmitting the acquired image data to the server, means for analyzing the images using a generative artificial intelligence model operating within the server to determine the classification of the waste and an appropriate sorting method, and means for an automated machine to move or process the waste in an appropriate storage device based on the analysis results. This automates waste processing within the factory, enabling efficient and consistent recycling operations.

[0695] "Image acquisition means" refers to a device or method for photographing and acquiring images of waste.

[0696] "Communication means" refers to the infrastructure or protocol used to transmit acquired image data to a server.

[0697] A "generative artificial intelligence model" is an algorithm or software that has the ability to learn from vast amounts of data, analyze images of waste, and derive classification and sorting methods.

[0698] "Separation information" refers to information about the classification and handling of waste derived from the analyzed images.

[0699] "Display means" refers to a device or method for visually conveying sorting information to the user.

[0700] An "automated machine" is a mechanical device that autonomously handles waste based on analysis results.

[0701] A "storage device" is an approach or device for storing or holding sorted waste.

[0702] In the system for implementing this invention, an automated waste treatment system is first introduced to efficiently classify and separate waste. This system is configured as follows:

[0703] The server acquires images of the waste using an image acquisition system equipped with a camera for photographing the waste. A possible camera for this system would be a Logitech C920. The acquired image data is transmitted to the server in real time via a communication system.

[0704] The server analyzes the received image data using a generative artificial intelligence model. This analysis utilizes machine learning frameworks such as TensorFlow and PyTorch. The generative AI model has been trained on a large amount of image data related to waste, and determines the appropriate classification and sorting method based on the type and condition of the waste.

[0705] Next, the server controls the automated machinery based on this decision, moving or processing the waste in the appropriate storage device. This enables efficient and accurate waste disposal without human intervention.

[0706] A concrete example is an automated machine positioned on a factory line that scans incoming waste in real time, instantly identifying plastics, metals, and other materials, and sorting them into their respective recycling containers. An example of a prompt message used in this process would be, "Please tell me which category this waste should be sorted into."

[0707] The introduction of this system will improve the efficiency of waste disposal, reduce environmental impact, and maximize resource recovery.

[0708] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0709] Step 1:

[0710] The device acquires images of waste using its camera. The input is the waste present at the location, and the output is digital image data. The camera photographs the waste and converts it into image data.

[0711] Step 2:

[0712] The terminal sends the acquired image data to the server. The input is digital image data, and the output is the transfer of image data to the server. A communication module is used to upload data to the server via a specific network protocol (e.g., HTTP, MQTT).

[0713] Step 3:

[0714] The server inputs the received image data into a generating AI model. In this step, the server uses the received image data as input and obtains the AI ​​analysis results as output. For data analysis, a TensorFlow or PyTorch-based image recognition model is used to analyze the features of each pixel data and identify the type of waste.

[0715] Step 4:

[0716] The server uses the analysis results from the generated AI model to determine the classification of waste and the appropriate sorting method. The input is the analysis results of the AI ​​model, and the output is specific classification instructions and sorting procedures. Based on the analysis results, it generates sorting guides such as "Plastic, remove caps" and "Metal, complete separation is required."

[0717] Step 5:

[0718] The server transmits the sorting information it has determined to the terminal. The input is the sorting information processed and generated by the server, and the output is the data displayed on the terminal. The data is transmitted using a communication method in a format that the terminal can receive.

[0719] Step 6:

[0720] The terminal displays the received sorting information to the user. The input is sorting information received from the server, and the output is a visual display for the user. A display device such as an LCD is used to present the sorting method in a way that the user can easily understand.

[0721] Step 7:

[0722] The automated machine moves or processes waste into appropriate storage devices based on analysis results, without waiting for user instructions. The input is the analyzed information and sorting guide, and the output is the physical movement or processing result of the waste. It controls robotic arms and transfer belts to perform the appropriate processing.

[0723] 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.

[0724] This invention provides a system that assists users in sorting waste in their daily lives, and further combines it with an emotion engine that recognizes the user's emotional state to provide more personalized guidance for eco-friendly activities. To implement this system, the user uses a smartphone or a dedicated terminal as an image acquisition device.

[0725] The user acquires images of waste through the camera. The device sends these images to a server, where the analysis begins. After receiving the images, the server analyzes them using a generative artificial intelligence model that has been previously trained on a large amount of data related to waste, and determines the classification and sorting method of the waste.

[0726] In addition, the present invention further includes an emotion engine. The terminal acquires the user's emotions through voice and facial expressions from the user's camera or microphone. This data is sent to a server, where the emotion engine analyzes the data. Based on this analysis, feedback is generated that corresponds to the user's current emotions. For example, if the user is feeling stressed, specific and concise instructions are provided so that they do not feel burdened by the process.

[0727] The server not only provides advice on sorting methods but also sends motivational messages tailored to the user's emotions to their device. Users receive this information on their device, review the emotionally sensitive instructions, and sort their waste accordingly. Furthermore, rewards and points are adjusted based on the user's emotional state when they engage in eco-friendly activities. For example, users who receive positive feedback may earn more points than usual.

[0728] As a concrete example, consider a scenario where a user tries to sort plastic bottles at the end of a busy day. If the emotion engine detects that the user is a little tired, the server provides concise instructions to help them sort the bottles quickly. These instructions may also include a calming message. With this support, the user can actively participate in eco-friendly activities and, as a result, develop more sustainable lifestyle habits.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] The user launches an application on their device and takes a picture of the waste with the camera. The device then captures this image data and prepares to send it to the server.

[0732] Step 2:

[0733] The device acquires emotional data through the user's facial expressions and voice. This data is collected using the camera or microphone and sent to a server.

[0734] Step 3:

[0735] The server receives image data and emotion data sent from the terminal. After verifying the integrity of the data, the analysis process begins.

[0736] Step 4:

[0737] The AI ​​model on the server analyzes the received image data to identify waste and determine the appropriate sorting method. The analysis results are then generated as specific instructions.

[0738] Step 5:

[0739] The server uses an emotion engine to analyze the user's emotional data. Based on the user's emotional state, it generates appropriate messages and motivational feedback to accompany the discriminatory advice.

[0740] Step 6:

[0741] The server integrates the image analysis results and emotion-based feedback, and sends it to the terminal as a single data package.

[0742] Step 7:

[0743] The terminal decodes the integrated data received from the server. It then displays discretionary advice and emotionally sensitive messages to the user on the screen.

[0744] Step 8:

[0745] Users check the instructions displayed on their device and sort their waste based on the analysis results and emotional feedback. This information supports users' eco-friendly activities and leads to sustainable behavior.

[0746] Step 9:

[0747] The device sends data to the server regarding the user's responsible behavior, eco-friendly activities, and their responses to feedback. The server analyzes this data and updates the points system as needed.

[0748] (Example 2)

[0749] 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".

[0750] While the importance of waste sorting in environmental conservation is increasing, its effective implementation is challenging due to individual users' emotional states and understanding of sorting methods. Many people are reluctant to sort waste, and this problem hinders the establishment of sustainable lifestyle habits.

[0751] 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.

[0752] In this invention, the server includes means for analyzing images of items to determine a sorting method, means for analyzing user emotional data to generate motivational messages, and means for providing the generated information to the user. This enables appropriate waste sorting guidance and motivation according to the user's emotional state.

[0753] "Image acquisition means" refers to devices or methods for users to acquire images of objects.

[0754] "Communication means" refers to methods and protocols for transmitting acquired data to another device or system.

[0755] An "information processing device" refers to a computer or system used to analyze data and make decisions.

[0756] An "intelligent model" refers to an algorithm or system that learns from data on objects and analyzes their characteristics.

[0757] "Means for acquiring emotional data" refers to devices or methods for detecting a user's emotional state.

[0758] An "emotion analysis engine" refers to a system that analyzes a user's emotional state and makes decisions based on the results.

[0759] A "motivational message" refers to a message designed to encourage a user to take a specific action.

[0760] "Sorting method" refers to the process or procedure of classifying items according to specific criteria.

[0761] Embodiments of this invention will be described.

[0762] First, the user acquires an image of the target object using a smartphone or dedicated terminal. This terminal has a camera function, allowing the user to photograph waste or other items. The terminal transmits the acquired image data to an information processing device using a communication method. Wireless communication technology is used for this communication, utilizing Wi-Fi or mobile data communication.

[0763] The information processing device analyzes received image data using an intelligent model. This model is pre-trained on a large amount of data and has the ability to determine how to classify and sort items. The information processing device also includes an emotion analysis engine that analyzes the user's emotional data. The user's terminal acquires facial expressions and voice through its camera and microphone and transmits them as emotional data.

[0764] The analysis results in the generation of motivational messages tailored to the user's emotions, along with methods for sorting items. For example, if the user is prone to stress, a message such as "You've worked hard! Please try this easy sorting method" will be generated.

[0765] The feedback generated by the information processing device is then transmitted back to the user's terminal via communication means. The user can then confirm these instructions via the terminal's display and proceed with sorting the items. This entire process allows the user to sort waste more effectively and contribute to environmental protection.

[0766] As a concrete example, when a user sorts plastic bottles, the information processing device acquires an image, analyzes the image, and determines that "this bottle is recyclable." Depending on the user's emotional state, a message encouraging them to take action is displayed, along with specific instructions such as "Please remove the bottle cap and place it in the recycling bin."

[0767] An example of a prompt to input into a generative AI model might be: "Identify the objects in this image and provide a message tailored to the user's emotional state, along with an appropriate sorting method."

[0768] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0769] Step 1:

[0770] The user acquires images of waste using a device. The input is a still image taken with the device's built-in camera. Ideally, this image should be high-resolution and clearly show the characteristics of the waste. At this stage, the image data is stored in the device's temporary memory.

[0771] Step 2:

[0772] The terminal transmits the acquired image data to the information processing device. The input is the image data acquired in step 1, and the output is the transmission of the image data to the server. A secure protocol (e.g., HTTPS) is used for communication, ensuring the reliability and security of the data.

[0773] Step 3:

[0774] The server inputs the received image data into a generating AI model for analysis. The input is image data, and the output is classification information of the items and the result of the sorting method determination. The AI ​​model is trained on a large dataset and achieves high-precision classification using image analysis algorithms.

[0775] Step 4:

[0776] The user's device uses its camera and microphone to acquire user emotion data in real time. Inputs include the user's facial expressions and voice, while output is quantified emotion data. This is done using an emotion recognition algorithm, which analyzes the data based on multiple emotion scales.

[0777] Step 5:

[0778] The server inputs emotional data into the emotion analysis engine for analysis. The input is the emotional data acquired in step 4, and the output is the judgment result regarding the user's emotional state. The engine can identify complex emotional patterns using machine learning techniques.

[0779] Step 6:

[0780] The server generates feedback for the user based on the waste sorting method and the analyzed emotional state. The input is the output of steps 3 and 5, and the output is the sorting procedure and motivational messages. This provides appropriate guidance that takes the user's emotions into consideration.

[0781] Step 7:

[0782] The server sends the generated feedback to the user's terminal. The input is the feedback information created in step 6, and the output is the feedback display on the user's terminal. This ensures that the feedback is accurately communicated to the user.

[0783] Step 8:

[0784] The user actually sorts the waste based on the feedback received on the device. The input is the instructions and messages received in step 7, and the output is the properly sorted waste. This encourages action toward environmental protection.

[0785] This series of steps allows users to receive personalized guidance that reflects their emotional state, enabling them to effectively sort their waste.

[0786] (Application Example 2)

[0787] 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".

[0788] Waste sorting is often a cumbersome and stressful process, leading to decreased motivation. This invention aims to improve the user experience by providing a system for efficiently sorting waste while considering the user's emotional state.

[0789] 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.

[0790] In this invention, the server includes means for acquiring images of objects using image acquisition means, means for acquiring the user's emotional state using emotion recognition means, and means for generating and sending appropriate motivational messages to the user based on the emotional state. This enables the user to efficiently sort objects while receiving instructions that take their emotions into consideration.

[0791] "Image acquisition means" refers to a function that uses a camera or similar device to capture an image of an object and acquire that data.

[0792] "Communication means" refers to the means for transmitting acquired data to other devices, and is a function that performs data transmission via a network.

[0793] An "information processing device" refers to a device that receives and analyzes data, and is generally called a server.

[0794] A "generative artificial intelligence model" refers to a software model that learns patterns from large amounts of data and performs image analysis and sentiment analysis.

[0795] "Means for determining classification and processing methods" refers to a function that uses a generative artificial intelligence model to analyze the characteristics of objects from analyzed images and determine the appropriate processing method.

[0796] "Emotion recognition means" refers to a method for acquiring and analyzing a user's emotions from voice and facial expression data.

[0797] "Means for generating and sending motivational messages" refers to a function that creates messages tailored to the user's emotional state and sends them to the user's device.

[0798] "Display means" refers to a method for visually presenting analysis results or messages to the user, and generally involves using a display.

[0799] The system for implementing this invention mainly consists of the following hardware and software. The hardware includes a smartphone or dedicated terminal equipped with a camera and microphone. The software consists of a generative artificial intelligence model and an emotion recognition engine running on a server.

[0800] When a user takes a picture of waste using their device's camera, the image data is transmitted to a server via a communication method. On the server side, a generative artificial intelligence model analyzes this image to determine the classification of the waste and the appropriate disposal method. Because this model has been pre-trained on a large amount of image data, it is capable of highly accurate analysis.

[0801] Simultaneously, the emotion recognition engine acquires the user's voice and facial expression data and analyzes their emotional state. If the user is feeling stressed, it generates motivational messages to help them sort waste in a more enjoyable way. These messages are sent from the server to the user's device and displayed.

[0802] As a concrete example, when a user attempts to use the application to put a plastic bottle into a smart trash can, the device receives instructions from the server to recycle the bottle and a message such as "Thank you for your eco-friendly efforts today!", which are then displayed on the user's screen.

[0803] Examples of prompt messages include the following:

[0804] "How would you give instructions if the user is holding a water bottle? Also, what special message would you provide if the user is a little tired?"

[0805] In this way, a system is provided that allows users to easily and correctly sort waste and support sustainable living.

[0806] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0807] Step 1:

[0808] The user takes an image of the waste using the camera on their device. The input is the image of the waste acquired through the camera, and the output is the image data. This image data is transmitted to the server via a communication method.

[0809] Step 2:

[0810] The server takes the received image data as input and performs image analysis using a generative artificial intelligence model. Here, the image data is processed as digital information, and waste is identified based on an object recognition algorithm. The output is the classified type of waste and the recommended disposal method.

[0811] Step 3:

[0812] Simultaneously, emotional data is acquired when the user speaks to the device or shows their facial expressions to the camera. The input is this voice and facial data, which is analyzed by the emotion recognition engine. The output is digital information indicating the user's current emotional state.

[0813] Step 4:

[0814] The server generates appropriate motivational messages based on classified waste and the user's emotional state. The input is waste classification information and emotional state information, and the output is the generated message. A generative AI model is used to create emotionally appropriate text.

[0815] Step 5:

[0816] The server sends the analyzed waste disposal method and generated motivational message to the terminal. The input is the disposal method and message generated by the server, and the output is the data transfer to the user terminal.

[0817] Step 6:

[0818] The user's device displays the received information on its screen. The input is information received from the server, and the output is a visual presentation to the user. This operation allows the user to receive instructions and enjoy participating in eco-friendly activities.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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."

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0840] The following is further disclosed regarding the embodiments described above.

[0841] (Claim 1)

[0842] A means for acquiring images of waste using an image acquisition means,

[0843] A communication means for transmitting the acquired image data to a server,

[0844] A means for analyzing the image using a generative artificial intelligence model operating within the server and determining the classification of waste and appropriate sorting methods,

[0845] Means for transmitting the determined sorting method and related environmental information to a terminal,

[0846] A system including a display means for displaying the transmitted sorting information to the user.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the generating artificial intelligence model is trained on a large amount of image data relating to waste.

[0849] (Claim 3)

[0850] The system according to claim 1, wherein users earn points by viewing environmental education content or participating in eco-activities, and these points are reflected in the users' sustainable activities.

[0851] "Example 1"

[0852] (Claim 1)

[0853] A means for acquiring image data of waste using an image data acquisition device,

[0854] A communication device that transmits the acquired image data to a data processing device,

[0855] A means for analyzing the image data using an automated analysis model operating within the data processing device to determine the classification of waste and the appropriate sorting method,

[0856] Means for transmitting the determined sorting method and related environmental information to a computer device,

[0857] A display device that displays the transmitted sorting method information to the user,

[0858] A means of calculating the incentives that users gain by using environmental awareness content or participating in sustainable activities,

[0859] A system that includes means for reflecting the aforementioned acquired incentives in the user's activity history.

[0860] (Claim 2)

[0861] The system according to claim 1, wherein the automated analysis model is trained on a variety of image data related to waste.

[0862] (Claim 3)

[0863] The system according to claim 1, which calculates an incentive for users to view environmental awareness content and participate in sustainable activities based on those activities, and reflects that incentive as points.

[0864] "Application Example 1"

[0865] (Claim 1)

[0866] A means for acquiring images of waste using an image acquisition means,

[0867] A communication means for transmitting the acquired image data to a server,

[0868] A means for analyzing the image using a generative artificial intelligence model operating within the server and determining the classification of waste and appropriate sorting methods,

[0869] Means for transmitting the determined sorting method and related environmental information to a terminal,

[0870] A display means for displaying the transmitted sorting information to the user,

[0871] The automated machine provides means for moving or processing waste in an appropriate storage device based on the analysis results,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, wherein the generating artificial intelligence model is trained on a large amount of image data relating to waste.

[0875] (Claim 3)

[0876] The system according to claim 1, wherein users earn points by viewing environmental education content or participating in eco-activities, and these points are reflected in the users' sustainable activities.

[0877] "Example 2 of combining an emotion engine"

[0878] (Claim 1)

[0879] A means for acquiring an image of an object using an image acquisition means,

[0880] A communication means for transmitting the acquired image data to an information processing device,

[0881] Means for analyzing the image using an intelligent model operating within the information processing device and determining the classification of items and appropriate sorting methods,

[0882] A means of acquiring user emotional data using an emotional data acquisition method,

[0883] A communication means for transmitting the acquired emotional data to an information processing device,

[0884] The emotion data is analyzed by an emotion analysis engine operating within the information processing device, and means for generating a selection method and motivational messages based on the analysis results,

[0885] The means for transmitting the generated selection method and motivational message to the terminal,

[0886] A system including a display means for displaying the transmitted information to the user.

[0887] (Claim 2)

[0888] The system according to claim 1, wherein the intelligent model is trained on a large amount of image data relating to an item.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein users earn rewards by participating in environmental improvement activities, and these rewards are reflected in the users' sustainable activities.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] Means for acquiring an image of an object using image acquisition means,

[0894] A communication means for transmitting the acquired image data to an information processing device,

[0895] A means for analyzing the image using a generative artificial intelligence model operating within the information processing device, and determining the classification of objects and appropriate processing methods,

[0896] A means for acquiring the user's emotional state using emotion recognition means,

[0897] A means for generating and sending an appropriate motivational message to the user based on the aforementioned emotional state,

[0898] A means for transmitting the determined processing method and instructions that take into consideration the user's feelings to the terminal,

[0899] A system including a display means for displaying the transmitted processing information to the user.

[0900] (Claim 2)

[0901] The system according to claim 1, wherein the generating artificial intelligence model is trained on a large amount of image data relating to an object.

[0902] (Claim 3)

[0903] The system according to claim 1, wherein users earn rewards by viewing educational content or participating in activities, and these rewards are reflected in the users' sustainable activities. [Explanation of Symbols]

[0904] 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

1. A means for acquiring images of waste using an image acquisition means, A communication means for transmitting the acquired image data to a server, A means for analyzing the image using a generative artificial intelligence model operating within the server and determining the classification of waste and appropriate sorting methods, Means for transmitting the determined sorting method and related environmental information to a terminal, A system including a display means for displaying the transmitted sorting information to the user.

2. The system according to claim 1, wherein the generating artificial intelligence model is trained on a large amount of image data relating to waste.

3. The system according to claim 1, wherein users earn points by viewing environmental education content or participating in eco-activities, and these points are reflected in the users' sustainable activities.

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