system

Abstract

We provide the system. [Solution] A means for analyzing images of waste and determining its sorting method, A means of presenting the determined sorting method, Means of providing information on recyclable resources, A means of searching for and presenting companies that handle large waste, A means of recording users' recycling activities and visualizing their environmental contribution, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Recycling of waste is important from the perspective of environmental protection, but it is difficult for individuals and enterprises to find appropriate sorting methods and recyclers. In addition, the environmental contribution of recycling activities is not easily visible, and it is difficult to maintain sustainable motivation. As a result, there are concerns about waste of resources and environmental load, and means for solving these problems are required.

Means for Solving the Problems

[0005] This invention provides a system that analyzes images of waste to determine the appropriate sorting method and presents it to the user. Furthermore, it provides information on recyclable resources and searches for and presents recycling companies based on location. This supports users in smoothly carrying out recycling activities and provides a means to maintain motivation by visualizing their environmental contribution.

[0006] "Images of waste" refer to photographs or digital image data taken of waste.

[0007] "Analysis" is the act of processing image data and understanding and classifying the information contained within it based on a specific purpose.

[0008] "Waste sorting methods" refer to the procedures and rules for properly classifying waste and processing it according to each category.

[0009] "Decision-making" is the process of drawing a specific result or conclusion based on the data and circumstances provided.

[0010] "Presentation" means showing information or results to the user, either visually or as text.

[0011] "Recyclable resources" refer to materials and items that can be reused and processed in an environmentally friendly manner.

[0012] "Information provision" refers to the act or method of transferring specific knowledge or data to others for the purpose of making it known to them.

[0013] "Large-sized waste" refers to waste that is too large or heavy to be treated as regular household waste.

[0014] A "vendor" is a company or individual business owner that specializes in providing specific tasks or services.

[0015] "Searching" is the act of investigating and verifying information or data that matches specific criteria.

[0016] "User" refers to an individual or a group that uses the system.

[0017] "Environmental contribution degree" is an indicator that shows the positive impact on society and the natural environment through recycling activities and other environmental protection activities.

[0018] "Visualization" is an act of expressing information and data in a visual form to facilitate understanding.

Brief Explanation of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0020] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0021] First, let's explain the terminology used in the following explanation.

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is designed to enable individuals and businesses to effectively carry out recycling activities through a system utilizing AI technology. Its main functions include image analysis of waste, suggestion of sorting methods, provision of recycling information, matching with recycling companies, and visualization of environmental contribution.

[0041] The server receives images of waste uploaded by the user from their device. Using a generation AI, the server analyzes the images to identify the type of waste. Simultaneously, the server consults a database to determine the most appropriate sorting method for the identified waste. The results are presented to the user via their device. The user can immediately confirm the sorting method on their device screen.

[0042] Furthermore, the server retrieves information about recyclable resources from a database and provides it to the user. This information includes recycling methods and related product information, and by conveying it to the user through the terminal, it supports the user's knowledge of recycling.

[0043] When a user wishes to dispose of large waste items, they notify the server via their terminal. The server uses registered vendor information to search for recycling companies based on the user's location and the type of waste, and presents suitable companies. The user can then select a preferred company from the presented options and check the transaction details on their terminal.

[0044] Ultimately, the server records the user's recycling activities and calculates their environmental contribution based on those activities. This environmental contribution is visually represented to the user on their device, encouraging continued motivation for recycling.

[0045] As a concrete example, consider a scenario where a user takes a picture of an empty plastic bottle at home and uploads it to the cloud via their device. In this case, the server analyzes the bottle and displays specific instructions on the device, such as "remove the label, take off the cap, crush it, and separate it as plastic waste." Furthermore, if information is also provided on how the plastic bottle is recycled and transformed into new products, it is expected that users' awareness of recycling will increase.

[0046] This invention reduces the effort required for sorting, improves the efficiency of recycling activities through information visualization and appropriate vendor selection, and enables contributions to a sustainable environment.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user takes a picture of the waste using their device's camera. The captured image is saved on the user's device.

[0050] Step 2:

[0051] Users upload images of waste to the server via a dedicated application on their device. The image data also includes the user's ID and location information.

[0052] Step 3:

[0053] The server sends the received image data to an AI module for analysis. The AI ​​module uses machine learning algorithms to analyze the images and identify the type of waste.

[0054] Step 4:

[0055] Based on the analysis results, the server identifies the appropriate sorting method for each type of waste. The sorting method is determined considering relevant laws and local guidelines.

[0056] Step 5:

[0057] The server sends the identified sorting method to the terminal. The terminal displays the sorting method to the user and provides specific instructions.

[0058] Step 6:

[0059] The server consults a database of recyclable resources and collects recycling information related to the relevant waste.

[0060] Step 7:

[0061] The server organizes the recycling information and sends it to the terminal. The terminal displays information to the user indicating the importance and possibility of recycling.

[0062] Step 8:

[0063] When a user wishes to have large waste items picked up, they send a request from their device to the server. The request includes the desired pickup date and time, as well as details about the waste.

[0064] Step 9:

[0065] The server searches the vendor database to identify recycling companies that match the user's location and request conditions.

[0066] Step 10:

[0067] The server sends information about the selected vendor to the terminal. The terminal displays the vendor's details and transaction options to the user.

[0068] Step 11:

[0069] The server records the user's recycling activity history and updates the database. It also calculates the user's environmental contribution based on their activities.

[0070] Step 12:

[0071] The server sends the calculation results to the terminal. The terminal displays the user's environmental contribution as a graph or numerical value, encouraging sustainable activity.

[0072] (Example 1)

[0073] 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."

[0074] The challenge lies in efficiently providing information on waste classification and recycling to support individuals and organizations in conducting appropriate and effective recycling activities, thereby promoting contributions to the environment. Furthermore, it is necessary to facilitate smooth collaboration with recycling companies and promote the proper disposal of waste.

[0075] 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.

[0076] In this invention, the server includes means for analyzing visual information of waste and determining its classification method, means for providing data on reusable resources, and means for recording the user's recycling activities through information processing and visualizing the degree of environmental contribution. This allows users to easily learn how to sort waste and effectively carry out recycling activities. Furthermore, the visualization of environmental contribution can provide motivation for continuous recycling activities.

[0077] "Visual information of waste" refers to data that describes the visual characteristics of waste, such as its shape, color, and texture.

[0078] "Analysis" is the process of extracting specific elements from visual information using generative models, etc., and then classifying and judging the information based on those elements.

[0079] "Means for determining classification methods" refer to methods and technologies for providing appropriate sorting instructions according to the type and material of waste.

[0080] "Means of display" refers to technologies that present analysis results and information to users through the screens or interfaces of electronic devices.

[0081] "Data on reusable resources" refers to information about how waste is recycled and utilized as new resources.

[0082] "Means of searching and presenting" refers to technology that searches for suitable vendors based on specific conditions and displays the results visually to the user.

[0083] "Information processing" is a series of computational processes that organize and analyze collected data and generate the necessary output.

[0084] "Degree of environmental contribution" is an indicator that quantitatively or qualitatively shows the positive environmental impact of users' recycling activities.

[0085] A "generative model" is an algorithm that uses artificial intelligence technology to generate or analyze specific information.

[0086] A description of the embodiment for carrying out the invention will be provided.

[0087] This invention is a system aimed at improving the efficiency of waste analysis and recycling activities. The system mainly consists of three components: a server, terminals, and users. The server plays a central role and exchanges information with users through terminals.

[0088] The server is equipped with high-performance computing units and data storage, and performs image analysis of waste using generative AI models. The image analysis analyzes the visual information of the waste and determines its classification. Specifically, server machines equipped with the latest GPUs are often used. The server also retrieves information on recyclable resources from a database and provides it to the user.

[0089] The terminal is a device used by users to photograph waste and send the images to a server. Smartphones and tablets can function as terminals. The terminal also displays analysis results and recycling information sent from the server, allowing users to learn how to handle the waste in detail.

[0090] Users routinely input the status of waste using their devices and utilize instructions and information from the server to carry out recycling activities. For example, a user might take a picture of an empty plastic bottle at home and upload the image from their device to the cloud. An example of a prompt message used in this process would be, "Analyze the plastic bottle image and suggest sorting methods."

[0091] This system allows users to easily understand and practice proper waste sorting and recycling methods. Furthermore, by visualizing their environmental contribution, users can see how much their recycling activities are positively impacting the environment, which helps them maintain sustained motivation.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The user takes a photograph of the waste using the device. At this time, the user inputs image data of the waste. The device then prepares to send this image data to the server.

[0095] Step 2:

[0096] The device uploads the image data of the captured waste to the server. The input is image data from the device, which is then received by the server. The output is the image saved on the server.

[0097] Step 3:

[0098] The server analyzes the received image data using a generation AI model. The input is image data of waste, and the data processing involves extracting features from the images and performing classification. The output is information about the type of waste and the classification result.

[0099] Step 4:

[0100] The server consults a database based on the determined type of waste to decide on the appropriate sorting method. The input is the classification result, and the output is specific sorting instructions. In this case, if the waste is a plastic bottle, the instruction "remove the label, remove the cap, and crush" is generated.

[0101] Step 5:

[0102] The server transmits the determined sorting method to the terminal. The input is data regarding the sorting method, and the output is the instructions displayed on the terminal. The terminal displays this information on its screen and provides it to the user.

[0103] Step 6:

[0104] The user checks the instructions displayed on the device screen and actually sorts the waste. Specifically, the user performs the appropriate recycling process according to the on-screen instructions.

[0105] Step 7:

[0106] The server further provides users with additional information about recyclable resources. Input is information based on database lookups, and output is sent to the terminal as recycling methods and product information. This allows users to learn how waste is reused.

[0107] Step 8:

[0108] When a user wishes to dispose of large waste items, they notify the server of their request from their terminal. The input is the user's request information, and the server processes the request based on this information.

[0109] Step 9:

[0110] The server searches for recycling companies based on the user's location information and identifies a suitable company. The input is the user's location information and data about bulky waste, and the output is a list of candidate companies.

[0111] Step 10:

[0112] The server sends the found vendor information to the terminal and presents it to the user. The user reviews the candidate vendors and decides on the transaction details.

[0113] Step 11:

[0114] The server records users' recycling activities and calculates their environmental contribution based on that data. The input is data about the user's recycling behavior, and the output is a numerical representation of their environmental contribution.

[0115] Step 12:

[0116] The server sends the environmental contribution to the terminal and displays it visually on the user's screen. This allows users to understand their own contribution to the environment and motivates them to take further action.

[0117] (Application Example 1)

[0118] 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."

[0119] In modern society, the importance of proper waste sorting and recycling is increasing. However, information and methods for individuals and businesses to efficiently carry out recycling activities are still insufficient. In particular, further support is needed for the disposal of large waste items and the selection of the most suitable recycling companies in the region. This challenge is faced by many people and results in an increased burden on the environment.

[0120] 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.

[0121] In this invention, the server includes means for analyzing waste data and determining its classification method, means for supplying information on reusable resources, and means for displaying the most suitable recycling company based on the user's location information. This enables the user to properly sort waste and select a recycling company.

[0122] "Waste" refers to materials and products that are no longer needed by users.

[0123] "Data" refers to a collection of information that represents the attributes of waste.

[0124] "Analysis" is the act of recognizing the characteristics contained in waste and identifying its type.

[0125] "Classification method" refers to guidelines for the proper reuse of specific types of waste.

[0126] "Determination" is the process of determining the optimal classification method based on the analysis results.

[0127] "Display" refers to the act of visually conveying analysis results and other information to the user.

[0128] "Reusable resources" are materials within waste that can be reused.

[0129] "Supplying" refers to the activity of providing users with information about reusable resources.

[0130] "Location information" refers to data used to determine the user's current location.

[0131] A "recycling company" is a specialized company that processes waste into a form that can be reused.

[0132] A "server" is the central computing unit that performs data processing in this invention.

[0133] The system of this invention begins with a server receiving image data of waste and analyzing it using an AI model. The analysis process extracts features of the waste from the image and determines the optimal classification method based on the results. The determined classification method is displayed on the user's terminal and communicated visually. In this process, a generative AI model equipped with computer vision technology is used for image analysis. The user takes photos of the waste using a mobile device and uploads them to the cloud environment.

[0134] The server also retrieves information about reusable resources from its database and presents it to users. This allows users to learn about waste recycling methods and raise awareness. The system also includes a function to display the most suitable recycling company based on the user's current location, enabling the provision of information that reflects local characteristics.

[0135] Furthermore, users' recycling activities are recorded within the system, and their environmental contribution is calculated based on this activity data. This contribution is visualized through a graphical user interface, helping to motivate users to engage in environmental activities. As a concrete example, consider a scenario where a user takes a picture of an empty bottle at home and uploads it via their device. In this case, the generating AI identifies the type of bottle and instructs it to remove the label before recycling it as plastic. It also presents information on how the bottle could be used as a renewable energy resource. This analysis is performed by supplying the AI ​​with the prompt, "Analyze the image of the PET bottle and tell me the appropriate recycling method."

[0136] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0137] Step 1:

[0138] The user takes pictures of waste using a device. These images will later become input data for analysis by a generated AI model. The captured images are sent to a server via the network.

[0139] Step 2:

[0140] The server inputs the received waste image data into a generating AI model. In this step, the AI ​​model analyzes the images to identify the type and characteristics of the waste. Through this data processing, the server determines which category the waste belongs to.

[0141] Step 3:

[0142] Based on the analysis results of the AI ​​model, the server queries the database to determine the appropriate classification method for the waste. The server then collects information about the classification method and generates specific instructions to present to the user.

[0143] Step 4:

[0144] The generated classification methods and information are sent to the user's device. The device visually displays this information, clearly indicating the waste sorting procedures that the user must follow.

[0145] Step 5:

[0146] In parallel, the server retrieves information about reusable resources from a database and provides it to the user's terminal. This information includes details on how waste is recycled and related products.

[0147] Step 6:

[0148] The server evaluates the user's location and searches its database for recycling companies optimized for the user's current location. It then sends the retrieved information to the user's device, displaying the name and location of the most suitable company.

[0149] Step 7:

[0150] Ultimately, the server records all of the user's recycling activities and calculates their environmental contribution. This value is sent to the terminal, visually representing the user's environmental contribution and helping to boost motivation.

[0151] 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.

[0152] This invention combines an emotion engine with a system that supports recycling activities to maintain user motivation and promote more effective resource reuse. The system includes features such as sorting guidance based on waste image analysis, provision of information on recyclable resources, matching of large waste disposal companies with users, and recording of users' recycling activities, as well as the ability to recognize users' emotions and provide information accordingly.

[0153] The server receives images of waste sent from the terminal and analyzes their contents using AI generation. Based on the analysis results, the appropriate sorting method is determined and presented to the user. The analysis results are also cross-referenced with a database of recyclable resource information, and relevant information is provided to the user.

[0154] The emotion engine analyzes users' emotions such as annoyance, anxiety, and interest, and based on the results, presents recycling information and advice optimized for the user. For example, users with high anxiety about recycling will be presented with easy-to-implement recycling methods and success stories, while users with high interest will be provided with information on advanced technologies and trends in recycling activities.

[0155] When a user needs to dispose of large waste items, they can request a contractor selection from the server via a terminal. The server then consults a contractor database and searches for a recycling company that matches the user's location and needs. Information on the selected contractor is presented to the user, and they are shown the options for the transaction.

[0156] The server calculates the user's environmental contribution based on recorded recycling activities and displays it on the terminal in a visualized form. This feedback also takes into account the results of the emotion engine analysis and includes messages designed to motivate the user.

[0157] For example, when a user's motivation for recycling declines, the emotion engine could detect this and send encouraging messages or past success stories to the user's device. This would help users regain interest in recycling and encourage sustained participation.

[0158] Thus, this invention not only enhances the efficiency and sustainability of recycling activities, but also provides support that is sensitive to the user's feelings, thereby contributing to the realization of a sustainable society.

[0159] The following describes the processing flow.

[0160] Step 1:

[0161] The user takes a picture of the waste using their device's camera. After the user reviews the image within the app, it is uploaded to the server.

[0162] Step 2:

[0163] The server receives the uploaded image and analyzes it using a generation AI module. The analysis identifies the type of waste.

[0164] Step 3:

[0165] The server determines the correct sorting method for recycling the user's items and sends the result to the terminal.

[0166] Step 4:

[0167] The terminal visually displays the sorting method received from the server to the user.

[0168] Step 5:

[0169] The server retrieves information on recyclable resources based on the type of waste from a database and provides it to the terminal. Users can then view detailed recycling information.

[0170] Step 6:

[0171] The device activates an emotion engine to analyze the user's emotions and processes input voice, text, and behavioral data.

[0172] Step 7:

[0173] The emotion engine recognizes the user's emotional state and sends the results to the server. Based on these results, the server creates personalized recycling advice.

[0174] Step 8:

[0175] The server searches for recycling companies based on the user's location, according to the user's request, and sends the information of the relevant companies to the terminal.

[0176] Step 9:

[0177] The device displays information about the recycling company selected by the user and their transaction options.

[0178] Step 10:

[0179] The server records the user's recycling activity history and calculates their environmental contribution based on that activity.

[0180] Step 11:

[0181] The device supports users in increasing their motivation for future recycling activities by displaying their calculated environmental contribution and messages generated by an emotion engine.

[0182] (Example 2)

[0183] 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".

[0184] Conventional recycling support systems lacked individual optimization that took user emotions into consideration, sometimes leading to decreased motivation and anxiety. Furthermore, selecting the appropriate disposal company for large waste items was difficult, burdening inexperienced users. Additionally, the lack of visual display of recycling results made it difficult for users to feel a sense of their own contribution.

[0185] 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.

[0186] In this invention, the server includes means for analyzing images of waste and determining its sorting method, means for analyzing the user's emotional state and providing information accordingly, and means for recording the user's recycling activities and visualizing their environmental contribution. This enables the provision of individually optimized recycling information and the selection of appropriate recycling companies, thereby improving user motivation and reducing their burden. Furthermore, by experiencing the results of their own actions, continuous recycling activities are promoted.

[0187] "Images of waste" refer to data that electronically records visual information, including waste.

[0188] "Means for analyzing images" refers to a function that performs the process of identifying objects from received visual information and analyzing their characteristics.

[0189] "Means for determining sorting methods" refers to a function that determines how to appropriately classify waste based on the results of image analysis.

[0190] "Information on recyclable resources" refers to data about materials and products that can be reused, including their characteristics and uses.

[0191] "Means for analyzing emotional states" refers to functions that analyze user input data and behavior to identify and evaluate emotional tendencies.

[0192] "Means of providing information" refers to functions that generate data and advice to present to users based on analysis results.

[0193] "Large-sized waste" refers to items that are difficult to handle using general waste disposal methods due to their size and weight.

[0194] "Means for searching for and presenting service providers" refers to a function that searches for appropriate service providers based on user requests and provides that information.

[0195] "Recycling activities" refer to actions that process waste into a reusable form and promote the recovery and reuse of resources.

[0196] "Means of visualizing environmental contribution" refers to a function that measures the results of users' recycling activities and displays them in an easy-to-understand format.

[0197] The system of this invention consists of a server, a terminal, and a user. The server receives images of waste and analyzes them using a generative AI model. Image recognition libraries such as OpenCV and TENSORFLOW® are used for image analysis. Based on the analysis results, the server identifies the type of waste and presents the user with appropriate sorting methods. For example, it provides specific instructions such as, "This waste is glass, so please dispose of it in the glass recycling bin."

[0198] Furthermore, the server has sentiment analysis capabilities to analyze the user's emotional state. This analysis uses natural language processing libraries, such as Hugging Face's Transformers. If a user is feeling anxious about recycling, the server will provide encouraging information such as, "Here are some easy ways to get started with recycling."

[0199] On the other hand, the terminal provides an interface for the user to interact with the server. The user sends images to the server via the terminal and receives instructions. If a large amount of waste needs to be disposed of, the user can request the server to select a contractor from the terminal. The server searches the contractor database and provides the user with information on a suitable contractor. For example, when a user is disposing of furniture, information such as "Company A and Company B are nearby recycling companies" might be presented.

[0200] Furthermore, the server tracks users' recycling activities, calculates and visualizes their environmental contribution. This information is displayed as graphs and numbers as the results of their activities, helping to raise user awareness. For example, a report such as "Today's recycling activities contributed to reducing CO2 emissions by XX kg" might be displayed on the terminal. This visualization uses software that supports data analysis and graphical representation.

[0201] An example of a prompt message to input into a generative AI model would be, "Analyze this image and suggest the appropriate recycling method." By using this prompt message, the generative AI model can quickly and accurately provide the user with the necessary information.

[0202] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0203] Step 1:

[0204] The user takes an image of the waste using a terminal and sends it to the server. This input serves as basic data for identifying the type of waste. The terminal initiates the image analysis process by transferring the captured image file to the server.

[0205] Step 2:

[0206] The server analyzes the received image. A generative AI model is used for this analysis, and the server instructs the model via a prompt message, saying, "Analyze this image and tell me the appropriate recycling method." The server extracts features from the image and applies a classification algorithm to determine the type of waste. As a result, the waste is classified into categories such as metal, plastic, and paper.

[0207] Step 3:

[0208] The server presents the user with appropriate sorting methods based on the analysis results. In this step, it generates instructions according to the waste category, such as "Please dispose of plastic in the designated recycling box." These instructions are then sent to the user's terminal.

[0209] Step 4:

[0210] The server analyzes the user's emotional state. It uses the user's past input data and behavioral history to apply an emotional analysis algorithm. For example, if a user tends to ignore recycling notifications, the server generates and sends encouraging information such as "Here's an easy way to get started with recycling."

[0211] Step 5:

[0212] When a user needs to dispose of large waste items, they request a contractor selection from their terminal. The server searches a contractor database based on the user's location and entered needs, and lists suitable contractors. The search results are displayed on the user's terminal, including the contractor's contact information and service details.

[0213] Step 6:

[0214] The server tracks users' recycling activities and calculates their environmental contribution. It analyzes activity history and calculates contributions in the form of energy savings and CO2 reductions. The calculation results are displayed on the user's device, for example, "Your activities this month have contributed to reducing carbon dioxide by XX kg."

[0215] (Application Example 2)

[0216] 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 device 14 will be referred to as the "terminal."

[0217] In modern society, recycling plays a crucial role in environmental protection, but declining individual motivation and the complexity of sorting are hindering its widespread adoption. Furthermore, a lack of specific information on proper waste sorting methods and the disposal of bulky waste places a burden on users. Additionally, the scarcity of appropriate, emotionally-based support makes it difficult to implement sustainable recycling activities.

[0218] 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.

[0219] In this invention, the server includes means for analyzing images of waste and determining how to sort it, means for analyzing the user's emotions and providing appropriate information, and means for displaying locations related to waste disposal using a geographic information system. This enables comprehensive support for easy and sustainable recycling while being sensitive to the user's emotions.

[0220] "Means for analyzing images of waste" refers to a processing device that receives image data, identifies its contents, and determines the type of waste.

[0221] "Means for determining sorting methods" refers to a device that has a calculation function to determine the appropriate sorting method based on the analyzed waste information.

[0222] "Means for presenting sorting methods" refers to a device that visually or audibly informs the user of the determined sorting method.

[0223] "Means for providing information on recyclable resources" refers to a device that has a display or notification function for providing users with detailed information about recyclable resources.

[0224] "A means of searching for and presenting companies that handle large waste" refers to a device that lists companies capable of handling large waste and provides company information that matches the user's requirements.

[0225] "A means of recording users' recycling activities and visualizing their environmental contribution" refers to a device that accumulates users' recycling behavior as data and evaluates and displays the results as an environmental contribution.

[0226] "A means of analyzing user emotions and providing appropriate information" refers to a device that senses the user's current emotional state and selects and provides information or messages accordingly.

[0227] "Means for displaying locations related to waste disposal using a geographic information system" refers to a device that displays waste disposal facilities and related locations on a map based on the user's location information.

[0228] This invention provides a system to support users' recycling activities, thereby providing appropriate recycling information tailored to the user's emotional state and enabling smart waste management. To achieve this, the following hardware and software configuration is used.

[0229] The main components of the system are the user terminal (such as a smartphone) and the server. The user terminal is responsible for taking pictures of waste with its built-in camera and sending the images to the server. The server uses a generative AI model combining Python and TensorFlow as image processing software to analyze the type of waste. Based on the analysis results, the server determines the appropriate sorting method and presents it to the user terminal.

[0230] Furthermore, the server incorporates an emotion analysis engine to analyze user sentiment. This evaluates users' feelings regarding waste disposal, such as interest or anxiety, and generates encouraging messages and simple suggestions accordingly. For example, it provides information on advanced recycling technologies to users who are enthusiastic about recycling, and introduces easy-to-implement methods to users who are reluctant.

[0231] The server also features a Geographic Information System (GIS), which displays nearby recycling facilities and large waste disposal companies on a map based on the user's location. Users can utilize this information to easily carry out recycling activities in their local communities.

[0232] The server also includes a feature that records users' past recycling activities and visually displays their contribution to the environment. This allows users to see that their actions are contributing to building a sustainable society, leading to increased motivation.

[0233] For example, a user struggling with how to dispose of a large amount of waste paper generated while cleaning their house on a holiday can upload images of the paper taken with their device and instantly receive information on appropriate disposal methods and recycling facilities. Furthermore, based on sentiment analysis, messages such as "Your recycling is contributing to the environment!" are displayed to motivate the user.

[0234] Examples of prompts for a generative AI model are as follows:

[0235] "You are an assistant supporting recycling activities. Based on the type of waste the user has, provide them with appropriate waste sorting methods and recycling information. Also, send encouraging messages and past success stories tailored to the user's mood to pique their interest in recycling."

[0236] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0237] Step 1:

[0238] The user takes a picture of the waste with their device and sends that image to the server via the application. The input in this process is image data generated by the user's actions, and the output is the image file uploaded to the server.

[0239] Step 2:

[0240] The server analyzes the received image data using a generation AI model. This analysis uses TensorFlow to recognize objects in the image and identify the type of waste. The input is an image file uploaded to the server, and the output is data about the type of waste.

[0241] Step 3:

[0242] Based on the analysis results, the server determines the appropriate sorting method. Here, the server compares the type data derived by the generating AI model with sorting methods registered in the database beforehand and selects the appropriate method. The input is the analyzed waste type data, and the output is information on sorting methods.

[0243] Step 4:

[0244] The server provides the user's terminal with information on the determined sorting method and recyclable resources. Here, the sorting method and related information are presented to the user as text and images. The input is sorting method information, and the output is the display on the user's terminal.

[0245] Step 5:

[0246] The server uses an emotion analysis engine to analyze the user's emotional state and select appropriate information. It extracts emotional data using simple questionnaires and usage history entered by the user and generates corresponding messages. The input is the user's emotional data, and the output is a message corresponding to that emotion.

[0247] Step 6:

[0248] The server acquires the user's location information and uses a geographic information system to display nearby recycling facilities and businesses. Here, GPS data is used to map locations onto a map. The input is the user's location information, and the output is the map display information.

[0249] Step 7:

[0250] The server records the user's recycling activities and calculates their environmental contribution. Using the recorded activity data, it calculates the contribution and provides feedback in a visualized format. The input is the user's activity record data, and the output is the evaluation result of their environmental contribution.

[0251] Step 8:

[0252] The user's device notifies the user of information and messages received from the server. Ultimately, the information is presented on the screen in a viewable format, allowing the user to decide on their next action. Input consists of various pieces of information from the server, and output consists of notifications to the user.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] [Second Embodiment]

[0257] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0258] 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.

[0259] 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).

[0260] 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.

[0261] 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.

[0262] 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).

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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".

[0269] This invention is designed to enable individuals and businesses to effectively carry out recycling activities through a system utilizing AI technology. Its main functions include image analysis of waste, suggestion of sorting methods, provision of recycling information, matching with recycling companies, and visualization of environmental contribution.

[0270] The server receives images of waste uploaded by the user from their device. Using a generation AI, the server analyzes the images to identify the type of waste. Simultaneously, the server consults a database to determine the most appropriate sorting method for the identified waste. The results are presented to the user via their device. The user can immediately confirm the sorting method on their device screen.

[0271] Furthermore, the server retrieves information about recyclable resources from a database and provides it to the user. This information includes recycling methods and related product information, and by conveying it to the user through the terminal, it supports the user's knowledge of recycling.

[0272] When a user wishes to dispose of large waste items, they notify the server via their terminal. The server uses registered vendor information to search for recycling companies based on the user's location and the type of waste, and presents suitable companies. The user can then select a preferred company from the presented options and check the transaction details on their terminal.

[0273] Ultimately, the server records the user's recycling activities and calculates their environmental contribution based on those activities. This environmental contribution is visually represented to the user on their device, encouraging continued motivation for recycling.

[0274] As a concrete example, consider a scenario where a user takes a picture of an empty plastic bottle at home and uploads it to the cloud via their device. In this case, the server analyzes the bottle and displays specific instructions on the device, such as "remove the label, take off the cap, crush it, and separate it as plastic waste." Furthermore, if information is also provided on how the plastic bottle is recycled and transformed into new products, it is expected that users' awareness of recycling will increase.

[0275] This invention reduces the effort required for sorting, improves the efficiency of recycling activities through information visualization and appropriate vendor selection, and enables contributions to a sustainable environment.

[0276] The process flow will be described below.

[0277] Step 1:

[0278] The user uses the camera of the terminal to take a picture of the waste. The captured image is saved on the user's terminal.

[0279] Step 2:

[0280] The user uploads the waste image to the server through the dedicated application on the terminal. The image data also includes the user's ID and location information. {{END]]

[0281] Step 3:

[0282] The server sends the received image data to the AI module for analysis. The AI module analyzes the image using machine learning algorithms to identify the type of waste.

[0283] Step 4:

[0284] Based on the analysis results, the server identifies the sorting method suitable for the type of waste. The sorting method is determined considering relevant laws and regional guidelines.

[0285] Step 5:

[0286] The server sends the identified sorting method to the terminal. The terminal displays the sorting method to the user and conveys the specific procedures.

[0287] Step 6:

[0288] The server refers to the database of recyclable resources and collects the recycling information related to the corresponding waste.

[0289] Step 7:

[0290] The server organizes the recycling information and sends it to the terminal. The terminal displays information to the user indicating the importance and possibility of recycling.

[0291] Step 8:

[0292] When a user wishes to have large waste items picked up, they send a request from their device to the server. The request includes the desired pickup date and time, as well as details about the waste.

[0293] Step 9:

[0294] The server searches the vendor database to identify recycling companies that match the user's location and request conditions.

[0295] Step 10:

[0296] The server sends information about the selected vendor to the terminal. The terminal displays the vendor's details and transaction options to the user.

[0297] Step 11:

[0298] The server records the user's recycling activity history and updates the database. It also calculates the user's environmental contribution based on their activities.

[0299] Step 12:

[0300] The server sends the calculation results to the terminal. The terminal displays the user's environmental contribution as a graph or numerical value, encouraging sustainable activity.

[0301] (Example 1)

[0302] 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."

[0303] The challenge is to support individuals and organizations in conducting recycling activities appropriately and effectively by efficiently providing information on waste classification and reuse, and to promote contributions to the environment. Furthermore, it is necessary to facilitate cooperation with reuse operators and promote the proper treatment of waste.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0305] In this invention, the server includes means for analyzing the visual information of waste and determining its classification method, means for providing data on reusable resources, and means for recording the user's reuse activities through information processing and visualizing the degree of environmental contribution. As a result, users can easily know the method of separating waste and can effectively carry out recycling activities. In addition, by visualizing the degree of environmental contribution, it is possible to provide motivation for continuous recycling activities.

[0306] "Visual information of waste" refers to data indicating visual features such as the shape, color, and texture of waste.

[0307] "Analysis" is a process of extracting specific elements from visual information using a generation model or the like and classifying and judging information based on it.

[0308] "Means for determining the classification method" is a method or technology for providing appropriate separation instructions according to the type and material of waste.

[0309] "Means for displaying" is a technology for presenting analysis results and information to users through the screen or interface of an electronic device.

[0310] "Data on reusable resources" refers to information on how waste is recycled and utilized as new resources.

[0311] "Means of searching and presenting" refers to technology that searches for suitable vendors based on specific conditions and displays the results visually to the user.

[0312] "Information processing" is a series of computational processes that organize and analyze collected data and generate the necessary output.

[0313] "Degree of environmental contribution" is an indicator that quantitatively or qualitatively shows the positive environmental impact of users' recycling activities.

[0314] A "generative model" is an algorithm that uses artificial intelligence technology to generate or analyze specific information.

[0315] A description of the embodiment for carrying out the invention will be provided.

[0316] This invention is a system aimed at improving the efficiency of waste analysis and recycling activities. The system mainly consists of three components: a server, terminals, and users. The server plays a central role and exchanges information with users through terminals.

[0317] The server is equipped with high-performance computing units and data storage, and performs image analysis of waste using generative AI models. The image analysis analyzes the visual information of the waste and determines its classification. Specifically, server machines equipped with the latest GPUs are often used. The server also retrieves information on recyclable resources from a database and provides it to the user.

[0318] The terminal is a device used by users to photograph waste and send the images to a server. Smartphones and tablets can function as terminals. The terminal also displays analysis results and recycling information sent from the server, allowing users to learn how to handle the waste in detail.

[0319] Users routinely input the status of waste using their devices and utilize instructions and information from the server to carry out recycling activities. For example, a user might take a picture of an empty plastic bottle at home and upload the image from their device to the cloud. An example of a prompt message used in this process would be, "Analyze the plastic bottle image and suggest sorting methods."

[0320] This system allows users to easily understand and practice proper waste sorting and recycling methods. Furthermore, by visualizing their environmental contribution, users can see how much their recycling activities are positively impacting the environment, which helps them maintain sustained motivation.

[0321] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0322] Step 1:

[0323] The user takes a photograph of the waste using the device. At this time, the user inputs image data of the waste. The device then prepares to send this image data to the server.

[0324] Step 2:

[0325] The device uploads the image data of the captured waste to the server. The input is image data from the device, which is then received by the server. The output is the image saved on the server.

[0326] Step 3:

[0327] The server analyzes the received image data using a generation AI model. The input is image data of waste, and the data processing involves extracting features from the images and performing classification. The output is information about the type of waste and the classification result.

[0328] Step 4:

[0329] The server consults a database based on the determined type of waste to decide on the appropriate sorting method. The input is the classification result, and the output is specific sorting instructions. In this case, if the waste is a plastic bottle, the instruction "remove the label, remove the cap, and crush" is generated.

[0330] Step 5:

[0331] The server transmits the determined sorting method to the terminal. The input is data regarding the sorting method, and the output is the instructions displayed on the terminal. The terminal displays this information on its screen and provides it to the user.

[0332] Step 6:

[0333] The user checks the instructions displayed on the device screen and actually sorts the waste. Specifically, the user performs the appropriate recycling process according to the on-screen instructions.

[0334] Step 7:

[0335] The server further provides users with additional information about recyclable resources. Input is information based on database lookups, and output is sent to the terminal as recycling methods and product information. This allows users to learn how waste is reused.

[0336] Step 8:

[0337] When a user wishes to dispose of large waste items, they notify the server of their request from their terminal. The input is the user's request information, and the server processes the request based on this information.

[0338] Step 9:

[0339] The server searches for recycling companies based on the user's location information and identifies a suitable company. The input is the user's location information and data about bulky waste, and the output is a list of candidate companies.

[0340] Step 10:

[0341] The server sends the found vendor information to the terminal and presents it to the user. The user reviews the candidate vendors and decides on the transaction details.

[0342] Step 11:

[0343] The server records users' recycling activities and calculates their environmental contribution based on that data. The input is data about the user's recycling behavior, and the output is a numerical representation of their environmental contribution.

[0344] Step 12:

[0345] The server sends the environmental contribution to the terminal and displays it visually on the user's screen. This allows users to understand their own contribution to the environment and motivates them to take further action.

[0346] (Application Example 1)

[0347] 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."

[0348] In modern society, the importance of proper waste sorting and recycling is increasing. However, information and methods for individuals and businesses to efficiently carry out recycling activities are still insufficient. In particular, further support is needed for the disposal of large waste items and the selection of the most suitable recycling companies in the region. This challenge is faced by many people and results in an increased burden on the environment.

[0349] 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.

[0350] In this invention, the server includes means for analyzing waste data and determining its classification method, means for supplying information on reusable resources, and means for displaying the most suitable recycling company based on the user's location information. This enables the user to properly sort waste and select a recycling company.

[0351] "Waste" refers to materials and products that are no longer needed by users.

[0352] "Data" refers to a collection of information that represents the attributes of waste.

[0353] "Analysis" is the act of recognizing the characteristics contained in waste and identifying its type.

[0354] "Classification method" refers to guidelines for the proper reuse of specific types of waste.

[0355] "Determination" is the process of determining the optimal classification method based on the analysis results.

[0356] "Display" refers to the act of visually conveying analysis results and other information to the user.

[0357] "Reusable resources" are materials within waste that can be reused.

[0358] "Supplying" refers to the activity of providing users with information about reusable resources.

[0359] "Location information" refers to data used to determine the user's current location.

[0360] A "recycling company" is a specialized company that processes waste into a form that can be reused.

[0361] A "server" is the central computing unit that performs data processing in this invention.

[0362] The system of this invention begins with a server receiving image data of waste and analyzing it using an AI model. The analysis process extracts features of the waste from the image and determines the optimal classification method based on the results. The determined classification method is displayed on the user's terminal and communicated visually. In this process, a generative AI model equipped with computer vision technology is used for image analysis. The user takes photos of the waste using a mobile device and uploads them to the cloud environment.

[0363] The server also retrieves information about reusable resources from its database and presents it to users. This allows users to learn about waste recycling methods and raise awareness. The system also includes a function to display the most suitable recycling company based on the user's current location, enabling the provision of information that reflects local characteristics.

[0364] Furthermore, users' recycling activities are recorded within the system, and their environmental contribution is calculated based on this activity data. This contribution is visualized through a graphical user interface, helping to motivate users to engage in environmental activities. As a concrete example, consider a scenario where a user takes a picture of an empty bottle at home and uploads it via their device. In this case, the generating AI identifies the type of bottle and instructs it to remove the label before recycling it as plastic. It also presents information on how the bottle could be used as a renewable energy resource. This analysis is performed by supplying the AI ​​with the prompt, "Analyze the image of the PET bottle and tell me the appropriate recycling method."

[0365] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0366] Step 1:

[0367] The user takes pictures of waste using a device. These images will later become input data for analysis by a generated AI model. The captured images are sent to a server via the network.

[0368] Step 2:

[0369] The server inputs the received waste image data into a generating AI model. In this step, the AI ​​model analyzes the images to identify the type and characteristics of the waste. Through this data processing, the server determines which category the waste belongs to.

[0370] Step 3:

[0371] Based on the analysis results of the AI ​​model, the server queries the database to determine the appropriate classification method for the waste. The server then collects information about the classification method and generates specific instructions to present to the user.

[0372] Step 4:

[0373] The generated classification methods and information are sent to the user's device. The device visually displays this information, clearly indicating the waste sorting procedures that the user must follow.

[0374] Step 5:

[0375] In parallel, the server retrieves information about reusable resources from a database and provides it to the user's terminal. This information includes details on how waste is recycled and related products.

[0376] Step 6:

[0377] The server evaluates the user's location and searches its database for recycling companies optimized for the user's current location. It then sends the retrieved information to the user's device, displaying the name and location of the most suitable company.

[0378] Step 7:

[0379] Ultimately, the server records all of the user's recycling activities and calculates their environmental contribution. This value is sent to the terminal, visually representing the user's environmental contribution and helping to boost motivation.

[0380] 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.

[0381] This invention combines an emotion engine with a system that supports recycling activities to maintain user motivation and promote more effective resource reuse. The system includes features such as sorting guidance based on waste image analysis, provision of information on recyclable resources, matching of large waste disposal companies with users, and recording of users' recycling activities, as well as the ability to recognize users' emotions and provide information accordingly.

[0382] The server receives images of waste sent from the terminal and analyzes their contents using AI generation. Based on the analysis results, the appropriate sorting method is determined and presented to the user. The analysis results are also cross-referenced with a database of recyclable resource information, and relevant information is provided to the user.

[0383] The emotion engine analyzes users' emotions such as annoyance, anxiety, and interest, and based on the results, presents recycling information and advice optimized for the user. For example, users with high anxiety about recycling will be presented with easy-to-implement recycling methods and success stories, while users with high interest will be provided with information on advanced technologies and trends in recycling activities.

[0384] When a user needs to dispose of large waste items, they can request a contractor selection from the server via a terminal. The server then consults a contractor database and searches for a recycling company that matches the user's location and needs. Information on the selected contractor is presented to the user, and they are shown the options for the transaction.

[0385] The server calculates the user's environmental contribution based on recorded recycling activities and displays it on the terminal in a visualized form. This feedback also takes into account the results of the emotion engine analysis and includes messages designed to motivate the user.

[0386] For example, when a user's motivation for recycling declines, the emotion engine could detect this and send encouraging messages or past success stories to the user's device. This would help users regain interest in recycling and encourage sustained participation.

[0387] Thus, this invention not only enhances the efficiency and sustainability of recycling activities, but also provides support that is sensitive to the user's feelings, thereby contributing to the realization of a sustainable society.

[0388] The following describes the processing flow.

[0389] Step 1:

[0390] The user takes a picture of the waste using their device's camera. After the user reviews the image within the app, it is uploaded to the server.

[0391] Step 2:

[0392] The server receives the uploaded image and analyzes it using a generation AI module. The analysis identifies the type of waste.

[0393] Step 3:

[0394] The server determines the correct sorting method for recycling the user's items and sends the result to the terminal.

[0395] Step 4:

[0396] The terminal visually displays the sorting method received from the server to the user.

[0397] Step 5:

[0398] The server retrieves information on recyclable resources based on the type of waste from a database and provides it to the terminal. Users can then view detailed recycling information.

[0399] Step 6:

[0400] The device activates an emotion engine to analyze the user's emotions and processes input voice, text, and behavioral data.

[0401] Step 7:

[0402] The emotion engine recognizes the user's emotional state and sends the results to the server. Based on these results, the server creates personalized recycling advice.

[0403] Step 8:

[0404] The server searches for recycling companies based on the user's location, according to the user's request, and sends the information of the relevant companies to the terminal.

[0405] Step 9:

[0406] The device displays information about the recycling company selected by the user and their transaction options.

[0407] Step 10:

[0408] The server records the user's recycling activity history and calculates their environmental contribution based on that activity.

[0409] Step 11:

[0410] The device supports users in increasing their motivation for future recycling activities by displaying their calculated environmental contribution and messages generated by an emotion engine.

[0411] (Example 2)

[0412] 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".

[0413] Conventional recycling support systems lacked individual optimization that took user emotions into consideration, sometimes leading to decreased motivation and anxiety. Furthermore, selecting the appropriate disposal company for large waste items was difficult, burdening inexperienced users. Additionally, the lack of visual display of recycling results made it difficult for users to feel a sense of their own contribution.

[0414] 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.

[0415] In this invention, the server includes means for analyzing images of waste and determining its sorting method, means for analyzing the user's emotional state and providing information accordingly, and means for recording the user's recycling activities and visualizing their environmental contribution. This enables the provision of individually optimized recycling information and the selection of appropriate recycling companies, thereby improving user motivation and reducing their burden. Furthermore, by experiencing the results of their own actions, continuous recycling activities are promoted.

[0416] "Images of waste" refer to data that electronically records visual information, including waste.

[0417] "Means for analyzing images" refers to a function that performs the process of identifying objects from received visual information and analyzing their characteristics.

[0418] "Means for determining sorting methods" refers to a function that determines how to appropriately classify waste based on the results of image analysis.

[0419] "Information on recyclable resources" refers to data about materials and products that can be reused, including their characteristics and uses.

[0420] "Means for analyzing emotional states" refers to functions that analyze user input data and behavior to identify and evaluate emotional tendencies.

[0421] "Means of providing information" refers to functions that generate data and advice to present to users based on analysis results.

[0422] "Large-sized waste" refers to items that are difficult to handle using general waste disposal methods due to their size and weight.

[0423] "Means for searching for and presenting service providers" refers to a function that searches for appropriate service providers based on user requests and provides that information.

[0424] "Recycling activities" refer to actions that process waste into a reusable form and promote the recovery and reuse of resources.

[0425] "Means of visualizing environmental contribution" refers to a function that measures the results of users' recycling activities and displays them in an easy-to-understand format.

[0426] The system of this invention consists of a server, a terminal, and a user. The server receives images of waste and analyzes them using a generative AI model. Image recognition libraries such as OpenCV and TensorFlow are used for image analysis. Based on the analysis results, the server identifies the type of waste and suggests appropriate sorting methods to the user. For example, it may give specific instructions such as, "This waste is glass, so please dispose of it in the glass recycling bin."

[0427] Furthermore, the server has sentiment analysis capabilities to analyze the user's emotional state. This analysis uses natural language processing libraries, such as Hugging Face's Transformers. If a user is feeling anxious about recycling, the server will provide encouraging information such as, "Here are some easy ways to get started with recycling."

[0428] On the other hand, the terminal provides an interface for the user to interact with the server. The user sends images to the server via the terminal and receives instructions. If a large amount of waste needs to be disposed of, the user can request the server to select a contractor from the terminal. The server searches the contractor database and provides the user with information on a suitable contractor. For example, when a user is disposing of furniture, information such as "Company A and Company B are nearby recycling companies" might be presented.

[0429] Furthermore, the server tracks users' recycling activities, calculates and visualizes their environmental contribution. This information is displayed as graphs and numbers as the results of their activities, helping to raise user awareness. For example, a report such as "Today's recycling activities contributed to reducing CO2 emissions by XX kg" might be displayed on the terminal. This visualization uses software that supports data analysis and graphical representation.

[0430] An example of a prompt message to input into a generative AI model would be, "Analyze this image and suggest the appropriate recycling method." By using this prompt message, the generative AI model can quickly and accurately provide the user with the necessary information.

[0431] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0432] Step 1:

[0433] The user takes an image of the waste using a terminal and sends it to the server. This input serves as basic data for identifying the type of waste. The terminal initiates the image analysis process by transferring the captured image file to the server.

[0434] Step 2:

[0435] The server analyzes the received image. A generative AI model is used for this analysis, and the server instructs the model via a prompt message, saying, "Analyze this image and tell me the appropriate recycling method." The server extracts features from the image and applies a classification algorithm to determine the type of waste. As a result, the waste is classified into categories such as metal, plastic, and paper.

[0436] Step 3:

[0437] The server presents the user with appropriate sorting methods based on the analysis results. In this step, it generates instructions according to the waste category, such as "Please dispose of plastic in the designated recycling box." These instructions are then sent to the user's terminal.

[0438] Step 4:

[0439] The server analyzes the user's emotional state. It uses the user's past input data and behavioral history to apply an emotional analysis algorithm. For example, if a user tends to ignore recycling notifications, the server generates and sends encouraging information such as "Here's an easy way to get started with recycling."

[0440] Step 5:

[0441] When a user needs to dispose of large waste items, they request a contractor selection from their terminal. The server searches a contractor database based on the user's location and entered needs, and lists suitable contractors. The search results are displayed on the user's terminal, including the contractor's contact information and service details.

[0442] Step 6:

[0443] The server tracks users' recycling activities and calculates their environmental contribution. It analyzes activity history and calculates contributions in the form of energy savings and CO2 reductions. The calculation results are displayed on the user's device, for example, "Your activities this month have contributed to reducing carbon dioxide by XX kg."

[0444] (Application Example 2)

[0445] 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 as the "terminal".

[0446] In modern society, recycling plays a crucial role in environmental protection, but declining individual motivation and the complexity of sorting are hindering its widespread adoption. Furthermore, a lack of specific information on proper waste sorting methods and the disposal of bulky waste places a burden on users. Additionally, the scarcity of appropriate, emotionally-based support makes it difficult to implement sustainable recycling activities.

[0447] 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.

[0448] In this invention, the server includes means for analyzing images of waste and determining how to sort it, means for analyzing the user's emotions and providing appropriate information, and means for displaying locations related to waste disposal using a geographic information system. This enables comprehensive support for easy and sustainable recycling while being sensitive to the user's emotions.

[0449] "Means for analyzing images of waste" refers to a processing device that receives image data, identifies its contents, and determines the type of waste.

[0450] "Means for determining sorting methods" refers to a device that has a calculation function to determine the appropriate sorting method based on the analyzed waste information.

[0451] "Means for presenting sorting methods" refers to a device that visually or audibly informs the user of the determined sorting method.

[0452] "Means for providing information on recyclable resources" refers to a device that has a display or notification function for providing users with detailed information about recyclable resources.

[0453] "A means of searching for and presenting companies that handle large waste" refers to a device that lists companies capable of handling large waste and provides company information that matches the user's requirements.

[0454] "A means of recording users' recycling activities and visualizing their environmental contribution" refers to a device that accumulates users' recycling behavior as data and evaluates and displays the results as an environmental contribution.

[0455] "A means of analyzing user emotions and providing appropriate information" refers to a device that senses the user's current emotional state and selects and provides information or messages accordingly.

[0456] "Means for displaying locations related to waste disposal using a geographic information system" refers to a device that displays waste disposal facilities and related locations on a map based on the user's location information.

[0457] This invention provides a system to support users' recycling activities, thereby providing appropriate recycling information tailored to the user's emotional state and enabling smart waste management. To achieve this, the following hardware and software configuration is used.

[0458] The main components of the system are the user terminal (such as a smartphone) and the server. The user terminal is responsible for taking pictures of waste with its built-in camera and sending the images to the server. The server uses a generative AI model combining Python and TensorFlow as image processing software to analyze the type of waste. Based on the analysis results, the server determines the appropriate sorting method and presents it to the user terminal.

[0459] Furthermore, the server incorporates an emotion analysis engine to analyze user sentiment. This evaluates users' feelings regarding waste disposal, such as interest or anxiety, and generates encouraging messages and simple suggestions accordingly. For example, it provides information on advanced recycling technologies to users who are enthusiastic about recycling, and introduces easy-to-implement methods to users who are reluctant.

[0460] The server also features a Geographic Information System (GIS), which displays nearby recycling facilities and large waste disposal companies on a map based on the user's location. Users can utilize this information to easily carry out recycling activities in their local communities.

[0461] The server also includes a feature that records users' past recycling activities and visually displays their contribution to the environment. This allows users to see that their actions are contributing to building a sustainable society, leading to increased motivation.

[0462] For example, a user struggling with how to dispose of a large amount of waste paper generated while cleaning their house on a holiday can upload images of the paper taken with their device and instantly receive information on appropriate disposal methods and recycling facilities. Furthermore, based on sentiment analysis, messages such as "Your recycling is contributing to the environment!" are displayed to motivate the user.

[0463] Examples of prompts for a generative AI model are as follows:

[0464] "You are an assistant supporting recycling activities. Based on the type of waste the user has, provide them with appropriate waste sorting methods and recycling information. Also, send encouraging messages and past success stories tailored to the user's mood to pique their interest in recycling."

[0465] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0466] Step 1:

[0467] The user takes a picture of the waste with their device and sends that image to the server via the application. The input in this process is image data generated by the user's actions, and the output is the image file uploaded to the server.

[0468] Step 2:

[0469] The server analyzes the received image data using a generation AI model. This analysis uses TensorFlow to recognize objects in the image and identify the type of waste. The input is an image file uploaded to the server, and the output is data about the type of waste.

[0470] Step 3:

[0471] Based on the analysis results, the server determines the appropriate sorting method. Here, the server compares the type data derived by the generating AI model with sorting methods registered in the database beforehand and selects the appropriate method. The input is the analyzed waste type data, and the output is information on sorting methods.

[0472] Step 4:

[0473] The server provides the user's terminal with information on the determined sorting method and recyclable resources. Here, the sorting method and related information are presented to the user as text and images. The input is sorting method information, and the output is the display on the user's terminal.

[0474] Step 5:

[0475] The server uses an emotion analysis engine to analyze the user's emotional state and select appropriate information. It extracts emotional data using simple questionnaires and usage history entered by the user and generates corresponding messages. The input is the user's emotional data, and the output is a message corresponding to that emotion.

[0476] Step 6:

[0477] The server acquires the user's location information and uses a geographic information system to display nearby recycling facilities and businesses. Here, GPS data is used to map locations onto a map. The input is the user's location information, and the output is the map display information.

[0478] Step 7:

[0479] The server records the user's recycling activities and calculates their environmental contribution. Using the recorded activity data, it calculates the contribution and provides feedback in a visualized format. The input is the user's activity record data, and the output is the evaluation result of their environmental contribution.

[0480] Step 8:

[0481] The user's device notifies the user of information and messages received from the server. Ultimately, the information is presented on the screen in a viewable format, allowing the user to decide on their next action. Input consists of various pieces of information from the server, and output consists of notifications to the user.

[0482] 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.

[0483] 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.

[0484] 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.

[0485] [Third Embodiment]

[0486] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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).

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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".

[0498] This invention is designed to enable individuals and businesses to effectively carry out recycling activities through a system utilizing AI technology. Its main functions include image analysis of waste, suggestion of sorting methods, provision of recycling information, matching with recycling companies, and visualization of environmental contribution.

[0499] The server receives images of waste uploaded by the user from their device. Using a generation AI, the server analyzes the images to identify the type of waste. Simultaneously, the server consults a database to determine the most appropriate sorting method for the identified waste. The results are presented to the user via their device. The user can immediately confirm the sorting method on their device screen.

[0500] Furthermore, the server retrieves information about recyclable resources from a database and provides it to the user. This information includes recycling methods and related product information, and by conveying it to the user through the terminal, it supports the user's knowledge of recycling.

[0501] When a user wishes to dispose of large waste items, they notify the server via their terminal. The server uses registered vendor information to search for recycling companies based on the user's location and the type of waste, and presents suitable companies. The user can then select a preferred company from the presented options and check the transaction details on their terminal.

[0502] Ultimately, the server records the user's recycling activities and calculates their environmental contribution based on those activities. This environmental contribution is visually represented to the user on their device, encouraging continued motivation for recycling.

[0503] As a concrete example, consider a scenario where a user takes a picture of an empty plastic bottle at home and uploads it to the cloud via their device. In this case, the server analyzes the bottle and displays specific instructions on the device, such as "remove the label, take off the cap, crush it, and separate it as plastic waste." Furthermore, if information is also provided on how the plastic bottle is recycled and transformed into new products, it is expected that users' awareness of recycling will increase.

[0504] This invention reduces the effort required for sorting, improves the efficiency of recycling activities through information visualization and appropriate vendor selection, and enables contributions to a sustainable environment.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The user takes a picture of the waste using their device's camera. The captured image is saved on the user's device.

[0508] Step 2:

[0509] Users upload images of waste to the server via a dedicated application on their device. The image data also includes the user's ID and location information.

[0510] Step 3:

[0511] The server sends the received image data to an AI module for analysis. The AI ​​module uses machine learning algorithms to analyze the images and identify the type of waste.

[0512] Step 4:

[0513] Based on the analysis results, the server identifies the appropriate sorting method for each type of waste. The sorting method is determined considering relevant laws and local guidelines.

[0514] Step 5:

[0515] The server sends the identified sorting method to the terminal. The terminal displays the sorting method to the user and provides specific instructions.

[0516] Step 6:

[0517] The server consults a database of recyclable resources and collects recycling information related to the relevant waste.

[0518] Step 7:

[0519] The server organizes the recycling information and sends it to the terminal. The terminal displays information to the user indicating the importance and possibility of recycling.

[0520] Step 8:

[0521] When a user wishes to have large waste items picked up, they send a request from their device to the server. The request includes the desired pickup date and time, as well as details about the waste.

[0522] Step 9:

[0523] The server searches the vendor database to identify recycling companies that match the user's location and request conditions.

[0524] Step 10:

[0525] The server sends information about the selected vendor to the terminal. The terminal displays the vendor's details and transaction options to the user.

[0526] Step 11:

[0527] The server records the user's recycling activity history and updates the database. It also calculates the user's environmental contribution based on their activities.

[0528] Step 12:

[0529] The server sends the calculation results to the terminal. The terminal displays the user's environmental contribution as a graph or numerical value, encouraging sustainable activity.

[0530] (Example 1)

[0531] 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."

[0532] The challenge lies in efficiently providing information on waste classification and recycling to support individuals and organizations in conducting appropriate and effective recycling activities, thereby promoting contributions to the environment. Furthermore, it is necessary to facilitate smooth collaboration with recycling companies and promote the proper disposal of waste.

[0533] 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.

[0534] In this invention, the server includes means for analyzing visual information of waste and determining its classification method, means for providing data on reusable resources, and means for recording the user's recycling activities through information processing and visualizing the degree of environmental contribution. This allows users to easily learn how to sort waste and effectively carry out recycling activities. Furthermore, the visualization of environmental contribution can provide motivation for continuous recycling activities.

[0535] "Visual information of waste" refers to data that describes the visual characteristics of waste, such as its shape, color, and texture.

[0536] "Analysis" is the process of extracting specific elements from visual information using generative models, etc., and then classifying and judging the information based on those elements.

[0537] "Means for determining classification methods" refer to methods and technologies for providing appropriate sorting instructions according to the type and material of waste.

[0538] "Means of display" refers to technologies that present analysis results and information to users through the screens or interfaces of electronic devices.

[0539] "Data on reusable resources" refers to information about how waste is recycled and utilized as new resources.

[0540] "Means of searching and presenting" refers to technology that searches for suitable vendors based on specific conditions and displays the results visually to the user.

[0541] "Information processing" is a series of computational processes that organize and analyze collected data and generate the necessary output.

[0542] "Degree of environmental contribution" is an indicator that quantitatively or qualitatively shows the positive environmental impact of users' recycling activities.

[0543] A "generative model" is an algorithm that uses artificial intelligence technology to generate or analyze specific information.

[0544] A description of the embodiment for carrying out the invention will be provided.

[0545] This invention is a system aimed at improving the efficiency of waste analysis and recycling activities. The system mainly consists of three components: a server, terminals, and users. The server plays a central role and exchanges information with users through terminals.

[0546] The server is equipped with high-performance computing units and data storage, and performs image analysis of waste using generative AI models. The image analysis analyzes the visual information of the waste and determines its classification. Specifically, server machines equipped with the latest GPUs are often used. The server also retrieves information on recyclable resources from a database and provides it to the user.

[0547] The terminal is a device used by users to photograph waste and send the images to a server. Smartphones and tablets can function as terminals. The terminal also displays analysis results and recycling information sent from the server, allowing users to learn how to handle the waste in detail.

[0548] Users routinely input the status of waste using their devices and utilize instructions and information from the server to carry out recycling activities. For example, a user might take a picture of an empty plastic bottle at home and upload the image from their device to the cloud. An example of a prompt message used in this process would be, "Analyze the plastic bottle image and suggest sorting methods."

[0549] This system allows users to easily understand and practice proper waste sorting and recycling methods. Furthermore, by visualizing their environmental contribution, users can see how much their recycling activities are positively impacting the environment, which helps them maintain sustained motivation.

[0550] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0551] Step 1:

[0552] The user takes a photograph of the waste using the device. At this time, the user inputs image data of the waste. The device then prepares to send this image data to the server.

[0553] Step 2:

[0554] The device uploads the image data of the captured waste to the server. The input is image data from the device, which is then received by the server. The output is the image saved on the server.

[0555] Step 3:

[0556] The server analyzes the received image data using a generation AI model. The input is image data of waste, and the data processing involves extracting features from the images and performing classification. The output is information about the type of waste and the classification result.

[0557] Step 4:

[0558] The server consults a database based on the determined type of waste to decide on the appropriate sorting method. The input is the classification result, and the output is specific sorting instructions. In this case, if the waste is a plastic bottle, the instruction "remove the label, remove the cap, and crush" is generated.

[0559] Step 5:

[0560] The server transmits the determined sorting method to the terminal. The input is data regarding the sorting method, and the output is the instructions displayed on the terminal. The terminal displays this information on its screen and provides it to the user.

[0561] Step 6:

[0562] The user checks the instructions displayed on the device screen and actually sorts the waste. Specifically, the user performs the appropriate recycling process according to the on-screen instructions.

[0563] Step 7:

[0564] The server further provides users with additional information about recyclable resources. Input is information based on database lookups, and output is sent to the terminal as recycling methods and product information. This allows users to learn how waste is reused.

[0565] Step 8:

[0566] When a user wishes to dispose of large waste items, they notify the server of their request from their terminal. The input is the user's request information, and the server processes the request based on this information.

[0567] Step 9:

[0568] The server searches for recycling companies based on the user's location information and identifies a suitable company. The input is the user's location information and data about bulky waste, and the output is a list of candidate companies.

[0569] Step 10:

[0570] The server sends the found vendor information to the terminal and presents it to the user. The user reviews the candidate vendors and decides on the transaction details.

[0571] Step 11:

[0572] The server records users' recycling activities and calculates their environmental contribution based on that data. The input is data about the user's recycling behavior, and the output is a numerical representation of their environmental contribution.

[0573] Step 12:

[0574] The server sends the environmental contribution to the terminal and displays it visually on the user's screen. This allows users to understand their own contribution to the environment and motivates them to take further action.

[0575] (Application Example 1)

[0576] 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."

[0577] In modern society, the importance of proper waste sorting and recycling is increasing. However, information and methods for individuals and businesses to efficiently carry out recycling activities are still insufficient. In particular, further support is needed for the disposal of large waste items and the selection of the most suitable recycling companies in the region. This challenge is faced by many people and results in an increased burden on the environment.

[0578] 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.

[0579] In this invention, the server includes means for analyzing waste data and determining its classification method, means for supplying information on reusable resources, and means for displaying the most suitable recycling company based on the user's location information. This enables the user to properly sort waste and select a recycling company.

[0580] "Waste" refers to materials and products that are no longer needed by users.

[0581] "Data" refers to a collection of information that represents the attributes of waste.

[0582] "Analysis" is the act of recognizing the characteristics contained in waste and identifying its type.

[0583] "Classification method" refers to guidelines for the proper reuse of specific types of waste.

[0584] "Determination" is the process of determining the optimal classification method based on the analysis results.

[0585] "Display" refers to the act of visually conveying analysis results and other information to the user.

[0586] "Reusable resources" are materials within waste that can be reused.

[0587] "Supplying" refers to the activity of providing users with information about reusable resources.

[0588] "Location information" refers to data used to determine the user's current location.

[0589] A "recycling company" is a specialized company that processes waste into a form that can be reused.

[0590] A "server" is the central computing unit that performs data processing in this invention.

[0591] The system of this invention begins with a server receiving image data of waste and analyzing it using an AI model. The analysis process extracts features of the waste from the image and determines the optimal classification method based on the results. The determined classification method is displayed on the user's terminal and communicated visually. In this process, a generative AI model equipped with computer vision technology is used for image analysis. The user takes photos of the waste using a mobile device and uploads them to the cloud environment.

[0592] The server also retrieves information about reusable resources from its database and presents it to users. This allows users to learn about waste recycling methods and raise awareness. The system also includes a function to display the most suitable recycling company based on the user's current location, enabling the provision of information that reflects local characteristics.

[0593] Furthermore, users' recycling activities are recorded within the system, and their environmental contribution is calculated based on this activity data. This contribution is visualized through a graphical user interface, helping to motivate users to engage in environmental activities. As a concrete example, consider a scenario where a user takes a picture of an empty bottle at home and uploads it via their device. In this case, the generating AI identifies the type of bottle and instructs it to remove the label before recycling it as plastic. It also presents information on how the bottle could be used as a renewable energy resource. This analysis is performed by supplying the AI ​​with the prompt, "Analyze the image of the PET bottle and tell me the appropriate recycling method."

[0594] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0595] Step 1:

[0596] The user takes pictures of waste using a device. These images will later become input data for analysis by a generated AI model. The captured images are sent to a server via the network.

[0597] Step 2:

[0598] The server inputs the received waste image data into a generating AI model. In this step, the AI ​​model analyzes the images to identify the type and characteristics of the waste. Through this data processing, the server determines which category the waste belongs to.

[0599] Step 3:

[0600] Based on the analysis results of the AI ​​model, the server queries the database to determine the appropriate classification method for the waste. The server then collects information about the classification method and generates specific instructions to present to the user.

[0601] Step 4:

[0602] The generated classification methods and information are sent to the user's device. The device visually displays this information, clearly indicating the waste sorting procedures that the user must follow.

[0603] Step 5:

[0604] In parallel, the server retrieves information about reusable resources from a database and provides it to the user's terminal. This information includes details on how waste is recycled and related products.

[0605] Step 6:

[0606] The server evaluates the user's location and searches its database for recycling companies optimized for the user's current location. It then sends the retrieved information to the user's device, displaying the name and location of the most suitable company.

[0607] Step 7:

[0608] Ultimately, the server records all of the user's recycling activities and calculates their environmental contribution. This value is sent to the terminal, visually representing the user's environmental contribution and helping to boost motivation.

[0609] 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.

[0610] This invention combines an emotion engine with a system that supports recycling activities to maintain user motivation and promote more effective resource reuse. The system includes features such as sorting guidance based on waste image analysis, provision of information on recyclable resources, matching of large waste disposal companies with users, and recording of users' recycling activities, as well as the ability to recognize users' emotions and provide information accordingly.

[0611] The server receives images of waste sent from the terminal and analyzes their contents using AI generation. Based on the analysis results, the appropriate sorting method is determined and presented to the user. The analysis results are also cross-referenced with a database of recyclable resource information, and relevant information is provided to the user.

[0612] The emotion engine analyzes users' emotions such as annoyance, anxiety, and interest, and based on the results, presents recycling information and advice optimized for the user. For example, users with high anxiety about recycling will be presented with easy-to-implement recycling methods and success stories, while users with high interest will be provided with information on advanced technologies and trends in recycling activities.

[0613] When a user needs to dispose of large waste items, they can request a contractor selection from the server via a terminal. The server then consults a contractor database and searches for a recycling company that matches the user's location and needs. Information on the selected contractor is presented to the user, and they are shown the options for the transaction.

[0614] The server calculates the user's environmental contribution based on recorded recycling activities and displays it on the terminal in a visualized form. This feedback also takes into account the results of the emotion engine analysis and includes messages designed to motivate the user.

[0615] For example, when a user's motivation for recycling declines, the emotion engine could detect this and send encouraging messages or past success stories to the user's device. This would help users regain interest in recycling and encourage sustained participation.

[0616] Thus, this invention not only enhances the efficiency and sustainability of recycling activities, but also provides support that is sensitive to the user's feelings, thereby contributing to the realization of a sustainable society.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The user takes a picture of the waste using their device's camera. After the user reviews the image within the app, it is uploaded to the server.

[0620] Step 2:

[0621] The server receives the uploaded image and analyzes it using a generation AI module. The analysis identifies the type of waste.

[0622] Step 3:

[0623] The server determines the correct sorting method for recycling the user's items and sends the result to the terminal.

[0624] Step 4:

[0625] The terminal visually displays the sorting method received from the server to the user.

[0626] Step 5:

[0627] The server retrieves information on recyclable resources based on the type of waste from a database and provides it to the terminal. Users can then view detailed recycling information.

[0628] Step 6:

[0629] The device activates an emotion engine to analyze the user's emotions and processes input voice, text, and behavioral data.

[0630] Step 7:

[0631] The emotion engine recognizes the user's emotional state and sends the results to the server. Based on these results, the server creates personalized recycling advice.

[0632] Step 8:

[0633] The server searches for recycling companies based on the user's location, according to the user's request, and sends the information of the relevant companies to the terminal.

[0634] Step 9:

[0635] The device displays information about the recycling company selected by the user and their transaction options.

[0636] Step 10:

[0637] The server records the user's recycling activity history and calculates their environmental contribution based on that activity.

[0638] Step 11:

[0639] The device supports users in increasing their motivation for future recycling activities by displaying their calculated environmental contribution and messages generated by an emotion engine.

[0640] (Example 2)

[0641] 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."

[0642] Conventional recycling support systems lacked individual optimization that took user emotions into consideration, sometimes leading to decreased motivation and anxiety. Furthermore, selecting the appropriate disposal company for large waste items was difficult, burdening inexperienced users. Additionally, the lack of visual display of recycling results made it difficult for users to feel a sense of their own contribution.

[0643] 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.

[0644] In this invention, the server includes means for analyzing images of waste and determining its sorting method, means for analyzing the user's emotional state and providing information accordingly, and means for recording the user's recycling activities and visualizing their environmental contribution. This enables the provision of individually optimized recycling information and the selection of appropriate recycling companies, thereby improving user motivation and reducing their burden. Furthermore, by experiencing the results of their own actions, continuous recycling activities are promoted.

[0645] "Images of waste" refer to data that electronically records visual information, including waste.

[0646] "Means for analyzing images" refers to a function that performs the process of identifying objects from received visual information and analyzing their characteristics.

[0647] "Means for determining sorting methods" refers to a function that determines how to appropriately classify waste based on the results of image analysis.

[0648] "Information on recyclable resources" refers to data about materials and products that can be reused, including their characteristics and uses.

[0649] "Means for analyzing emotional states" refers to functions that analyze user input data and behavior to identify and evaluate emotional tendencies.

[0650] "Means of providing information" refers to functions that generate data and advice to present to users based on analysis results.

[0651] "Large-sized waste" refers to items that are difficult to handle using general waste disposal methods due to their size and weight.

[0652] "Means for searching for and presenting service providers" refers to a function that searches for appropriate service providers based on user requests and provides that information.

[0653] "Recycling activities" refer to actions that process waste into a reusable form and promote the recovery and reuse of resources.

[0654] "Means of visualizing environmental contribution" refers to a function that measures the results of users' recycling activities and displays them in an easy-to-understand format.

[0655] The system of this invention consists of a server, a terminal, and a user. The server receives images of waste and analyzes them using a generative AI model. Image recognition libraries such as OpenCV and TensorFlow are used for image analysis. Based on the analysis results, the server identifies the type of waste and suggests appropriate sorting methods to the user. For example, it may give specific instructions such as, "This waste is glass, so please dispose of it in the glass recycling bin."

[0656] Furthermore, the server has sentiment analysis capabilities to analyze the user's emotional state. This analysis uses natural language processing libraries, such as Hugging Face's Transformers. If a user is feeling anxious about recycling, the server will provide encouraging information such as, "Here are some easy ways to get started with recycling."

[0657] On the other hand, the terminal provides an interface for the user to interact with the server. The user sends images to the server via the terminal and receives instructions. If a large amount of waste needs to be disposed of, the user can request the server to select a contractor from the terminal. The server searches the contractor database and provides the user with information on a suitable contractor. For example, when a user is disposing of furniture, information such as "Company A and Company B are nearby recycling companies" might be presented.

[0658] Furthermore, the server tracks users' recycling activities, calculates and visualizes their environmental contribution. This information is displayed as graphs and numbers as the results of their activities, helping to raise user awareness. For example, a report such as "Today's recycling activities contributed to reducing CO2 emissions by XX kg" might be displayed on the terminal. This visualization uses software that supports data analysis and graphical representation.

[0659] An example of a prompt message to input into a generative AI model would be, "Analyze this image and suggest the appropriate recycling method." By using this prompt message, the generative AI model can quickly and accurately provide the user with the necessary information.

[0660] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0661] Step 1:

[0662] The user takes an image of the waste using a terminal and sends it to the server. This input serves as basic data for identifying the type of waste. The terminal initiates the image analysis process by transferring the captured image file to the server.

[0663] Step 2:

[0664] The server analyzes the received image. A generative AI model is used for this analysis, and the server instructs the model via a prompt message, saying, "Analyze this image and tell me the appropriate recycling method." The server extracts features from the image and applies a classification algorithm to determine the type of waste. As a result, the waste is classified into categories such as metal, plastic, and paper.

[0665] Step 3:

[0666] The server presents the user with appropriate sorting methods based on the analysis results. In this step, it generates instructions according to the waste category, such as "Please dispose of plastic in the designated recycling box." These instructions are then sent to the user's terminal.

[0667] Step 4:

[0668] The server analyzes the user's emotional state. It uses the user's past input data and behavioral history to apply an emotional analysis algorithm. For example, if a user tends to ignore recycling notifications, the server generates and sends encouraging information such as "Here's an easy way to get started with recycling."

[0669] Step 5:

[0670] When a user needs to dispose of large waste items, they request a contractor selection from their terminal. The server searches a contractor database based on the user's location and entered needs, and lists suitable contractors. The search results are displayed on the user's terminal, including the contractor's contact information and service details.

[0671] Step 6:

[0672] The server tracks users' recycling activities and calculates their environmental contribution. It analyzes activity history and calculates contributions in the form of energy savings and CO2 reductions. The calculation results are displayed on the user's device, for example, "Your activities this month have contributed to reducing carbon dioxide by XX kg."

[0673] (Application Example 2)

[0674] 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."

[0675] In modern society, recycling plays a crucial role in environmental protection, but declining individual motivation and the complexity of sorting are hindering its widespread adoption. Furthermore, a lack of specific information on proper waste sorting methods and the disposal of bulky waste places a burden on users. Additionally, the scarcity of appropriate, emotionally-based support makes it difficult to implement sustainable recycling activities.

[0676] 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.

[0677] In this invention, the server includes means for analyzing images of waste and determining how to sort it, means for analyzing the user's emotions and providing appropriate information, and means for displaying locations related to waste disposal using a geographic information system. This enables comprehensive support for easy and sustainable recycling while being sensitive to the user's emotions.

[0678] "Means for analyzing images of waste" refers to a processing device that receives image data, identifies its contents, and determines the type of waste.

[0679] "Means for determining sorting methods" refers to a device that has a calculation function to determine the appropriate sorting method based on the analyzed waste information.

[0680] "Means for presenting sorting methods" refers to a device that visually or audibly informs the user of the determined sorting method.

[0681] "Means for providing information on recyclable resources" refers to a device that has a display or notification function for providing users with detailed information about recyclable resources.

[0682] "A means of searching for and presenting companies that handle large waste" refers to a device that lists companies capable of handling large waste and provides company information that matches the user's requirements.

[0683] "A means of recording users' recycling activities and visualizing their environmental contribution" refers to a device that accumulates users' recycling behavior as data and evaluates and displays the results as an environmental contribution.

[0684] "A means of analyzing user emotions and providing appropriate information" refers to a device that senses the user's current emotional state and selects and provides information or messages accordingly.

[0685] "Means for displaying locations related to waste disposal using a geographic information system" refers to a device that displays waste disposal facilities and related locations on a map based on the user's location information.

[0686] This invention provides a system to support users' recycling activities, thereby providing appropriate recycling information tailored to the user's emotional state and enabling smart waste management. To achieve this, the following hardware and software configuration is used.

[0687] The main components of the system are the user terminal (such as a smartphone) and the server. The user terminal is responsible for taking pictures of waste with its built-in camera and sending the images to the server. The server uses a generative AI model combining Python and TensorFlow as image processing software to analyze the type of waste. Based on the analysis results, the server determines the appropriate sorting method and presents it to the user terminal.

[0688] Furthermore, the server incorporates an emotion analysis engine to analyze user sentiment. This evaluates users' feelings regarding waste disposal, such as interest or anxiety, and generates encouraging messages and simple suggestions accordingly. For example, it provides information on advanced recycling technologies to users who are enthusiastic about recycling, and introduces easy-to-implement methods to users who are reluctant.

[0689] The server also features a Geographic Information System (GIS), which displays nearby recycling facilities and large waste disposal companies on a map based on the user's location. Users can utilize this information to easily carry out recycling activities in their local communities.

[0690] The server also includes a feature that records users' past recycling activities and visually displays their contribution to the environment. This allows users to see that their actions are contributing to building a sustainable society, leading to increased motivation.

[0691] For example, a user struggling with how to dispose of a large amount of waste paper generated while cleaning their house on a holiday can upload images of the paper taken with their device and instantly receive information on appropriate disposal methods and recycling facilities. Furthermore, based on sentiment analysis, messages such as "Your recycling is contributing to the environment!" are displayed to motivate the user.

[0692] Examples of prompts for a generative AI model are as follows:

[0693] "You are an assistant supporting recycling activities. Based on the type of waste the user has, provide them with appropriate waste sorting methods and recycling information. Also, send encouraging messages and past success stories tailored to the user's mood to pique their interest in recycling."

[0694] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0695] Step 1:

[0696] The user takes a picture of the waste with their device and sends that image to the server via the application. The input in this process is image data generated by the user's actions, and the output is the image file uploaded to the server.

[0697] Step 2:

[0698] The server analyzes the received image data using a generation AI model. This analysis uses TensorFlow to recognize objects in the image and identify the type of waste. The input is an image file uploaded to the server, and the output is data about the type of waste.

[0699] Step 3:

[0700] Based on the analysis results, the server determines the appropriate sorting method. Here, the server compares the type data derived by the generating AI model with sorting methods registered in the database beforehand and selects the appropriate method. The input is the analyzed waste type data, and the output is information on sorting methods.

[0701] Step 4:

[0702] The server provides the user's terminal with information on the determined sorting method and recyclable resources. Here, the sorting method and related information are presented to the user as text and images. The input is sorting method information, and the output is the display on the user's terminal.

[0703] Step 5:

[0704] The server uses an emotion analysis engine to analyze the user's emotional state and select appropriate information. It extracts emotional data using simple questionnaires and usage history entered by the user and generates corresponding messages. The input is the user's emotional data, and the output is a message corresponding to that emotion.

[0705] Step 6:

[0706] The server acquires the user's location information and uses a geographic information system to display nearby recycling facilities and businesses. Here, GPS data is used to map locations onto a map. The input is the user's location information, and the output is the map display information.

[0707] Step 7:

[0708] The server records the user's recycling activities and calculates their environmental contribution. Using the recorded activity data, it calculates the contribution and provides feedback in a visualized format. The input is the user's activity record data, and the output is the evaluation result of their environmental contribution.

[0709] Step 8:

[0710] The user's device notifies the user of information and messages received from the server. Ultimately, the information is presented on the screen in a viewable format, allowing the user to decide on their next action. Input consists of various pieces of information from the server, and output consists of notifications to the user.

[0711] 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.

[0712] 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.

[0713] 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.

[0714] [Fourth Embodiment]

[0715] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0716] 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.

[0717] 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).

[0718] 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.

[0719] 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.

[0720] 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).

[0721] 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.

[0722] 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.

[0723] 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.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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".

[0728] This invention is designed to enable individuals and businesses to effectively carry out recycling activities through a system utilizing AI technology. Its main functions include image analysis of waste, suggestion of sorting methods, provision of recycling information, matching with recycling companies, and visualization of environmental contribution.

[0729] The server receives images of waste uploaded by the user from their device. Using a generation AI, the server analyzes the images to identify the type of waste. Simultaneously, the server consults a database to determine the most appropriate sorting method for the identified waste. The results are presented to the user via their device. The user can immediately confirm the sorting method on their device screen.

[0730] Furthermore, the server retrieves information about recyclable resources from a database and provides it to the user. This information includes recycling methods and related product information, and by conveying it to the user through the terminal, it supports the user's knowledge of recycling.

[0731] When a user wishes to dispose of large waste items, they notify the server via their terminal. The server uses registered vendor information to search for recycling companies based on the user's location and the type of waste, and presents suitable companies. The user can then select a preferred company from the presented options and check the transaction details on their terminal.

[0732] Ultimately, the server records the user's recycling activities and calculates their environmental contribution based on those activities. This environmental contribution is visually represented to the user on their device, encouraging continued motivation for recycling.

[0733] As a concrete example, consider a scenario where a user takes a picture of an empty plastic bottle at home and uploads it to the cloud via their device. In this case, the server analyzes the bottle and displays specific instructions on the device, such as "remove the label, take off the cap, crush it, and separate it as plastic waste." Furthermore, if information is also provided on how the plastic bottle is recycled and transformed into new products, it is expected that users' awareness of recycling will increase.

[0734] This invention reduces the effort required for sorting, improves the efficiency of recycling activities through information visualization and appropriate vendor selection, and enables contributions to a sustainable environment.

[0735] The following describes the processing flow.

[0736] Step 1:

[0737] The user takes a picture of the waste using their device's camera. The captured image is saved on the user's device.

[0738] Step 2:

[0739] Users upload images of waste to the server via a dedicated application on their device. The image data also includes the user's ID and location information.

[0740] Step 3:

[0741] The server sends the received image data to an AI module for analysis. The AI ​​module uses machine learning algorithms to analyze the images and identify the type of waste.

[0742] Step 4:

[0743] Based on the analysis results, the server identifies the appropriate sorting method for each type of waste. The sorting method is determined considering relevant laws and local guidelines.

[0744] Step 5:

[0745] The server sends the identified sorting method to the terminal. The terminal displays the sorting method to the user and provides specific instructions.

[0746] Step 6:

[0747] The server consults a database of recyclable resources and collects recycling information related to the relevant waste.

[0748] Step 7:

[0749] The server organizes the recycling information and sends it to the terminal. The terminal displays information to the user indicating the importance and possibility of recycling.

[0750] Step 8:

[0751] When a user wishes to have large waste items picked up, they send a request from their device to the server. The request includes the desired pickup date and time, as well as details about the waste.

[0752] Step 9:

[0753] The server searches the vendor database to identify recycling companies that match the user's location and request conditions.

[0754] Step 10:

[0755] The server sends information about the selected vendor to the terminal. The terminal displays the vendor's details and transaction options to the user.

[0756] Step 11:

[0757] The server records the user's recycling activity history and updates the database. It also calculates the user's environmental contribution based on their activities.

[0758] Step 12:

[0759] The server sends the calculation results to the terminal. The terminal displays the user's environmental contribution as a graph or numerical value, encouraging sustainable activity.

[0760] (Example 1)

[0761] 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".

[0762] The challenge lies in efficiently providing information on waste classification and recycling to support individuals and organizations in conducting appropriate and effective recycling activities, thereby promoting contributions to the environment. Furthermore, it is necessary to facilitate smooth collaboration with recycling companies and promote the proper disposal of waste.

[0763] 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.

[0764] In this invention, the server includes means for analyzing visual information of waste and determining its classification method, means for providing data on reusable resources, and means for recording the user's recycling activities through information processing and visualizing the degree of environmental contribution. This allows users to easily learn how to sort waste and effectively carry out recycling activities. Furthermore, the visualization of environmental contribution can provide motivation for continuous recycling activities.

[0765] "Visual information of waste" refers to data that describes the visual characteristics of waste, such as its shape, color, and texture.

[0766] "Analysis" is the process of extracting specific elements from visual information using generative models, etc., and then classifying and judging the information based on those elements.

[0767] "Means for determining classification methods" refer to methods and technologies for providing appropriate sorting instructions according to the type and material of waste.

[0768] "Means of display" refers to technologies that present analysis results and information to users through the screens or interfaces of electronic devices.

[0769] "Data on reusable resources" refers to information about how waste is recycled and utilized as new resources.

[0770] "Means of searching and presenting" refers to technology that searches for suitable vendors based on specific conditions and displays the results visually to the user.

[0771] "Information processing" is a series of computational processes that organize and analyze collected data and generate the necessary output.

[0772] "Degree of environmental contribution" is an indicator that quantitatively or qualitatively shows the positive environmental impact of users' recycling activities.

[0773] A "generative model" is an algorithm that uses artificial intelligence technology to generate or analyze specific information.

[0774] A description of the embodiment for carrying out the invention will be provided.

[0775] This invention is a system aimed at improving the efficiency of waste analysis and recycling activities. The system mainly consists of three components: a server, terminals, and users. The server plays a central role and exchanges information with users through terminals.

[0776] The server is equipped with high-performance computing units and data storage, and performs image analysis of waste using generative AI models. The image analysis analyzes the visual information of the waste and determines its classification. Specifically, server machines equipped with the latest GPUs are often used. The server also retrieves information on recyclable resources from a database and provides it to the user.

[0777] The terminal is a device used by users to photograph waste and send the images to a server. Smartphones and tablets can function as terminals. The terminal also displays analysis results and recycling information sent from the server, allowing users to learn how to handle the waste in detail.

[0778] Users routinely input the status of waste using their devices and utilize instructions and information from the server to carry out recycling activities. For example, a user might take a picture of an empty plastic bottle at home and upload the image from their device to the cloud. An example of a prompt message used in this process would be, "Analyze the plastic bottle image and suggest sorting methods."

[0779] This system allows users to easily understand and practice proper waste sorting and recycling methods. Furthermore, by visualizing their environmental contribution, users can see how much their recycling activities are positively impacting the environment, which helps them maintain sustained motivation.

[0780] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0781] Step 1:

[0782] The user takes a photograph of the waste using the device. At this time, the user inputs image data of the waste. The device then prepares to send this image data to the server.

[0783] Step 2:

[0784] The device uploads the image data of the captured waste to the server. The input is image data from the device, which is then received by the server. The output is the image saved on the server.

[0785] Step 3:

[0786] The server analyzes the received image data using a generation AI model. The input is image data of waste, and the data processing involves extracting features from the images and performing classification. The output is information about the type of waste and the classification result.

[0787] Step 4:

[0788] The server consults a database based on the determined type of waste to decide on the appropriate sorting method. The input is the classification result, and the output is specific sorting instructions. In this case, if the waste is a plastic bottle, the instruction "remove the label, remove the cap, and crush" is generated.

[0789] Step 5:

[0790] The server transmits the determined sorting method to the terminal. The input is data regarding the sorting method, and the output is the instructions displayed on the terminal. The terminal displays this information on its screen and provides it to the user.

[0791] Step 6:

[0792] The user checks the instructions displayed on the device screen and actually sorts the waste. Specifically, the user performs the appropriate recycling process according to the on-screen instructions.

[0793] Step 7:

[0794] The server further provides users with additional information about recyclable resources. Input is information based on database lookups, and output is sent to the terminal as recycling methods and product information. This allows users to learn how waste is reused.

[0795] Step 8:

[0796] When a user wishes to dispose of large waste items, they notify the server of their request from their terminal. The input is the user's request information, and the server processes the request based on this information.

[0797] Step 9:

[0798] The server searches for recycling companies based on the user's location information and identifies a suitable company. The input is the user's location information and data about bulky waste, and the output is a list of candidate companies.

[0799] Step 10:

[0800] The server sends the found vendor information to the terminal and presents it to the user. The user reviews the candidate vendors and decides on the transaction details.

[0801] Step 11:

[0802] The server records users' recycling activities and calculates their environmental contribution based on that data. The input is data about the user's recycling behavior, and the output is a numerical representation of their environmental contribution.

[0803] Step 12:

[0804] The server sends the environmental contribution to the terminal and displays it visually on the user's screen. This allows users to understand their own contribution to the environment and motivates them to take further action.

[0805] (Application Example 1)

[0806] 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".

[0807] In modern society, the importance of proper waste sorting and recycling is increasing. However, information and methods for individuals and businesses to efficiently carry out recycling activities are still insufficient. In particular, further support is needed for the disposal of large waste items and the selection of the most suitable recycling companies in the region. This challenge is faced by many people and results in an increased burden on the environment.

[0808] 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.

[0809] In this invention, the server includes means for analyzing waste data and determining its classification method, means for supplying information on reusable resources, and means for displaying the most suitable recycling company based on the user's location information. This enables the user to properly sort waste and select a recycling company.

[0810] "Waste" refers to materials and products that are no longer needed by users.

[0811] "Data" refers to a collection of information that represents the attributes of waste.

[0812] "Analysis" is the act of recognizing the characteristics contained in waste and identifying its type.

[0813] "Classification method" refers to guidelines for the proper reuse of specific types of waste.

[0814] "Determination" is the process of determining the optimal classification method based on the analysis results.

[0815] "Display" refers to the act of visually conveying analysis results and other information to the user.

[0816] "Reusable resources" are materials within waste that can be reused.

[0817] "Supplying" refers to the activity of providing users with information about reusable resources.

[0818] "Location information" refers to data used to determine the user's current location.

[0819] A "recycling company" is a specialized company that processes waste into a form that can be reused.

[0820] A "server" is the central computing unit that performs data processing in this invention.

[0821] The system of this invention begins with a server receiving image data of waste and analyzing it using an AI model. The analysis process extracts features of the waste from the image and determines the optimal classification method based on the results. The determined classification method is displayed on the user's terminal and communicated visually. In this process, a generative AI model equipped with computer vision technology is used for image analysis. The user takes photos of the waste using a mobile device and uploads them to the cloud environment.

[0822] The server also retrieves information about reusable resources from its database and presents it to users. This allows users to learn about waste recycling methods and raise awareness. The system also includes a function to display the most suitable recycling company based on the user's current location, enabling the provision of information that reflects local characteristics.

[0823] Furthermore, users' recycling activities are recorded within the system, and their environmental contribution is calculated based on this activity data. This contribution is visualized through a graphical user interface, helping to motivate users to engage in environmental activities. As a concrete example, consider a scenario where a user takes a picture of an empty bottle at home and uploads it via their device. In this case, the generating AI identifies the type of bottle and instructs it to remove the label before recycling it as plastic. It also presents information on how the bottle could be used as a renewable energy resource. This analysis is performed by supplying the AI ​​with the prompt, "Analyze the image of the PET bottle and tell me the appropriate recycling method."

[0824] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0825] Step 1:

[0826] The user takes pictures of waste using a device. These images will later become input data for analysis by a generated AI model. The captured images are sent to a server via the network.

[0827] Step 2:

[0828] The server inputs the received waste image data into a generating AI model. In this step, the AI ​​model analyzes the images to identify the type and characteristics of the waste. Through this data processing, the server determines which category the waste belongs to.

[0829] Step 3:

[0830] Based on the analysis results of the AI ​​model, the server queries the database to determine the appropriate classification method for the waste. The server then collects information about the classification method and generates specific instructions to present to the user.

[0831] Step 4:

[0832] The generated classification methods and information are sent to the user's device. The device visually displays this information, clearly indicating the waste sorting procedures that the user must follow.

[0833] Step 5:

[0834] In parallel, the server retrieves information about reusable resources from a database and provides it to the user's terminal. This information includes details on how waste is recycled and related products.

[0835] Step 6:

[0836] The server evaluates the user's location and searches its database for recycling companies optimized for the user's current location. It then sends the retrieved information to the user's device, displaying the name and location of the most suitable company.

[0837] Step 7:

[0838] Ultimately, the server records all of the user's recycling activities and calculates their environmental contribution. This value is sent to the terminal, visually representing the user's environmental contribution and helping to boost motivation.

[0839] 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.

[0840] This invention combines an emotion engine with a system that supports recycling activities to maintain user motivation and promote more effective resource reuse. The system includes features such as sorting guidance based on waste image analysis, provision of information on recyclable resources, matching of large waste disposal companies with users, and recording of users' recycling activities, as well as the ability to recognize users' emotions and provide information accordingly.

[0841] The server receives images of waste sent from the terminal and analyzes their contents using AI generation. Based on the analysis results, the appropriate sorting method is determined and presented to the user. The analysis results are also cross-referenced with a database of recyclable resource information, and relevant information is provided to the user.

[0842] The emotion engine analyzes users' emotions such as annoyance, anxiety, and interest, and based on the results, presents recycling information and advice optimized for the user. For example, users with high anxiety about recycling will be presented with easy-to-implement recycling methods and success stories, while users with high interest will be provided with information on advanced technologies and trends in recycling activities.

[0843] When a user needs to dispose of large waste items, they can request a contractor selection from the server via a terminal. The server then consults a contractor database and searches for a recycling company that matches the user's location and needs. Information on the selected contractor is presented to the user, and they are shown the options for the transaction.

[0844] The server calculates the user's environmental contribution based on recorded recycling activities and displays it on the terminal in a visualized form. This feedback also takes into account the results of the emotion engine analysis and includes messages designed to motivate the user.

[0845] For example, when a user's motivation for recycling declines, the emotion engine could detect this and send encouraging messages or past success stories to the user's device. This would help users regain interest in recycling and encourage sustained participation.

[0846] Thus, this invention not only enhances the efficiency and sustainability of recycling activities, but also provides support that is sensitive to the user's feelings, thereby contributing to the realization of a sustainable society.

[0847] The following describes the processing flow.

[0848] Step 1:

[0849] The user takes a picture of the waste using their device's camera. After the user reviews the image within the app, it is uploaded to the server.

[0850] Step 2:

[0851] The server receives the uploaded image and analyzes it using a generation AI module. The analysis identifies the type of waste.

[0852] Step 3:

[0853] The server determines the correct sorting method for recycling the user's items and sends the result to the terminal.

[0854] Step 4:

[0855] The terminal visually displays the sorting method received from the server to the user.

[0856] Step 5:

[0857] The server retrieves information on recyclable resources based on the type of waste from a database and provides it to the terminal. Users can then view detailed recycling information.

[0858] Step 6:

[0859] The device activates an emotion engine to analyze the user's emotions and processes input voice, text, and behavioral data.

[0860] Step 7:

[0861] The emotion engine recognizes the user's emotional state and sends the results to the server. Based on these results, the server creates personalized recycling advice.

[0862] Step 8:

[0863] The server searches for recycling companies based on the user's location, according to the user's request, and sends the information of the relevant companies to the terminal.

[0864] Step 9:

[0865] The device displays information about the recycling company selected by the user and their transaction options.

[0866] Step 10:

[0867] The server records the user's recycling activity history and calculates their environmental contribution based on that activity.

[0868] Step 11:

[0869] The device supports users in increasing their motivation for future recycling activities by displaying their calculated environmental contribution and messages generated by an emotion engine.

[0870] (Example 2)

[0871] 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".

[0872] Conventional recycling support systems lacked individual optimization that took user emotions into consideration, sometimes leading to decreased motivation and anxiety. Furthermore, selecting the appropriate disposal company for large waste items was difficult, burdening inexperienced users. Additionally, the lack of visual display of recycling results made it difficult for users to feel a sense of their own contribution.

[0873] 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.

[0874] In this invention, the server includes means for analyzing images of waste and determining its sorting method, means for analyzing the user's emotional state and providing information accordingly, and means for recording the user's recycling activities and visualizing their environmental contribution. This enables the provision of individually optimized recycling information and the selection of appropriate recycling companies, thereby improving user motivation and reducing their burden. Furthermore, by experiencing the results of their own actions, continuous recycling activities are promoted.

[0875] "Images of waste" refer to data that electronically records visual information, including waste.

[0876] "Means for analyzing images" refers to a function that performs the process of identifying objects from received visual information and analyzing their characteristics.

[0877] "Means for determining sorting methods" refers to a function that determines how to appropriately classify waste based on the results of image analysis.

[0878] "Information on recyclable resources" refers to data about materials and products that can be reused, including their characteristics and uses.

[0879] "Means for analyzing emotional states" refers to functions that analyze user input data and behavior to identify and evaluate emotional tendencies.

[0880] "Means of providing information" refers to functions that generate data and advice to present to users based on analysis results.

[0881] "Large-sized waste" refers to items that are difficult to handle using general waste disposal methods due to their size and weight.

[0882] "Means for searching for and presenting service providers" refers to a function that searches for appropriate service providers based on user requests and provides that information.

[0883] "Recycling activities" refer to actions that process waste into a reusable form and promote the recovery and reuse of resources.

[0884] "Means of visualizing environmental contribution" refers to a function that measures the results of users' recycling activities and displays them in an easy-to-understand format.

[0885] The system of this invention consists of a server, a terminal, and a user. The server receives images of waste and analyzes them using a generative AI model. Image recognition libraries such as OpenCV and TensorFlow are used for image analysis. Based on the analysis results, the server identifies the type of waste and suggests appropriate sorting methods to the user. For example, it may give specific instructions such as, "This waste is glass, so please dispose of it in the glass recycling bin."

[0886] Furthermore, the server has sentiment analysis capabilities to analyze the user's emotional state. This analysis uses natural language processing libraries, such as Hugging Face's Transformers. If a user is feeling anxious about recycling, the server will provide encouraging information such as, "Here are some easy ways to get started with recycling."

[0887] On the other hand, the terminal provides an interface for the user to interact with the server. The user sends images to the server via the terminal and receives instructions. If a large amount of waste needs to be disposed of, the user can request the server to select a contractor from the terminal. The server searches the contractor database and provides the user with information on a suitable contractor. For example, when a user is disposing of furniture, information such as "Company A and Company B are nearby recycling companies" might be presented.

[0888] Furthermore, the server tracks users' recycling activities, calculates and visualizes their environmental contribution. This information is displayed as graphs and numbers as the results of their activities, helping to raise user awareness. For example, a report such as "Today's recycling activities contributed to reducing CO2 emissions by XX kg" might be displayed on the terminal. This visualization uses software that supports data analysis and graphical representation.

[0889] An example of a prompt message to input into a generative AI model would be, "Analyze this image and suggest the appropriate recycling method." By using this prompt message, the generative AI model can quickly and accurately provide the user with the necessary information.

[0890] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0891] Step 1:

[0892] The user takes an image of the waste using a terminal and sends it to the server. This input serves as basic data for identifying the type of waste. The terminal initiates the image analysis process by transferring the captured image file to the server.

[0893] Step 2:

[0894] The server analyzes the received image. A generative AI model is used for this analysis, and the server instructs the model via a prompt message, saying, "Analyze this image and tell me the appropriate recycling method." The server extracts features from the image and applies a classification algorithm to determine the type of waste. As a result, the waste is classified into categories such as metal, plastic, and paper.

[0895] Step 3:

[0896] The server presents the user with appropriate sorting methods based on the analysis results. In this step, it generates instructions according to the waste category, such as "Please dispose of plastic in the designated recycling box." These instructions are then sent to the user's terminal.

[0897] Step 4:

[0898] The server analyzes the user's emotional state. It uses the user's past input data and behavioral history to apply an emotional analysis algorithm. For example, if a user tends to ignore recycling notifications, the server generates and sends encouraging information such as "Here's an easy way to get started with recycling."

[0899] Step 5:

[0900] When a user needs to dispose of large waste items, they request a contractor selection from their terminal. The server searches a contractor database based on the user's location and entered needs, and lists suitable contractors. The search results are displayed on the user's terminal, including the contractor's contact information and service details.

[0901] Step 6:

[0902] The server tracks users' recycling activities and calculates their environmental contribution. It analyzes activity history and calculates contributions in the form of energy savings and CO2 reductions. The calculation results are displayed on the user's device, for example, "Your activities this month have contributed to reducing carbon dioxide by XX kg."

[0903] (Application Example 2)

[0904] 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".

[0905] In modern society, recycling plays a crucial role in environmental protection, but declining individual motivation and the complexity of sorting are hindering its widespread adoption. Furthermore, a lack of specific information on proper waste sorting methods and the disposal of bulky waste places a burden on users. Additionally, the scarcity of appropriate, emotionally-based support makes it difficult to implement sustainable recycling activities.

[0906] 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.

[0907] In this invention, the server includes means for analyzing images of waste and determining how to sort it, means for analyzing the user's emotions and providing appropriate information, and means for displaying locations related to waste disposal using a geographic information system. This enables comprehensive support for easy and sustainable recycling while being sensitive to the user's emotions.

[0908] "Means for analyzing images of waste" refers to a processing device that receives image data, identifies its contents, and determines the type of waste.

[0909] "Means for determining sorting methods" refers to a device that has a calculation function to determine the appropriate sorting method based on the analyzed waste information.

[0910] "Means for presenting sorting methods" refers to a device that visually or audibly informs the user of the determined sorting method.

[0911] "Means for providing information on recyclable resources" refers to a device that has a display or notification function for providing users with detailed information about recyclable resources.

[0912] "A means of searching for and presenting companies that handle large waste" refers to a device that lists companies capable of handling large waste and provides company information that matches the user's requirements.

[0913] "A means of recording users' recycling activities and visualizing their environmental contribution" refers to a device that accumulates users' recycling behavior as data and evaluates and displays the results as an environmental contribution.

[0914] "A means of analyzing user emotions and providing appropriate information" refers to a device that senses the user's current emotional state and selects and provides information or messages accordingly.

[0915] "Means for displaying locations related to waste disposal using a geographic information system" refers to a device that displays waste disposal facilities and related locations on a map based on the user's location information.

[0916] This invention provides a system to support users' recycling activities, thereby providing appropriate recycling information tailored to the user's emotional state and enabling smart waste management. To achieve this, the following hardware and software configuration is used.

[0917] The main components of the system are the user terminal (such as a smartphone) and the server. The user terminal is responsible for taking pictures of waste with its built-in camera and sending the images to the server. The server uses a generative AI model combining Python and TensorFlow as image processing software to analyze the type of waste. Based on the analysis results, the server determines the appropriate sorting method and presents it to the user terminal.

[0918] Furthermore, the server incorporates an emotion analysis engine to analyze user sentiment. This evaluates users' feelings regarding waste disposal, such as interest or anxiety, and generates encouraging messages and simple suggestions accordingly. For example, it provides information on advanced recycling technologies to users who are enthusiastic about recycling, and introduces easy-to-implement methods to users who are reluctant.

[0919] The server also features a Geographic Information System (GIS), which displays nearby recycling facilities and large waste disposal companies on a map based on the user's location. Users can utilize this information to easily carry out recycling activities in their local communities.

[0920] The server also includes a feature that records users' past recycling activities and visually displays their contribution to the environment. This allows users to see that their actions are contributing to building a sustainable society, leading to increased motivation.

[0921] For example, a user struggling with how to dispose of a large amount of waste paper generated while cleaning their house on a holiday can upload images of the paper taken with their device and instantly receive information on appropriate disposal methods and recycling facilities. Furthermore, based on sentiment analysis, messages such as "Your recycling is contributing to the environment!" are displayed to motivate the user.

[0922] Examples of prompts for a generative AI model are as follows:

[0923] "You are an assistant supporting recycling activities. Based on the type of waste the user has, provide them with appropriate waste sorting methods and recycling information. Also, send encouraging messages and past success stories tailored to the user's mood to pique their interest in recycling."

[0924] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0925] Step 1:

[0926] The user takes a picture of the waste with their device and sends that image to the server via the application. The input in this process is image data generated by the user's actions, and the output is the image file uploaded to the server.

[0927] Step 2:

[0928] The server analyzes the received image data using a generation AI model. This analysis uses TensorFlow to recognize objects in the image and identify the type of waste. The input is an image file uploaded to the server, and the output is data about the type of waste.

[0929] Step 3:

[0930] Based on the analysis results, the server determines the appropriate sorting method. Here, the server compares the type data derived by the generating AI model with sorting methods registered in the database beforehand and selects the appropriate method. The input is the analyzed waste type data, and the output is information on sorting methods.

[0931] Step 4:

[0932] The server provides the user's terminal with information on the determined sorting method and recyclable resources. Here, the sorting method and related information are presented to the user as text and images. The input is sorting method information, and the output is the display on the user's terminal.

[0933] Step 5:

[0934] The server uses an emotion analysis engine to analyze the user's emotional state and select appropriate information. It extracts emotional data using simple questionnaires and usage history entered by the user and generates corresponding messages. The input is the user's emotional data, and the output is a message corresponding to that emotion.

[0935] Step 6:

[0936] The server acquires the user's location information and uses a geographic information system to display nearby recycling facilities and businesses. Here, GPS data is used to map locations onto a map. The input is the user's location information, and the output is the map display information.

[0937] Step 7:

[0938] The server records the user's recycling activities and calculates their environmental contribution. Using the recorded activity data, it calculates the contribution and provides feedback in a visualized format. The input is the user's activity record data, and the output is the evaluation result of their environmental contribution.

[0939] Step 8:

[0940] The user's device notifies the user of information and messages received from the server. Ultimately, the information is presented on the screen in a viewable format, allowing the user to decide on their next action. Input consists of various pieces of information from the server, and output consists of notifications to the user.

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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."

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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.

[0961] 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.

[0962] The following is further disclosed regarding the embodiments described above.

[0963] (Claim 1)

[0964] A means for analyzing images of waste and determining its sorting method,

[0965] A means of presenting the determined sorting method,

[0966] Means of providing information on recyclable resources,

[0967] A means of searching for and presenting companies that handle large waste,

[0968] A means of recording users' recycling activities and visualizing their environmental contribution,

[0969] A system that includes this.

[0970] (Claim 2)

[0971] The system according to claim 1, which performs waste type determination based on image analysis.

[0972] (Claim 3)

[0973] The system according to claim 1 for searching for a recycling company based on the user's location.

[0974] "Example 1"

[0975] (Claim 1)

[0976] A means for analyzing the visual information of waste and determining its classification method,

[0977] A means for displaying the determined classification method,

[0978] Means for providing data on reusable resources,

[0979] A means of searching for and presenting a company that handles large waste,

[0980] A means of recording users' reuse activities through information processing and visualizing the degree of environmental contribution,

[0981] Methods for performing analysis using generative models,

[0982] A system that includes this.

[0983] (Claim 2)

[0984] The system according to claim 1 for performing waste classification determination based on image analysis.

[0985] (Claim 3)

[0986] The system according to claim 1, which searches for a recycling company based on the user's location information.

[0987] "Application Example 1"

[0988] (Claim 1)

[0989] A means for analyzing waste data and determining its classification method,

[0990] A means for displaying the determined classification method,

[0991] Means for supplying information on reusable resources,

[0992] A means of searching for and displaying companies that handle large waste,

[0993] A means of recording users' recycling activities and visualizing their environmental contribution,

[0994] A means of displaying the most suitable recycling company based on the user's location information,

[0995] A system that includes this.

[0996] (Claim 2)

[0997] The system according to claim 1, which performs attribute determination of waste based on image analysis.

[0998] (Claim 3)

[0999] The system according to claim 1, which provides the optimal recycling provider based on the user's location information.

[1000] "Example 2 of combining an emotion engine"

[1001] (Claim 1)

[1002] A means for analyzing images of waste and determining its sorting method,

[1003] A means of presenting the determined sorting method,

[1004] A means of providing information on recyclable resources based on the analysis results,

[1005] A means of analyzing the user's emotional state and providing information based on the results,

[1006] A means of searching for and presenting companies that handle large waste,

[1007] A means of recording users' recycling activities and visualizing their environmental contribution,

[1008] A system that includes this.

[1009] (Claim 2)

[1010] The system according to claim 1, which determines the type of waste based on image analysis and suggests an appropriate sorting method based on that information.

[1011] (Claim 3)

[1012] The system according to claim 1, which searches for and presents a suitable service provider based on the user's location and needs.

[1013] "Application example 2 when combining with an emotional engine"

[1014] (Claim 1)

[1015] A means for analyzing images of waste and determining its sorting method,

[1016] A means of presenting the determined sorting method,

[1017] Means of providing information on recyclable resources,

[1018] A means of searching for and presenting companies that handle large waste,

[1019] A means of recording users' recycling activities and visualizing their environmental contribution,

[1020] A means of analyzing user emotions and providing appropriate information,

[1021] A means of displaying locations related to waste disposal using a geographic information system,

[1022] A system that includes this.

[1023] (Claim 2)

[1024] The system according to claim 1, which performs waste type determination based on image analysis.

[1025] (Claim 3)

[1026] The system according to claim 1 for searching for a recycling company based on the user's location. [Explanation of symbols]

[1027] 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 analyzing images of waste and determining its sorting method, A means of presenting the determined sorting method, Means of providing information on recyclable resources, A means of searching for and presenting companies that handle large waste, A means of recording users' recycling activities and visualizing their environmental contribution, A system that includes this.

2. The system according to claim 1, which performs waste type determination based on image analysis.

3. The system according to claim 1, which searches for a recycling company based on the user's location.

Images