System

Abstract

A system is provided.SOLUTION: A system comprising: means for causing a user to input information on a name, an image, and a residential area of waste; means for transmitting the input information; means for receiving the transmitted information and analyzing the name and the image of the waste to identify a type of the waste; means for searching for a sorting rule based on the residential area to determine an appropriate sorting method corresponding to the identified waste; and means for notifying the user of the determined sorting method.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Japan's annual waste output is approximately 41.67 million tons, and proper waste sorting is expected to reduce waste output by 20% to 50%. However, the complexity of sorting rules and regional differences make accurate sorting difficult, resulting in a lack of thoroughness. For this reason, there is a need for a method that allows users to easily sort their waste properly. [Means for solving the problem]

[0005] This invention is a system that includes means for allowing a user to input the name, image, and residential area information of waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, and means for notifying the user of the determined sorting method. This system makes it easy for users to accurately sort waste based on the sorting rules for each area, thereby ensuring thorough and accurate sorting.

[0006] "User" refers to a person who uses this system to check how to separate garbage.

[0007] "Waste" refers to items that are no longer needed and discarded by households and businesses.

[0008] "Name" refers to information expressed as a string of characters that identifies the type of waste.

[0009] "Image" refers to data that captures the visual characteristics of waste.

[0010] "Residence area" refers to the geographic area, such as the city or town, where the user lives.

[0011] The "means for inputting information" refers to an interface through which a user inputs the name, image, and area of ​​residence of the waste.

[0012] "Means for transmitting information" refers to communication means for transmitting information input from a terminal to a server.

[0013] The "means for receiving" refers to the means by which the server receives information transmitted from the terminal.

[0014] "Means for analyzing and identifying" refers to the algorithms and processes that the server uses to identify the type of waste using its name and image.

[0015] "Sorting rules" refer to local regulations that stipulate the appropriate disposal methods for different types of waste.

[0016] "Means for determining" refers to the process of applying sorting rules and selecting an appropriate sorting method based on the identified type of waste.

[0017] The "notification means" refers to an output means for informing the user of the determined separation method.

[0018] "System" refers to the overall computer network and software configuration including each of the above means. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

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

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

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

[0029] 1, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0036] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0037] The storage 32 stores a data generation model 58 and an 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 process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is a system that uses generative AI to support proper waste sorting. Below, we will explain how to implement this system in concrete terms.

[0041] Overall system configuration

[0042] This system is composed of a terminal with an interface where users can input the name of the waste, an image, and information about their residential area, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. As a specific embodiment, we will describe how each of these elements works together.

[0043] System operation flow

[0044] 1. User Input

[0045] The user starts the application using a device such as a smartphone or PC, enters the name of the waste into the input form within the application, and takes a picture of the waste using the camera function.

[0046] Also, enter your area of ​​residence (city, town, village, etc.).

[0047] 2. Data Transmission

[0048] The device sends the entered name, image, and location information to the server via an HTTP request over the internet connection.

[0049] 3. Data reception and preparation for analysis

[0050] The server receives the input data, parses it as structured data in JSON format, and separates it into the name of the garbage, an image, and the residential area.

[0051] 4. Identifying the garbage

[0052] The server uses generative AI to analyze the received images and names, and identifies the type of waste using a specific algorithm. Image analysis algorithms extract waste characteristics from the uploaded images.

[0053] By combining both the name of the waste and image information, the type of waste can be identified more accurately.

[0054] 5. Application of sorting rules

[0055] The server accesses the database and searches for the waste separation rules for the specified area, which include waste types, corresponding separation methods, collection dates, etc.

[0056] The identified types of waste are compared with local sorting rules to determine the appropriate sorting method.

[0057] 6. Sending and notifying results

[0058] The server then sends the analysis results and the determined sorting method to the user's device, including specific instructions such as the waste type, sorting method, and collection date.

[0059] The results are displayed on the user's terminal, and the user can confirm the appropriate sorting method and sort the waste accordingly.

[0060] Specific examples

[0061] For example, let's consider the case where a user uses this system to process a "PET bottle" that they plan to discard at home. The user takes a picture of the PET bottle with their smartphone and enters "PET bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area. This information is sent to the server, which uses generative AI to identify it as a "PET bottle." Furthermore, the server retrieves the sorting rules for the residential area from a database and determines that the PET bottle should be collected as "plastic waste" every Wednesday. This information is notified to the user, and instructions to put it out as "plastic waste" on Wednesday are displayed on the screen.

[0062] In this way, this system allows users to easily and accurately separate their garbage, which promotes proper garbage disposal in accordance with local garbage separation rules.

[0063] The processing flow will be explained below.

[0064] Step 1: User Input

[0065] The user uses the terminal to launch the dedicated application.

[0066] The user enters the name of the waste material into the input form (e.g., "PET bottle").

[0067] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[0068] Enter your area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture") and click the submit button.

[0069] Step 2: Send data

[0070] The terminal compiles the name of the waste, an image, and information on the area of ​​residence entered by the user into a single data packet.

[0071] The terminal sends this data packet as an HTTP request to a server via the Internet.

[0072] Step 3: Receiving data

[0073] The server receives the HTTP request and parses the data in the request body.

[0074] The server divides the received data into the name of the waste, image, and residential area, and stores them in the respective variables.

[0075] Step 4: Identify the debris

[0076] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0077] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0078] Where necessary, both name and image may be combined for more precise identification.

[0079] Step 5: Applying separation rules

[0080] The server searches the database for the sorting rules for each residential area.

[0081] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area to determine the appropriate sorting method.

[0082] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[0083] Step 6: Sending the results

[0084] The server generates result data including the determined separation method.

[0085] The server sends the result data to the user's terminal as an HTTP response.

[0086] Step 7: View the results

[0087] The terminal receives the response data from the server.

[0088] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[0089] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] Conventional waste sorting systems required users to manually determine the type of waste and look up the appropriate sorting method. This made sorting cumbersome and prone to mistakes, making it difficult to properly dispose of waste in accordance with local sorting rules. Furthermore, the criteria for identifying waste types were vague, leading to confusion. There is a need to provide a system that can solve these issues and support users in easily and accurately sorting waste.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for analyzing the image and name of the waste using a generative AI model, means for searching a database for the sorting rules for the specified residential area, and means for notifying the user of the determined sorting method. This enables the user to accurately identify the type of waste and easily carry out appropriate waste disposal in accordance with the sorting rules for each area.

[0095] "User" refers to a person who uses the system to separate waste.

[0096] "Waste" refers to items that a user is disposing of.

[0097] "Name" refers to text information entered by the user to identify the waste.

[0098] "Image" refers to a photograph taken by a user using the device's camera to identify waste.

[0099] "Residence area" refers to the administrative division where a user lives.

[0100] "Input means" refers to an interface for a user to input information about the name, image, and residential area of ​​the waste into the system.

[0101] "Transmission means" refers to a function for transmitting information entered by the user to the server.

[0102] "Receiving means" refers to a function that allows the server to receive information sent from the user.

[0103] "Analysis means" refers to the function of analyzing the name and image of the waste received by the server and identifying the type of waste.

[0104] "Generative AI model" refers to an algorithm that uses artificial intelligence technology to analyze received images and names and determine the characteristics of the waste.

[0105] The "database" refers to a collection of information that stores sorting rules for each residential area.

[0106] "Search means" refers to the function for searching the database for waste separation rules for a specified residential area.

[0107] "Notification means" refers to a function for notifying the user of the sorting method determined by the server.

[0108] "Sorting rules" refers to information on the appropriate sorting methods and collection dates for each type of waste in each residential area.

[0109] "Analysis preparation" refers to the process of parsing the received data and dividing it into waste names, images, and residential areas.

[0110] MODE FOR CARRYING OUT THE INVENTION

[0111] The present invention is a system that uses generative AI to support proper waste sorting. This system consists of a terminal with an interface for users to input the name, image, and residential area information of the waste, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. Specific embodiments are described below.

[0112] Hardware and software used

[0113] 1. Device:

[0114] It is used by users to enter information and upload images. Examples include smartphones and personal computers.

[0115] The application includes a photo capture function and an input form.

[0116] 2. Server:

[0117] The program receives and analyzes information and is implemented using Python.

[0118] The TensorFlow library is used for image analysis, and a generative AI model is applied.

[0119] The server also queries the database with the analysis results to retrieve the classification rules.

[0120] 3. Database:

[0121] Sorting rules for each region are stored using a database such as MySQL.

[0122] System Operation Overview

[0123] User input:

[0124] Users launch the application using their smartphone or computer, enter the name of the waste in the input form within the application, take a picture of the waste using the device's camera, and select their area of ​​residence.

[0125] Data transmission:

[0126] The device sends the entered name, image, and location information to a server over its internet connection via an HTTP POST request.

[0127] Data reception and preparation for analysis:

[0128] The server receives an HTTP request, parses the received data in JSON format, and separates it into the waste name, image, and residential area information.

[0129] Garbage Identification:

[0130] The server uses a generative AI model to analyze the received images and names to identify the type of waste. This process is done using the TensorFlow library. The waste characteristics are extracted and analyzed along with the name.

[0131] Separation rules apply:

[0132] The server accesses the database and searches for the waste separation rules for the specified residential area. The rules include waste types, corresponding separation methods, collection dates, etc. The identified waste type is compared with the local separation rules to determine the appropriate separation method.

[0133] Sending and notifying results:

[0134] The server sends the analysis results and the determined sorting method to the user's device in JSON format. The user can then use the application to check specific information such as the type of waste, sorting method, and collection date.

[0135] Specific examples

[0136] For example, let's say a user wants to throw away a "plastic bottle." The user takes a picture of the plastic bottle with their smartphone at home, and then enters "plastic bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area in the application. This information is sent to the server.

[0137] The server uses a generative AI to identify the item as a "plastic bottle" using the following prompt:

[0138] "Please identify what kind of garbage is in this image and tell me the proper way to separate it in Naka Ward, Yokohama City, Kanagawa Prefecture."

[0139] The server then retrieves the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture from the database and determines that the PET bottles should be collected as "plastic waste" every Wednesday. The result is notified to the user, and specific instructions are displayed on the application screen.

[0140] In this way, users can accurately separate their garbage and dispose of it properly in accordance with local rules.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] System program processing flow

[0143] Step 1:

[0144] Input: The user launches the application on the terminal and inputs the name of the waste, an image, and information about the area of ​​residence.

[0145] Specific operation: The user enters "PET bottle" into the application's input form, takes a picture of the PET bottle with the smartphone camera, and selects "Naka-ku, Yokohama City, Kanagawa Prefecture" as their area of ​​residence.

[0146] Output: The system temporarily stores the name entered by the user, the image taken, and the residential area information.

[0147] Step 2:

[0148] Input: The user presses a "Submit" button within the application.

[0149] Specific operation: The terminal collects the input information, converts it into JSON format, and sends it to the server via an HTTP POST request.

[0150] Output: The entered name, image, and area information are sent to the server.

[0151] Step 3:

[0152] Input: Parse the HTTP request received by the server.

[0153] Specific operation: The server parses the received data in JSON format and divides it into the waste name, image, and residential area information.

[0154] Output: The server stores the received data internally as name, image, and residential area information.

[0155] Step 4:

[0156] Input: The server receives the name and image of the waste and feeds it to the generative AI model.

[0157] Specific operation: The server uses the TensorFlow library to drive a generative AI model to analyze the image and its name. The prompt text is "Please identify what kind of garbage is in this image and tell me the appropriate method for sorting it in Naka-ku, Yokohama City, Kanagawa Prefecture."

[0158] Output: Obtain the determination result that it is a "plastic bottle" from the generative AI model.

[0159] Step 5:

[0160] Input: The server searches the database for sorting rules based on the type of waste and residential area information identified.

[0161] What it does: The server queries the MySQL database to get the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture, specifically, whether "PET bottles" are considered "plastic waste" and the collection schedule.

[0162] Output: The rule information retrieved from the database is sent back to the server.

[0163] Step 6:

[0164] Input: The sorting rules obtained by the server and the garbage judgment results of the generating AI.

[0165] Specific operation: The server integrates this information and determines the appropriate sorting method and collection date, while also compiling the analysis results and specific instructions on how to sort the waste.

[0166] Output: Information about the determined sorting method and collection date is prepared in JSON format.

[0167] Step 7:

[0168] Input: JSON data prepared by the server to notify the user.

[0169] Specific operation: The server sends the result to the user's device as an HTTP response.

[0170] Output: The user terminal receives the analysis results along with information on sorting methods and collection schedules.

[0171] Step 8:

[0172] Input: Analysis results and classification methods displayed on the user's device.

[0173] Specific operation: The user confirms the instruction on the application screen that "PET bottles should be disposed of as plastic waste every Wednesday."

[0174] Output: The user follows the correct sorting method and puts out the garbage on the specified date.

[0175] This allows users to accurately separate their garbage and dispose of it properly in accordance with local rules.

[0176] (Application example 1)

[0177] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0178] While existing waste sorting systems focus on encouraging users to properly sort their waste at home, they do not address the disposal methods of used products in brick-and-mortar stores. Furthermore, the lack of effective methods to promote proper waste sorting within stores results in insufficient customer service and environmental protection activities.

[0179] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0180] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for providing real-time in-store guidance to customers on how to dispose of used products. This enables efficient proper waste sorting in physical stores, improving both customer service and environmental protection.

[0181] "User" means an individual or organization that uses the waste sorting system.

[0182] "Waste" refers to items or materials that are no longer needed and are discarded, and require appropriate disposal methods.

[0183] "Name" is a character string entered by the user to identify the waste.

[0184] "Images" are visual data obtained by photographing the appearance of waste with a camera.

[0185] The "residential area" refers to the area such as a city, town, or village where the user lives, and the rules for separating garbage vary from area to area.

[0186] The "means for inputting" is an interface that allows a user to input the name, image, and residential area information of the waste into the system.

[0187] The "transmitting means" refers to a communication technology for transmitting the input information to the server.

[0188] "Means of receiving and identifying" refers to the process by which the server receives the transmitted information and identifies the type of waste using generative AI and image analysis algorithms.

[0189] The "means for searching and determining sorting rules" refers to a process in which the server searches the database for waste sorting rules based on the residential area and determines an appropriate sorting method for the waste.

[0190] The "means for notifying" is an interface for notifying the user of the determined separation method.

[0191] "In-store guidance" refers to a system that allows customers to receive real-time information on how to sort used products in-store.

[0192] "Barcode scanning" is the process of reading the barcode of a product and obtaining details of the waste based on that information.

[0193] MODE FOR CARRYING OUT THE INVENTION

[0194] The present invention provides a specific embodiment of a waste sorting system, particularly for efficiently sorting used products in brick-and-mortar stores.

[0195] Overall system configuration

[0196] This system consists of the following elements:

[0197] 1. User Device:

[0198] Applications installed on devices such as smartphones and smart glasses.

[0199] It provides an interface for users to input waste name, image, and residential area information.

[0200] Ability to scan waste barcodes within the application.

[0201] 2. Server:

[0202] The received data is analyzed and the type of waste is identified using generative AI.

[0203] Based on the specified residential area, sorting rules are searched from a database to determine the appropriate sorting method.

[0204] Users will be notified of sorting methods and collection dates.

[0205] 3. Database:

[0206] It stores waste separation rules for each region.

[0207] Specifically, this includes sorting methods and collection schedules according to the type of waste.

[0208] Specific operation methods of the system

[0209] 1. Enter your information:

[0210] Users scan the barcode of used products using an application on their smartphone or smart glasses.

[0211] The user takes a picture of the waste and enters its name and area of ​​residence (such as city, ward, town, or village).

[0212] 2. Data transmission:

[0213] The entered information is sent to the server via the Internet using an HTTP request.

[0214] 3. Data reception and analysis:

[0215] The server receives the transmitted data and uses generative AI and image analysis algorithms to extract the characteristics of the waste and identify its type.

[0216] 4. Search and apply sorting rules:

[0217] The server searches the database for appropriate sorting rules based on the identified type of waste and the user's designated residential area.

[0218] For example, obtain information from the database that "PET bottles are collected as plastic waste every Wednesday."

[0219] 5. Notification of Results:

[0220] The server transmits the determined sorting method to the user terminal and notifies the user in real time.

[0221] For example, a message may be displayed saying, "Please separate plastic bottles as plastic waste. Collection day is every Wednesday."

[0222] Hardware and software used

[0223] Hardware: Smartphones, smart glasses

[0224] Software: Python 3, Requests library, Pillow library

[0225] Specific examples

[0226] If the user wants to know how to dispose of a "plastic bottle," they can use the following prompt:

[0227] How do I dispose of plastic bottles? I live in Shibuya Ward, Tokyo.

[0228] Based on this prompt, the generative AI model analyzes and generates specific disposal instructions, such as:

[0229] Please separate plastic bottles as plastic waste. Collection day is every Wednesday.

[0230] As described above, the system of the present invention allows users to easily and accurately separate waste, which will enable proper waste separation and promote eco-friendly activities in brick-and-mortar stores.

[0231] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0232] Step 1:

[0233] Enter information

[0234] Users launch an application on their smartphone or smart glasses and enter information about the waste.

[0235] The input information is threefold: "Name of waste," "Image of waste," and "Area of ​​residence (city, town, or village)."

[0236] If a barcode scanning function is used, the entered information also includes the identification information obtained by the barcode.

[0237] Input: waste name, image, residential area

[0238] Output: A set of information to be sent to the server

[0239] Step 2:

[0240] Data transmission

[0241] The terminal transmits the input information to the server via an HTTP request.

[0242] Specific operation: Using the terminal's communication function, the input data (name of waste, image, and residential area information) is sent to the server.

[0243] Input: Waste information entered by the user

[0244] Output: Request sent to the server

[0245] Step 3:

[0246] Data reception and analysis

[0247] The server receives the sent data, which is structured in JSON format.

[0248] The server analyzes the name, image, and residential area of ​​the waste from the received data and identifies the type of waste using a generative AI model.

[0249] How it works: The server uses analytical tools (e.g., Python and the Pillow library) to extract waste characteristics using image analysis algorithms, and then uses generative AI models (e.g., TensorFlow or PyTorch) to combine waste name and image information to identify the type of waste.

[0250] Input: JSON data received by the server (name, image, location)

[0251] Output: Identified waste types

[0252] Step 4:

[0253] Searching and applying sorting rules

[0254] The server searches the database for sorting rules based on the identified waste type and residential area.

[0255] The database includes regional waste sorting methods and corresponding collection dates.

[0256] Specific operation: The server uses an SQL query to search the database for the relevant sorting rules and determines the appropriate sorting method based on the identified waste type.

[0257] Input: Identified waste type, residential area

[0258] Output: Proper sorting method and collection schedule

[0259] Step 5:

[0260] Notification of results

[0261] The server transmits the determined sorting method and collection schedule to the user terminal.

[0262] The user terminal displays the received information on the user interface and notifies the user.

[0263] Specific operation: The server sends the sorting information as an HTTP response, and the device receives it and displays it on the screen. For example, the user receives a notification such as, "Please separate PET bottles as plastic waste. Collection day is every Wednesday."

[0264] Input: Proper sorting method and collection schedule

[0265] Output: Classification information notified to the user

[0266] These processes will enable proper waste sorting in physical stores efficiently, improving both customer service and environmental protection.

[0267] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0268] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. Below, we will explain how this system is implemented in concrete terms.

[0269] Overall system configuration

[0270] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[0271] System operation flow

[0272] 1. User Input

[0273] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[0274] Also, enter information about your area of ​​residence (city, town, village, etc.).

[0275] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[0276] 2. Data Transmission

[0277] The terminal combines the entered name, image, and residential area information into a single data packet.

[0278] The terminal sends this data packet as an HTTP request to a server via the Internet.

[0279] 3. Data reception and preparation for analysis

[0280] The server receives the HTTP request and parses the data in the request body.

[0281] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[0282] 4. Identifying the garbage

[0283] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0284] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0285] Where necessary, both name and image may be combined for more precise identification.

[0286] 5. Application of sorting rules

[0287] The server searches the database for the sorting rules for each residential area.

[0288] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[0289] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[0290] 6. Sending and notifying results

[0291] The server generates result data including the determined separation method.

[0292] The generated result data is sent to the user's terminal as an HTTP response.

[0293] 7. Displaying the results

[0294] The terminal receives the response data from the server.

[0295] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[0296] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0297] Examples of emotion engines

[0298] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[0299] In this way, this system allows users to easily and accurately separate their garbage, and with the help of the emotion engine, they can operate the system without stress. This will encourage thorough separation and lead to more efficient garbage disposal in the community.

[0300] The processing flow will be explained below.

[0301] Step 1: User Input

[0302] The user uses the terminal to launch the dedicated application.

[0303] The user enters the name of the waste material in text into the input form (e.g., "PET bottle").

[0304] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[0305] The user enters information about their area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture").

[0306] The emotion engine recognizes the user's emotions through voice input and facial expression analysis, and displays additional guidance and support as needed.

[0307] Step 2: Send data

[0308] The terminal combines the name of the waste, the image, the residential area information entered by the user, and the emotion data from the emotion engine into one data packet.

[0309] The terminal sends this data packet to a server over the Internet.

[0310] Step 3: Receiving data

[0311] The server receives the HTTP request and parses the data in the request body.

[0312] The data received by the server is divided into the waste name, image, residential area, and emotion data, and stored in the respective variables.

[0313] Step 4: Identify the debris

[0314] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0315] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0316] Where necessary, both name and image may be combined for more precise identification.

[0317] Step 5: Applying separation rules

[0318] The server searches the database for the sorting rules for each residential area.

[0319] The server matches the identified waste type (e.g., "PET bottle") with the sorting rules for the area in which the user lives and determines the appropriate sorting method.

[0320] The determined sorting method (e.g., "Plastic bottles should be disposed of as plastic waste every Wednesday") is generated as data.

[0321] Step 6: Sending the results

[0322] The server generates result data including the determined sorting method.

[0323] The server sends the result data to the user's terminal as an HTTP response.

[0324] Step 7: View the results

[0325] The terminal receives the response data from the server.

[0326] The device analyzes the response data and displays it on the screen in a user-friendly format (e.g., "Please dispose of PET bottles as plastic waste every Wednesday").

[0327] If the user feels stressed while operating the device, the emotion engine will recognize this and display appropriate support and guidance on the screen.

[0328] This series of steps allows users to separate their garbage simply and accurately without even thinking about it. Utilizing an emotion engine improves the user experience, allowing for stress-free input of information and garbage separation.

[0329] Example 2

[0330] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0331] In recent years, proper waste sorting has required users to easily understand how to sort waste and to perform the task appropriately. However, in many cases, users lack understanding of the types of waste and the sorting rules, resulting in incorrect sorting. Furthermore, users may feel stressed during the sorting process, which can reduce the accuracy and efficiency of sorting. Therefore, there is a need for a system that supports users in sorting their waste easily and accurately.

[0332] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0333] In this invention, the server includes means for allowing a user to input the name, image, and residential area information of the waste, means for transmitting the input information, and means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, thereby enabling the user to easily identify the type of waste and understand the appropriate sorting method.

[0334] "User" refers to the person who operates the terminal to input the name, image, and residential area information of the waste.

[0335] "Terminal" refers to an information processing device such as a smartphone or computer on which a user inputs and sends information about waste.

[0336] "Server" refers to a central information processing device that receives and analyzes information sent by users and determines the appropriate method of sorting.

[0337] The "emotion engine" refers to a component that analyzes the user's voice, text, and facial expressions to recognize the user's emotions.

[0338] A "data packet" refers to a unit of digital data that compiles user input information.

[0339] An "HTTP request" refers to an information request, which is a form of communication protocol sent from a user's terminal to a server.

[0340] "Image analysis algorithm" refers to a computational procedure for processing waste image data and extracting waste characteristics.

[0341] "Waste identification" refers to the process of determining what waste is based on the input name and analyzed image.

[0342] "Sorting rules" refer to regulations that stipulate the proper method of sorting waste in a particular area.

[0343] "HTTP response" refers to response data, which is a form of communication protocol sent from a server to a user's terminal.

[0344] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. The system identifies the type of waste based on information entered by the user and provides instructions on the appropriate sorting method based on the sorting rules for each residential area. Furthermore, it improves the user experience by recognizing the user's emotions and providing an interface that responds to them.

[0345] System configuration

[0346] The system consists of the following main components:

[0347] A device (smartphone or PC) with an interface that allows users to input the name of the garbage, an image, and information about their residential area

[0348] A server that receives and analyzes input information

[0349] Database that stores regional sorting rules

[0350] A means of informing users of the analysis results and classification methods

[0351] An emotion engine that recognizes user emotions and adjusts the interface

[0352] Hardware and Software

[0353] Terminal: The user inputs the name of the waste, takes a picture of the waste with a camera, and inputs information about the area where they live. This is done using a smartphone or computer.

[0354] Server: Receives and analyzes input information. The server is equipped with a generative AI model (e.g., OpenAI) and an emotion engine.

[0355] Database: Stores sorting rules for each residential area.

[0356] Emotion Engine: Recognizes user emotions from voice, text, and facial expressions.

[0357] Example of a system

[0358] 1. User Input

[0359] The user launches the application on their smartphone and enters the name of the waste item: "PET bottle." They then take a picture of the PET bottle with their device's camera and upload it within the app. Next, they enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). While they are entering information, the emotion engine analyzes the user's tone of voice and facial expression, and if it detects stress, it displays additional guidance such as, "If you don't know how to operate it, please see this guide."

[0360] 2. Data Transmission

[0361] Once the user has completed the input, the device assembles the name "PET bottle," the image of the PET bottle, and the residential area "Shinjuku Ward, Tokyo" into a single data packet and sends it to the server as an HTTP request.

[0362] 3. Data reception and preparation for analysis

[0363] The server analyzes the received HTTP request and extracts the name, image, and residential area information from the request body. For example, the name "PET bottle," the image data, and the residential area "Shinjuku-ku, Tokyo" are stored in individual variables.

[0364] 4. Identifying the garbage

[0365] The server uses a generative AI model (for example, OpenAI's image analysis model) to analyze the received image of the plastic bottle. As a result, the generative AI determines that "this is a plastic bottle" and identifies it by matching the name and characteristics of the waste.

[0366] 5. Application of sorting rules

[0367] The server searches the database for the sorting rules for the area where the user lives, "Shinjuku Ward, Tokyo." It then applies the rule that "PET bottles" are classified as "plastic waste" and are collected every Wednesday.

[0368] 6. Sending and notifying results

[0369] The server generates result data including the determined sorting method;

[0370] "Plastic bottles should be disposed of as plastic waste every Wednesday."

[0371] This is sent to the user's device as an HTTP response.

[0372] 7. Displaying the results

[0373] The device receives the response data from the server, analyzes it, and displays it on the screen. When the user looks at the screen, they can see specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0374] Prompt Sentence Examples

[0375] "Please identify what this waste is based on the name and image of the waste."

[0376] "Please tell me the proper way to separate the identified waste based on the waste separation rules in your area."

[0377] This allows users to easily separate their waste properly, and with the help of an emotion engine, the operation can be performed smoothly, improving the user experience and making local waste management more effective.

[0378] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0379] Step 1:

[0380] The user uses their device to launch the application. They enter the name of the waste in the input form, take a picture of the waste using the camera, and upload it. They also enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). The emotion engine recognizes emotions from voice, text, and facial expressions, and provides additional guidance and support if the user is feeling stressed.

[0381] Input: waste name, image, residential area information, user emotion data.

[0382] Output: Waste information and emotion data organized as data packets.

[0383] Step 2:

[0384] The terminal combines the inputted waste name, image, and residential area information into a single data packet, which is then sent to the server as an HTTP request.

[0385] Input: Waste name, image, and residential area information from the user.

[0386] Output: Data packets sent to the server.

[0387] Step 3:

[0388] The server receives the HTTP request, analyzes the received data, and stores it in variables for the waste name, image, and residential area information.

[0389] Input: Data packets sent from the terminal.

[0390] Output: Name of waste, image, and residential area information stored in separate variables.

[0391] Step 4:

[0392] The server's generation AI analyzes the uploaded image and extracts the characteristics of the waste from the image. Then, it uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is. For example, it uses an image analysis algorithm to extract the characteristics of a plastic bottle and identify it as a plastic bottle.

[0393] Input: Name of waste, image.

[0394] Output: Waste identification result (e.g. "PET bottles").

[0395] Step 5:

[0396] The server searches for the sorting rules for the residential area (e.g., Shinjuku Ward, Tokyo) stored in a database. It matches the identified waste type (e.g., PET bottles) with the sorting rules for the residential area to determine the appropriate sorting method. For example, it determines that "PET bottles are classified as plastic waste and are collected every Wednesday."

[0397] Input: Waste identification results, sorting rules for your area.

[0398] Output: The optimal sorting method (e.g. "PET bottles are collected as plastic waste every Wednesday").

[0399] Step 6:

[0400] The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[0401] Input: Optimal separation method.

[0402] Output: The result data sent to the user's device (e.g., "Please put out plastic bottles as plastic waste every Wednesday").

[0403] Step 7:

[0404] The device receives the response data from the server. The response data is analyzed and displayed on the screen in a format that is easy for the user to understand. For example, the data may be read and displayed on the UI as "Please dispose of PET bottles as plastic waste every Wednesday."

[0405] Input: Response data from the server.

[0406] Output: A display of the sorting method that can be checked by the user.

[0407] (Application example 2)

[0408] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0409] In conventional waste sorting systems, it is difficult for users to obtain instructions on how to sort waste accurately, and this can often be stressful for users, leading to inappropriate sorting. As a result, improper waste sorting can cause environmental problems and reduce recycling efficiency.

[0410] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for analyzing the user's emotions using emotion recognition means and providing guidance and support to reduce the user's stress. This enables the user to sort waste accurately and without stress.

[0411] "User" refers to the entity that uses the system to receive instructions on how to separate waste.

[0412] "Waste" refers to items that a user wishes to discard and that require proper separation.

[0413] "Name" refers to text information entered by the user to identify the waste.

[0414] "Image" refers to photographic data uploaded by users to extract visual information about waste.

[0415] "Residential area" is area information such as the city or town to which the user belongs, and is information that affects the sorting rules for that area.

[0416] "Means for input" refers to an interface for collecting information from a user.

[0417] "Means for sending" refers to the technical means for sending the information entered by the user to the server.

[0418] "Means for receiving and analyzing" refers to the technical means by which the server receives information sent from the user and performs processing to analyze its contents.

[0419] "Means of identification" means the technical means for clearly identifying the type of waste based on the information received.

[0420] "Means for searching and determining sorting rules" refers to technical methods for searching for and determining appropriate waste sorting methods based on information about the residential area.

[0421] "Means of notification" refers to a technical method for communicating the determined separation method to the user.

[0422] "Emotion recognition means" refers to technology that analyzes the user's emotions and provides appropriate support to reduce stress.

[0423] "Means for providing guidance and support" refers to technical methods for providing additional explanations or assistance depending on the user's emotional state.

[0424] This invention is a system that supports proper waste sorting by combining a generative AI model and an emotion engine. Below, we will explain how to specifically implement this system.

[0425] Overall system configuration

[0426] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[0427] System operation flow

[0428] 1. User Input

[0429] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[0430] Also, enter information about your area of ​​residence (city, town, village, etc.).

[0431] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[0432] 2. Data Transmission

[0433] The terminal combines the entered name, image, and residential area information into a single data packet.

[0434] The terminal sends this data packet as an HTTP request to a server via the Internet.

[0435] 3. Data reception and preparation for analysis

[0436] The server receives the HTTP request and parses the data in the request body.

[0437] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[0438] 4. Identifying the garbage

[0439] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0440] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0441] Where necessary, both name and image may be combined for more precise identification.

[0442] 5. Application of sorting rules

[0443] The server searches the database for the sorting rules for each residential area.

[0444] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[0445] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[0446] 6. Sending and notifying results

[0447] The server generates result data including the determined separation method.

[0448] The generated result data is sent to the user's terminal as an HTTP response.

[0449] 7. Displaying the results

[0450] The terminal receives the response data from the server.

[0451] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[0452] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0453] Examples of emotion engines

[0454] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[0455] Hardware and software used

[0456] This system uses the following hardware and software:

[0457] User interface: Smartphone or tablet

[0458] Server: Built using the Frust framework

[0459] Generative AI model: built with TensorFlow

[0460] Emotion Recognition Software: Uses Emotion Recognition API

[0461] Examples of concrete examples and prompts

[0462] For example, when a user scans the label of a plastic bottle purchased at the supermarket, the app analyzes the image. If the emotion engine detects stress, it provides guidance such as, "Please throw this plastic bottle in the recycling bin. The recycling bin is located at the entrance of the store."

[0463] Example prompt sentence:

[0464] "A user uploaded an image of a plastic bottle from their smartphone. The emotion engine detected stress. Please provide appropriate waste sorting instructions."

[0465] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0466] Step 1:

[0467] Input: The user inputs the name of the waste, an image, and information about the area of ​​residence.

[0468] Processing: The user launches the application using a device such as a smartphone or PC. They enter the name of the waste into the input form within the application, take a picture of the waste using the camera function, and upload it. They also enter their residential area.

[0469] Output: The entered name, image, and residential area are stored as data on the device.

[0470] Step 2:

[0471] Input: Data collected in Step 1 (name, image, location)

[0472] Processing: The device assembles the entered name, picture, and location information into a single data packet.

[0473] Output: A data packet is generated.

[0474] Step 3:

[0475] Input: Data packet generated in step 2

[0476] Processing: The device sends the data packet as an HTTP request to the server over the Internet.

[0477] Output: The server receives the data packet.

[0478] Step 4:

[0479] Input: Data packet received by the server

[0480] Processing: The server receives the HTTP request and analyzes the data in the request body. The server divides the data into the waste name, image, and residential area and stores them in variables.

[0481] Output: Name, image, and location stored in separate variables.

[0482] Step 5:

[0483] Input: waste image and waste name

[0484] Processing: The server's generation AI analyzes the uploaded image and extracts the waste characteristics from the image. The generation AI uses TensorFlow to perform image analysis. By combining both the name and the image, the type of waste is identified based on the algorithm.

[0485] Output: The type of waste is identified (e.g., "PET bottle").

[0486] Step 6:

[0487] Input: Waste type and residential area identified in step 5

[0488] Processing: The server searches the database for the sorting rules for each residential area. The server matches the identified waste type with the local sorting rules and determines the appropriate sorting method.

[0489] Output: The sorting method is determined (e.g., "Plastic bottles are classified as plastic waste").

[0490] Step 7:

[0491] Input: The classification method determined in step 6 and the user's emotion data

[0492] Processing: The server uses the emotion recognition API to analyze the user's emotions and generate guidance and support according to their emotional state. If the emotion engine detects stress, it applies additional guidance and support.

[0493] Output: The final classification method and emotional support guide are generated.

[0494] Step 8:

[0495] Enter: The Ultimate Sort-Out and Emotional Support Guide

[0496] Processing: The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[0497] Output: The response with the result data is sent to the user's device.

[0498] Step 9:

[0499] Input: Response data from the server

[0500] Processing: The device analyzes the response data received from the server and displays it on the screen in a format that is easy for the user to understand. The device also notifies the user of the sorting method and additional guidance.

[0501] Output: The user looks at the device screen and sees specific instructions and guidance for sorting waste (e.g., "Plastic bottles should be put out as plastic waste every Wednesday").

[0502] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0503] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0504] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0505] [Second embodiment]

[0506] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0507] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0508] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0509] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0510] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0511] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0512] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0513] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0514] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0515] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0516] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0517] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0518] The present invention is a system that uses generative AI to support proper waste sorting. Below, we will explain how to implement this system in concrete terms.

[0519] Overall system configuration

[0520] This system is composed of a terminal with an interface where users can input the name of the waste, an image, and information about their residential area, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. As a specific embodiment, we will describe how each of these elements works together.

[0521] System operation flow

[0522] 1. User Input

[0523] The user starts the application using a device such as a smartphone or PC, enters the name of the waste into the input form within the application, and takes a picture of the waste using the camera function.

[0524] Also, enter your area of ​​residence (city, town, village, etc.).

[0525] 2. Data Transmission

[0526] The device sends the entered name, image, and location information to the server via an HTTP request over the internet connection.

[0527] 3. Data reception and preparation for analysis

[0528] The server receives the input data, parses it as structured data in JSON format, and separates it into the name of the garbage, an image, and the residential area.

[0529] 4. Identifying the garbage

[0530] The server uses generative AI to analyze the received images and names, and identifies the type of waste using a specific algorithm. Image analysis algorithms extract waste characteristics from the uploaded images.

[0531] By combining both the name of the waste and image information, the type of waste can be identified more accurately.

[0532] 5. Application of sorting rules

[0533] The server accesses the database and searches for the waste separation rules for the specified area, which include waste types, corresponding separation methods, collection dates, etc.

[0534] The identified types of waste are compared with local sorting rules to determine the appropriate sorting method.

[0535] 6. Sending and notifying results

[0536] The server then sends the analysis results and the determined sorting method to the user's device, including specific instructions such as the waste type, sorting method, and collection date.

[0537] The results are displayed on the user's terminal, and the user can confirm the appropriate sorting method and sort the waste accordingly.

[0538] Specific examples

[0539] For example, let's consider the case where a user uses this system to process a "PET bottle" that they plan to discard at home. The user takes a picture of the PET bottle with their smartphone and enters "PET bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area. This information is sent to the server, which uses generative AI to identify it as a "PET bottle." Furthermore, the server retrieves the sorting rules for the residential area from a database and determines that the PET bottle should be collected as "plastic waste" every Wednesday. This information is notified to the user, and instructions to put it out as "plastic waste" on Wednesday are displayed on the screen.

[0540] In this way, this system allows users to easily and accurately separate their garbage, which promotes proper garbage disposal in accordance with local garbage separation rules.

[0541] The processing flow will be explained below.

[0542] Step 1: User Input

[0543] The user uses the terminal to launch the dedicated application.

[0544] The user enters the name of the waste material into the input form (e.g., "PET bottle").

[0545] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[0546] Enter your area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture") and click the submit button.

[0547] Step 2: Send data

[0548] The terminal compiles the name of the waste, an image, and information on the area of ​​residence entered by the user into a single data packet.

[0549] The terminal sends this data packet as an HTTP request to a server via the Internet.

[0550] Step 3: Receiving data

[0551] The server receives the HTTP request and parses the data in the request body.

[0552] The server divides the received data into the name of the waste, image, and residential area, and stores them in the respective variables.

[0553] Step 4: Identify the debris

[0554] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0555] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0556] Where necessary, both name and image may be combined for more precise identification.

[0557] Step 5: Applying separation rules

[0558] The server searches the database for the sorting rules for each residential area.

[0559] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area to determine the appropriate sorting method.

[0560] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[0561] Step 6: Sending the results

[0562] The server generates result data including the determined separation method.

[0563] The server sends the result data to the user's terminal as an HTTP response.

[0564] Step 7: View the results

[0565] The terminal receives the response data from the server.

[0566] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[0567] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0568] Example 1

[0569] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0570] Conventional waste sorting systems required users to manually determine the type of waste and look up the appropriate sorting method. This made sorting cumbersome and prone to mistakes, making it difficult to properly dispose of waste in accordance with local sorting rules. Furthermore, the criteria for identifying waste types were vague, leading to confusion. There is a need to provide a system that can solve these issues and support users in easily and accurately sorting waste.

[0571] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0572] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for analyzing the image and name of the waste using a generative AI model, means for searching a database for the sorting rules for the specified residential area, and means for notifying the user of the determined sorting method. This enables the user to accurately identify the type of waste and easily carry out appropriate waste disposal in accordance with the sorting rules for each area.

[0573] "User" refers to a person who uses the system to separate waste.

[0574] "Waste" refers to items that a user is disposing of.

[0575] "Name" refers to text information entered by the user to identify the waste.

[0576] "Image" refers to a photograph taken by a user using the device's camera to identify waste.

[0577] "Residence area" refers to the administrative division where a user lives.

[0578] "Input means" refers to an interface for a user to input information about the name, image, and residential area of ​​the waste into the system.

[0579] "Transmission means" refers to a function for transmitting information entered by the user to the server.

[0580] "Receiving means" refers to a function that allows the server to receive information sent from the user.

[0581] "Analysis means" refers to the function of analyzing the name and image of the waste received by the server and identifying the type of waste.

[0582] "Generative AI model" refers to an algorithm that uses artificial intelligence technology to analyze received images and names and determine the characteristics of the waste.

[0583] The "database" refers to a collection of information that stores sorting rules for each residential area.

[0584] "Search means" refers to the function for searching the database for waste separation rules for a specified residential area.

[0585] "Notification means" refers to a function for notifying the user of the sorting method determined by the server.

[0586] "Sorting rules" refers to information on the appropriate sorting methods and collection dates for each type of waste in each residential area.

[0587] "Analysis preparation" refers to the process of parsing the received data and dividing it into waste names, images, and residential areas.

[0588] MODE FOR CARRYING OUT THE INVENTION

[0589] The present invention is a system that uses generative AI to support proper waste sorting. This system consists of a terminal with an interface for users to input the name, image, and residential area information of the waste, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. Specific embodiments are described below.

[0590] Hardware and software used

[0591] 1. Device:

[0592] It is used by users to enter information and upload images. Examples include smartphones and personal computers.

[0593] The application includes a photo capture function and an input form.

[0594] 2. Server:

[0595] The program receives and analyzes information and is implemented using Python.

[0596] The TensorFlow library is used for image analysis, and a generative AI model is applied.

[0597] The server also queries the database with the analysis results to retrieve the classification rules.

[0598] 3. Database:

[0599] Sorting rules for each region are stored using a database such as MySQL.

[0600] System Operation Overview

[0601] User input:

[0602] Users launch the application using their smartphone or computer, enter the name of the waste in the input form within the application, take a picture of the waste using the device's camera, and select their area of ​​residence.

[0603] Data transmission:

[0604] The device sends the entered name, image, and location information to a server over its internet connection via an HTTP POST request.

[0605] Data reception and preparation for analysis:

[0606] The server receives an HTTP request, parses the received data in JSON format, and separates it into the waste name, image, and residential area information.

[0607] Garbage Identification:

[0608] The server uses a generative AI model to analyze the received images and names to identify the type of waste. This process is done using the TensorFlow library. The waste characteristics are extracted and analyzed along with the name.

[0609] Separation rules apply:

[0610] The server accesses the database and searches for the waste separation rules for the specified residential area. The rules include waste types, corresponding separation methods, collection dates, etc. The identified waste type is compared with the local separation rules to determine the appropriate separation method.

[0611] Sending and notifying results:

[0612] The server sends the analysis results and the determined sorting method to the user's device in JSON format. The user can then use the application to check specific information such as the type of waste, sorting method, and collection date.

[0613] Specific examples

[0614] For example, let's say a user wants to throw away a "plastic bottle." The user takes a picture of the plastic bottle with their smartphone at home, and then enters "plastic bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area in the application. This information is sent to the server.

[0615] The server uses a generative AI to identify the item as a "plastic bottle" using the following prompt:

[0616] "Please identify what kind of garbage is in this image and tell me the proper way to separate it in Naka Ward, Yokohama City, Kanagawa Prefecture."

[0617] The server then retrieves the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture from the database and determines that the PET bottles should be collected as "plastic waste" every Wednesday. The result is notified to the user, and specific instructions are displayed on the application screen.

[0618] In this way, users can accurately separate their garbage and dispose of it properly in accordance with local rules.

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] System program processing flow

[0621] Step 1:

[0622] Input: The user launches the application on the terminal and inputs the name of the waste, an image, and information about the area of ​​residence.

[0623] Specific operation: The user enters "PET bottle" into the application's input form, takes a picture of the PET bottle with the smartphone camera, and selects "Naka-ku, Yokohama City, Kanagawa Prefecture" as their area of ​​residence.

[0624] Output: The system temporarily stores the name entered by the user, the image taken, and the residential area information.

[0625] Step 2:

[0626] Input: The user presses a "Submit" button within the application.

[0627] Specific operation: The terminal collects the input information, converts it into JSON format, and sends it to the server via an HTTP POST request.

[0628] Output: The entered name, image, and area information are sent to the server.

[0629] Step 3:

[0630] Input: Parse the HTTP request received by the server.

[0631] Specific operation: The server parses the received data in JSON format and divides it into the waste name, image, and residential area information.

[0632] Output: The server stores the received data internally as name, image, and residential area information.

[0633] Step 4:

[0634] Input: The server receives the name and image of the waste and feeds it to the generative AI model.

[0635] Specific operation: The server uses the TensorFlow library to drive a generative AI model to analyze the image and its name. The prompt text is "Please identify what kind of garbage is in this image and tell me the appropriate method for sorting it in Naka-ku, Yokohama City, Kanagawa Prefecture."

[0636] Output: Obtain the determination result that it is a "plastic bottle" from the generative AI model.

[0637] Step 5:

[0638] Input: The server searches the database for sorting rules based on the type of waste and residential area information identified.

[0639] What it does: The server queries the MySQL database to get the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture, specifically, whether "PET bottles" are considered "plastic waste" and the collection schedule.

[0640] Output: The rule information retrieved from the database is sent back to the server.

[0641] Step 6:

[0642] Input: The sorting rules obtained by the server and the garbage judgment results of the generating AI.

[0643] Specific operation: The server integrates this information and determines the appropriate sorting method and collection date, while also compiling the analysis results and specific instructions on how to sort the waste.

[0644] Output: Information about the determined sorting method and collection date is prepared in JSON format.

[0645] Step 7:

[0646] Input: JSON data prepared by the server to notify the user.

[0647] Specific operation: The server sends the result to the user's device as an HTTP response.

[0648] Output: The user terminal receives the analysis results along with information on sorting methods and collection schedules.

[0649] Step 8:

[0650] Input: Analysis results and classification methods displayed on the user's device.

[0651] Specific operation: The user confirms the instruction on the application screen that "PET bottles should be disposed of as plastic waste every Wednesday."

[0652] Output: The user follows the correct sorting method and puts out the garbage on the specified date.

[0653] This allows users to accurately separate their garbage and dispose of it properly in accordance with local rules.

[0654] (Application example 1)

[0655] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0656] While existing waste sorting systems focus on encouraging users to properly sort their waste at home, they do not address the disposal methods of used products in brick-and-mortar stores. Furthermore, the lack of effective methods to promote proper waste sorting within stores results in insufficient customer service and environmental protection activities.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0658] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for providing real-time in-store guidance to customers on how to dispose of used products. This enables efficient proper waste sorting in physical stores, improving both customer service and environmental protection.

[0659] "User" means an individual or organization that uses the waste sorting system.

[0660] "Waste" refers to items or materials that are no longer needed and are discarded, and require appropriate disposal methods.

[0661] "Name" is a character string entered by the user to identify the waste.

[0662] "Images" are visual data obtained by photographing the appearance of waste with a camera.

[0663] The "residential area" refers to the area such as a city, town, or village where the user lives, and the rules for separating garbage vary from area to area.

[0664] The "means for inputting" is an interface that allows a user to input the name, image, and residential area information of the waste into the system.

[0665] The "transmitting means" refers to a communication technology for transmitting the input information to the server.

[0666] "Means of receiving and identifying" refers to the process by which the server receives the transmitted information and identifies the type of waste using generative AI and image analysis algorithms.

[0667] The "means for searching and determining sorting rules" refers to a process in which the server searches the database for waste sorting rules based on the residential area and determines an appropriate sorting method for the waste.

[0668] The "means for notifying" is an interface for notifying the user of the determined separation method.

[0669] "In-store guidance" refers to a system that allows customers to receive real-time information on how to sort used products in-store.

[0670] "Barcode scanning" is the process of reading the barcode of a product and obtaining details of the waste based on that information.

[0671] MODE FOR CARRYING OUT THE INVENTION

[0672] The present invention provides a specific embodiment of a waste sorting system, particularly for efficiently sorting used products in brick-and-mortar stores.

[0673] Overall system configuration

[0674] This system consists of the following elements:

[0675] 1. User Device:

[0676] Applications installed on devices such as smartphones and smart glasses.

[0677] It provides an interface for users to input waste name, image, and residential area information.

[0678] Ability to scan waste barcodes within the application.

[0679] 2. Server:

[0680] The received data is analyzed and the type of waste is identified using generative AI.

[0681] Based on the specified residential area, sorting rules are searched from a database to determine the appropriate sorting method.

[0682] Users will be notified of sorting methods and collection dates.

[0683] 3. Database:

[0684] It stores waste separation rules for each region.

[0685] Specifically, this includes sorting methods and collection schedules according to the type of waste.

[0686] Specific operation methods of the system

[0687] 1. Enter your information:

[0688] Users scan the barcode of used products using an application on their smartphone or smart glasses.

[0689] The user takes a picture of the waste and enters its name and area of ​​residence (such as city, ward, town, or village).

[0690] 2. Data transmission:

[0691] The entered information is sent to the server via the Internet using an HTTP request.

[0692] 3. Data reception and analysis:

[0693] The server receives the transmitted data and uses generative AI and image analysis algorithms to extract the characteristics of the waste and identify its type.

[0694] 4. Search and apply sorting rules:

[0695] The server searches the database for appropriate sorting rules based on the identified type of waste and the user's designated residential area.

[0696] For example, obtain information from the database that "PET bottles are collected as plastic waste every Wednesday."

[0697] 5. Notification of Results:

[0698] The server transmits the determined sorting method to the user terminal and notifies the user in real time.

[0699] For example, a message may be displayed saying, "Please separate plastic bottles as plastic waste. Collection day is every Wednesday."

[0700] Hardware and software used

[0701] Hardware: Smartphones, smart glasses

[0702] Software: Python 3, Requests library, Pillow library

[0703] Specific examples

[0704] If the user wants to know how to dispose of a "plastic bottle," they can use the following prompt:

[0705] How do I dispose of plastic bottles? I live in Shibuya Ward, Tokyo.

[0706] Based on this prompt, the generative AI model analyzes and generates specific disposal instructions, such as:

[0707] Please separate plastic bottles as plastic waste. Collection day is every Wednesday.

[0708] As described above, the system of the present invention allows users to easily and accurately separate waste, which will enable proper waste separation and promote eco-friendly activities in brick-and-mortar stores.

[0709] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0710] Step 1:

[0711] Enter information

[0712] Users launch an application on their smartphone or smart glasses and enter information about the waste.

[0713] The input information is threefold: "Name of waste," "Image of waste," and "Area of ​​residence (city, town, or village)."

[0714] If a barcode scanning function is used, the entered information also includes the identification information obtained by the barcode.

[0715] Input: waste name, image, residential area

[0716] Output: A set of information to be sent to the server

[0717] Step 2:

[0718] Data transmission

[0719] The terminal transmits the input information to the server via an HTTP request.

[0720] Specific operation: Using the terminal's communication function, the input data (name of waste, image, and residential area information) is sent to the server.

[0721] Input: Waste information entered by the user

[0722] Output: Request sent to the server

[0723] Step 3:

[0724] Data reception and analysis

[0725] The server receives the sent data, which is structured in JSON format.

[0726] The server analyzes the name, image, and residential area of ​​the waste from the received data and identifies the type of waste using a generative AI model.

[0727] How it works: The server uses analytical tools (e.g., Python and the Pillow library) to extract waste characteristics using image analysis algorithms, and then uses generative AI models (e.g., TensorFlow or PyTorch) to combine waste name and image information to identify the type of waste.

[0728] Input: JSON data received by the server (name, image, location)

[0729] Output: Identified waste types

[0730] Step 4:

[0731] Searching and applying sorting rules

[0732] The server searches the database for sorting rules based on the identified waste type and residential area.

[0733] The database includes regional waste sorting methods and corresponding collection dates.

[0734] Specific operation: The server uses an SQL query to search the database for the relevant sorting rules and determines the appropriate sorting method based on the identified waste type.

[0735] Input: Identified waste type, residential area

[0736] Output: Proper sorting method and collection schedule

[0737] Step 5:

[0738] Notification of results

[0739] The server transmits the determined sorting method and collection schedule to the user terminal.

[0740] The user terminal displays the received information on the user interface and notifies the user.

[0741] Specific operation: The server sends the sorting information as an HTTP response, and the device receives it and displays it on the screen. For example, the user receives a notification such as, "Please separate PET bottles as plastic waste. Collection day is every Wednesday."

[0742] Input: Proper sorting method and collection schedule

[0743] Output: Classification information notified to the user

[0744] These processes will enable proper waste sorting in physical stores efficiently, improving both customer service and environmental protection.

[0745] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0746] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. Below, we will explain how this system is implemented in concrete terms.

[0747] Overall system configuration

[0748] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[0749] System operation flow

[0750] 1. User Input

[0751] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[0752] Also, enter information about your area of ​​residence (city, town, village, etc.).

[0753] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[0754] 2. Data Transmission

[0755] The terminal combines the entered name, image, and residential area information into a single data packet.

[0756] The terminal sends this data packet as an HTTP request to a server via the Internet.

[0757] 3. Data reception and preparation for analysis

[0758] The server receives the HTTP request and parses the data in the request body.

[0759] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[0760] 4. Identifying the garbage

[0761] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0762] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0763] Where necessary, both name and image may be combined for more precise identification.

[0764] 5. Application of sorting rules

[0765] The server searches the database for the sorting rules for each residential area.

[0766] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[0767] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[0768] 6. Sending and notifying results

[0769] The server generates result data including the determined separation method.

[0770] The generated result data is sent to the user's terminal as an HTTP response.

[0771] 7. Displaying the results

[0772] The terminal receives the response data from the server.

[0773] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[0774] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0775] Examples of emotion engines

[0776] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[0777] In this way, this system allows users to easily and accurately separate their garbage, and with the help of the emotion engine, they can operate the system without stress. This will encourage thorough separation and lead to more efficient garbage disposal in the community.

[0778] The processing flow will be explained below.

[0779] Step 1: User Input

[0780] The user uses the terminal to launch the dedicated application.

[0781] The user enters the name of the waste material in text into the input form (e.g., "PET bottle").

[0782] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[0783] The user enters information about their area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture").

[0784] The emotion engine recognizes the user's emotions through voice input and facial expression analysis, and displays additional guidance and support as needed.

[0785] Step 2: Send data

[0786] The terminal combines the name of the waste, the image, the residential area information entered by the user, and the emotion data from the emotion engine into one data packet.

[0787] The terminal sends this data packet to a server over the Internet.

[0788] Step 3: Receiving data

[0789] The server receives the HTTP request and parses the data in the request body.

[0790] The data received by the server is divided into the waste name, image, residential area, and emotion data, and stored in the respective variables.

[0791] Step 4: Identify the debris

[0792] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0793] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0794] Where necessary, both name and image may be combined for more precise identification.

[0795] Step 5: Applying separation rules

[0796] The server searches the database for the sorting rules for each residential area.

[0797] The server matches the identified waste type (e.g., "PET bottle") with the sorting rules for the area in which the user lives and determines the appropriate sorting method.

[0798] The determined sorting method (e.g., "Plastic bottles should be disposed of as plastic waste every Wednesday") is generated as data.

[0799] Step 6: Sending the results

[0800] The server generates result data including the determined sorting method.

[0801] The server sends the result data to the user's terminal as an HTTP response.

[0802] Step 7: View the results

[0803] The terminal receives the response data from the server.

[0804] The device analyzes the response data and displays it on the screen in a user-friendly format (e.g., "Please dispose of PET bottles as plastic waste every Wednesday").

[0805] If the user feels stressed while operating the device, the emotion engine will recognize this and display appropriate support and guidance on the screen.

[0806] This series of steps allows users to separate their garbage simply and accurately without even thinking about it. Utilizing an emotion engine improves the user experience, allowing for stress-free input of information and garbage separation.

[0807] Example 2

[0808] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0809] In recent years, proper waste sorting has required users to easily understand how to sort waste and to perform the task appropriately. However, in many cases, users lack understanding of the types of waste and the sorting rules, resulting in incorrect sorting. Furthermore, users may feel stressed during the sorting process, which can reduce the accuracy and efficiency of sorting. Therefore, there is a need for a system that supports users in sorting their waste easily and accurately.

[0810] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0811] In this invention, the server includes means for allowing a user to input the name, image, and residential area information of the waste, means for transmitting the input information, and means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, thereby enabling the user to easily identify the type of waste and understand the appropriate sorting method.

[0812] "User" refers to the person who operates the terminal to input the name, image, and residential area information of the waste.

[0813] "Terminal" refers to an information processing device such as a smartphone or computer on which a user inputs and sends information about waste.

[0814] "Server" refers to a central information processing device that receives and analyzes information sent by users and determines the appropriate method of sorting.

[0815] The "emotion engine" refers to a component that analyzes the user's voice, text, and facial expressions to recognize the user's emotions.

[0816] A "data packet" refers to a unit of digital data that compiles user input information.

[0817] An "HTTP request" refers to an information request, which is a form of communication protocol sent from a user's terminal to a server.

[0818] "Image analysis algorithm" refers to a computational procedure for processing waste image data and extracting waste characteristics.

[0819] "Waste identification" refers to the process of determining what waste is based on the input name and analyzed image.

[0820] "Sorting rules" refer to regulations that stipulate the proper method of sorting waste in a particular area.

[0821] "HTTP response" refers to response data, which is a form of communication protocol sent from a server to a user's terminal.

[0822] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. The system identifies the type of waste based on information entered by the user and provides instructions on the appropriate sorting method based on the sorting rules for each residential area. Furthermore, it improves the user experience by recognizing the user's emotions and providing an interface that responds to them.

[0823] System configuration

[0824] The system consists of the following main components:

[0825] A device (smartphone or PC) with an interface that allows users to input the name of the garbage, an image, and information about their residential area

[0826] A server that receives and analyzes input information

[0827] Database that stores regional sorting rules

[0828] A means of informing users of the analysis results and classification methods

[0829] An emotion engine that recognizes user emotions and adjusts the interface

[0830] Hardware and Software

[0831] Terminal: The user inputs the name of the waste, takes a picture of the waste with a camera, and inputs information about the area where they live. This is done using a smartphone or computer.

[0832] Server: Receives and analyzes input information. The server is equipped with a generative AI model (e.g., OpenAI) and an emotion engine.

[0833] Database: Stores sorting rules for each residential area.

[0834] Emotion Engine: Recognizes user emotions from voice, text, and facial expressions.

[0835] Example of a system

[0836] 1. User Input

[0837] The user launches the application on their smartphone and enters the name of the waste item: "PET bottle." They then take a picture of the PET bottle with their device's camera and upload it within the app. Next, they enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). While they are entering information, the emotion engine analyzes the user's tone of voice and facial expression, and if it detects stress, it displays additional guidance such as, "If you don't know how to operate it, please see this guide."

[0838] 2. Data Transmission

[0839] Once the user has completed the input, the device assembles the name "PET bottle," the image of the PET bottle, and the residential area "Shinjuku Ward, Tokyo" into a single data packet and sends it to the server as an HTTP request.

[0840] 3. Data reception and preparation for analysis

[0841] The server analyzes the received HTTP request and extracts the name, image, and residential area information from the request body. For example, the name "PET bottle," the image data, and the residential area "Shinjuku-ku, Tokyo" are stored in individual variables.

[0842] 4. Identifying the garbage

[0843] The server uses a generative AI model (for example, OpenAI's image analysis model) to analyze the received image of the plastic bottle. As a result, the generative AI determines that "this is a plastic bottle" and identifies it by matching the name and characteristics of the waste.

[0844] 5. Application of sorting rules

[0845] The server searches the database for the sorting rules for the area where the user lives, "Shinjuku Ward, Tokyo." It then applies the rule that "PET bottles" are classified as "plastic waste" and are collected every Wednesday.

[0846] 6. Sending and notifying results

[0847] The server generates result data including the determined sorting method;

[0848] "Plastic bottles should be disposed of as plastic waste every Wednesday."

[0849] This is sent to the user's device as an HTTP response.

[0850] 7. Displaying the results

[0851] The device receives the response data from the server, analyzes it, and displays it on the screen. When the user looks at the screen, they can see specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0852] Prompt Sentence Examples

[0853] "Please identify what this waste is based on the name and image of the waste."

[0854] "Please tell me the proper way to separate the identified waste based on the waste separation rules in your area."

[0855] This allows users to easily separate their waste properly, and with the help of an emotion engine, the operation can be performed smoothly, improving the user experience and making local waste management more effective.

[0856] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0857] Step 1:

[0858] The user uses their device to launch the application. They enter the name of the waste in the input form, take a picture of the waste using the camera, and upload it. They also enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). The emotion engine recognizes emotions from voice, text, and facial expressions, and provides additional guidance and support if the user is feeling stressed.

[0859] Input: waste name, image, residential area information, user emotion data.

[0860] Output: Waste information and emotion data organized as data packets.

[0861] Step 2:

[0862] The terminal combines the inputted waste name, image, and residential area information into a single data packet, which is then sent to the server as an HTTP request.

[0863] Input: Waste name, image, and residential area information from the user.

[0864] Output: Data packets sent to the server.

[0865] Step 3:

[0866] The server receives the HTTP request, analyzes the received data, and stores it in variables for the waste name, image, and residential area information.

[0867] Input: Data packets sent from the terminal.

[0868] Output: Name of waste, image, and residential area information stored in separate variables.

[0869] Step 4:

[0870] The server's generation AI analyzes the uploaded image and extracts the characteristics of the waste from the image. Then, it uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is. For example, it uses an image analysis algorithm to extract the characteristics of a plastic bottle and identify it as a plastic bottle.

[0871] Input: Name of waste, image.

[0872] Output: Waste identification result (e.g. "PET bottles").

[0873] Step 5:

[0874] The server searches for the sorting rules for the residential area (e.g., Shinjuku Ward, Tokyo) stored in a database. It matches the identified waste type (e.g., PET bottles) with the sorting rules for the residential area to determine the appropriate sorting method. For example, it determines that "PET bottles are classified as plastic waste and are collected every Wednesday."

[0875] Input: Waste identification results, sorting rules for your area.

[0876] Output: The optimal sorting method (e.g. "PET bottles are collected as plastic waste every Wednesday").

[0877] Step 6:

[0878] The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[0879] Input: Optimal separation method.

[0880] Output: The result data sent to the user's device (e.g., "Please put out plastic bottles as plastic waste every Wednesday").

[0881] Step 7:

[0882] The device receives the response data from the server. The response data is analyzed and displayed on the screen in a format that is easy for the user to understand. For example, the data may be read and displayed on the UI as "Please dispose of PET bottles as plastic waste every Wednesday."

[0883] Input: Response data from the server.

[0884] Output: A display of the sorting method that can be checked by the user.

[0885] (Application example 2)

[0886] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0887] In conventional waste sorting systems, it is difficult for users to obtain instructions on how to sort waste accurately, and this can often be stressful for users, leading to inappropriate sorting. As a result, improper waste sorting can cause environmental problems and reduce recycling efficiency.

[0888] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for analyzing the user's emotions using emotion recognition means and providing guidance and support to reduce the user's stress. This enables the user to sort waste accurately and without stress.

[0889] "User" refers to the entity that uses the system to receive instructions on how to separate waste.

[0890] "Waste" refers to items that a user wishes to discard and that require proper separation.

[0891] "Name" refers to text information entered by the user to identify the waste.

[0892] "Image" refers to photographic data uploaded by users to extract visual information about waste.

[0893] "Residential area" is area information such as the city or town to which the user belongs, and is information that affects the sorting rules for that area.

[0894] "Means for input" refers to an interface for collecting information from a user.

[0895] "Means for sending" refers to the technical means for sending the information entered by the user to the server.

[0896] "Means for receiving and analyzing" refers to the technical means by which the server receives information sent from the user and performs processing to analyze its contents.

[0897] "Means of identification" means the technical means for clearly identifying the type of waste based on the information received.

[0898] "Means for searching and determining sorting rules" refers to technical methods for searching for and determining appropriate waste sorting methods based on information about the residential area.

[0899] "Means of notification" refers to a technical method for communicating the determined separation method to the user.

[0900] "Emotion recognition means" refers to technology that analyzes the user's emotions and provides appropriate support to reduce stress.

[0901] "Means for providing guidance and support" refers to technical methods for providing additional explanations or assistance depending on the user's emotional state.

[0902] This invention is a system that supports proper waste sorting by combining a generative AI model and an emotion engine. Below, we will explain how to specifically implement this system.

[0903] Overall system configuration

[0904] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[0905] System operation flow

[0906] 1. User Input

[0907] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[0908] Also, enter information about your area of ​​residence (city, town, village, etc.).

[0909] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[0910] 2. Data Transmission

[0911] The terminal combines the entered name, image, and residential area information into a single data packet.

[0912] The terminal sends this data packet as an HTTP request to a server via the Internet.

[0913] 3. Data reception and preparation for analysis

[0914] The server receives the HTTP request and parses the data in the request body.

[0915] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[0916] 4. Identifying the garbage

[0917] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[0918] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[0919] Where necessary, both name and image may be combined for more precise identification.

[0920] 5. Application of sorting rules

[0921] The server searches the database for the sorting rules for each residential area.

[0922] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[0923] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[0924] 6. Sending and notifying results

[0925] The server generates result data including the determined separation method.

[0926] The generated result data is sent to the user's terminal as an HTTP response.

[0927] 7. Displaying the results

[0928] The terminal receives the response data from the server.

[0929] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[0930] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[0931] Examples of emotion engines

[0932] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[0933] Hardware and software used

[0934] This system uses the following hardware and software:

[0935] User interface: Smartphone or tablet

[0936] Server: Built using the Frust framework

[0937] Generative AI model: built with TensorFlow

[0938] Emotion Recognition Software: Uses Emotion Recognition API

[0939] Examples of concrete examples and prompts

[0940] For example, when a user scans the label of a plastic bottle purchased at the supermarket, the app analyzes the image. If the emotion engine detects stress, it provides guidance such as, "Please throw this plastic bottle in the recycling bin. The recycling bin is located at the entrance of the store."

[0941] Example prompt sentence:

[0942] "A user uploaded an image of a plastic bottle from their smartphone. The emotion engine detected stress. Please provide appropriate waste sorting instructions."

[0943] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0944] Step 1:

[0945] Input: The user inputs the name of the waste, an image, and information about the area of ​​residence.

[0946] Processing: The user launches the application using a device such as a smartphone or PC. They enter the name of the waste into the input form within the application, take a picture of the waste using the camera function, and upload it. They also enter their residential area.

[0947] Output: The entered name, image, and residential area are stored as data on the device.

[0948] Step 2:

[0949] Input: Data collected in Step 1 (name, image, location)

[0950] Processing: The device assembles the entered name, picture, and location information into a single data packet.

[0951] Output: A data packet is generated.

[0952] Step 3:

[0953] Input: Data packet generated in step 2

[0954] Processing: The device sends the data packet as an HTTP request to the server over the Internet.

[0955] Output: The server receives the data packet.

[0956] Step 4:

[0957] Input: Data packet received by the server

[0958] Processing: The server receives the HTTP request and analyzes the data in the request body. The server divides the data into the waste name, image, and residential area and stores them in variables.

[0959] Output: Name, image, and location stored in separate variables.

[0960] Step 5:

[0961] Input: waste image and waste name

[0962] Processing: The server's generation AI analyzes the uploaded image and extracts the waste characteristics from the image. The generation AI uses TensorFlow to perform image analysis. By combining both the name and the image, the type of waste is identified based on the algorithm.

[0963] Output: The type of waste is identified (e.g., "PET bottle").

[0964] Step 6:

[0965] Input: Waste type and residential area identified in step 5

[0966] Processing: The server searches the database for the sorting rules for each residential area. The server matches the identified waste type with the local sorting rules and determines the appropriate sorting method.

[0967] Output: The sorting method is determined (e.g., "Plastic bottles are classified as plastic waste").

[0968] Step 7:

[0969] Input: The classification method determined in step 6 and the user's emotion data

[0970] Processing: The server uses the emotion recognition API to analyze the user's emotions and generate guidance and support according to their emotional state. If the emotion engine detects stress, it applies additional guidance and support.

[0971] Output: The final classification method and emotional support guide are generated.

[0972] Step 8:

[0973] Enter: The Ultimate Sort-Out and Emotional Support Guide

[0974] Processing: The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[0975] Output: The response with the result data is sent to the user's device.

[0976] Step 9:

[0977] Input: Response data from the server

[0978] Processing: The device analyzes the response data received from the server and displays it on the screen in a format that is easy for the user to understand. The device also notifies the user of the sorting method and additional guidance.

[0979] Output: The user looks at the device screen and sees specific instructions and guidance for sorting waste (e.g., "Plastic bottles should be put out as plastic waste every Wednesday").

[0980] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0981] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0982] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0983] [Third embodiment]

[0984] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0985] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0986] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0987] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0988] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0989] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0990] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0991] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0992] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0993] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0994] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0995] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0996] The present invention is a system that uses generative AI to support proper waste sorting. Below, we will explain how to implement this system in concrete terms.

[0997] Overall system configuration

[0998] This system is composed of a terminal with an interface where users can input the name of the waste, an image, and information about their residential area, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. As a specific embodiment, we will describe how each of these elements works together.

[0999] System operation flow

[1000] 1. User Input

[1001] The user starts the application using a device such as a smartphone or PC, enters the name of the waste into the input form within the application, and takes a picture of the waste using the camera function.

[1002] Also, enter your area of ​​residence (city, town, village, etc.).

[1003] 2. Data Transmission

[1004] The device sends the entered name, image, and location information to the server via an HTTP request over the internet connection.

[1005] 3. Data reception and preparation for analysis

[1006] The server receives the input data, parses it as structured data in JSON format, and separates it into the name of the garbage, an image, and the residential area.

[1007] 4. Identifying the garbage

[1008] The server uses generative AI to analyze the received images and names, and identifies the type of waste using a specific algorithm. Image analysis algorithms extract waste characteristics from the uploaded images.

[1009] By combining both the name of the waste and image information, the type of waste can be identified more accurately.

[1010] 5. Application of sorting rules

[1011] The server accesses the database and searches for the waste separation rules for the specified area, which include waste types, corresponding separation methods, collection dates, etc.

[1012] The identified types of waste are compared with local sorting rules to determine the appropriate sorting method.

[1013] 6. Sending and notifying results

[1014] The server then sends the analysis results and the determined sorting method to the user's device, including specific instructions such as the waste type, sorting method, and collection date.

[1015] The results are displayed on the user's terminal, and the user can confirm the appropriate sorting method and sort the waste accordingly.

[1016] Specific examples

[1017] For example, let's consider the case where a user uses this system to process a "PET bottle" that they plan to discard at home. The user takes a picture of the PET bottle with their smartphone and enters "PET bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area. This information is sent to the server, which uses generative AI to identify it as a "PET bottle." Furthermore, the server retrieves the sorting rules for the residential area from a database and determines that the PET bottle should be collected as "plastic waste" every Wednesday. This information is notified to the user, and instructions to put it out as "plastic waste" on Wednesday are displayed on the screen.

[1018] In this way, this system allows users to easily and accurately separate their garbage, which promotes proper garbage disposal in accordance with local garbage separation rules.

[1019] The processing flow will be explained below.

[1020] Step 1: User Input

[1021] The user uses the terminal to launch the dedicated application.

[1022] The user enters the name of the waste material into the input form (e.g., "PET bottle").

[1023] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[1024] Enter your area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture") and click the submit button.

[1025] Step 2: Send data

[1026] The terminal compiles the name of the waste, an image, and information on the area of ​​residence entered by the user into a single data packet.

[1027] The terminal sends this data packet as an HTTP request to a server via the Internet.

[1028] Step 3: Receiving data

[1029] The server receives the HTTP request and parses the data in the request body.

[1030] The server divides the received data into the name of the waste, image, and residential area, and stores them in the respective variables.

[1031] Step 4: Identify the debris

[1032] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1033] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1034] Where necessary, both name and image may be combined for more precise identification.

[1035] Step 5: Applying separation rules

[1036] The server searches the database for the sorting rules for each residential area.

[1037] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area to determine the appropriate sorting method.

[1038] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[1039] Step 6: Sending the results

[1040] The server generates result data including the determined separation method.

[1041] The server sends the result data to the user's terminal as an HTTP response.

[1042] Step 7: View the results

[1043] The terminal receives the response data from the server.

[1044] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[1045] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1046] Example 1

[1047] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1048] Conventional waste sorting systems required users to manually determine the type of waste and look up the appropriate sorting method. This made sorting cumbersome and prone to mistakes, making it difficult to properly dispose of waste in accordance with local sorting rules. Furthermore, the criteria for identifying waste types were vague, leading to confusion. There is a need to provide a system that can solve these issues and support users in easily and accurately sorting waste.

[1049] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1050] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for analyzing the image and name of the waste using a generative AI model, means for searching a database for the sorting rules for the specified residential area, and means for notifying the user of the determined sorting method. This enables the user to accurately identify the type of waste and easily carry out appropriate waste disposal in accordance with the sorting rules for each area.

[1051] "User" refers to a person who uses the system to separate waste.

[1052] "Waste" refers to items that a user is disposing of.

[1053] "Name" refers to text information entered by the user to identify the waste.

[1054] "Image" refers to a photograph taken by a user using the device's camera to identify waste.

[1055] "Residence area" refers to the administrative division where a user lives.

[1056] "Input means" refers to an interface for a user to input information about the name, image, and residential area of ​​the waste into the system.

[1057] "Transmission means" refers to a function for transmitting information entered by the user to the server.

[1058] "Receiving means" refers to a function that allows the server to receive information sent from the user.

[1059] "Analysis means" refers to the function of analyzing the name and image of the waste received by the server and identifying the type of waste.

[1060] "Generative AI model" refers to an algorithm that uses artificial intelligence technology to analyze received images and names and determine the characteristics of the waste.

[1061] The "database" refers to a collection of information that stores sorting rules for each residential area.

[1062] "Search means" refers to the function for searching the database for waste separation rules for a specified residential area.

[1063] "Notification means" refers to a function for notifying the user of the sorting method determined by the server.

[1064] "Sorting rules" refers to information on the appropriate sorting methods and collection dates for each type of waste in each residential area.

[1065] "Analysis preparation" refers to the process of parsing the received data and dividing it into waste names, images, and residential areas.

[1066] MODE FOR CARRYING OUT THE INVENTION

[1067] The present invention is a system that uses generative AI to support proper waste sorting. This system consists of a terminal with an interface for users to input the name, image, and residential area information of the waste, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. Specific embodiments are described below.

[1068] Hardware and software used

[1069] 1. Device:

[1070] It is used by users to enter information and upload images. Examples include smartphones and personal computers.

[1071] The application includes a photo capture function and an input form.

[1072] 2. Server:

[1073] The program receives and analyzes information and is implemented using Python.

[1074] The TensorFlow library is used for image analysis, and a generative AI model is applied.

[1075] The server also queries the database with the analysis results to retrieve the classification rules.

[1076] 3. Database:

[1077] Sorting rules for each region are stored using a database such as MySQL.

[1078] System Operation Overview

[1079] User input:

[1080] Users launch the application using their smartphone or computer, enter the name of the waste in the input form within the application, take a picture of the waste using the device's camera, and select their area of ​​residence.

[1081] Data transmission:

[1082] The device sends the entered name, image, and location information to a server over its internet connection via an HTTP POST request.

[1083] Data reception and preparation for analysis:

[1084] The server receives an HTTP request, parses the received data in JSON format, and separates it into the waste name, image, and residential area information.

[1085] Garbage Identification:

[1086] The server uses a generative AI model to analyze the received images and names to identify the type of waste. This process is done using the TensorFlow library. The waste characteristics are extracted and analyzed along with the name.

[1087] Separation rules apply:

[1088] The server accesses the database and searches for the waste separation rules for the specified residential area. The rules include waste types, corresponding separation methods, collection dates, etc. The identified waste type is compared with the local separation rules to determine the appropriate separation method.

[1089] Sending and notifying results:

[1090] The server sends the analysis results and the determined sorting method to the user's device in JSON format. The user can then use the application to check specific information such as the type of waste, sorting method, and collection date.

[1091] Specific examples

[1092] For example, let's say a user wants to throw away a "plastic bottle." The user takes a picture of the plastic bottle with their smartphone at home, and then enters "plastic bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area in the application. This information is sent to the server.

[1093] The server uses a generative AI to identify the item as a "plastic bottle" using the following prompt:

[1094] "Please identify what kind of garbage is in this image and tell me the proper way to separate it in Naka Ward, Yokohama City, Kanagawa Prefecture."

[1095] The server then retrieves the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture from the database and determines that the PET bottles should be collected as "plastic waste" every Wednesday. The result is notified to the user, and specific instructions are displayed on the application screen.

[1096] In this way, users can accurately separate their garbage and dispose of it properly in accordance with local rules.

[1097] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1098] System program processing flow

[1099] Step 1:

[1100] Input: The user launches the application on the terminal and inputs the name of the waste, an image, and information about the area of ​​residence.

[1101] Specific operation: The user enters "PET bottle" into the application's input form, takes a picture of the PET bottle with the smartphone camera, and selects "Naka-ku, Yokohama City, Kanagawa Prefecture" as their area of ​​residence.

[1102] Output: The system temporarily stores the name entered by the user, the image taken, and the residential area information.

[1103] Step 2:

[1104] Input: The user presses a "Submit" button within the application.

[1105] Specific operation: The terminal collects the input information, converts it into JSON format, and sends it to the server via an HTTP POST request.

[1106] Output: The entered name, image, and area information are sent to the server.

[1107] Step 3:

[1108] Input: Parse the HTTP request received by the server.

[1109] Specific operation: The server parses the received data in JSON format and divides it into the waste name, image, and residential area information.

[1110] Output: The server stores the received data internally as name, image, and residential area information.

[1111] Step 4:

[1112] Input: The server receives the name and image of the waste and feeds it to the generative AI model.

[1113] Specific operation: The server uses the TensorFlow library to drive a generative AI model to analyze the image and its name. The prompt text is "Please identify what kind of garbage is in this image and tell me the appropriate method for sorting it in Naka-ku, Yokohama City, Kanagawa Prefecture."

[1114] Output: Obtain the determination result that it is a "plastic bottle" from the generative AI model.

[1115] Step 5:

[1116] Input: The server searches the database for sorting rules based on the type of waste and residential area information identified.

[1117] What it does: The server queries the MySQL database to get the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture, specifically, whether "PET bottles" are considered "plastic waste" and the collection schedule.

[1118] Output: The rule information retrieved from the database is sent back to the server.

[1119] Step 6:

[1120] Input: The sorting rules obtained by the server and the garbage judgment results of the generating AI.

[1121] Specific operation: The server integrates this information and determines the appropriate sorting method and collection date, while also compiling the analysis results and specific instructions on how to sort the waste.

[1122] Output: Information about the determined sorting method and collection date is prepared in JSON format.

[1123] Step 7:

[1124] Input: JSON data prepared by the server to notify the user.

[1125] Specific operation: The server sends the result to the user's device as an HTTP response.

[1126] Output: The user terminal receives the analysis results along with information on sorting methods and collection schedules.

[1127] Step 8:

[1128] Input: Analysis results and classification methods displayed on the user's device.

[1129] Specific operation: The user confirms the instruction on the application screen that "PET bottles should be disposed of as plastic waste every Wednesday."

[1130] Output: The user follows the correct sorting method and puts out the garbage on the specified date.

[1131] This allows users to accurately separate their garbage and dispose of it properly in accordance with local rules.

[1132] (Application example 1)

[1133] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1134] While existing waste sorting systems focus on encouraging users to properly sort their waste at home, they do not address the disposal methods of used products in brick-and-mortar stores. Furthermore, the lack of effective methods to promote proper waste sorting within stores results in insufficient customer service and environmental protection activities.

[1135] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1136] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for providing real-time in-store guidance to customers on how to dispose of used products. This enables efficient proper waste sorting in physical stores, improving both customer service and environmental protection.

[1137] "User" means an individual or organization that uses the waste sorting system.

[1138] "Waste" refers to items or materials that are no longer needed and are discarded, and require appropriate disposal methods.

[1139] "Name" is a character string entered by the user to identify the waste.

[1140] "Images" are visual data obtained by photographing the appearance of waste with a camera.

[1141] The "residential area" refers to the area such as a city, town, or village where the user lives, and the rules for separating garbage vary from area to area.

[1142] The "means for inputting" is an interface that allows a user to input the name, image, and residential area information of the waste into the system.

[1143] The "transmitting means" refers to a communication technology for transmitting the input information to the server.

[1144] "Means of receiving and identifying" refers to the process by which the server receives the transmitted information and identifies the type of waste using generative AI and image analysis algorithms.

[1145] The "means for searching and determining sorting rules" refers to a process in which the server searches the database for waste sorting rules based on the residential area and determines an appropriate sorting method for the waste.

[1146] The "means for notifying" is an interface for notifying the user of the determined separation method.

[1147] "In-store guidance" refers to a system that allows customers to receive real-time information on how to sort used products in-store.

[1148] "Barcode scanning" is the process of reading the barcode of a product and obtaining details of the waste based on that information.

[1149] MODE FOR CARRYING OUT THE INVENTION

[1150] The present invention provides a specific embodiment of a waste sorting system, particularly for efficiently sorting used products in brick-and-mortar stores.

[1151] Overall system configuration

[1152] This system consists of the following elements:

[1153] 1. User Device:

[1154] Applications installed on devices such as smartphones and smart glasses.

[1155] It provides an interface for users to input waste name, image, and residential area information.

[1156] Ability to scan waste barcodes within the application.

[1157] 2. Server:

[1158] The received data is analyzed and the type of waste is identified using generative AI.

[1159] Based on the specified residential area, sorting rules are searched from a database to determine the appropriate sorting method.

[1160] Users will be notified of sorting methods and collection dates.

[1161] 3. Database:

[1162] It stores waste separation rules for each region.

[1163] Specifically, this includes sorting methods and collection schedules according to the type of waste.

[1164] Specific operation methods of the system

[1165] 1. Enter your information:

[1166] Users scan the barcode of used products using an application on their smartphone or smart glasses.

[1167] The user takes a picture of the waste and enters its name and area of ​​residence (such as city, ward, town, or village).

[1168] 2. Data transmission:

[1169] The entered information is sent to the server via the Internet using an HTTP request.

[1170] 3. Data reception and analysis:

[1171] The server receives the transmitted data and uses generative AI and image analysis algorithms to extract the characteristics of the waste and identify its type.

[1172] 4. Search and apply sorting rules:

[1173] The server searches the database for appropriate sorting rules based on the identified type of waste and the user's designated residential area.

[1174] For example, obtain information from the database that "PET bottles are collected as plastic waste every Wednesday."

[1175] 5. Notification of Results:

[1176] The server transmits the determined sorting method to the user terminal and notifies the user in real time.

[1177] For example, a message may be displayed saying, "Please separate plastic bottles as plastic waste. Collection day is every Wednesday."

[1178] Hardware and software used

[1179] Hardware: Smartphones, smart glasses

[1180] Software: Python 3, Requests library, Pillow library

[1181] Specific examples

[1182] If the user wants to know how to dispose of a "plastic bottle," they can use the following prompt:

[1183] How do I dispose of plastic bottles? I live in Shibuya Ward, Tokyo.

[1184] Based on this prompt, the generative AI model analyzes and generates specific disposal instructions, such as:

[1185] Please separate plastic bottles as plastic waste. Collection day is every Wednesday.

[1186] As described above, the system of the present invention allows users to easily and accurately separate waste, which will enable proper waste separation and promote eco-friendly activities in brick-and-mortar stores.

[1187] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1188] Step 1:

[1189] Enter information

[1190] Users launch an application on their smartphone or smart glasses and enter information about the waste.

[1191] The input information is threefold: "Name of waste," "Image of waste," and "Area of ​​residence (city, town, or village)."

[1192] If a barcode scanning function is used, the entered information also includes the identification information obtained by the barcode.

[1193] Input: waste name, image, residential area

[1194] Output: A set of information to be sent to the server

[1195] Step 2:

[1196] Data transmission

[1197] The terminal transmits the input information to the server via an HTTP request.

[1198] Specific operation: Using the terminal's communication function, the input data (name of waste, image, and residential area information) is sent to the server.

[1199] Input: Waste information entered by the user

[1200] Output: Request sent to the server

[1201] Step 3:

[1202] Data reception and analysis

[1203] The server receives the sent data, which is structured in JSON format.

[1204] The server analyzes the name, image, and residential area of ​​the waste from the received data and identifies the type of waste using a generative AI model.

[1205] How it works: The server uses analytical tools (e.g., Python and the Pillow library) to extract waste characteristics using image analysis algorithms, and then uses generative AI models (e.g., TensorFlow or PyTorch) to combine waste name and image information to identify the type of waste.

[1206] Input: JSON data received by the server (name, image, location)

[1207] Output: Identified waste types

[1208] Step 4:

[1209] Searching and applying sorting rules

[1210] The server searches the database for sorting rules based on the identified waste type and residential area.

[1211] The database includes regional waste sorting methods and corresponding collection dates.

[1212] Specific operation: The server uses an SQL query to search the database for the relevant sorting rules and determines the appropriate sorting method based on the identified waste type.

[1213] Input: Identified waste type, residential area

[1214] Output: Proper sorting method and collection schedule

[1215] Step 5:

[1216] Notification of results

[1217] The server transmits the determined sorting method and collection schedule to the user terminal.

[1218] The user terminal displays the received information on the user interface and notifies the user.

[1219] Specific operation: The server sends the sorting information as an HTTP response, and the device receives it and displays it on the screen. For example, the user receives a notification such as, "Please separate PET bottles as plastic waste. Collection day is every Wednesday."

[1220] Input: Proper sorting method and collection schedule

[1221] Output: Classification information notified to the user

[1222] These processes will enable proper waste sorting in physical stores efficiently, improving both customer service and environmental protection.

[1223] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1224] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. Below, we will explain how this system is implemented in concrete terms.

[1225] Overall system configuration

[1226] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[1227] System operation flow

[1228] 1. User Input

[1229] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[1230] Also, enter information about your area of ​​residence (city, town, village, etc.).

[1231] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[1232] 2. Data Transmission

[1233] The terminal combines the entered name, image, and residential area information into a single data packet.

[1234] The terminal sends this data packet as an HTTP request to a server via the Internet.

[1235] 3. Data reception and preparation for analysis

[1236] The server receives the HTTP request and parses the data in the request body.

[1237] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[1238] 4. Identifying the garbage

[1239] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1240] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1241] Where necessary, both name and image may be combined for more precise identification.

[1242] 5. Application of sorting rules

[1243] The server searches the database for the sorting rules for each residential area.

[1244] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[1245] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[1246] 6. Sending and notifying results

[1247] The server generates result data including the determined separation method.

[1248] The generated result data is sent to the user's terminal as an HTTP response.

[1249] 7. Displaying the results

[1250] The terminal receives the response data from the server.

[1251] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[1252] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1253] Examples of emotion engines

[1254] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[1255] In this way, this system allows users to easily and accurately separate their garbage, and with the help of the emotion engine, they can operate the system without stress. This will encourage thorough separation and lead to more efficient garbage disposal in the community.

[1256] The processing flow will be explained below.

[1257] Step 1: User Input

[1258] The user uses the terminal to launch the dedicated application.

[1259] The user enters the name of the waste material in text into the input form (e.g., "PET bottle").

[1260] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[1261] The user enters information about their area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture").

[1262] The emotion engine recognizes the user's emotions through voice input and facial expression analysis, and displays additional guidance and support as needed.

[1263] Step 2: Send data

[1264] The terminal combines the name of the waste, the image, the residential area information entered by the user, and the emotion data from the emotion engine into one data packet.

[1265] The terminal sends this data packet to a server over the Internet.

[1266] Step 3: Receiving data

[1267] The server receives the HTTP request and parses the data in the request body.

[1268] The data received by the server is divided into the waste name, image, residential area, and emotion data, and stored in the respective variables.

[1269] Step 4: Identify the debris

[1270] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1271] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1272] Where necessary, both name and image may be combined for more precise identification.

[1273] Step 5: Applying separation rules

[1274] The server searches the database for the sorting rules for each residential area.

[1275] The server matches the identified waste type (e.g., "PET bottle") with the sorting rules for the area in which the user lives and determines the appropriate sorting method.

[1276] The determined sorting method (e.g., "Plastic bottles should be disposed of as plastic waste every Wednesday") is generated as data.

[1277] Step 6: Sending the results

[1278] The server generates result data including the determined sorting method.

[1279] The server sends the result data to the user's terminal as an HTTP response.

[1280] Step 7: View the results

[1281] The terminal receives the response data from the server.

[1282] The device analyzes the response data and displays it on the screen in a user-friendly format (e.g., "Please dispose of PET bottles as plastic waste every Wednesday").

[1283] If the user feels stressed while operating the device, the emotion engine will recognize this and display appropriate support and guidance on the screen.

[1284] This series of steps allows users to separate their garbage simply and accurately without even thinking about it. Utilizing an emotion engine improves the user experience, allowing for stress-free input of information and garbage separation.

[1285] Example 2

[1286] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1287] In recent years, proper waste sorting has required users to easily understand how to sort waste and to perform the task appropriately. However, in many cases, users lack understanding of the types of waste and the sorting rules, resulting in incorrect sorting. Furthermore, users may feel stressed during the sorting process, which can reduce the accuracy and efficiency of sorting. Therefore, there is a need for a system that supports users in sorting their waste easily and accurately.

[1288] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1289] In this invention, the server includes means for allowing a user to input the name, image, and residential area information of the waste, means for transmitting the input information, and means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, thereby enabling the user to easily identify the type of waste and understand the appropriate sorting method.

[1290] "User" refers to the person who operates the terminal to input the name, image, and residential area information of the waste.

[1291] "Terminal" refers to an information processing device such as a smartphone or computer on which a user inputs and sends information about waste.

[1292] "Server" refers to a central information processing device that receives and analyzes information sent by users and determines the appropriate method of sorting.

[1293] The "emotion engine" refers to a component that analyzes the user's voice, text, and facial expressions to recognize the user's emotions.

[1294] A "data packet" refers to a unit of digital data that compiles user input information.

[1295] An "HTTP request" refers to an information request, which is a form of communication protocol sent from a user's terminal to a server.

[1296] "Image analysis algorithm" refers to a computational procedure for processing waste image data and extracting waste characteristics.

[1297] "Waste identification" refers to the process of determining what waste is based on the input name and analyzed image.

[1298] "Sorting rules" refer to regulations that stipulate the proper method of sorting waste in a particular area.

[1299] "HTTP response" refers to response data, which is a form of communication protocol sent from a server to a user's terminal.

[1300] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. The system identifies the type of waste based on information entered by the user and provides instructions on the appropriate sorting method based on the sorting rules for each residential area. Furthermore, it improves the user experience by recognizing the user's emotions and providing an interface that responds to them.

[1301] System configuration

[1302] The system consists of the following main components:

[1303] A device (smartphone or PC) with an interface that allows users to input the name of the garbage, an image, and information about their residential area

[1304] A server that receives and analyzes input information

[1305] Database that stores regional sorting rules

[1306] A means of informing users of the analysis results and classification methods

[1307] An emotion engine that recognizes user emotions and adjusts the interface

[1308] Hardware and Software

[1309] Terminal: The user inputs the name of the waste, takes a picture of the waste with a camera, and inputs information about the area where they live. This is done using a smartphone or computer.

[1310] Server: Receives and analyzes input information. The server is equipped with a generative AI model (e.g., OpenAI) and an emotion engine.

[1311] Database: Stores sorting rules for each residential area.

[1312] Emotion Engine: Recognizes user emotions from voice, text, and facial expressions.

[1313] Example of a system

[1314] 1. User Input

[1315] The user launches the application on their smartphone and enters the name of the waste item: "PET bottle." They then take a picture of the PET bottle with their device's camera and upload it within the app. Next, they enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). While they are entering information, the emotion engine analyzes the user's tone of voice and facial expression, and if it detects stress, it displays additional guidance such as, "If you don't know how to operate it, please see this guide."

[1316] 2. Data Transmission

[1317] Once the user has completed the input, the device assembles the name "PET bottle," the image of the PET bottle, and the residential area "Shinjuku Ward, Tokyo" into a single data packet and sends it to the server as an HTTP request.

[1318] 3. Data reception and preparation for analysis

[1319] The server analyzes the received HTTP request and extracts the name, image, and residential area information from the request body. For example, the name "PET bottle," the image data, and the residential area "Shinjuku-ku, Tokyo" are stored in individual variables.

[1320] 4. Identifying the garbage

[1321] The server uses a generative AI model (for example, OpenAI's image analysis model) to analyze the received image of the plastic bottle. As a result, the generative AI determines that "this is a plastic bottle" and identifies it by matching the name and characteristics of the waste.

[1322] 5. Application of sorting rules

[1323] The server searches the database for the sorting rules for the area where the user lives, "Shinjuku Ward, Tokyo." It then applies the rule that "PET bottles" are classified as "plastic waste" and are collected every Wednesday.

[1324] 6. Sending and notifying results

[1325] The server generates result data including the determined sorting method;

[1326] "Plastic bottles should be disposed of as plastic waste every Wednesday."

[1327] This is sent to the user's device as an HTTP response.

[1328] 7. Displaying the results

[1329] The device receives the response data from the server, analyzes it, and displays it on the screen. When the user looks at the screen, they can see specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1330] Prompt Sentence Examples

[1331] "Please identify what this waste is based on the name and image of the waste."

[1332] "Please tell me the proper way to separate the identified waste based on the waste separation rules in your area."

[1333] This allows users to easily separate their waste properly, and with the help of an emotion engine, the operation can be performed smoothly, improving the user experience and making local waste management more effective.

[1334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1335] Step 1:

[1336] The user uses their device to launch the application. They enter the name of the waste in the input form, take a picture of the waste using the camera, and upload it. They also enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). The emotion engine recognizes emotions from voice, text, and facial expressions, and provides additional guidance and support if the user is feeling stressed.

[1337] Input: waste name, image, residential area information, user emotion data.

[1338] Output: Waste information and emotion data organized as data packets.

[1339] Step 2:

[1340] The terminal combines the inputted waste name, image, and residential area information into a single data packet, which is then sent to the server as an HTTP request.

[1341] Input: Waste name, image, and residential area information from the user.

[1342] Output: Data packets sent to the server.

[1343] Step 3:

[1344] The server receives the HTTP request, analyzes the received data, and stores it in variables for the waste name, image, and residential area information.

[1345] Input: Data packets sent from the terminal.

[1346] Output: Name of waste, image, and residential area information stored in separate variables.

[1347] Step 4:

[1348] The server's generation AI analyzes the uploaded image and extracts the characteristics of the waste from the image. Then, it uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is. For example, it uses an image analysis algorithm to extract the characteristics of a plastic bottle and identify it as a plastic bottle.

[1349] Input: Name of waste, image.

[1350] Output: Waste identification result (e.g. "PET bottles").

[1351] Step 5:

[1352] The server searches for the sorting rules for the residential area (e.g., Shinjuku Ward, Tokyo) stored in a database. It matches the identified waste type (e.g., PET bottles) with the sorting rules for the residential area to determine the appropriate sorting method. For example, it determines that "PET bottles are classified as plastic waste and are collected every Wednesday."

[1353] Input: Waste identification results, sorting rules for your area.

[1354] Output: The optimal sorting method (e.g. "PET bottles are collected as plastic waste every Wednesday").

[1355] Step 6:

[1356] The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[1357] Input: Optimal separation method.

[1358] Output: The result data sent to the user's device (e.g., "Please put out plastic bottles as plastic waste every Wednesday").

[1359] Step 7:

[1360] The device receives the response data from the server. The response data is analyzed and displayed on the screen in a format that is easy for the user to understand. For example, the data may be read and displayed on the UI as "Please dispose of PET bottles as plastic waste every Wednesday."

[1361] Input: Response data from the server.

[1362] Output: A display of the sorting method that can be checked by the user.

[1363] (Application example 2)

[1364] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1365] In conventional waste sorting systems, it is difficult for users to obtain instructions on how to sort waste accurately, and this can often be stressful for users, leading to inappropriate sorting. As a result, improper waste sorting can cause environmental problems and reduce recycling efficiency.

[1366] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for analyzing the user's emotions using emotion recognition means and providing guidance and support to reduce the user's stress. This enables the user to sort waste accurately and without stress.

[1367] "User" refers to the entity that uses the system to receive instructions on how to separate waste.

[1368] "Waste" refers to items that a user wishes to discard and that require proper separation.

[1369] "Name" refers to text information entered by the user to identify the waste.

[1370] "Image" refers to photographic data uploaded by users to extract visual information about waste.

[1371] "Residential area" is area information such as the city or town to which the user belongs, and is information that affects the sorting rules for that area.

[1372] "Means for input" refers to an interface for collecting information from a user.

[1373] "Means for sending" refers to the technical means for sending the information entered by the user to the server.

[1374] "Means for receiving and analyzing" refers to the technical means by which the server receives information sent from the user and performs processing to analyze its contents.

[1375] "Means of identification" means the technical means for clearly identifying the type of waste based on the information received.

[1376] "Means for searching and determining sorting rules" refers to technical methods for searching for and determining appropriate waste sorting methods based on information about the residential area.

[1377] "Means of notification" refers to a technical method for communicating the determined separation method to the user.

[1378] "Emotion recognition means" refers to technology that analyzes the user's emotions and provides appropriate support to reduce stress.

[1379] "Means for providing guidance and support" refers to technical methods for providing additional explanations or assistance depending on the user's emotional state.

[1380] This invention is a system that supports proper waste sorting by combining a generative AI model and an emotion engine. Below, we will explain how to specifically implement this system.

[1381] Overall system configuration

[1382] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[1383] System operation flow

[1384] 1. User Input

[1385] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[1386] Also, enter information about your area of ​​residence (city, town, village, etc.).

[1387] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[1388] 2. Data Transmission

[1389] The terminal combines the entered name, image, and residential area information into a single data packet.

[1390] The terminal sends this data packet as an HTTP request to a server via the Internet.

[1391] 3. Data reception and preparation for analysis

[1392] The server receives the HTTP request and parses the data in the request body.

[1393] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[1394] 4. Identifying the garbage

[1395] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1396] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1397] Where necessary, both name and image may be combined for more precise identification.

[1398] 5. Application of sorting rules

[1399] The server searches the database for the sorting rules for each residential area.

[1400] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[1401] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[1402] 6. Sending and notifying results

[1403] The server generates result data including the determined separation method.

[1404] The generated result data is sent to the user's terminal as an HTTP response.

[1405] 7. Displaying the results

[1406] The terminal receives the response data from the server.

[1407] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[1408] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1409] Examples of emotion engines

[1410] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[1411] Hardware and software used

[1412] This system uses the following hardware and software:

[1413] User interface: Smartphone or tablet

[1414] Server: Built using the Frust framework

[1415] Generative AI model: built with TensorFlow

[1416] Emotion Recognition Software: Uses Emotion Recognition API

[1417] Examples of concrete examples and prompts

[1418] For example, when a user scans the label of a plastic bottle purchased at the supermarket, the app analyzes the image. If the emotion engine detects stress, it provides guidance such as, "Please throw this plastic bottle in the recycling bin. The recycling bin is located at the entrance of the store."

[1419] Example prompt sentence:

[1420] "A user uploaded an image of a plastic bottle from their smartphone. The emotion engine detected stress. Please provide appropriate waste sorting instructions."

[1421] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1422] Step 1:

[1423] Input: The user inputs the name of the waste, an image, and information about the area of ​​residence.

[1424] Processing: The user launches the application using a device such as a smartphone or PC. They enter the name of the waste into the input form within the application, take a picture of the waste using the camera function, and upload it. They also enter their residential area.

[1425] Output: The entered name, image, and residential area are stored as data on the device.

[1426] Step 2:

[1427] Input: Data collected in Step 1 (name, image, location)

[1428] Processing: The device assembles the entered name, picture, and location information into a single data packet.

[1429] Output: A data packet is generated.

[1430] Step 3:

[1431] Input: Data packet generated in step 2

[1432] Processing: The device sends the data packet as an HTTP request to the server over the Internet.

[1433] Output: The server receives the data packet.

[1434] Step 4:

[1435] Input: Data packet received by the server

[1436] Processing: The server receives the HTTP request and analyzes the data in the request body. The server divides the data into the waste name, image, and residential area and stores them in variables.

[1437] Output: Name, image, and location stored in separate variables.

[1438] Step 5:

[1439] Input: waste image and waste name

[1440] Processing: The server's generation AI analyzes the uploaded image and extracts the waste characteristics from the image. The generation AI uses TensorFlow to perform image analysis. By combining both the name and the image, the type of waste is identified based on the algorithm.

[1441] Output: The type of waste is identified (e.g., "PET bottle").

[1442] Step 6:

[1443] Input: Waste type and residential area identified in step 5

[1444] Processing: The server searches the database for the sorting rules for each residential area. The server matches the identified waste type with the local sorting rules and determines the appropriate sorting method.

[1445] Output: The sorting method is determined (e.g., "Plastic bottles are classified as plastic waste").

[1446] Step 7:

[1447] Input: The classification method determined in step 6 and the user's emotion data

[1448] Processing: The server uses the emotion recognition API to analyze the user's emotions and generate guidance and support according to their emotional state. If the emotion engine detects stress, it applies additional guidance and support.

[1449] Output: The final classification method and emotional support guide are generated.

[1450] Step 8:

[1451] Enter: The Ultimate Sort-Out and Emotional Support Guide

[1452] Processing: The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[1453] Output: The response with the result data is sent to the user's device.

[1454] Step 9:

[1455] Input: Response data from the server

[1456] Processing: The device analyzes the response data received from the server and displays it on the screen in a format that is easy for the user to understand. The device also notifies the user of the sorting method and additional guidance.

[1457] Output: The user looks at the device screen and sees specific instructions and guidance for sorting waste (e.g., "Plastic bottles should be put out as plastic waste every Wednesday").

[1458] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1459] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1460] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1461] [Fourth embodiment]

[1462] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1463] 7, a 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.

[1464] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1465] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1466] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1467] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1468] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1469] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1470] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1471] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1472] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1473] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1474] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1475] The present invention is a system that uses generative AI to support proper waste sorting. Below, we will explain how to implement this system in concrete terms.

[1476] Overall system configuration

[1477] This system is composed of a terminal with an interface where users can input the name of the waste, an image, and information about their residential area, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. As a specific embodiment, we will describe how each of these elements works together.

[1478] System operation flow

[1479] 1. User Input

[1480] The user starts the application using a device such as a smartphone or PC, enters the name of the waste into the input form within the application, and takes a picture of the waste using the camera function.

[1481] Also, enter your area of ​​residence (city, town, village, etc.).

[1482] 2. Data Transmission

[1483] The device sends the entered name, image, and location information to the server via an HTTP request over the internet connection.

[1484] 3. Data reception and preparation for analysis

[1485] The server receives the input data, parses it as structured data in JSON format, and separates it into the name of the garbage, an image, and the residential area.

[1486] 4. Identifying the garbage

[1487] The server uses generative AI to analyze the received images and names, and identifies the type of waste using a specific algorithm. Image analysis algorithms extract waste characteristics from the uploaded images.

[1488] By combining both the name of the waste and image information, the type of waste can be identified more accurately.

[1489] 5. Application of sorting rules

[1490] The server accesses the database and searches for the waste separation rules for the specified area, which include waste types, corresponding separation methods, collection dates, etc.

[1491] The identified types of waste are compared with local sorting rules to determine the appropriate sorting method.

[1492] 6. Sending and notifying results

[1493] The server then sends the analysis results and the determined sorting method to the user's device, including specific instructions such as the waste type, sorting method, and collection date.

[1494] The results are displayed on the user's terminal, and the user can confirm the appropriate sorting method and sort the waste accordingly.

[1495] Specific examples

[1496] For example, let's consider the case where a user uses this system to process a "PET bottle" that they plan to discard at home. The user takes a picture of the PET bottle with their smartphone and enters "PET bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area. This information is sent to the server, which uses generative AI to identify it as a "PET bottle." Furthermore, the server retrieves the sorting rules for the residential area from a database and determines that the PET bottle should be collected as "plastic waste" every Wednesday. This information is notified to the user, and instructions to put it out as "plastic waste" on Wednesday are displayed on the screen.

[1497] In this way, this system allows users to easily and accurately separate their garbage, which promotes proper garbage disposal in accordance with local garbage separation rules.

[1498] The processing flow will be explained below.

[1499] Step 1: User Input

[1500] The user uses the terminal to launch the dedicated application.

[1501] The user enters the name of the waste material into the input form (e.g., "PET bottle").

[1502] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[1503] Enter your area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture") and click the submit button.

[1504] Step 2: Send data

[1505] The terminal compiles the name of the waste, an image, and information on the area of ​​residence entered by the user into a single data packet.

[1506] The terminal sends this data packet as an HTTP request to a server via the Internet.

[1507] Step 3: Receiving data

[1508] The server receives the HTTP request and parses the data in the request body.

[1509] The server divides the received data into the name of the waste, image, and residential area, and stores them in the respective variables.

[1510] Step 4: Identify the debris

[1511] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1512] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1513] Where necessary, both name and image may be combined for more precise identification.

[1514] Step 5: Applying separation rules

[1515] The server searches the database for the sorting rules for each residential area.

[1516] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area to determine the appropriate sorting method.

[1517] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[1518] Step 6: Sending the results

[1519] The server generates result data including the determined separation method.

[1520] The server sends the result data to the user's terminal as an HTTP response.

[1521] Step 7: View the results

[1522] The terminal receives the response data from the server.

[1523] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[1524] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1525] Example 1

[1526] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1527] Conventional waste sorting systems required users to manually determine the type of waste and look up the appropriate sorting method. This made sorting cumbersome and prone to mistakes, making it difficult to properly dispose of waste in accordance with local sorting rules. Furthermore, the criteria for identifying waste types were vague, leading to confusion. There is a need to provide a system that can solve these issues and support users in easily and accurately sorting waste.

[1528] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1529] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for analyzing the image and name of the waste using a generative AI model, means for searching a database for the sorting rules for the specified residential area, and means for notifying the user of the determined sorting method. This enables the user to accurately identify the type of waste and easily carry out appropriate waste disposal in accordance with the sorting rules for each area.

[1530] "User" refers to a person who uses the system to separate waste.

[1531] "Waste" refers to items that a user is disposing of.

[1532] "Name" refers to text information entered by the user to identify the waste.

[1533] "Image" refers to a photograph taken by a user using the device's camera to identify waste.

[1534] "Residence area" refers to the administrative division where a user lives.

[1535] "Input means" refers to an interface for a user to input information about the name, image, and residential area of ​​the waste into the system.

[1536] "Transmission means" refers to a function for transmitting information entered by the user to the server.

[1537] "Receiving means" refers to a function that allows the server to receive information sent from the user.

[1538] "Analysis means" refers to the function of analyzing the name and image of the waste received by the server and identifying the type of waste.

[1539] "Generative AI model" refers to an algorithm that uses artificial intelligence technology to analyze received images and names and determine the characteristics of the waste.

[1540] The "database" refers to a collection of information that stores sorting rules for each residential area.

[1541] "Search means" refers to the function for searching the database for waste separation rules for a specified residential area.

[1542] "Notification means" refers to a function for notifying the user of the sorting method determined by the server.

[1543] "Sorting rules" refers to information on the appropriate sorting methods and collection dates for each type of waste in each residential area.

[1544] "Analysis preparation" refers to the process of parsing the received data and dividing it into waste names, images, and residential areas.

[1545] MODE FOR CARRYING OUT THE INVENTION

[1546] The present invention is a system that uses generative AI to support proper waste sorting. This system consists of a terminal with an interface for users to input the name, image, and residential area information of the waste, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, and a means for notifying the user of the analysis results. Specific embodiments are described below.

[1547] Hardware and software used

[1548] 1. Device:

[1549] It is used by users to enter information and upload images. Examples include smartphones and personal computers.

[1550] The application includes a photo capture function and an input form.

[1551] 2. Server:

[1552] The program receives and analyzes information and is implemented using Python.

[1553] The TensorFlow library is used for image analysis, and a generative AI model is applied.

[1554] The server also queries the database with the analysis results to retrieve the classification rules.

[1555] 3. Database:

[1556] Sorting rules for each region are stored using a database such as MySQL.

[1557] System Operation Overview

[1558] User input:

[1559] Users launch the application using their smartphone or computer, enter the name of the waste in the input form within the application, take a picture of the waste using the device's camera, and select their area of ​​residence.

[1560] Data transmission:

[1561] The device sends the entered name, image, and location information to a server over its internet connection via an HTTP POST request.

[1562] Data reception and preparation for analysis:

[1563] The server receives an HTTP request, parses the received data in JSON format, and separates it into the waste name, image, and residential area information.

[1564] Garbage Identification:

[1565] The server uses a generative AI model to analyze the received images and names to identify the type of waste. This process is done using the TensorFlow library. The waste characteristics are extracted and analyzed along with the name.

[1566] Separation rules apply:

[1567] The server accesses the database and searches for the waste separation rules for the specified residential area. The rules include waste types, corresponding separation methods, collection dates, etc. The identified waste type is compared with the local separation rules to determine the appropriate separation method.

[1568] Sending and notifying results:

[1569] The server sends the analysis results and the determined sorting method to the user's device in JSON format. The user can then use the application to check specific information such as the type of waste, sorting method, and collection date.

[1570] Specific examples

[1571] For example, let's say a user wants to throw away a "plastic bottle." The user takes a picture of the plastic bottle with their smartphone at home, and then enters "plastic bottle" as the name and "Naka-ku, Yokohama City, Kanagawa Prefecture" as the residential area in the application. This information is sent to the server.

[1572] The server uses a generative AI to identify the item as a "plastic bottle" using the following prompt:

[1573] "Please identify what kind of garbage is in this image and tell me the proper way to separate it in Naka Ward, Yokohama City, Kanagawa Prefecture."

[1574] The server then retrieves the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture from the database and determines that the PET bottles should be collected as "plastic waste" every Wednesday. The result is notified to the user, and specific instructions are displayed on the application screen.

[1575] In this way, users can accurately separate their garbage and dispose of it properly in accordance with local rules.

[1576] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1577] System program processing flow

[1578] Step 1:

[1579] Input: The user launches the application on the terminal and inputs the name of the waste, an image, and information about the area of ​​residence.

[1580] Specific operation: The user enters "PET bottle" into the application's input form, takes a picture of the PET bottle with the smartphone camera, and selects "Naka-ku, Yokohama City, Kanagawa Prefecture" as their area of ​​residence.

[1581] Output: The system temporarily stores the name entered by the user, the image taken, and the residential area information.

[1582] Step 2:

[1583] Input: The user presses a "Submit" button within the application.

[1584] Specific operation: The terminal collects the input information, converts it into JSON format, and sends it to the server via an HTTP POST request.

[1585] Output: The entered name, image, and area information are sent to the server.

[1586] Step 3:

[1587] Input: Parse the HTTP request received by the server.

[1588] Specific operation: The server parses the received data in JSON format and divides it into the waste name, image, and residential area information.

[1589] Output: The server stores the received data internally as name, image, and residential area information.

[1590] Step 4:

[1591] Input: The server receives the name and image of the waste and feeds it to the generative AI model.

[1592] Specific operation: The server uses the TensorFlow library to drive a generative AI model to analyze the image and its name. The prompt text is "Please identify what kind of garbage is in this image and tell me the appropriate method for sorting it in Naka-ku, Yokohama City, Kanagawa Prefecture."

[1593] Output: Obtain the determination result that it is a "plastic bottle" from the generative AI model.

[1594] Step 5:

[1595] Input: The server searches the database for sorting rules based on the type of waste and residential area information identified.

[1596] What it does: The server queries the MySQL database to get the sorting rules for Naka-ku, Yokohama City, Kanagawa Prefecture, specifically, whether "PET bottles" are considered "plastic waste" and the collection schedule.

[1597] Output: The rule information retrieved from the database is sent back to the server.

[1598] Step 6:

[1599] Input: The sorting rules obtained by the server and the garbage judgment results of the generating AI.

[1600] Specific operation: The server integrates this information and determines the appropriate sorting method and collection date, while also compiling the analysis results and specific instructions on how to sort the waste.

[1601] Output: Information about the determined sorting method and collection date is prepared in JSON format.

[1602] Step 7:

[1603] Input: JSON data prepared by the server to notify the user.

[1604] Specific operation: The server sends the result to the user's device as an HTTP response.

[1605] Output: The user terminal receives the analysis results along with information on sorting methods and collection schedules.

[1606] Step 8:

[1607] Input: Analysis results and classification methods displayed on the user's device.

[1608] Specific operation: The user confirms the instruction on the application screen that "PET bottles should be disposed of as plastic waste every Wednesday."

[1609] Output: The user follows the correct sorting method and puts out the garbage on the specified date.

[1610] This allows users to accurately separate their garbage and dispose of it properly in accordance with local rules.

[1611] (Application example 1)

[1612] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] While existing waste sorting systems focus on encouraging users to properly sort their waste at home, they do not address the disposal methods of used products in brick-and-mortar stores. Furthermore, the lack of effective methods to promote proper waste sorting within stores results in insufficient customer service and environmental protection activities.

[1614] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1615] In this invention, the server includes means for having a user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for providing real-time in-store guidance to customers on how to dispose of used products. This enables efficient proper waste sorting in physical stores, improving both customer service and environmental protection.

[1616] "User" means an individual or organization that uses the waste sorting system.

[1617] "Waste" refers to items or materials that are no longer needed and are discarded, and require appropriate disposal methods.

[1618] "Name" is a character string entered by the user to identify the waste.

[1619] "Images" are visual data obtained by photographing the appearance of waste with a camera.

[1620] The "residential area" refers to the area such as a city, town, or village where the user lives, and the rules for separating garbage vary from area to area.

[1621] The "means for inputting" is an interface that allows a user to input the name, image, and residential area information of the waste into the system.

[1622] The "transmitting means" refers to a communication technology for transmitting the input information to the server.

[1623] "Means of receiving and identifying" refers to the process by which the server receives the transmitted information and identifies the type of waste using generative AI and image analysis algorithms.

[1624] The "means for searching and determining sorting rules" refers to a process in which the server searches the database for waste sorting rules based on the residential area and determines an appropriate sorting method for the waste.

[1625] The "means for notifying" is an interface for notifying the user of the determined separation method.

[1626] "In-store guidance" refers to a system that allows customers to receive real-time information on how to sort used products in-store.

[1627] "Barcode scanning" is the process of reading the barcode of a product and obtaining details of the waste based on that information.

[1628] MODE FOR CARRYING OUT THE INVENTION

[1629] The present invention provides a specific embodiment of a waste sorting system, particularly for efficiently sorting used products in brick-and-mortar stores.

[1630] Overall system configuration

[1631] This system consists of the following elements:

[1632] 1. User Device:

[1633] Applications installed on devices such as smartphones and smart glasses.

[1634] It provides an interface for users to input waste name, image, and residential area information.

[1635] Ability to scan waste barcodes within the application.

[1636] 2. Server:

[1637] The received data is analyzed and the type of waste is identified using generative AI.

[1638] Based on the specified residential area, sorting rules are searched from a database to determine the appropriate sorting method.

[1639] Users will be notified of sorting methods and collection dates.

[1640] 3. Database:

[1641] It stores waste separation rules for each region.

[1642] Specifically, this includes sorting methods and collection schedules according to the type of waste.

[1643] Specific operation methods of the system

[1644] 1. Enter your information:

[1645] Users scan the barcode of used products using an application on their smartphone or smart glasses.

[1646] The user takes a picture of the waste and enters its name and area of ​​residence (such as city, ward, town, or village).

[1647] 2. Data transmission:

[1648] The entered information is sent to the server via the Internet using an HTTP request.

[1649] 3. Data reception and analysis:

[1650] The server receives the transmitted data and uses generative AI and image analysis algorithms to extract the characteristics of the waste and identify its type.

[1651] 4. Search and apply sorting rules:

[1652] The server searches the database for appropriate sorting rules based on the identified type of waste and the user's designated residential area.

[1653] For example, obtain information from the database that "PET bottles are collected as plastic waste every Wednesday."

[1654] 5. Notification of Results:

[1655] The server transmits the determined sorting method to the user terminal and notifies the user in real time.

[1656] For example, a message may be displayed saying, "Please separate plastic bottles as plastic waste. Collection day is every Wednesday."

[1657] Hardware and software used

[1658] Hardware: Smartphones, smart glasses

[1659] Software: Python 3, Requests library, Pillow library

[1660] Specific examples

[1661] If the user wants to know how to dispose of a "plastic bottle," they can use the following prompt:

[1662] How do I dispose of plastic bottles? I live in Shibuya Ward, Tokyo.

[1663] Based on this prompt, the generative AI model analyzes and generates specific disposal instructions, such as:

[1664] Please separate plastic bottles as plastic waste. Collection day is every Wednesday.

[1665] As described above, the system of the present invention allows users to easily and accurately separate waste, which will enable proper waste separation and promote eco-friendly activities in brick-and-mortar stores.

[1666] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1667] Step 1:

[1668] Enter information

[1669] Users launch an application on their smartphone or smart glasses and enter information about the waste.

[1670] The input information is threefold: "Name of waste," "Image of waste," and "Area of ​​residence (city, town, or village)."

[1671] If a barcode scanning function is used, the entered information also includes the identification information obtained by the barcode.

[1672] Input: waste name, image, residential area

[1673] Output: A set of information to be sent to the server

[1674] Step 2:

[1675] Data transmission

[1676] The terminal transmits the input information to the server via an HTTP request.

[1677] Specific operation: Using the terminal's communication function, the input data (name of waste, image, and residential area information) is sent to the server.

[1678] Input: Waste information entered by the user

[1679] Output: Request sent to the server

[1680] Step 3:

[1681] Data reception and analysis

[1682] The server receives the sent data, which is structured in JSON format.

[1683] The server analyzes the name, image, and residential area of ​​the waste from the received data and identifies the type of waste using a generative AI model.

[1684] How it works: The server uses analytical tools (e.g., Python and the Pillow library) to extract waste characteristics using image analysis algorithms, and then uses generative AI models (e.g., TensorFlow or PyTorch) to combine waste name and image information to identify the type of waste.

[1685] Input: JSON data received by the server (name, image, location)

[1686] Output: Identified waste types

[1687] Step 4:

[1688] Searching and applying sorting rules

[1689] The server searches the database for sorting rules based on the identified waste type and residential area.

[1690] The database includes regional waste sorting methods and corresponding collection dates.

[1691] Specific operation: The server uses an SQL query to search the database for the relevant sorting rules and determines the appropriate sorting method based on the identified waste type.

[1692] Input: Identified waste type, residential area

[1693] Output: Proper sorting method and collection schedule

[1694] Step 5:

[1695] Notification of results

[1696] The server transmits the determined sorting method and collection schedule to the user terminal.

[1697] The user terminal displays the received information on the user interface and notifies the user.

[1698] Specific operation: The server sends the sorting information as an HTTP response, and the device receives it and displays it on the screen. For example, the user receives a notification such as, "Please separate PET bottles as plastic waste. Collection day is every Wednesday."

[1699] Input: Proper sorting method and collection schedule

[1700] Output: Classification information notified to the user

[1701] These processes will enable proper waste sorting in physical stores efficiently, improving both customer service and environmental protection.

[1702] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1703] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. Below, we will explain how this system is implemented in concrete terms.

[1704] Overall system configuration

[1705] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[1706] System operation flow

[1707] 1. User Input

[1708] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[1709] Also, enter information about your area of ​​residence (city, town, village, etc.).

[1710] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[1711] 2. Data Transmission

[1712] The terminal combines the entered name, image, and residential area information into a single data packet.

[1713] The terminal sends this data packet as an HTTP request to a server via the Internet.

[1714] 3. Data reception and preparation for analysis

[1715] The server receives the HTTP request and parses the data in the request body.

[1716] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[1717] 4. Identifying the garbage

[1718] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1719] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1720] Where necessary, both name and image may be combined for more precise identification.

[1721] 5. Application of sorting rules

[1722] The server searches the database for the sorting rules for each residential area.

[1723] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[1724] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[1725] 6. Sending and notifying results

[1726] The server generates result data including the determined separation method.

[1727] The generated result data is sent to the user's terminal as an HTTP response.

[1728] 7. Displaying the results

[1729] The terminal receives the response data from the server.

[1730] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[1731] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1732] Examples of emotion engines

[1733] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[1734] In this way, this system allows users to easily and accurately separate their garbage, and with the help of the emotion engine, they can operate the system without stress. This will encourage thorough separation and lead to more efficient garbage disposal in the community.

[1735] The processing flow will be explained below.

[1736] Step 1: User Input

[1737] The user uses the terminal to launch the dedicated application.

[1738] The user enters the name of the waste material in text into the input form (e.g., "PET bottle").

[1739] The user takes a picture of the waste using the camera function of the device and uploads the image to the application.

[1740] The user enters information about their area of ​​residence (e.g., "Naka-ku, Yokohama City, Kanagawa Prefecture").

[1741] The emotion engine recognizes the user's emotions through voice input and facial expression analysis, and displays additional guidance and support as needed.

[1742] Step 2: Send data

[1743] The terminal combines the name of the waste, the image, the residential area information entered by the user, and the emotion data from the emotion engine into one data packet.

[1744] The terminal sends this data packet to a server over the Internet.

[1745] Step 3: Receiving data

[1746] The server receives the HTTP request and parses the data in the request body.

[1747] The data received by the server is divided into the waste name, image, residential area, and emotion data, and stored in the respective variables.

[1748] Step 4: Identify the debris

[1749] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1750] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1751] Where necessary, both name and image may be combined for more precise identification.

[1752] Step 5: Applying separation rules

[1753] The server searches the database for the sorting rules for each residential area.

[1754] The server matches the identified waste type (e.g., "PET bottle") with the sorting rules for the area in which the user lives and determines the appropriate sorting method.

[1755] The determined sorting method (e.g., "Plastic bottles should be disposed of as plastic waste every Wednesday") is generated as data.

[1756] Step 6: Sending the results

[1757] The server generates result data including the determined sorting method.

[1758] The server sends the result data to the user's terminal as an HTTP response.

[1759] Step 7: View the results

[1760] The terminal receives the response data from the server.

[1761] The device analyzes the response data and displays it on the screen in a user-friendly format (e.g., "Please dispose of PET bottles as plastic waste every Wednesday").

[1762] If the user feels stressed while operating the device, the emotion engine will recognize this and display appropriate support and guidance on the screen.

[1763] This series of steps allows users to separate their garbage simply and accurately without even thinking about it. Utilizing an emotion engine improves the user experience, allowing for stress-free input of information and garbage separation.

[1764] Example 2

[1765] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1766] In recent years, proper waste sorting has required users to easily understand how to sort waste and to perform the task appropriately. However, in many cases, users lack understanding of the types of waste and the sorting rules, resulting in incorrect sorting. Furthermore, users may feel stressed during the sorting process, which can reduce the accuracy and efficiency of sorting. Therefore, there is a need for a system that supports users in sorting their waste easily and accurately.

[1767] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1768] In this invention, the server includes means for allowing a user to input the name, image, and residential area information of the waste, means for transmitting the input information, and means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, thereby enabling the user to easily identify the type of waste and understand the appropriate sorting method.

[1769] "User" refers to the person who operates the terminal to input the name, image, and residential area information of the waste.

[1770] "Terminal" refers to an information processing device such as a smartphone or computer on which a user inputs and sends information about waste.

[1771] "Server" refers to a central information processing device that receives and analyzes information sent by users and determines the appropriate method of sorting.

[1772] The "emotion engine" refers to a component that analyzes the user's voice, text, and facial expressions to recognize the user's emotions.

[1773] A "data packet" refers to a unit of digital data that compiles user input information.

[1774] An "HTTP request" refers to an information request, which is a form of communication protocol sent from a user's terminal to a server.

[1775] "Image analysis algorithm" refers to a computational procedure for processing waste image data and extracting waste characteristics.

[1776] "Waste identification" refers to the process of determining what waste is based on the input name and analyzed image.

[1777] "Sorting rules" refer to regulations that stipulate the proper method of sorting waste in a particular area.

[1778] "HTTP response" refers to response data, which is a form of communication protocol sent from a server to a user's terminal.

[1779] This invention is a system that supports proper waste sorting by combining generative AI and an emotion engine. The system identifies the type of waste based on information entered by the user and provides instructions on the appropriate sorting method based on the sorting rules for each residential area. Furthermore, it improves the user experience by recognizing the user's emotions and providing an interface that responds to them.

[1780] System configuration

[1781] The system consists of the following main components:

[1782] A device (smartphone or PC) with an interface that allows users to input the name of the garbage, an image, and information about their residential area

[1783] A server that receives and analyzes input information

[1784] Database that stores regional sorting rules

[1785] A means of informing users of the analysis results and classification methods

[1786] An emotion engine that recognizes user emotions and adjusts the interface

[1787] Hardware and Software

[1788] Terminal: The user inputs the name of the waste, takes a picture of the waste with a camera, and inputs information about the area where they live. This is done using a smartphone or computer.

[1789] Server: Receives and analyzes input information. The server is equipped with a generative AI model (e.g., OpenAI) and an emotion engine.

[1790] Database: Stores sorting rules for each residential area.

[1791] Emotion Engine: Recognizes user emotions from voice, text, and facial expressions.

[1792] Example of a system

[1793] 1. User Input

[1794] The user launches the application on their smartphone and enters the name of the waste item: "PET bottle." They then take a picture of the PET bottle with their device's camera and upload it within the app. Next, they enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). While they are entering information, the emotion engine analyzes the user's tone of voice and facial expression, and if it detects stress, it displays additional guidance such as, "If you don't know how to operate it, please see this guide."

[1795] 2. Data Transmission

[1796] Once the user has completed the input, the device assembles the name "PET bottle," the image of the PET bottle, and the residential area "Shinjuku Ward, Tokyo" into a single data packet and sends it to the server as an HTTP request.

[1797] 3. Data reception and preparation for analysis

[1798] The server analyzes the received HTTP request and extracts the name, image, and residential area information from the request body. For example, the name "PET bottle," the image data, and the residential area "Shinjuku-ku, Tokyo" are stored in individual variables.

[1799] 4. Identifying the garbage

[1800] The server uses a generative AI model (for example, OpenAI's image analysis model) to analyze the received image of the plastic bottle. As a result, the generative AI determines that "this is a plastic bottle" and identifies it by matching the name and characteristics of the waste.

[1801] 5. Application of sorting rules

[1802] The server searches the database for the sorting rules for the area where the user lives, "Shinjuku Ward, Tokyo." It then applies the rule that "PET bottles" are classified as "plastic waste" and are collected every Wednesday.

[1803] 6. Sending and notifying results

[1804] The server generates result data including the determined sorting method;

[1805] "Plastic bottles should be disposed of as plastic waste every Wednesday."

[1806] This is sent to the user's device as an HTTP response.

[1807] 7. Displaying the results

[1808] The device receives the response data from the server, analyzes it, and displays it on the screen. When the user looks at the screen, they can see specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1809] Prompt Sentence Examples

[1810] "Please identify what this waste is based on the name and image of the waste."

[1811] "Please tell me the proper way to separate the identified waste based on the waste separation rules in your area."

[1812] This allows users to easily separate their waste properly, and with the help of an emotion engine, the operation can be performed smoothly, improving the user experience and making local waste management more effective.

[1813] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1814] Step 1:

[1815] The user uses their device to launch the application. They enter the name of the waste in the input form, take a picture of the waste using the camera, and upload it. They also enter information about their area of ​​residence (e.g., Shinjuku Ward, Tokyo). The emotion engine recognizes emotions from voice, text, and facial expressions, and provides additional guidance and support if the user is feeling stressed.

[1816] Input: waste name, image, residential area information, user emotion data.

[1817] Output: Waste information and emotion data organized as data packets.

[1818] Step 2:

[1819] The terminal combines the inputted waste name, image, and residential area information into a single data packet, which is then sent to the server as an HTTP request.

[1820] Input: Waste name, image, and residential area information from the user.

[1821] Output: Data packets sent to the server.

[1822] Step 3:

[1823] The server receives the HTTP request, analyzes the received data, and stores it in variables for the waste name, image, and residential area information.

[1824] Input: Data packets sent from the terminal.

[1825] Output: Name of waste, image, and residential area information stored in separate variables.

[1826] Step 4:

[1827] The server's generation AI analyzes the uploaded image and extracts the characteristics of the waste from the image. Then, it uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is. For example, it uses an image analysis algorithm to extract the characteristics of a plastic bottle and identify it as a plastic bottle.

[1828] Input: Name of waste, image.

[1829] Output: Waste identification result (e.g. "PET bottles").

[1830] Step 5:

[1831] The server searches for the sorting rules for the residential area (e.g., Shinjuku Ward, Tokyo) stored in a database. It matches the identified waste type (e.g., PET bottles) with the sorting rules for the residential area to determine the appropriate sorting method. For example, it determines that "PET bottles are classified as plastic waste and are collected every Wednesday."

[1832] Input: Waste identification results, sorting rules for your area.

[1833] Output: The optimal sorting method (e.g. "PET bottles are collected as plastic waste every Wednesday").

[1834] Step 6:

[1835] The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[1836] Input: Optimal separation method.

[1837] Output: The result data sent to the user's device (e.g., "Please put out plastic bottles as plastic waste every Wednesday").

[1838] Step 7:

[1839] The device receives the response data from the server. The response data is analyzed and displayed on the screen in a format that is easy for the user to understand. For example, the data may be read and displayed on the UI as "Please dispose of PET bottles as plastic waste every Wednesday."

[1840] Input: Response data from the server.

[1841] Output: A display of the sorting method that can be checked by the user.

[1842] (Application example 2)

[1843] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1844] In conventional waste sorting systems, it is difficult for users to obtain instructions on how to sort waste accurately, and this can often be stressful for users, leading to inappropriate sorting. As a result, improper waste sorting can cause environmental problems and reduce recycling efficiency.

[1845] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for having the user input the name, image, and residential area information of the waste, means for transmitting the input information, means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste, means for searching for sorting rules based on the residential area and determining an appropriate sorting method for the identified waste, means for notifying the user of the determined sorting method, and means for analyzing the user's emotions using emotion recognition means and providing guidance and support to reduce the user's stress. This enables the user to sort waste accurately and without stress.

[1846] "User" refers to the entity that uses the system to receive instructions on how to separate waste.

[1847] "Waste" refers to items that a user wishes to discard and that require proper separation.

[1848] "Name" refers to text information entered by the user to identify the waste.

[1849] "Image" refers to photographic data uploaded by users to extract visual information about waste.

[1850] "Residential area" is area information such as the city or town to which the user belongs, and is information that affects the sorting rules for that area.

[1851] "Means for input" refers to an interface for collecting information from a user.

[1852] "Means for sending" refers to the technical means for sending the information entered by the user to the server.

[1853] "Means for receiving and analyzing" refers to the technical means by which the server receives information sent from the user and performs processing to analyze its contents.

[1854] "Means of identification" means the technical means for clearly identifying the type of waste based on the information received.

[1855] "Means for searching and determining sorting rules" refers to technical methods for searching for and determining appropriate waste sorting methods based on information about the residential area.

[1856] "Means of notification" refers to a technical method for communicating the determined separation method to the user.

[1857] "Emotion recognition means" refers to technology that analyzes the user's emotions and provides appropriate support to reduce stress.

[1858] "Means for providing guidance and support" refers to technical methods for providing additional explanations or assistance depending on the user's emotional state.

[1859] This invention is a system that supports proper waste sorting by combining a generative AI model and an emotion engine. Below, we will explain how to specifically implement this system.

[1860] Overall system configuration

[1861] This system is composed of a terminal with an interface where users input the name, image, and information about their residential area of ​​the garbage, a server that receives and analyzes the input information, a database that stores the sorting rules for each area, a means for notifying the user of the analysis results and sorting methods, and an emotion engine that recognizes the user's emotions and adjusts the interface. As a specific embodiment, we will describe how each of these elements works together.

[1862] System operation flow

[1863] 1. User Input

[1864] The user starts the application using a device such as a smartphone or PC, enters the name of the waste in the input form within the application, and uses the camera function to take and upload an image of the waste.

[1865] Also, enter information about your area of ​​residence (city, town, village, etc.).

[1866] As the user types, the emotion engine recognizes emotions from voice, text, and facial expressions, providing additional guidance and support if the user is feeling stressed.

[1867] 2. Data Transmission

[1868] The terminal combines the entered name, image, and residential area information into a single data packet.

[1869] The terminal sends this data packet as an HTTP request to a server via the Internet.

[1870] 3. Data reception and preparation for analysis

[1871] The server receives the HTTP request and parses the data in the request body.

[1872] The server divides the received data into the name of the waste, an image, and the residential area, and stores them in variables.

[1873] 4. Identifying the garbage

[1874] The server's generated AI analyzes the uploaded images and extracts characteristics of the waste from the images.

[1875] The server uses an algorithm to analyze the name of the waste and the extracted characteristics to identify what the waste is (e.g., "plastic bottle").

[1876] Where necessary, both name and image may be combined for more precise identification.

[1877] 5. Application of sorting rules

[1878] The server searches the database for the sorting rules for each residential area.

[1879] The server compares the identified type of waste ("PET bottles") with the sorting rules in the area and determines the appropriate sorting method.

[1880] For example, "PET bottles" are classified as "plastic waste" and are determined to be collected every Wednesday.

[1881] 6. Sending and notifying results

[1882] The server generates result data including the determined separation method.

[1883] The generated result data is sent to the user's terminal as an HTTP response.

[1884] 7. Displaying the results

[1885] The terminal receives the response data from the server.

[1886] The terminal analyzes the response data and displays it on the screen in a user-friendly format.

[1887] The user looks at the device screen and sees specific instructions such as "Please put out PET bottles as plastic waste every Wednesday."

[1888] Examples of emotion engines

[1889] For example, when a user disposes of a "plastic bottle," the system receives the user's input information, and at the same time, the emotion engine analyzes the user's tone of voice and facial expression to recognize that the user is feeling stressed. Based on this information, the emotion engine displays additional guidance to the user or initiates voice guidance to guide the user to operate the system smoothly. This improves the user experience and promotes accurate waste sorting.

[1890] Hardware and software used

[1891] This system uses the following hardware and software:

[1892] User interface: Smartphone or tablet

[1893] Server: Built using the Frust framework

[1894] Generative AI model: built with TensorFlow

[1895] Emotion Recognition Software: Uses Emotion Recognition API

[1896] Examples of concrete examples and prompts

[1897] For example, when a user scans the label of a plastic bottle purchased at the supermarket, the app analyzes the image. If the emotion engine detects stress, it provides guidance such as, "Please throw this plastic bottle in the recycling bin. The recycling bin is located at the entrance of the store."

[1898] Example prompt sentence:

[1899] "A user uploaded an image of a plastic bottle from their smartphone. The emotion engine detected stress. Please provide appropriate waste sorting instructions."

[1900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1901] Step 1:

[1902] Input: The user inputs the name of the waste, an image, and information about the area of ​​residence.

[1903] Processing: The user launches the application using a device such as a smartphone or PC. They enter the name of the waste into the input form within the application, take a picture of the waste using the camera function, and upload it. They also enter their residential area.

[1904] Output: The entered name, image, and residential area are stored as data on the device.

[1905] Step 2:

[1906] Input: Data collected in Step 1 (name, image, location)

[1907] Processing: The device assembles the entered name, picture, and location information into a single data packet.

[1908] Output: A data packet is generated.

[1909] Step 3:

[1910] Input: Data packet generated in step 2

[1911] Processing: The device sends the data packet as an HTTP request to the server over the Internet.

[1912] Output: The server receives the data packet.

[1913] Step 4:

[1914] Input: Data packet received by the server

[1915] Processing: The server receives the HTTP request and analyzes the data in the request body. The server divides the data into the waste name, image, and residential area and stores them in variables.

[1916] Output: Name, image, and location stored in separate variables.

[1917] Step 5:

[1918] Input: waste image and waste name

[1919] Processing: The server's generation AI analyzes the uploaded image and extracts the waste characteristics from the image. The generation AI uses TensorFlow to perform image analysis. By combining both the name and the image, the type of waste is identified based on the algorithm.

[1920] Output: The type of waste is identified (e.g., "PET bottle").

[1921] Step 6:

[1922] Input: Waste type and residential area identified in step 5

[1923] Processing: The server searches the database for the sorting rules for each residential area. The server matches the identified waste type with the local sorting rules and determines the appropriate sorting method.

[1924] Output: The sorting method is determined (e.g., "Plastic bottles are classified as plastic waste").

[1925] Step 7:

[1926] Input: The classification method determined in step 6 and the user's emotion data

[1927] Processing: The server uses the emotion recognition API to analyze the user's emotions and generate guidance and support according to their emotional state. If the emotion engine detects stress, it applies additional guidance and support.

[1928] Output: The final classification method and emotional support guide are generated.

[1929] Step 8:

[1930] Enter: The Ultimate Sort-Out and Emotional Support Guide

[1931] Processing: The server generates result data including the determined sorting method and sends it to the user's terminal as an HTTP response.

[1932] Output: The response with the result data is sent to the user's device.

[1933] Step 9:

[1934] Input: Response data from the server

[1935] Processing: The device analyzes the response data received from the server and displays it on the screen in a format that is easy for the user to understand. The device also notifies the user of the sorting method and additional guidance.

[1936] Output: The user looks at the device screen and sees specific instructions and guidance for sorting waste (e.g., "Plastic bottles should be put out as plastic waste every Wednesday").

[1937] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1938] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1939] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1940] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1941] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1942] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1943] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1944] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1945] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1946] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1947] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1948] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1949] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1950] 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.

[1951] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1952] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1953] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1954] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1955] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1956] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1957] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1958] The following is further disclosed regarding the above embodiment.

[1959] (Claim 1)

[1960] A means for allowing a user to input the name, image, and residential area information of the waste;

[1961] means for transmitting the input information;

[1962] A means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste;

[1963] A means to search for sorting rules based on the area of ​​residence and determine the appropriate sorting method for the identified waste;

[1964] a means for notifying a user of the determined sorting method;

[1965] A system including:

[1966] (Claim 2)

[1967] 2. The system according to claim 1, wherein the means for analyzing the image of the waste is means for extracting characteristics of the waste using an image analysis algorithm and identifying the type of waste based on the results.

[1968] (Claim 3)

[1969] 10. The system of claim 1, further comprising means for combining both the name and image of the waste to identify the type of waste.

[1970] "Example 1"

[1971] (Claim 1)

[1972] A means for allowing a user to input the name, image, and residential area information of the waste;

[1973] means for transmitting the input information;

[1974] A means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste;

[1975] A means to search for sorting rules based on the area of ​​residence and determine the appropriate sorting method for the identified waste;

[1976] a means for notifying a user of the determined sorting method;

[1977] A means for analyzing waste images and names using a generative AI model;

[1978] A means for searching the database for the separation rules for a specified residential area;

[1979] A system including:

[1980] (Claim 2)

[1981] 2. The system according to claim 1, wherein the means for analyzing the image of the waste is means for extracting characteristics of the waste using an image analysis algorithm and identifying the type of waste based on the results.

[1982] (Claim 3)

[1983] 10. The system of claim 1, further comprising means for combining both the name and image of the waste to identify the type of waste.

[1984] "Application Example 1"

[1985] (Claim 1)

[1986] A means for allowing a user to input the name, image, and residential area information of the waste;

[1987] means for transmitting the input information;

[1988] A means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste;

[1989] A means to search for sorting rules based on the area of ​​residence and determine the appropriate sorting method for the identified waste;

[1990] a means for notifying a user of the determined sorting method;

[1991] The system includes a means for providing real-time guidance to customers in stores on how to dispose of used products.

[1992] (Claim 2)

[1993] 2. The system according to claim 1, wherein the means for analyzing the image of the waste is means for extracting characteristics of the waste using an image analysis algorithm and identifying the type of waste based on the results.

[1994] (Claim 3)

[1995] The system according to claim 1, further comprising means for acquiring waste information by scanning barcodes and providing guidance on proper sorting methods so that customers can properly sort used products in the store.

[1996] "Example 2: Combining Emotion Engines"

[1997] (Claim 1)

[1998] A means for allowing a user to input the name, image, and residential area information of the waste;

[1999] means for transmitting the input information;

[2000] A means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste;

[2001] A means to search for sorting rules based on the area of ​​residence and determine the appropriate sorting method for the identified waste;

[2002] means for recognizing a user's emotion and adjusting the interface by means of emotion recognition means;

[2003] a means for notifying a user of the determined sorting method;

[2004] A system including:

[2005] (Claim 2)

[2006] 2. The system according to claim 1, wherein the means for analyzing the image of the waste is means for extracting characteristics of the waste using an image analysis algorithm and identifying the type of waste based on the results.

[2007] (Claim 3)

[2008] 10. The system of claim 1, further comprising means for combining both the name and image of the waste to identify the type of waste.

[2009] "Application example 2 when combining emotion engines"

[2010] (Claim 1)

[2011] A means for allowing a user to input the name, image, and residential area information of the waste;

[2012] means for transmitting the input information;

[2013] A means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste;

[2014] A means to search for sorting rules based on the area of ​​residence and determine the appropriate sorting method for the identified waste;

[2015] a means for notifying a user of the determined sorting method;

[2016] A means for analyzing the user's emotions using an emotion recognition means and providing guidance and support to reduce the user's stress;

[2017] A system including:

[2018] (Claim 2)

[2019] 2. The system according to claim 1, wherein the means for analyzing the image of the waste is means for extracting characteristics of the waste using an image analysis algorithm and identifying the type of waste based on the results.

[2020] (Claim 3)

[2021] 10. The system of claim 1, further comprising means for combining both the name and image of the waste to identify the type of waste. [Explanation of symbols]

[2022] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for allowing a user to input the name, image, and residential area information of the waste; means for transmitting the input information; A means for receiving the transmitted information and analyzing the name and image of the waste to identify the type of waste; A means to search for sorting rules based on the area of ​​residence and determine the appropriate sorting method for the identified waste; a means for notifying a user of the determined sorting method; A system including:

2. The system according to claim 1 , wherein the means for analyzing the image of the waste is means for extracting characteristics of the waste using an image analysis algorithm and identifying the type of waste based on the results.

3. The system of claim 1 , further comprising means for combining both the name and image of the waste to identify the type of waste.

Images