System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting information on waste; means for transmitting the information on the waste; means for analyzing the information on the waste and determining a type of the waste; means for referring to waste sorting rules of municipalities on the basis of residential area information of a user and specifying an appropriate sorting method; means for determining a possibility of reuse of the waste; and means for notifying the user of the sorting method and a reuse proposal.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] In modern society, garbage sorting rules vary from city to city, town to town, and village to town, making it complicated to understand the rules, especially for those who have just moved to an area. Furthermore, the disposal costs for bulky garbage and reusable items can be expensive. This makes it difficult for residents to properly separate and dispose of their garbage, leading to the issue of inappropriate waste disposal. Furthermore, there is also the problem of reusable items being discarded without being properly reused, resulting in wasted resources and increased environmental impact. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: a system including means for inputting garbage information, means for transmitting the garbage information, means for analyzing the garbage information and determining the type of garbage, means for consulting municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method, means for determining whether the garbage can be reused, and means for notifying the user of the sorting method and reuse suggestions. This allows users to easily learn appropriate garbage sorting methods and effectively reuse reusable items.

[0006] "Garbage information" is data that allows a user to provide characteristics of specific garbage, and includes image data and text data.

[0007] "Means" refer to components or functions that are provided to perform specific functions or roles within a system.

[0008] "Transmission means" refers to a communication function for sending garbage information acquired from the user to the server.

[0009] "Means of analysis" refers to the processing functions and algorithms used to understand and determine the type and characteristics of garbage based on the received garbage information.

[0010] "Means of determination" refers to the functions and algorithms for identifying and classifying the type and attributes of garbage.

[0011] "User's residence information" is data for identifying the specific area where the user resides, and includes the name of the city, town, or village, address, and the like.

[0012] "Querying means" refers to the ability to research and obtain the required information from a particular database or source.

[0013] "Means of identification" refers to the function of using acquired information to arrive at a specific conclusion or result.

[0014] "Reusability" refers to the criteria for determining whether a particular item is reusable and can be reused instead of being discarded.

[0015] "Means for notifying" refers to a communication function that enables the server to provide information and instructions to the user.

[0016] "System" refers to the overall structure in which multiple means work together to provide the functions of waste sorting and reuse suggestions. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

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

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

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

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0032] 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.

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

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] The present invention is a system that includes a series of processes, starting with the user inputting information about the garbage, followed by an analysis by the server and notification of the appropriate disposal method and reuse proposal to the user. The specific processes and operations of each part of the system are explained below.

[0039] System Overview

[0040] Input Method

[0041] A user uses a smartphone to take a photo of the trash or input text information. For example, a user can take a photo of a broken plastic cup or input "old T-shirt" as text. This information is important data for identifying the characteristics of the trash.

[0042] Transmission method

[0043] The device sends the entered garbage information to a server. This transmission is done via the Internet, and the server receives the data through an API (Application Program Interface).

[0044] Analysis means

[0045] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from text.

[0046] Inquiry method

[0047] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server then retrieves the sorting rules corresponding to the analyzed garbage type.

[0048] Specific means

[0049] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information from a city or ward / ward database that "plastic waste should be collected as plastic resources" and notifies the user of that method.

[0050] Reuse decision method

[0051] Based on the analysis results, the server determines whether the garbage can be reused, for example, by determining whether it is a plastic product that can still be used or clothing that can still be worn, and if so, suggests that the item be put up for sale at a flea market or auction service.

[0052] Notification means

[0053] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. Notifications are sent via a smartphone app, allowing users to check the information immediately. For example, a message might appear saying, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0054] Specific examples

[0055] For plastic products

[0056] 1. User takes a photo of a plastic cup and inputs it into the app:

[0057] A user takes a photo of a plastic cup with their smartphone camera.

[0058] 2. The device sends the photo data to the server:

[0059] The device sends the photo to the server.

[0060] 3. The server analyzes using the AI ​​model:

[0061] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0062] 4. The server queries the city / ward / town / village's sorting rules:

[0063] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0064] 5. The server determines whether the item can be reused:

[0065] The server verifies that the cup is still usable and generates a reuse suggestion.

[0066] 6. The server notifies the user of the result:

[0067] The notice reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0068] In the case of clothing

[0069] 1. User enters text information about an old T-shirt:

[0070] A user types "old T-shirt" into the app.

[0071] 2. The device sends the text data to the server:

[0072] The terminal transmits the text information to the server.

[0073] 3. The server analyzes using the generated AI model:

[0074] The server inputs the text data into an AI model and determines that it is "clothing."

[0075] 4. The server queries the city / ward / town / village's sorting rules:

[0076] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0077] 5. The server determines whether the item can be reused:

[0078] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0079] 6. The server notifies the user of the result:

[0080] The notice reads, "Please separate it as fabric waste. Also, since it is still usable, we recommend that you sell it on a flea market site."

[0081] The above is an embodiment of the present invention. This system allows users to easily check the appropriate garbage sorting method and reusability of garbage, and dispose of garbage in accordance with local garbage sorting rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective resource utilization and reduce environmental impact.

[0082] The processing flow will be explained below.

[0083] Step 1:

[0084] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, a user might take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[0085] Step 2:

[0086] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[0087] Step 3:

[0088] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. Specifically, the server can identify a plastic cup based on photo data, or determine that it is clothing by analyzing text data.

[0089] Step 4:

[0090] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[0091] Step 5:

[0092] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[0093] Step 6:

[0094] The server then checks the analysis results again to determine whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0095] Step 7:

[0096] The server then aggregates all the information and creates a final notification for the user, combining the analysis results and reuse suggestions into a single notification message.

[0097] Step 8:

[0098] The device receives a notification message from the server and displays it to the user. The user can then use their smartphone to view specific sorting methods and reuse suggestions, such as, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0099] Example 1

[0100] 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."

[0101] Current waste sorting and reuse systems have the problem that it is difficult for users to accurately identify the type of waste and understand the appropriate disposal method. As a result, potential reuse of waste is overlooked, or incorrect sorting increases the environmental burden. To solve this problem, a system is needed that allows users to easily identify the type of waste and learn the correct sorting and reuse methods.

[0102] 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.

[0103] In this invention, the server includes means for a user to input garbage information, means for transmitting the garbage information from a terminal to the server, means for the server to input the garbage information into a generative AI model and analyze it, means for querying a database of garbage sorting rules for the city, ward, town, or village based on the user's residential information and identifying an appropriate sorting method, means for determining whether the garbage can be reused, and means for notifying the user of the sorting method and reuse suggestions. This enables the user to easily identify the type of garbage and receive accurate sorting methods and reuse suggestions.

[0104] "Garbage information" is data relating to waste input by the user, and includes image data or text data.

[0105] A "terminal" is a device such as a smartphone or tablet operated by a user, which provides a means for inputting garbage information and sending it to a server.

[0106] The "server" is a central computer system that receives garbage information sent from the terminals and performs analysis and query processing.

[0107] A "generative AI model" is an artificial intelligence model that uses image recognition and natural language processing technologies to analyze information about garbage and determine its specific type and characteristics.

[0108] The "database" is an information aggregation system that stores municipal garbage sorting rules and accepts inquiries based on the user's residential address information.

[0109] "Querying" is the process of sending a query to a database to obtain specific information and retrieving the required data.

[0110] "Sorting method" refers to the procedures and rules for separating waste into appropriate categories and following designated collection and disposal methods.

[0111] "Reusability" refers to the criteria or assessment that determines whether waste can be reused or resold.

[0112] "Notification" refers to the act of informing users of the analysis results, sorting methods, and reuse suggestions, and is a message provided via a smartphone app.

[0113] The system of the present invention involves a series of processes in which a user inputs information about the waste, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. The specific configuration and operation for implementing this system are described below.

[0114] System Overview

[0115] Input Method

[0116] Users use a device such as a smartphone to take a photo of the trash or enter text information into the application. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This information becomes important data for the server to analyze the characteristics of the trash.

[0117] Transmission method

[0118] The terminal sends the garbage information entered by the user to a server. This transmission is carried out via the Internet, and the server receives the data through an application program interface (API).

[0119] Analysis means

[0120] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[0121] Inquiry method

[0122] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server retrieves the sorting rules corresponding to the analyzed garbage type.

[0123] Specific means

[0124] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information such as "plastic waste should be collected as plastic resources" from the municipal waste separation rules database and notifies the user of that method.

[0125] Reuse decision method

[0126] Based on the analysis results, the server determines whether the trash can be reused, for example, by determining whether the plastic products or clothing can still be used, and if so, suggests putting those items up for sale at a flea market or auction service.

[0127] Notification means

[0128] The server integrates all the data and notifies the user of appropriate sorting methods and reuse suggestions. This notification is sent via a smartphone application, allowing the user to immediately check the information. For example, a message such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site" may be displayed.

[0129] Specific examples

[0130] Examples of plastic products

[0131] 1. The user inputs a photo of a plastic cup into the app.

[0132] 2. The device sends the photo data to the server.

[0133] 3. The server inputs the photo into a generative AI model and determines that it is a "plastic cup."

[0134] 4. Based on the user's residential address information, the server queries the city / ward / town / village's database of garbage sorting rules and obtains the information that the item should be collected as a plastic resource.

[0135] 5. The server determines whether the cup is reusable and verifies that it is usable.

[0136] 6. The server notifies the user of the sorting method and reuse suggestions.

[0137] Examples of clothing

[0138] 1. The user enters the text information "old T-shirt" into the app.

[0139] 2. The device sends the text information to the server.

[0140] 3. The server inputs the text data into the generative AI model and determines that it is "clothing."

[0141] 4. The server queries the garbage sorting rules database based on the user's residential address information and obtains the information that the item should be treated as fabric garbage.

[0142] 5. The server determines whether the T-shirt can be reused and verifies that it is usable.

[0143] 6. The server notifies the user of the sorting method and reuse suggestions.

[0144] Examples of prompt statements used:

[0145] Below are some example prompts used as input to generative AI models:

[0146] When inputting a photo

[0147] "Identify the object in this photo and determine what it is made of."

[0148] For text input

[0149] "Based on the text entered, determine which category this item belongs to."

[0150] The above is a specific embodiment for carrying out the invention. This system allows users to easily check the appropriate method for separating waste and the reusability of waste, and allows for appropriate disposal in accordance with local waste separation rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective use of resources and reduce environmental impact.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] The user enters information about the garbage. The system's application is launched, and an input screen is used to take a photo of the garbage or to enter text information. The user takes a photo of the garbage with their smartphone camera and imports it into the app, or enters text about the garbage. This input data is saved on the device as photo data or text data.

[0154] Input: photo of trash or text information

[0155] Output: Garbage photo data or text data

[0156] Step 2:

[0157] The device sends the entered garbage information to the server. When the user taps the "Send" button, the device sends the photo data and text data to the server via API. The API receives this data via the Internet.

[0158] Input: Photo data or text data of garbage sent from the device

[0159] Output: Photo data or text data of the garbage stored on the server side

[0160] Step 3:

[0161] The server inputs the received garbage information into a generative AI model for analysis. The server passes the received data to an analysis engine, which performs image recognition or natural language processing. In the case of photo data, an image recognition algorithm is used to identify the object and distinguish its material and type. In the case of text data, a natural language processing model is used to determine the item category.

[0162] Input: garbage photo data or text data

[0163] Output: Identification of type and material of garbage

[0164] Step 4:

[0165] The server queries the municipality's garbage sorting rules database based on the user's residential address information. The user's residential address information is used as a condition for the database query to retrieve the garbage sorting rules for that area from the database.

[0166] Input: User's residential information, garbage type and material identification results

[0167] Output: Applicable garbage sorting rules

[0168] Step 5:

[0169] The server identifies the appropriate sorting method based on the garbage sorting rules it obtains from the database. It then refers to the analysis results and determines the specific disposal method for that garbage. For example, this data could be, "Plastic garbage should be collected as plastic resources."

[0170] Input: Garbage sorting rules, type and material of garbage

[0171] Output: How to separate garbage

[0172] Step 6:

[0173] The server determines whether the trash can be reused. Based on the trash identification results and its condition (e.g., whether it has scratches or not, and its functionality), it evaluates whether it can be reused. If it can be reused, it generates a proposal to put it up for sale at a flea market or auction.

[0174] Input: type and condition of garbage, sorting method

[0175] Output: Possibility of reuse, reuse proposal

[0176] Step 7:

[0177] The server consolidates all data and notifies the user of appropriate sorting methods and reuse suggestions. The server generates notification messages and sends them to the device via API, which the user receives on their smartphone. The user can check the notifications within the application and take appropriate action.

[0178] Input: Garbage sorting method, reuse suggestions

[0179] Output: A notification message to the user

[0180] The above are the processing steps of the program for this system, and an explanation including the specific operations and data flow at each step.

[0181] (Application example 1)

[0182] 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."

[0183] In modern society, the importance of waste separation and reuse is increasing, but it is difficult for individual citizens to properly determine the correct separation method and the possibility of reuse. In particular, there is a lack of means to quickly learn the appropriate separation and reuse methods for the waste brought to physical stores. In addition, there are technical challenges in efficiently obtaining waste information using smart devices and presenting it in real time.

[0184] 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.

[0185] In this invention, the server includes means for inputting garbage information using a user's terminal, means for transmitting the garbage information to the server via the Internet, means for inputting the garbage information into a generative AI model and determining the type and characteristics of the garbage, means for referencing a database of municipal garbage sorting rules based on the user's residential information and identifying an appropriate sorting method, means for determining the reuse potential of the garbage and generating reuse suggestions, means for notifying the user of the identified sorting method and reuse suggestions, and means for capturing images of the garbage with a camera in a device worn by the user and displaying the analysis results in real time. This allows users to learn appropriate sorting and reuse methods in real time when disposing of their garbage, thereby reducing environmental impact and making effective use of resources.

[0186] A "user-owned terminal" is a hardware device that can be used by any user and has the function of inputting garbage information and transmitting it to a server via the Internet.

[0187] "Via the Internet" refers to a communication method for sending and receiving data based on the Internet Protocol (IP), which realizes data linkage with a server.

[0188] A "generative AI model" is a model trained using artificial intelligence technology that has algorithms for analyzing garbage information and determining its type and characteristics.

[0189] "User's residence information" is information about the district or area where the user lives, and is data for inquiring about the garbage separation rules of the city, ward, town, or village based on that information.

[0190] "Municipal garbage sorting rules" are regulations established by a particular city, town, or village that outline the standards and methods for sorting garbage for each type of garbage.

[0191] A "database" is a system for storing and searching large amounts of data in a structured manner, and manages information including municipal waste sorting rules.

[0192] "Reusability" is a criterion that indicates whether an item classified as garbage can be reused.

[0193] "Reuse suggestions" are recommendations on how items classified as garbage can be reused, and are notified to users.

[0194] "Means for notifying the user" refers to a method for transmitting analysis results and suggestions from the server to the user, and is usually done through the user's terminal.

[0195] A "user-worn device" is a hardware device worn by a user and has the function of capturing images of dust.

[0196] "Means for displaying analysis results in real time" refers to a method for instantly analyzing the acquired garbage information and visually presenting the results to the user.

[0197] Overall system overview

[0198] This system allows users to input information about their waste using their own devices, analyzes that information using a generative AI model, and notifies the user of appropriate sorting methods and reuse suggestions. Furthermore, the system can display the analysis results in real time using the camera on the device worn by the user.

[0199] Hardware and software used

[0200] User devices: Use devices such as smartphones or smart glasses. These devices have a camera, internet connection, and display capabilities.

[0201] Server: A cloud server is used to receive data, analyze it using the generative AI model, query the municipality's garbage sorting rules database, and notify the results.

[0202] Generative AI models: Use TENSORFLOW (registered trademark) and PyTorch for image recognition and natural language processing.

[0203] Database: PostgreSQL is used to store municipal waste sorting rules.

[0204] Program processing

[0205] Entering and sending garbage information

[0206] Users input information about the garbage using their own devices. For example, they can take a photo of the garbage with their smartphone camera and enter supplementary information in text. This information is then sent to a server via the Internet.

[0207] Garbage information analysis

[0208] The server inputs the received garbage information into a generative AI model to analyze the type and characteristics of the garbage. Image recognition technology is used to identify the material and shape of the garbage from the photo, and natural language processing technology is used to determine the category from the text information.

[0209] Identifying sorting methods

[0210] The server queries the municipality's database of waste separation rules based on the user's residential address information and identifies the separation method that corresponds to the analyzed type of waste. For example, plastic items might be classified as "plastic recyclable waste," and glass products might be classified as "glass waste."

[0211] Generating reuse suggestions

[0212] Based on the analysis results, the server determines whether the garbage can be reused. If so, it generates a method and proposal for how to reuse the garbage. For example, if the clothing is still usable, it may suggest selling it on a flea market app.

[0213] Notifications and real-time display

[0214] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. The analysis results are displayed in real time on users' smart devices, allowing them to take appropriate action immediately.

[0215] Specific examples

[0216] As a specific example, consider a scenario in which a user brings a broken mobile phone to an eco-station in a brick-and-mortar store.

[0217] 1. A user brings in a broken phone and puts on smart glasses:

[0218] The camera in the smart glasses captures the mobile phone and obtains the image data.

[0219] 2. The smart glasses send the image data to the server:

[0220] Image data is sent to a server via the Internet.

[0221] 3. The server analyzes using the generated AI model:

[0222] The sent image is input into the AI ​​model and determined to be a "mobile phone."

[0223] 4. The server queries the store's garbage sorting rules:

[0224] The appropriate sorting method is identified based on the analysis results and the user's residential information (location information of the physical store).

[0225] 5. The server determines whether the item can be reused:

[0226] To determine whether a mobile phone is reusable and generate an appropriate reuse proposal.

[0227] 6. Notification of Results:

[0228] The server displays on the smart glasses in real time, "Please put this mobile phone in the electronics recycling box. Also, since there are still usable parts, please consider consulting the reuse corner of a store."

[0229] Example prompt sentence:

[0230] Q: I took a picture of my broken cell phone with my smart glasses. What are the proper ways to separate this item and what are some suggestions for reuse?

[0231] A: Please put this mobile phone in the electronics recycling bin. Also, since there are still usable parts, please consider asking at the reuse corner of a store.

[0232] As a result, users can learn the appropriate methods for separating and reusing waste in real time when disposing of it, which reduces the burden on the environment and makes effective use of resources.

[0233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0234] Step 1:

[0235] The user inputs information about the garbage using a device they own. Specifically, they wear smart glasses and use their camera to capture images of the garbage. The input data is image data of the captured garbage. This allows the physical characteristics and condition of the garbage to be visually recorded.

[0236] Step 2:

[0237] The device sends the captured image data of the garbage to a server via the Internet. Specifically, the image data is uploaded to the server's API using the HTTP protocol. The input of this step is the image data stored on the user's device, and the output is the image data sent to the server.

[0238] Step 3:

[0239] The server inputs the received image data into a generative AI model to analyze the type and characteristics of the waste. Data processing involves using an image recognition algorithm to read the image and identify the specific type of waste (e.g., plastic, metal, paper, etc.). The input is the received image data, and the output is the type and characteristics of the waste as a result of the analysis.

[0240] Step 4:

[0241] Based on the analysis results, the server compares the user's residential address information and queries the database for the city / ward / town / village's garbage sorting rules. Specifically, it executes a database query to obtain the corresponding sorting method. The input for this step is the type of garbage and the user's residential address information, and the output is the correct sorting method for that garbage.

[0242] Step 5:

[0243] The server determines whether the waste can be reused and generates a reuse proposal. This is an operation that applies an algorithm to evaluate the possibility of reuse based on the analysis results and the sorting rules queried. The input is the sorting method and waste characteristics information, and the output is a reuse proposal.

[0244] Step 6:

[0245] The server notifies the user of the identified sorting method and reuse suggestions. Specifically, the server sends a notification message to the terminal, which receives it and displays it to the user. The input is the sorting method and reuse suggestion data, and the output is a notification message in a format that the user can check.

[0246] Step 7:

[0247] The analysis results and suggestions are displayed in real time on a smart device worn by the user. Specifically, text information and icons are displayed on the glasses' display, visually conveying the results to the user. The input is a notification message from the server, and the output is the analysis results and reuse suggestions displayed in the user's field of vision.

[0248] The above are the specific processing steps of the system that realizes the application example.

[0249] 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.

[0250] This invention combines a system that effectively separates garbage and suggests reuse methods with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[0251] System Overview

[0252] Input Method

[0253] The user uses their smartphone to take a photo of the trash or enter text information about the trash. For example, the user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[0254] Transmission method

[0255] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[0256] Analysis means

[0257] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[0258] Inquiry method

[0259] The server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[0260] Specific means

[0261] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[0262] Reuse decision method

[0263] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0264] Emotion Engine

[0265] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," when inputting, or if emotions are expressed through voice input, the server recognizes those emotions.

[0266] Notification means

[0267] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, the server combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[0268] Specific examples

[0269] For plastic products

[0270] 1. User takes a photo of a plastic cup and inputs it into the app:

[0271] A user takes a photo of a plastic cup with their smartphone camera.

[0272] 2. The device sends the photo data to the server:

[0273] The device sends the photo to the server.

[0274] 3. The server analyzes using the AI ​​model:

[0275] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0276] 4. The server queries the city / ward / town / village's sorting rules:

[0277] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0278] 5. The server determines whether the item can be reused:

[0279] The server verifies that the cup is still usable and generates a reuse suggestion.

[0280] 6. The server uses the emotion engine to recognize the user's emotion:

[0281] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[0282] 7. The server notifies the user of the result:

[0283] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is a hassle, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[0284] In the case of clothing

[0285] 1. User enters text information about an old T-shirt:

[0286] A user types "old T-shirt" into the app.

[0287] 2. The device sends the text data to the server:

[0288] The terminal transmits the text information to the server.

[0289] 3. The server analyzes using the generated AI model:

[0290] The server inputs the text data into an AI model and determines that it is "clothing."

[0291] 4. The server queries the city / ward / town / village's sorting rules:

[0292] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0293] 5. The server determines whether the item can be reused:

[0294] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0295] 6. The server uses the emotion engine to recognize the user's emotion:

[0296] The server checks for sentiment, such as "no longer using."

[0297] 7. The server notifies the user of the result:

[0298] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[0299] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, they can take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[0303] Step 2:

[0304] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[0305] Step 3:

[0306] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[0307] Step 4:

[0308] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server then obtains the sorting information corresponding to the analysis results.

[0309] Step 5:

[0310] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[0311] Step 6:

[0312] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0313] Step 7:

[0314] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user enters a keyword that expresses an emotion, such as "annoying," or if an emotion is expressed through voice input, the server recognizes that emotion.

[0315] Step 8:

[0316] The server integrates all the data and creates a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and changes the content and tone of the message depending on the user's emotions.

[0317] Step 9:

[0318] The device receives notification messages from the server and displays them to the user. Through their smartphone, users can check specific sorting methods and reuse suggestions, such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the emotion engine analyzes the user's emotions and determines that the process is "troublesome," the notification message can be flexibly changed to something like "It's easy to do, so give it a try."

[0319] Example 2

[0320] 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."

[0321] Conventional garbage sorting systems often make it difficult for users to understand the garbage sorting rules in their area. Furthermore, they rarely suggest reusability and do not provide notifications that take the user's feelings into consideration. This reduces the efficiency of garbage sorting and reuse, causing users to feel stressed. Therefore, a system is needed that can easily identify garbage sorting rules for the user's area, determine reusability, and provide notifications that take the user's feelings into consideration.

[0322] 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.

[0323] In this invention, the server includes means for analyzing garbage information and determining the type of garbage, means for inquiring about garbage sorting rules for each region based on the user's residential information and identifying an appropriate sorting method, and means for recognizing the user's emotions and notifying them of reuse suggestions based on the analysis results and emotions. This allows the user to easily understand the garbage sorting rules according to their residential area and receive reuse suggestions without stress.

[0324] "Means for inputting garbage information" refers to the means by which users use devices such as smartphones or computers to input photos of garbage and text information into the system.

[0325] The "means for transmitting the garbage information" is a means for transmitting the garbage information input by the user to the server.

[0326] The "means for analyzing the information about the garbage and determining the type of garbage" refers to a means for analyzing the received garbage information using artificial intelligence or a machine learning model and determining the type and characteristics of the garbage.

[0327] "Means for inquiring about local garbage sorting rules based on the user's residential information and identifying the appropriate sorting method" refers to means for searching a database of garbage sorting rules for the area where the user lives and identifying the correct garbage sorting method for that area.

[0328] The "means for determining the reusability of the waste" is a means for determining whether the waste can be reused based on the analyzed information of the waste.

[0329] "Means for recognizing user emotions and notifying reuse suggestions based on the analysis results and emotions" refers to a means for analyzing user emotions from the user's input data and interactions, and making appropriate reuse suggestions to the user based on the analysis results and garbage information.

[0330] This invention is a system that effectively separates garbage and suggests reuse methods, and is combined with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[0331] Input Method

[0332] Users enter information about the garbage using a device such as a smartphone or computer. Specifically, they must take a photo of the garbage using the smartphone camera or enter text information. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[0333] Transmission method

[0334] The device sends the captured photo or entered text information to the server. Specifically, image data or text data is sent via the Internet, and the server receives the data via API.

[0335] Analysis means

[0336] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, the server might identify a "plastic cup" from a photo and then determine categories such as "clothing" or "broken parts" from the text.

[0337] Inquiry method

[0338] The server queries a database of garbage sorting rules for each region corresponding to the user's residence. This database contains detailed garbage sorting rules for each region. The server then obtains the sorting information corresponding to the analysis results.

[0339] Specific means

[0340] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines that it should be disposed of as "plastic waste."

[0341] Reuse decision method

[0342] Based on the analysis results, the server determines whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0343] Emotion Engine

[0344] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," or if emotions are expressed through voice input, the server recognizes those emotions.

[0345] Notification means

[0346] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[0347] Specific examples

[0348] For plastic products

[0349] 1. User takes a photo of a plastic cup and inputs it into the app:

[0350] A user takes a photo of a plastic cup with their smartphone camera.

[0351] 2. The device sends the photo data to the server:

[0352] The device sends the photo to the server.

[0353] 3. The server analyzes using the AI ​​model:

[0354] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0355] 4. The server queries the local sorting rules:

[0356] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0357] 5. The server determines whether the item can be reused:

[0358] The server verifies that the cup is still usable and generates a reuse suggestion.

[0359] 6. The server uses the emotion engine to recognize the user's emotion:

[0360] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[0361] 7. The server notifies the user of the result:

[0362] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is a hassle, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[0363] In the case of clothing

[0364] 1. User enters text information about an old T-shirt:

[0365] A user types "old T-shirt" into the app.

[0366] 2. The device sends the text data to the server:

[0367] The terminal transmits the text information to the server.

[0368] 3. The server analyzes using the AI ​​model:

[0369] The server inputs the text data into an AI model and determines that it is "clothing."

[0370] 4. The server queries the local sorting rules:

[0371] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0372] 5. The server determines whether the item can be reused:

[0373] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0374] 6. The server uses the emotion engine to recognize the user's emotion:

[0375] The server checks for sentiment, such as "no longer using."

[0376] 7. The server notifies the user of the result:

[0377] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[0378] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[0379] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0380] Step 1:

[0381] The user inputs information about the garbage.

[0382] Input: The user opens the app on their smartphone and takes a photo of the trash or enters text information.

[0383] Specific action: The user takes a photo of a broken plastic cup with their smartphone camera or types in the text "old T-shirt."

[0384] Output: The input photo data or text data is saved in the device's app.

[0385] Step 2:

[0386] The terminal sends the input data to the server.

[0387] Input: Photo or text data entered by the user.

[0388] Specific operation: The device clicks the send button and sends the input data to the server via the Internet.

[0389] Output: Photo data or text data arrives at the server.

[0390] Step 3:

[0391] The server analyzes the received data using an AI model.

[0392] Input: Photo or text data received by the server.

[0393] Specific operation: The server analyzes the photo data using image recognition technology and determines that it is a "plastic cup." It also uses natural language processing technology to classify the text data as "clothing."

[0394] Output: Analysis results showing the type and characteristics of the waste.

[0395] Step 4:

[0396] The server queries the classification rule database.

[0397] Input: Server analysis results (type and characteristics of garbage) and user's location information.

[0398] Specific operation: The server searches the sorting rule database and obtains the sorting rules for the relevant area.

[0399] Output: How to properly separate waste.

[0400] Step 5:

[0401] The server identifies the appropriate way to dispose of the waste.

[0402] Input: Separation information obtained from the Separation Rules Database.

[0403] Specific behavior: The server classifies the plastic cup as "plastic waste."

[0404] Output: Proper disposal of waste.

[0405] Step 6:

[0406] The server determines whether it can be reused.

[0407] Input: Garbage analysis results.

[0408] Specific operation: The server determines whether the garbage is recyclable and evaluates it as, for example, "Plastic cups can still be used."

[0409] Output: Judgment result on reusability.

[0410] Step 7:

[0411] The server recognizes the user's emotions using an emotion engine.

[0412] Input: User input data and interaction records.

[0413] Specific operation: The server uses an emotion engine to analyze what the user feels is "annoying."

[0414] Output: User sentiment analysis results.

[0415] Step 8:

[0416] The server notifies the user of the results.

[0417] Input: Garbage analysis results, sorting method, reuse decision results, emotion analysis results.

[0418] Specific operation: The server integrates these results and generates a notification message for the user. For example, it may say, "Please separate the item as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." Flexible messages based on emotions may also be included.

[0419] Output: The message that is displayed to the user.

[0420] (Application example 2)

[0421] 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."

[0422] Current technology in waste sorting and reuse suggestion systems provides uniform notifications without considering the user's emotional state, resulting in a decline in user satisfaction and willingness to use the service. Furthermore, there are limitations to the ability of store staff to provide appropriate recycling information and reuse suggestions to customers in real time, which can lead to a decline in service quality. Furthermore, the lack of flexibility in reuse suggestions can result in a cumbersome and inefficient experience for users.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0424] In this invention, the server includes: a means for inputting garbage information; a means for transmitting the garbage information; a means for analyzing the garbage information and determining the type of garbage; a means for consulting municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method; a means for determining whether the garbage can be reused; a means for notifying the user of the sorting method and reuse suggestions; an emotion engine for recognizing the user's emotions and adjusting the content of the notification message in accordance with the emotion; and a means for displaying the notification message based on the emotion. This enables flexible notifications based on the user's emotions, thereby improving user satisfaction and willingness to use the service. Furthermore, in brick-and-mortar stores, staff can provide appropriate recycling information and reuse suggestions in real time through the smart glasses, improving service quality and enabling more efficient operations.

[0425] "Garbage information" is data about garbage entered by the user, and includes photo data and text data.

[0426] A "means" is a method or apparatus for performing a particular function or role.

[0427] A "server" is a central computer system that processes and analyzes data over a network.

[0428] "Analysis" is the process of examining data scientifically and technically to arrive at a conclusion.

[0429] "Type of waste" refers to the specific classification of waste, such as plastic, paper, metal, clothing, etc.

[0430] "Residence information" is information about the place where the user lives, and includes local information such as city, ward, town, and village.

[0431] "Garbage sorting rules" are instructions on how to classify and dispose of garbage set by a city, town, village, or other local area.

[0432] "Reusability" is a characteristic that indicates whether or not garbage can be reused.

[0433] "Notification" is the transmission of information from the system to the user.

[0434] An "emotion engine" is a combination of software and algorithms for recognizing and analyzing a user's emotions.

[0435] "Adjusting the content of the notification message" means changing the content of the notification message in accordance with the user's emotions.

[0436] "Smart glasses" are a wearable device in the form of glasses that can display information.

[0437] To implement this invention, a user first inputs information about the garbage using smart glasses. Specifically, the user provides the garbage information to the system by tagging or taking a photo. The image or text data of the garbage captured using smart glasses (e.g., Google® Glass®) is sent to the server via the terminal.

[0438] The server inputs the received image or text data of the garbage into a generative AI model for analysis. This analysis identifies the type of garbage. Based on the analyzed data, the server uses the user's residential address information to query the city, town, or village's garbage sorting rules from a database. This identifies the appropriate sorting method.

[0439] Next, the server determines whether the trash can be reused. If the trash can be reused, a reuse suggestion is generated. This suggestion may include, for example, a suggestion for reuse on a flea market site or an auction site.

[0440] The server also uses an emotion engine to analyze the text and voice entered by the user and recognize the user's emotions. Based on this result, the content and tone of the notification message are adjusted. For example, if the user feels that something is "troublesome" based on the analysis results of the emotion engine, a message such as "It's easy, so give it a try" will be displayed.

[0441] These notification messages are displayed to users through smart glasses or other display means, allowing users to receive real-time information on appropriate waste sorting methods and reuse suggestions, as well as emotional guidance.

[0442] As a concrete example, suppose a store staff member wearing smart glasses is serving a customer and the customer asks, "How should I dispose of this disposable plastic bottle?" The staff member uses the smart glasses to take an image of the bottle and sends the data to a server. After analysis, a notification is displayed saying, "Simply put this plastic bottle in a collection box. It's easy, so please give it a try."

[0443] Example prompt sentence:

[0444] When a customer brings in a discolored plastic bottle and asks a staff member, "What should I do with this?"

[0445] The hardware used includes smart glasses as an image capture device and a server for data processing and analysis, while the software includes a generative AI model for image recognition and natural language processing, an emotion engine for emotion analysis, and a communication program (such as a requests library) for data transmission.

[0446] By using this system, users can smoothly separate their trash and receive suggestions for reuse, thereby contributing to environmental protection. Furthermore, especially in brick-and-mortar stores, this system improves the quality of service by enabling staff to provide customers with prompt and appropriate answers.

[0447] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0448] Step 1:

[0449] The user uses the smart glasses to input information about the garbage. Specifically, the user can take a photo of the garbage using the smart glasses' camera or input text data about the garbage using voice input. The input data includes information about the type and characteristics of the garbage.

[0450] Input: Photos of garbage or audio text data

[0451] Output: Captured photo data or text data

[0452] Step 2:

[0453] The device sends the captured photo data or text data to the server. The requests library is used for communication, and each piece of data is sent via API.

[0454] Input: Captured photo data or text data

[0455] Output: Photo data or text data sent to the server

[0456] Step 3:

[0457] The server inputs the received photo or text data into a generative AI model for analysis, which uses image recognition and natural language processing techniques to identify the type of garbage.

[0458] Input: Photo data or text data sent to the server

[0459] Output: Information about the types of garbage analyzed

[0460] Step 4:

[0461] Based on the user's residential information, the server queries the database for the city, ward, town, or village's garbage sorting rules and identifies the sorting method that corresponds to the analysis results.

[0462] Input: Information about the type of garbage analyzed, and the user's location

[0463] Output: Information about the identified waste sorting methods

[0464] Step 5:

[0465] The server determines whether the trash can be reused based on the analysis results, and if so, generates a reuse proposal for the trash on a flea market or auction site.

[0466] Input: Information about the type of garbage analyzed

[0467] Output: Judgment of reusability and reuse proposal

[0468] Step 6:

[0469] The server uses an emotion engine to analyze emotions based on user input and interactions. The emotion engine uses natural language processing to identify the emotions the user is feeling (e.g., annoyed, happy).

[0470] Input: User input data and interactions

[0471] Output: Information about the identified user's emotions

[0472] Step 7:

[0473] The server combines the analysis results with the emotion engine results to generate flexible notification messages that respond to the user's emotions. The notifications include garbage sorting methods and reuse suggestions, and the content and tone are adjusted according to the user's emotions.

[0474] Input: Garbage sorting method, reuse suggestions, and user sentiment

[0475] Output: Flexible notification messages that respond to emotions

[0476] Step 8:

[0477] The server generates a notification message and notifies the user through smart glasses or other display means, allowing the user to receive relevant information in real time.

[0478] Input: Flexible notification messages that respond to emotions

[0479] Output: Notification message displayed on the smart glasses

[0480] 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.

[0481] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0482] 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.

[0483] [Second embodiment]

[0484] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0485] 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.

[0486] 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).

[0487] 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.

[0488] 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.

[0489] 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).

[0490] 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. 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.

[0495] 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."

[0496] The present invention is a system that includes a series of processes, starting with the user inputting information about the garbage, followed by an analysis by the server and notification of the appropriate disposal method and reuse proposal to the user. The specific processes and operations of each part of the system are explained below.

[0497] System Overview

[0498] Input Method

[0499] A user uses a smartphone to take a photo of the trash or input text information. For example, a user can take a photo of a broken plastic cup or input "old T-shirt" as text. This information is important data for identifying the characteristics of the trash.

[0500] Transmission method

[0501] The device sends the entered garbage information to a server. This transmission is done via the Internet, and the server receives the data through an API (Application Program Interface).

[0502] Analysis means

[0503] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from text.

[0504] Inquiry method

[0505] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server then retrieves the sorting rules corresponding to the analyzed garbage type.

[0506] Specific means

[0507] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information from a city or ward / ward database that "plastic waste should be collected as plastic resources" and notifies the user of that method.

[0508] Reuse decision method

[0509] Based on the analysis results, the server determines whether the garbage can be reused, for example, by determining whether it is a plastic product that can still be used or clothing that can still be worn, and if so, suggests that the item be put up for sale at a flea market or auction service.

[0510] Notification means

[0511] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. Notifications are sent via a smartphone app, allowing users to check the information immediately. For example, a message might appear saying, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0512] Specific examples

[0513] For plastic products

[0514] 1. User takes a photo of a plastic cup and inputs it into the app:

[0515] A user takes a photo of a plastic cup with their smartphone camera.

[0516] 2. The device sends the photo data to the server:

[0517] The device sends the photo to the server.

[0518] 3. The server analyzes using the AI ​​model:

[0519] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0520] 4. The server queries the city / ward / town / village's sorting rules:

[0521] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0522] 5. The server determines whether the item can be reused:

[0523] The server verifies that the cup is still usable and generates a reuse suggestion.

[0524] 6. The server notifies the user of the result:

[0525] The notice reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0526] In the case of clothing

[0527] 1. User enters text information about an old T-shirt:

[0528] A user types "old T-shirt" into the app.

[0529] 2. The device sends the text data to the server:

[0530] The terminal transmits the text information to the server.

[0531] 3. The server analyzes using the generated AI model:

[0532] The server inputs the text data into an AI model and determines that it is "clothing."

[0533] 4. The server queries the city / ward / town / village's sorting rules:

[0534] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0535] 5. The server determines whether the item can be reused:

[0536] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0537] 6. The server notifies the user of the result:

[0538] The notice reads, "Please separate it as fabric waste. Also, since it is still usable, we recommend that you sell it on a flea market site."

[0539] The above is an embodiment of the present invention. This system allows users to easily check the appropriate garbage sorting method and reusability of garbage, and dispose of garbage in accordance with local garbage sorting rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective resource utilization and reduce environmental impact.

[0540] The processing flow will be explained below.

[0541] Step 1:

[0542] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, a user might take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[0543] Step 2:

[0544] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[0545] Step 3:

[0546] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. Specifically, the server can identify a plastic cup based on photo data, or determine that it is clothing by analyzing text data.

[0547] Step 4:

[0548] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[0549] Step 5:

[0550] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[0551] Step 6:

[0552] The server then checks the analysis results again to determine whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0553] Step 7:

[0554] The server then aggregates all the information and creates a final notification for the user, combining the analysis results and reuse suggestions into a single notification message.

[0555] Step 8:

[0556] The device receives a notification message from the server and displays it to the user. The user can then use their smartphone to view specific sorting methods and reuse suggestions, such as, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0557] Example 1

[0558] 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."

[0559] Current waste sorting and reuse systems have the problem that it is difficult for users to accurately identify the type of waste and understand the appropriate disposal method. As a result, potential reuse of waste is overlooked, or incorrect sorting increases the environmental burden. To solve this problem, a system is needed that allows users to easily identify the type of waste and learn the correct sorting and reuse methods.

[0560] 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.

[0561] In this invention, the server includes means for a user to input garbage information, means for transmitting the garbage information from a terminal to the server, means for the server to input the garbage information into a generative AI model and analyze it, means for querying a database of garbage sorting rules for the city, ward, town, or village based on the user's residential information and identifying an appropriate sorting method, means for determining whether the garbage can be reused, and means for notifying the user of the sorting method and reuse suggestions. This enables the user to easily identify the type of garbage and receive accurate sorting methods and reuse suggestions.

[0562] "Garbage information" is data relating to waste input by the user, and includes image data or text data.

[0563] A "terminal" is a device such as a smartphone or tablet operated by a user, which provides a means for inputting garbage information and sending it to a server.

[0564] The "server" is a central computer system that receives garbage information sent from the terminals and performs analysis and query processing.

[0565] A "generative AI model" is an artificial intelligence model that uses image recognition and natural language processing technologies to analyze information about garbage and determine its specific type and characteristics.

[0566] The "database" is an information aggregation system that stores municipal garbage sorting rules and accepts inquiries based on the user's residential address information.

[0567] "Querying" is the process of sending a query to a database to obtain specific information and retrieving the required data.

[0568] "Sorting method" refers to the procedures and rules for separating waste into appropriate categories and following designated collection and disposal methods.

[0569] "Reusability" refers to the criteria or assessment that determines whether waste can be reused or resold.

[0570] "Notification" refers to the act of informing users of the analysis results, sorting methods, and reuse suggestions, and is a message provided via a smartphone app.

[0571] The system of the present invention involves a series of processes in which a user inputs information about the waste, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. The specific configuration and operation for implementing this system are described below.

[0572] System Overview

[0573] Input Method

[0574] Users use a device such as a smartphone to take a photo of the trash or enter text information into the application. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This information becomes important data for the server to analyze the characteristics of the trash.

[0575] Transmission method

[0576] The terminal sends the garbage information entered by the user to a server. This transmission is carried out via the Internet, and the server receives the data through an application program interface (API).

[0577] Analysis means

[0578] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[0579] Inquiry method

[0580] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server retrieves the sorting rules corresponding to the analyzed garbage type.

[0581] Specific means

[0582] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information such as "plastic waste should be collected as plastic resources" from the municipal waste separation rules database and notifies the user of that method.

[0583] Reuse decision method

[0584] Based on the analysis results, the server determines whether the trash can be reused, for example, by determining whether the plastic products or clothing can still be used, and if so, suggests putting those items up for sale at a flea market or auction service.

[0585] Notification means

[0586] The server integrates all the data and notifies the user of appropriate sorting methods and reuse suggestions. This notification is sent via a smartphone application, allowing the user to immediately check the information. For example, a message such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site" may be displayed.

[0587] Specific examples

[0588] Examples of plastic products

[0589] 1. The user inputs a photo of a plastic cup into the app.

[0590] 2. The device sends the photo data to the server.

[0591] 3. The server inputs the photo into a generative AI model and determines that it is a "plastic cup."

[0592] 4. Based on the user's residential address information, the server queries the city / ward / town / village's database of garbage sorting rules and obtains the information that the item should be collected as a plastic resource.

[0593] 5. The server determines whether the cup is reusable and verifies that it is usable.

[0594] 6. The server notifies the user of the sorting method and reuse suggestions.

[0595] Examples of clothing

[0596] 1. The user enters the text information "old T-shirt" into the app.

[0597] 2. The device sends the text information to the server.

[0598] 3. The server inputs the text data into the generative AI model and determines that it is "clothing."

[0599] 4. The server queries the garbage sorting rules database based on the user's residential address information and obtains the information that the item should be treated as fabric garbage.

[0600] 5. The server determines whether the T-shirt can be reused and verifies that it is usable.

[0601] 6. The server notifies the user of the sorting method and reuse suggestions.

[0602] Examples of prompt statements used:

[0603] Below are some example prompts used as input to generative AI models:

[0604] When inputting a photo

[0605] "Identify the object in this photo and determine what it is made of."

[0606] For text input

[0607] "Based on the text entered, determine which category this item belongs to."

[0608] The above is a specific embodiment for carrying out the invention. This system allows users to easily check the appropriate method for separating waste and the reusability of waste, and allows for appropriate disposal in accordance with local waste separation rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective use of resources and reduce environmental impact.

[0609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0610] Step 1:

[0611] The user enters information about the garbage. The system's application is launched, and an input screen is used to take a photo of the garbage or to enter text information. The user takes a photo of the garbage with their smartphone camera and imports it into the app, or enters text about the garbage. This input data is saved on the device as photo data or text data.

[0612] Input: photo of trash or text information

[0613] Output: Garbage photo data or text data

[0614] Step 2:

[0615] The device sends the entered garbage information to the server. When the user taps the "Send" button, the device sends the photo data and text data to the server via API. The API receives this data via the Internet.

[0616] Input: Photo data or text data of garbage sent from the device

[0617] Output: Photo data or text data of the garbage stored on the server side

[0618] Step 3:

[0619] The server inputs the received garbage information into a generative AI model for analysis. The server passes the received data to an analysis engine, which performs image recognition or natural language processing. In the case of photo data, an image recognition algorithm is used to identify the object and distinguish its material and type. In the case of text data, a natural language processing model is used to determine the item category.

[0620] Input: garbage photo data or text data

[0621] Output: Identification of type and material of garbage

[0622] Step 4:

[0623] The server queries the municipality's garbage sorting rules database based on the user's residential address information. The user's residential address information is used as a condition for the database query to retrieve the garbage sorting rules for that area from the database.

[0624] Input: User's residential information, garbage type and material identification results

[0625] Output: Applicable garbage sorting rules

[0626] Step 5:

[0627] The server identifies the appropriate sorting method based on the garbage sorting rules it obtains from the database. It then refers to the analysis results and determines the specific disposal method for that garbage. For example, this data could be, "Plastic garbage should be collected as plastic resources."

[0628] Input: Garbage sorting rules, type and material of garbage

[0629] Output: How to separate garbage

[0630] Step 6:

[0631] The server determines whether the trash can be reused. Based on the trash identification results and its condition (e.g., whether it has scratches or not, and its functionality), it evaluates whether it can be reused. If it can be reused, it generates a proposal to put it up for sale at a flea market or auction.

[0632] Input: type and condition of garbage, sorting method

[0633] Output: Possibility of reuse, reuse proposal

[0634] Step 7:

[0635] The server consolidates all data and notifies the user of appropriate sorting methods and reuse suggestions. The server generates notification messages and sends them to the device via API, which the user receives on their smartphone. The user can check the notifications within the application and take appropriate action.

[0636] Input: Garbage sorting method, reuse suggestions

[0637] Output: A notification message to the user

[0638] The above are the processing steps of the program for this system, and an explanation including the specific operations and data flow at each step.

[0639] (Application example 1)

[0640] 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."

[0641] In modern society, the importance of waste separation and reuse is increasing, but it is difficult for individual citizens to properly determine the correct separation method and the possibility of reuse. In particular, there is a lack of means to quickly learn the appropriate separation and reuse methods for the waste brought to physical stores. In addition, there are technical challenges in efficiently obtaining waste information using smart devices and presenting it in real time.

[0642] 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.

[0643] In this invention, the server includes means for inputting garbage information using a user's terminal, means for transmitting the garbage information to the server via the Internet, means for inputting the garbage information into a generative AI model and determining the type and characteristics of the garbage, means for referencing a database of municipal garbage sorting rules based on the user's residential information and identifying an appropriate sorting method, means for determining the reuse potential of the garbage and generating reuse suggestions, means for notifying the user of the identified sorting method and reuse suggestions, and means for capturing images of the garbage with a camera in a device worn by the user and displaying the analysis results in real time. This allows users to learn appropriate sorting and reuse methods in real time when disposing of their garbage, thereby reducing environmental impact and making effective use of resources.

[0644] A "user-owned terminal" is a hardware device that can be used by any user and has the function of inputting garbage information and transmitting it to a server via the Internet.

[0645] "Via the Internet" refers to a communication method for sending and receiving data based on the Internet Protocol (IP), which realizes data linkage with a server.

[0646] A "generative AI model" is a model trained using artificial intelligence technology that has algorithms for analyzing garbage information and determining its type and characteristics.

[0647] "User's residence information" is information about the district or area where the user lives, and is data for inquiring about the garbage separation rules of the city, ward, town, or village based on that information.

[0648] "Municipal garbage sorting rules" are regulations established by a particular city, town, or village that outline the standards and methods for sorting garbage for each type of garbage.

[0649] A "database" is a system for storing and searching large amounts of data in a structured manner, and manages information including municipal waste sorting rules.

[0650] "Reusability" is a criterion that indicates whether an item classified as garbage can be reused.

[0651] "Reuse suggestions" are recommendations on how items classified as garbage can be reused, and are notified to users.

[0652] "Means for notifying the user" refers to a method for transmitting analysis results and suggestions from the server to the user, and is usually done through the user's terminal.

[0653] A "user-worn device" is a hardware device worn by a user and has the function of capturing images of dust.

[0654] "Means for displaying analysis results in real time" refers to a method for instantly analyzing the acquired garbage information and visually presenting the results to the user.

[0655] Overall system overview

[0656] This system allows users to input information about their waste using their own devices, analyzes that information using a generative AI model, and notifies the user of appropriate sorting methods and reuse suggestions. Furthermore, the system can display the analysis results in real time using the camera on the device worn by the user.

[0657] Hardware and software used

[0658] User devices: Use devices such as smartphones or smart glasses. These devices have a camera, internet connection, and display capabilities.

[0659] Server: A cloud server is used to receive data, analyze it using the generative AI model, query the municipality's garbage sorting rules database, and notify the results.

[0660] Generative AI models: Use TensorFlow and PyTorch for image recognition and natural language processing.

[0661] Database: PostgreSQL is used to store municipal waste sorting rules.

[0662] Program processing

[0663] Entering and sending garbage information

[0664] Users input information about the garbage using their own devices. For example, they can take a photo of the garbage with their smartphone camera and enter supplementary information in text. This information is then sent to a server via the Internet.

[0665] Garbage information analysis

[0666] The server inputs the received garbage information into a generative AI model to analyze the type and characteristics of the garbage. Image recognition technology is used to identify the material and shape of the garbage from the photo, and natural language processing technology is used to determine the category from the text information.

[0667] Identifying sorting methods

[0668] The server queries the municipality's database of waste separation rules based on the user's residential address information and identifies the separation method that corresponds to the analyzed type of waste. For example, plastic items might be classified as "plastic recyclable waste," and glass products might be classified as "glass waste."

[0669] Generating reuse suggestions

[0670] Based on the analysis results, the server determines whether the garbage can be reused. If so, it generates a method and proposal for how to reuse the garbage. For example, if the clothing is still usable, it may suggest selling it on a flea market app.

[0671] Notifications and real-time display

[0672] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. The analysis results are displayed in real time on users' smart devices, allowing them to take appropriate action immediately.

[0673] Specific examples

[0674] As a specific example, consider a scenario in which a user brings a broken mobile phone to an eco-station in a brick-and-mortar store.

[0675] 1. A user brings in a broken phone and puts on smart glasses:

[0676] The camera in the smart glasses captures the mobile phone and obtains the image data.

[0677] 2. The smart glasses send the image data to the server:

[0678] Image data is sent to a server via the Internet.

[0679] 3. The server analyzes using the generated AI model:

[0680] The sent image is input into the AI ​​model and determined to be a "mobile phone."

[0681] 4. The server queries the store's garbage sorting rules:

[0682] The appropriate sorting method is identified based on the analysis results and the user's residential information (location information of the physical store).

[0683] 5. The server determines whether the item can be reused:

[0684] To determine whether a mobile phone is reusable and generate an appropriate reuse proposal.

[0685] 6. Notification of Results:

[0686] The server displays on the smart glasses in real time, "Please put this mobile phone in the electronics recycling box. Also, since there are still usable parts, please consider consulting the reuse corner of a store."

[0687] Example prompt sentence:

[0688] Q: I took a picture of my broken cell phone with my smart glasses. What are the proper ways to separate this item and what are some suggestions for reuse?

[0689] A: Please put this mobile phone in the electronics recycling bin. Also, since there are still usable parts, please consider asking at the reuse corner of a store.

[0690] As a result, users can learn the appropriate methods for separating and reusing waste in real time when disposing of it, which reduces the burden on the environment and makes effective use of resources.

[0691] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0692] Step 1:

[0693] The user inputs information about the garbage using a device they own. Specifically, they wear smart glasses and use their camera to capture images of the garbage. The input data is image data of the captured garbage. This allows the physical characteristics and condition of the garbage to be visually recorded.

[0694] Step 2:

[0695] The device sends the captured image data of the garbage to a server via the Internet. Specifically, the image data is uploaded to the server's API using the HTTP protocol. The input of this step is the image data stored on the user's device, and the output is the image data sent to the server.

[0696] Step 3:

[0697] The server inputs the received image data into a generative AI model to analyze the type and characteristics of the waste. Data processing involves using an image recognition algorithm to read the image and identify the specific type of waste (e.g., plastic, metal, paper, etc.). The input is the received image data, and the output is the type and characteristics of the waste as a result of the analysis.

[0698] Step 4:

[0699] Based on the analysis results, the server compares the user's residential address information and queries the database for the city / ward / town / village's garbage sorting rules. Specifically, it executes a database query to obtain the corresponding sorting method. The input for this step is the type of garbage and the user's residential address information, and the output is the correct sorting method for that garbage.

[0700] Step 5:

[0701] The server determines whether the waste can be reused and generates a reuse proposal. This is an operation that applies an algorithm to evaluate the possibility of reuse based on the analysis results and the sorting rules queried. The input is the sorting method and waste characteristics information, and the output is a reuse proposal.

[0702] Step 6:

[0703] The server notifies the user of the identified sorting method and reuse suggestions. Specifically, the server sends a notification message to the terminal, which receives it and displays it to the user. The input is the sorting method and reuse suggestion data, and the output is a notification message in a format that the user can check.

[0704] Step 7:

[0705] The analysis results and suggestions are displayed in real time on a smart device worn by the user. Specifically, text information and icons are displayed on the glasses' display, visually conveying the results to the user. The input is a notification message from the server, and the output is the analysis results and reuse suggestions displayed in the user's field of vision.

[0706] The above are the specific processing steps of the system that realizes the application example.

[0707] 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.

[0708] This invention combines a system that effectively separates garbage and suggests reuse methods with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[0709] System Overview

[0710] Input Method

[0711] The user uses their smartphone to take a photo of the trash or enter text information about the trash. For example, the user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[0712] Transmission method

[0713] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[0714] Analysis means

[0715] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[0716] Inquiry method

[0717] The server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[0718] Specific means

[0719] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[0720] Reuse decision method

[0721] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0722] Emotion Engine

[0723] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," when inputting, or if emotions are expressed through voice input, the server recognizes those emotions.

[0724] Notification means

[0725] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, the server combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[0726] Specific examples

[0727] For plastic products

[0728] 1. User takes a photo of a plastic cup and inputs it into the app:

[0729] A user takes a photo of a plastic cup with their smartphone camera.

[0730] 2. The device sends the photo data to the server:

[0731] The device sends the photo to the server.

[0732] 3. The server analyzes using the AI ​​model:

[0733] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0734] 4. The server queries the city / ward / town / village's sorting rules:

[0735] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0736] 5. The server determines whether the item can be reused:

[0737] The server verifies that the cup is still usable and generates a reuse suggestion.

[0738] 6. The server uses the emotion engine to recognize the user's emotion:

[0739] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[0740] 7. The server notifies the user of the result:

[0741] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is a hassle, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[0742] In the case of clothing

[0743] 1. User enters text information about an old T-shirt:

[0744] A user types "old T-shirt" into the app.

[0745] 2. The device sends the text data to the server:

[0746] The terminal transmits the text information to the server.

[0747] 3. The server analyzes using the generated AI model:

[0748] The server inputs the text data into an AI model and determines that it is "clothing."

[0749] 4. The server queries the city / ward / town / village's sorting rules:

[0750] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0751] 5. The server determines whether the item can be reused:

[0752] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0753] 6. The server uses the emotion engine to recognize the user's emotion:

[0754] The server checks for sentiment, such as "no longer using."

[0755] 7. The server notifies the user of the result:

[0756] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[0757] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[0758] The processing flow will be explained below.

[0759] Step 1:

[0760] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, they can take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[0761] Step 2:

[0762] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[0763] Step 3:

[0764] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[0765] Step 4:

[0766] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server then obtains the sorting information corresponding to the analysis results.

[0767] Step 5:

[0768] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[0769] Step 6:

[0770] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0771] Step 7:

[0772] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user enters a keyword that expresses an emotion, such as "annoying," or if an emotion is expressed through voice input, the server recognizes that emotion.

[0773] Step 8:

[0774] The server integrates all the data and creates a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and changes the content and tone of the message depending on the user's emotions.

[0775] Step 9:

[0776] The device receives notification messages from the server and displays them to the user. Through their smartphone, users can check specific sorting methods and reuse suggestions, such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the emotion engine analyzes the user's emotions and determines that the process is "troublesome," the notification message can be flexibly changed to something like "It's easy to do, so give it a try."

[0777] Example 2

[0778] 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."

[0779] Conventional garbage sorting systems often make it difficult for users to understand the garbage sorting rules in their area. Furthermore, they rarely suggest reusability and do not provide notifications that take the user's feelings into consideration. This reduces the efficiency of garbage sorting and reuse, causing users to feel stressed. Therefore, a system is needed that can easily identify garbage sorting rules for the user's area, determine reusability, and provide notifications that take the user's feelings into consideration.

[0780] 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.

[0781] In this invention, the server includes means for analyzing garbage information and determining the type of garbage, means for inquiring about garbage sorting rules for each region based on the user's residential information and identifying an appropriate sorting method, and means for recognizing the user's emotions and notifying them of reuse suggestions based on the analysis results and emotions. This allows the user to easily understand the garbage sorting rules according to their residential area and receive reuse suggestions without stress.

[0782] "Means for inputting garbage information" refers to the means by which users use devices such as smartphones or computers to input photos of garbage and text information into the system.

[0783] The "means for transmitting the garbage information" is a means for transmitting the garbage information input by the user to the server.

[0784] The "means for analyzing the information about the garbage and determining the type of garbage" refers to a means for analyzing the received garbage information using artificial intelligence or a machine learning model and determining the type and characteristics of the garbage.

[0785] "Means for inquiring about local garbage sorting rules based on the user's residential information and identifying the appropriate sorting method" refers to means for searching a database of garbage sorting rules for the area where the user lives and identifying the correct garbage sorting method for that area.

[0786] The "means for determining the reusability of the waste" is a means for determining whether the waste can be reused based on the analyzed information of the waste.

[0787] "Means for recognizing user emotions and notifying reuse suggestions based on the analysis results and emotions" refers to a means for analyzing user emotions from the user's input data and interactions, and making appropriate reuse suggestions to the user based on the analysis results and garbage information.

[0788] This invention is a system that effectively separates garbage and suggests reuse methods, and is combined with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[0789] Input Method

[0790] Users enter information about the garbage using a device such as a smartphone or computer. Specifically, they must take a photo of the garbage using the smartphone camera or enter text information. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[0791] Transmission method

[0792] The device sends the captured photo or entered text information to the server. Specifically, image data or text data is sent via the Internet, and the server receives the data via API.

[0793] Analysis means

[0794] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, the server might identify a "plastic cup" from a photo and then determine categories such as "clothing" or "broken parts" from the text.

[0795] Inquiry method

[0796] The server queries a database of garbage sorting rules for each region corresponding to the user's residence. This database contains detailed garbage sorting rules for each region. The server then obtains the sorting information corresponding to the analysis results.

[0797] Specific means

[0798] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines that it should be disposed of as "plastic waste."

[0799] Reuse decision method

[0800] Based on the analysis results, the server determines whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[0801] Emotion Engine

[0802] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," or if emotions are expressed through voice input, the server recognizes those emotions.

[0803] Notification means

[0804] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[0805] Specific examples

[0806] For plastic products

[0807] 1. User takes a photo of a plastic cup and inputs it into the app:

[0808] A user takes a photo of a plastic cup with their smartphone camera.

[0809] 2. The device sends the photo data to the server:

[0810] The device sends the photo to the server.

[0811] 3. The server analyzes using the AI ​​model:

[0812] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0813] 4. The server queries the local sorting rules:

[0814] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0815] 5. The server determines whether the item can be reused:

[0816] The server verifies that the cup is still usable and generates a reuse suggestion.

[0817] 6. The server uses the emotion engine to recognize the user's emotion:

[0818] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[0819] 7. The server notifies the user of the result:

[0820] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is a hassle, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[0821] In the case of clothing

[0822] 1. User enters text information about an old T-shirt:

[0823] A user types "old T-shirt" into the app.

[0824] 2. The device sends the text data to the server:

[0825] The terminal transmits the text information to the server.

[0826] 3. The server analyzes using the AI ​​model:

[0827] The server inputs the text data into an AI model and determines that it is "clothing."

[0828] 4. The server queries the local sorting rules:

[0829] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0830] 5. The server determines whether the item can be reused:

[0831] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0832] 6. The server uses the emotion engine to recognize the user's emotion:

[0833] The server checks for sentiment, such as "no longer using."

[0834] 7. The server notifies the user of the result:

[0835] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[0836] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[0837] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0838] Step 1:

[0839] The user inputs information about the garbage.

[0840] Input: The user opens the app on their smartphone and takes a photo of the trash or enters text information.

[0841] Specific action: The user takes a photo of a broken plastic cup with their smartphone camera or types in the text "old T-shirt."

[0842] Output: The input photo data or text data is saved in the device's app.

[0843] Step 2:

[0844] The terminal sends the input data to the server.

[0845] Input: Photo or text data entered by the user.

[0846] Specific operation: The device clicks the send button and sends the input data to the server via the Internet.

[0847] Output: Photo data or text data arrives at the server.

[0848] Step 3:

[0849] The server analyzes the received data using an AI model.

[0850] Input: Photo or text data received by the server.

[0851] Specific operation: The server analyzes the photo data using image recognition technology and determines that it is a "plastic cup." It also uses natural language processing technology to classify the text data as "clothing."

[0852] Output: Analysis results showing the type and characteristics of the waste.

[0853] Step 4:

[0854] The server queries the classification rule database.

[0855] Input: Server analysis results (type and characteristics of garbage) and user's location information.

[0856] Specific operation: The server searches the sorting rule database and obtains the sorting rules for the relevant area.

[0857] Output: How to properly separate waste.

[0858] Step 5:

[0859] The server identifies the appropriate way to dispose of the waste.

[0860] Input: Separation information obtained from the Separation Rules Database.

[0861] Specific behavior: The server classifies the plastic cup as "plastic waste."

[0862] Output: Proper disposal of waste.

[0863] Step 6:

[0864] The server determines whether it can be reused.

[0865] Input: Garbage analysis results.

[0866] Specific operation: The server determines whether the garbage is recyclable and evaluates it as, for example, "Plastic cups can still be used."

[0867] Output: Judgment result on reusability.

[0868] Step 7:

[0869] The server recognizes the user's emotions using an emotion engine.

[0870] Input: User input data and interaction records.

[0871] Specific operation: The server uses an emotion engine to analyze what the user feels is "annoying."

[0872] Output: User sentiment analysis results.

[0873] Step 8:

[0874] The server notifies the user of the results.

[0875] Input: Garbage analysis results, sorting method, reuse decision results, emotion analysis results.

[0876] Specific operation: The server integrates these results and generates a notification message for the user. For example, it may say, "Please separate the item as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." Flexible messages based on emotions may also be included.

[0877] Output: The message that is displayed to the user.

[0878] (Application example 2)

[0879] 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."

[0880] Current technology in waste sorting and reuse suggestion systems provides uniform notifications without considering the user's emotional state, resulting in a decline in user satisfaction and willingness to use the service. Furthermore, there are limitations to the ability of store staff to provide appropriate recycling information and reuse suggestions to customers in real time, which can lead to a decline in service quality. Furthermore, the lack of flexibility in reuse suggestions can result in a cumbersome and inefficient experience for users.

[0881] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0882] In this invention, the server includes: a means for inputting garbage information; a means for transmitting the garbage information; a means for analyzing the garbage information and determining the type of garbage; a means for consulting municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method; a means for determining whether the garbage can be reused; a means for notifying the user of the sorting method and reuse suggestions; an emotion engine for recognizing the user's emotions and adjusting the content of the notification message in accordance with the emotion; and a means for displaying the notification message based on the emotion. This enables flexible notifications based on the user's emotions, thereby improving user satisfaction and willingness to use the service. Furthermore, in brick-and-mortar stores, staff can provide appropriate recycling information and reuse suggestions in real time through the smart glasses, improving service quality and enabling more efficient operations.

[0883] "Garbage information" is data about garbage entered by the user, and includes photo data and text data.

[0884] A "means" is a method or apparatus for performing a particular function or role.

[0885] A "server" is a central computer system that processes and analyzes data over a network.

[0886] "Analysis" is the process of examining data scientifically and technically to arrive at a conclusion.

[0887] "Type of waste" refers to the specific classification of waste, such as plastic, paper, metal, clothing, etc.

[0888] "Residence information" is information about the place where the user lives, and includes local information such as city, ward, town, and village.

[0889] "Garbage sorting rules" are instructions on how to classify and dispose of garbage set by a city, town, village, or other local area.

[0890] "Reusability" is a characteristic that indicates whether or not garbage can be reused.

[0891] "Notification" is the transmission of information from the system to the user.

[0892] An "emotion engine" is a combination of software and algorithms for recognizing and analyzing a user's emotions.

[0893] "Adjusting the content of the notification message" means changing the content of the notification message in accordance with the user's emotions.

[0894] "Smart glasses" are a wearable device in the form of glasses that can display information.

[0895] To implement this invention, a user first inputs information about the garbage using smart glasses. Specifically, the user provides the garbage information to the system by tagging or taking a photo. The image or text data of the garbage captured using smart glasses (e.g., Google Glass) is sent to the server via the terminal.

[0896] The server inputs the received image or text data of the garbage into a generative AI model for analysis. This analysis identifies the type of garbage. Based on the analyzed data, the server uses the user's residential address information to query the city, town, or village's garbage sorting rules from a database. This identifies the appropriate sorting method.

[0897] Next, the server determines whether the trash can be reused. If the trash can be reused, a reuse suggestion is generated. This suggestion may include, for example, a suggestion for reuse on a flea market site or an auction site.

[0898] The server also uses an emotion engine to analyze the text and voice entered by the user and recognize the user's emotions. Based on this result, the content and tone of the notification message are adjusted. For example, if the user feels that something is "troublesome" based on the analysis results of the emotion engine, a message such as "It's easy, so give it a try" will be displayed.

[0899] These notification messages are displayed to users through smart glasses or other display means, allowing users to receive real-time information on appropriate waste sorting methods and reuse suggestions, as well as emotional guidance.

[0900] As a concrete example, suppose a store staff member wearing smart glasses is serving a customer and the customer asks, "How should I dispose of this disposable plastic bottle?" The staff member uses the smart glasses to take an image of the bottle and sends the data to a server. After analysis, a notification is displayed saying, "Simply put this plastic bottle in a collection box. It's easy, so please give it a try."

[0901] Example prompt sentence:

[0902] When a customer brings in a discolored plastic bottle and asks a staff member, "What should I do with this?"

[0903] The hardware used includes smart glasses as an image capture device and a server for data processing and analysis, while the software includes a generative AI model for image recognition and natural language processing, an emotion engine for emotion analysis, and a communication program (such as a requests library) for data transmission.

[0904] By using this system, users can smoothly separate their trash and receive suggestions for reuse, thereby contributing to environmental protection. Furthermore, especially in brick-and-mortar stores, this system improves the quality of service by enabling staff to provide customers with prompt and appropriate answers.

[0905] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0906] Step 1:

[0907] The user uses the smart glasses to input information about the garbage. Specifically, the user can take a photo of the garbage using the smart glasses' camera or input text data about the garbage using voice input. The input data includes information about the type and characteristics of the garbage.

[0908] Input: Photos of garbage or audio text data

[0909] Output: Captured photo data or text data

[0910] Step 2:

[0911] The device sends the captured photo data or text data to the server. The requests library is used for communication, and each piece of data is sent via API.

[0912] Input: Captured photo data or text data

[0913] Output: Photo data or text data sent to the server

[0914] Step 3:

[0915] The server inputs the received photo or text data into a generative AI model for analysis, which uses image recognition and natural language processing techniques to identify the type of garbage.

[0916] Input: Photo data or text data sent to the server

[0917] Output: Information about the types of garbage analyzed

[0918] Step 4:

[0919] Based on the user's residential information, the server queries the database for the city, ward, town, or village's garbage sorting rules and identifies the sorting method that corresponds to the analysis results.

[0920] Input: Information about the type of garbage analyzed, and the user's location

[0921] Output: Information about the identified waste sorting methods

[0922] Step 5:

[0923] The server determines whether the trash can be reused based on the analysis results, and if so, generates a reuse proposal for the trash on a flea market or auction site.

[0924] Input: Information about the type of garbage analyzed

[0925] Output: Judgment of reusability and reuse proposal

[0926] Step 6:

[0927] The server uses an emotion engine to analyze emotions based on user input and interactions. The emotion engine uses natural language processing to identify the emotions the user is feeling (e.g., annoyed, happy).

[0928] Input: User input data and interactions

[0929] Output: Information about the identified user's emotions

[0930] Step 7:

[0931] The server combines the analysis results with the emotion engine results to generate flexible notification messages that respond to the user's emotions. The notifications include garbage sorting methods and reuse suggestions, and the content and tone are adjusted according to the user's emotions.

[0932] Input: Garbage sorting method, reuse suggestions, and user sentiment

[0933] Output: Flexible notification messages that respond to emotions

[0934] Step 8:

[0935] The server generates a notification message and notifies the user through smart glasses or other display means, allowing the user to receive relevant information in real time.

[0936] Input: Flexible notification messages that respond to emotions

[0937] Output: Notification message displayed on the smart glasses

[0938] 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.

[0939] 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.

[0940] 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.

[0941] [Third embodiment]

[0942] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0943] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0944] 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).

[0945] 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.

[0946] 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.

[0947] 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).

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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.

[0952] 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.

[0953] 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."

[0954] The present invention is a system that includes a series of processes, starting with the user inputting information about the garbage, followed by an analysis by the server and notification of the user of appropriate disposal methods and reuse suggestions. The specific processes and operations of each part of the system are explained below.

[0955] System Overview

[0956] Input Method

[0957] A user uses a smartphone to take a photo of the trash or input text information. For example, a user can take a photo of a broken plastic cup or input "old T-shirt" as text. This information is important data for identifying the characteristics of the trash.

[0958] Transmission method

[0959] The device sends the entered garbage information to a server. This transmission is done via the Internet, and the server receives the data through an API (Application Program Interface).

[0960] Analysis means

[0961] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from text.

[0962] Inquiry method

[0963] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server then retrieves the sorting rules corresponding to the analyzed garbage type.

[0964] Specific means

[0965] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information from a city or ward / ward database that "plastic waste should be collected as plastic resources" and notifies the user of that method.

[0966] Reuse decision method

[0967] Based on the analysis results, the server determines whether the garbage can be reused, for example, by determining whether it is a plastic product that can still be used or clothing that can still be worn, and if so, suggests putting the item up for sale at a flea market or auction service.

[0968] Notification means

[0969] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. Notifications are sent via a smartphone app, allowing users to check the information immediately. For example, a message might appear saying, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0970] Specific examples

[0971] For plastic products

[0972] 1. User takes a photo of a plastic cup and inputs it into the app:

[0973] A user takes a photo of a plastic cup with their smartphone camera.

[0974] 2. The device sends the photo data to the server:

[0975] The device sends the photo to the server.

[0976] 3. The server analyzes using the AI ​​model:

[0977] The server inputs the photo into an AI model and determines that it is a "plastic product."

[0978] 4. The server queries the city / ward / town / village's sorting rules:

[0979] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[0980] 5. The server determines whether the item can be reused:

[0981] The server verifies that the cup is still usable and generates a reuse suggestion.

[0982] 6. The server notifies the user of the result:

[0983] The notice reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[0984] In the case of clothing

[0985] 1. User enters text information about an old T-shirt:

[0986] A user types "old T-shirt" into the app.

[0987] 2. The device sends the text data to the server:

[0988] The terminal transmits the text information to the server.

[0989] 3. The server analyzes using the generated AI model:

[0990] The server inputs the text data into an AI model and determines that it is "clothing."

[0991] 4. The server queries the city / ward / town / village's sorting rules:

[0992] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[0993] 5. The server determines whether the item can be reused:

[0994] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[0995] 6. The server notifies the user of the result:

[0996] The notice reads, "Please separate it as fabric waste. Also, since it is still usable, we recommend that you sell it on a flea market site."

[0997] The above is an embodiment of the present invention. This system allows users to easily check the appropriate method for separating garbage and whether it can be reused, and allows users to dispose of garbage in accordance with local separation rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective use of resources and reduce environmental impact.

[0998] The processing flow will be explained below.

[0999] Step 1:

[1000] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, a user might take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[1001] Step 2:

[1002] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[1003] Step 3:

[1004] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. Specifically, the server can identify a plastic cup based on photo data, or determine that it is clothing by analyzing text data.

[1005] Step 4:

[1006] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[1007] Step 5:

[1008] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[1009] Step 6:

[1010] The server then checks the analysis results again to determine whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1011] Step 7:

[1012] The server then aggregates all the information and creates a final notification for the user, combining the analysis results and reuse suggestions into a single notification message.

[1013] Step 8:

[1014] The device receives a notification message from the server and displays it to the user. The user can then use their smartphone to view specific sorting methods and reuse suggestions, such as, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[1015] Example 1

[1016] 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."

[1017] Current waste sorting and reuse systems have the problem that it is difficult for users to accurately identify the type of waste and understand the appropriate disposal method. As a result, potential reuse of waste is overlooked, or incorrect sorting increases the environmental burden. To solve this problem, a system is needed that allows users to easily identify the type of waste and learn the correct sorting and reuse methods.

[1018] 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.

[1019] In this invention, the server includes means for a user to input garbage information, means for transmitting the garbage information from a terminal to the server, means for the server to input the garbage information into a generative AI model and analyze it, means for querying a database of garbage sorting rules for the city, ward, town, or village based on the user's residential information and identifying an appropriate sorting method, means for determining whether the garbage can be reused, and means for notifying the user of the sorting method and reuse suggestions. This enables the user to easily identify the type of garbage and receive accurate sorting methods and reuse suggestions.

[1020] "Garbage information" is data relating to waste input by the user, and includes image data or text data.

[1021] A "terminal" is a device such as a smartphone or tablet operated by a user, which provides a means for inputting garbage information and sending it to a server.

[1022] The "server" is a central computer system that receives garbage information sent from the terminals and performs analysis and query processing.

[1023] A "generative AI model" is an artificial intelligence model that uses image recognition and natural language processing technologies to analyze information about garbage and determine its specific type and characteristics.

[1024] The "database" is an information aggregation system that stores municipal garbage sorting rules and accepts inquiries based on the user's residential address information.

[1025] "Querying" is the process of sending a query to a database to obtain specific information and retrieving the required data.

[1026] "Sorting method" refers to the procedures and rules for separating waste into appropriate categories and following designated collection and disposal methods.

[1027] "Reusability" refers to the criteria or assessment that determines whether waste can be reused or resold.

[1028] "Notification" refers to the act of informing users of the analysis results, sorting methods, and reuse suggestions, and is a message provided via a smartphone app.

[1029] The system of the present invention involves a series of processes in which a user inputs information about the waste, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. The specific configuration and operation for implementing this system are described below.

[1030] System Overview

[1031] Input Method

[1032] Users use a device such as a smartphone to take a photo of the trash or enter text information into the application. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This information becomes important data for the server to analyze the characteristics of the trash.

[1033] Transmission method

[1034] The terminal sends the garbage information entered by the user to a server. This transmission is carried out via the Internet, and the server receives the data through an application program interface (API).

[1035] Analysis means

[1036] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[1037] Inquiry method

[1038] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server retrieves the sorting rules corresponding to the analyzed garbage type.

[1039] Specific means

[1040] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information such as "plastic waste should be collected as plastic resources" from the municipal waste separation rules database and notifies the user of that method.

[1041] Reuse decision method

[1042] Based on the analysis results, the server determines whether the trash can be reused, for example, by determining whether the plastic products or clothing can still be used, and if so, suggests putting those items up for sale at a flea market or auction service.

[1043] Notification means

[1044] The server integrates all the data and notifies the user of appropriate sorting methods and reuse suggestions. This notification is sent via a smartphone application, allowing the user to immediately check the information. For example, a message such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site" may be displayed.

[1045] Specific examples

[1046] Examples of plastic products

[1047] 1. The user inputs a photo of a plastic cup into the app.

[1048] 2. The device sends the photo data to the server.

[1049] 3. The server inputs the photo into a generative AI model and determines that it is a "plastic cup."

[1050] 4. Based on the user's residential address information, the server queries the city / ward / town / village's database of garbage sorting rules and obtains the information that the item should be collected as a plastic resource.

[1051] 5. The server determines whether the cup is reusable and verifies that it is usable.

[1052] 6. The server notifies the user of the sorting method and reuse suggestions.

[1053] Examples of clothing

[1054] 1. The user enters the text information "old T-shirt" into the app.

[1055] 2. The device sends the text information to the server.

[1056] 3. The server inputs the text data into the generative AI model and determines that it is "clothing."

[1057] 4. The server queries the garbage sorting rules database based on the user's residential address information and obtains the information that the item should be treated as fabric garbage.

[1058] 5. The server determines whether the T-shirt can be reused and verifies that it is usable.

[1059] 6. The server notifies the user of the sorting method and reuse suggestions.

[1060] Examples of prompt statements used:

[1061] Below are some example prompts used as input to generative AI models:

[1062] When inputting a photo

[1063] "Identify the object in this photo and determine what it is made of."

[1064] For text input

[1065] "Based on the text entered, determine which category this item belongs to."

[1066] The above is a specific embodiment for carrying out the invention. This system allows users to easily check the appropriate method for separating waste and the reusability of waste, and allows for appropriate disposal in accordance with local waste separation rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective use of resources and reduce environmental impact.

[1067] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1068] Step 1:

[1069] The user enters information about the garbage. The system's application is launched, and an input screen is used to take a photo of the garbage or to enter text information. The user takes a photo of the garbage with their smartphone camera and imports it into the app, or enters text about the garbage. This input data is saved on the device as photo data or text data.

[1070] Input: photo of trash or text information

[1071] Output: Garbage photo data or text data

[1072] Step 2:

[1073] The device sends the entered garbage information to the server. When the user taps the "Send" button, the device sends the photo data and text data to the server via API. The API receives this data via the Internet.

[1074] Input: Photo data or text data of garbage sent from the device

[1075] Output: Photo data or text data of the garbage stored on the server side

[1076] Step 3:

[1077] The server inputs the received garbage information into a generative AI model for analysis. The server passes the received data to an analysis engine, which performs image recognition or natural language processing. In the case of photo data, an image recognition algorithm is used to identify the object and distinguish its material and type. In the case of text data, a natural language processing model is used to determine the item category.

[1078] Input: garbage photo data or text data

[1079] Output: Identification of type and material of garbage

[1080] Step 4:

[1081] The server queries the municipality's garbage sorting rules database based on the user's residential address information. The user's residential address information is used as a condition for the database query to retrieve the garbage sorting rules for that area from the database.

[1082] Input: User's residential information, garbage type and material identification results

[1083] Output: Applicable garbage sorting rules

[1084] Step 5:

[1085] The server identifies the appropriate sorting method based on the garbage sorting rules it obtains from the database. It then refers to the analysis results and determines the specific disposal method for that garbage. For example, this data could be, "Plastic garbage should be collected as plastic resources."

[1086] Input: Garbage sorting rules, type and material of garbage

[1087] Output: How to separate garbage

[1088] Step 6:

[1089] The server determines whether the trash can be reused. Based on the trash identification results and its condition (e.g., whether it has scratches or not, and its functionality), it evaluates whether it can be reused. If it can be reused, it generates a proposal to put it up for sale at a flea market or auction.

[1090] Input: type and condition of garbage, sorting method

[1091] Output: Possibility of reuse, reuse proposal

[1092] Step 7:

[1093] The server consolidates all data and notifies the user of appropriate sorting methods and reuse suggestions. The server generates notification messages and sends them to the device via API, which the user receives on their smartphone. The user can check the notifications within the application and take appropriate action.

[1094] Input: Garbage sorting method, reuse suggestions

[1095] Output: A notification message to the user

[1096] The above are the processing steps of the program for this system, and an explanation including the specific operations and data flow at each step.

[1097] (Application example 1)

[1098] 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."

[1099] In modern society, the importance of waste separation and reuse is increasing, but it is difficult for individual citizens to properly determine the correct separation method and the possibility of reuse. In particular, there is a lack of means to quickly learn the appropriate separation and reuse methods for the waste brought to physical stores. In addition, there are technical challenges in efficiently obtaining waste information using smart devices and presenting it in real time.

[1100] 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.

[1101] In this invention, the server includes means for inputting garbage information using a user's terminal, means for transmitting the garbage information to the server via the Internet, means for inputting the garbage information into a generative AI model and determining the type and characteristics of the garbage, means for referencing a database of municipal garbage sorting rules based on the user's residential information and identifying an appropriate sorting method, means for determining the reuse potential of the garbage and generating reuse suggestions, means for notifying the user of the identified sorting method and reuse suggestions, and means for capturing images of the garbage with a camera in a device worn by the user and displaying the analysis results in real time. This allows users to learn appropriate sorting and reuse methods in real time when disposing of their garbage, thereby reducing environmental impact and making effective use of resources.

[1102] A "user-owned terminal" is a hardware device that can be used by any user and has the function of inputting garbage information and transmitting it to a server via the Internet.

[1103] "Via the Internet" refers to a communication method for sending and receiving data based on the Internet Protocol (IP), which realizes data linkage with a server.

[1104] A "generative AI model" is a model trained using artificial intelligence technology that has algorithms for analyzing garbage information and determining its type and characteristics.

[1105] "User's residence information" is information about the district or area where the user lives, and is data for inquiring about the garbage separation rules of the city, ward, town, or village based on that information.

[1106] "Municipal garbage sorting rules" are regulations established by a particular city, town, or village that outline the standards and methods for sorting garbage for each type of garbage.

[1107] A "database" is a system for storing and searching large amounts of data in a structured manner, and manages information including municipal waste sorting rules.

[1108] "Reusability" is a criterion that indicates whether an item classified as garbage can be reused.

[1109] "Reuse suggestions" are recommendations on how items classified as garbage can be reused, and are notified to users.

[1110] "Means for notifying the user" refers to a method for transmitting analysis results and suggestions from the server to the user, and is usually done through the user's terminal.

[1111] A "user-worn device" is a hardware device worn by a user and has the function of capturing images of dust.

[1112] "Means for displaying analysis results in real time" refers to a method for instantly analyzing the acquired garbage information and visually presenting the results to the user.

[1113] Overall system overview

[1114] This system allows users to input information about their waste using their own devices, analyzes that information using a generative AI model, and notifies the user of appropriate sorting methods and reuse suggestions. Furthermore, the system can display the analysis results in real time using the camera on the device worn by the user.

[1115] Hardware and software used

[1116] User devices: Use devices such as smartphones or smart glasses. These devices have a camera, internet connection, and display capabilities.

[1117] Server: A cloud server is used to receive data, analyze it using the generative AI model, query the municipality's garbage sorting rules database, and notify the results.

[1118] Generative AI models: Use TensorFlow and PyTorch for image recognition and natural language processing.

[1119] Database: PostgreSQL is used to store municipal waste sorting rules.

[1120] Program processing

[1121] Entering and sending garbage information

[1122] Users input information about the garbage using their own devices. For example, they can take a photo of the garbage with their smartphone camera and enter supplementary information in text. This information is then sent to a server via the Internet.

[1123] Garbage information analysis

[1124] The server inputs the received garbage information into a generative AI model to analyze the type and characteristics of the garbage. Image recognition technology is used to identify the material and shape of the garbage from the photo, and natural language processing technology is used to determine the category from the text information.

[1125] Identifying sorting methods

[1126] The server queries the municipality's database of waste separation rules based on the user's residential address information and identifies the separation method that corresponds to the analyzed type of waste. For example, plastic items might be classified as "plastic recyclable waste," and glass products might be classified as "glass waste."

[1127] Generating reuse suggestions

[1128] Based on the analysis results, the server determines whether the garbage can be reused. If so, it generates a method and proposal for how to reuse the garbage. For example, if the clothing is still usable, it may suggest selling it on a flea market app.

[1129] Notifications and real-time display

[1130] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. The analysis results are displayed in real time on users' smart devices, allowing them to take appropriate action immediately.

[1131] Specific examples

[1132] As a specific example, consider a scenario in which a user brings a broken mobile phone to an eco-station in a brick-and-mortar store.

[1133] 1. A user brings in a broken phone and puts on smart glasses:

[1134] The camera in the smart glasses captures the mobile phone and obtains the image data.

[1135] 2. The smart glasses send the image data to the server:

[1136] Image data is sent to a server via the Internet.

[1137] 3. The server analyzes using the generated AI model:

[1138] The sent image is input into the AI ​​model and determined to be a "mobile phone."

[1139] 4. The server queries the store's garbage sorting rules:

[1140] The appropriate sorting method is identified based on the analysis results and the user's residential information (location information of the physical store).

[1141] 5. The server determines whether the item can be reused:

[1142] To determine whether a mobile phone is reusable and generate an appropriate reuse proposal.

[1143] 6. Notification of Results:

[1144] The server displays on the smart glasses in real time, "Please put this mobile phone in the electronics recycling box. Also, since there are still usable parts, please consider consulting the reuse corner of a store."

[1145] Example prompt sentence:

[1146] Q: I took a picture of a broken cell phone with my smart glasses. What are the proper ways to separate this item and what are some suggestions for reuse?

[1147] A: Please put this mobile phone in the electronics recycling bin. Also, since there are still usable parts, please consider asking at the reuse corner of a store.

[1148] As a result, users can learn the appropriate methods for separating and reusing waste in real time when disposing of it, which reduces the burden on the environment and makes effective use of resources.

[1149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1150] Step 1:

[1151] The user inputs information about the garbage using a device they own. Specifically, they wear smart glasses and use their camera to capture images of the garbage. The input data is image data of the captured garbage. This allows the physical characteristics and condition of the garbage to be visually recorded.

[1152] Step 2:

[1153] The device sends the captured image data of the garbage to a server via the Internet. Specifically, the image data is uploaded to the server's API using the HTTP protocol. The input of this step is the image data stored on the user's device, and the output is the image data sent to the server.

[1154] Step 3:

[1155] The server inputs the received image data into a generative AI model to analyze the type and characteristics of the waste. Data processing involves using an image recognition algorithm to read the image and identify the specific type of waste (e.g., plastic, metal, paper, etc.). The input is the received image data, and the output is the type and characteristics of the waste as a result of the analysis.

[1156] Step 4:

[1157] Based on the analysis results, the server compares the user's residential address information and queries the database for the city / ward / town / village's garbage sorting rules. Specifically, it executes a database query to obtain the corresponding sorting method. The input for this step is the type of garbage and the user's residential address information, and the output is the correct sorting method for that garbage.

[1158] Step 5:

[1159] The server determines whether the waste can be reused and generates a reuse proposal. This is an operation that applies an algorithm to evaluate the possibility of reuse based on the analysis results and the sorting rules queried. The input is the sorting method and waste characteristics information, and the output is a reuse proposal.

[1160] Step 6:

[1161] The server notifies the user of the identified sorting method and reuse suggestions. Specifically, the server sends a notification message to the terminal, which receives it and displays it to the user. The input is the sorting method and reuse suggestion data, and the output is a notification message in a format that the user can check.

[1162] Step 7:

[1163] The analysis results and suggestions are displayed in real time on a smart device worn by the user. Specifically, text information and icons are displayed on the glasses' display, visually conveying the results to the user. The input is a notification message from the server, and the output is the analysis results and reuse suggestions displayed in the user's field of vision.

[1164] The above are the specific processing steps of the system that realizes the application example.

[1165] 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.

[1166] This invention combines a system that effectively separates garbage and suggests reuse methods with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[1167] System Overview

[1168] Input Method

[1169] The user uses their smartphone to take a photo of the trash or enter text information about the trash. For example, the user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[1170] Transmission method

[1171] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[1172] Analysis means

[1173] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[1174] Inquiry method

[1175] The server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[1176] Specific means

[1177] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[1178] Reuse decision method

[1179] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1180] Emotion Engine

[1181] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," when inputting, or if emotions are expressed through voice input, the server recognizes those emotions.

[1182] Notification means

[1183] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, the server combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[1184] Specific examples

[1185] For plastic products

[1186] 1. User takes a photo of a plastic cup and inputs it into the app:

[1187] A user takes a photo of a plastic cup with their smartphone camera.

[1188] 2. The device sends the photo data to the server:

[1189] The device sends the photo to the server.

[1190] 3. The server analyzes using the AI ​​model:

[1191] The server inputs the photo into an AI model and determines that it is a "plastic product."

[1192] 4. The server queries the city / ward / town / village's sorting rules:

[1193] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[1194] 5. The server determines whether the item can be reused:

[1195] The server verifies that the cup is still usable and generates a reuse suggestion.

[1196] 6. The server uses the emotion engine to recognize the user's emotion:

[1197] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[1198] 7. The server notifies the user of the result:

[1199] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is too much trouble, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[1200] In the case of clothing

[1201] 1. User enters text information about an old T-shirt:

[1202] A user types "old T-shirt" into the app.

[1203] 2. The device sends the text data to the server:

[1204] The terminal transmits the text information to the server.

[1205] 3. The server analyzes using the generated AI model:

[1206] The server inputs the text data into an AI model and determines that it is "clothing."

[1207] 4. The server queries the city / ward / town / village's sorting rules:

[1208] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[1209] 5. The server determines whether the item can be reused:

[1210] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[1211] 6. The server uses the emotion engine to recognize the user's emotion:

[1212] The server checks for sentiment, such as "no longer using."

[1213] 7. The server notifies the user of the result:

[1214] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[1215] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[1216] The processing flow will be explained below.

[1217] Step 1:

[1218] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, they can take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[1219] Step 2:

[1220] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[1221] Step 3:

[1222] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[1223] Step 4:

[1224] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server then obtains the sorting information corresponding to the analysis results.

[1225] Step 5:

[1226] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[1227] Step 6:

[1228] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1229] Step 7:

[1230] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user enters a keyword that expresses an emotion, such as "annoying," or if an emotion is expressed through voice input, the server recognizes that emotion.

[1231] Step 8:

[1232] The server integrates all the data and creates a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and changes the content and tone of the message depending on the user's emotions.

[1233] Step 9:

[1234] The device receives notification messages from the server and displays them to the user. Through their smartphone, users can check specific sorting methods and reuse suggestions, such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the emotion engine analyzes the user's emotions and determines that the process is "troublesome," the notification message can be flexibly changed to something like "It's easy to do, so give it a try."

[1235] Example 2

[1236] 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."

[1237] Conventional garbage sorting systems often make it difficult for users to understand the garbage sorting rules in their area. Furthermore, they rarely suggest reusability and do not provide notifications that take the user's feelings into consideration. This reduces the efficiency of garbage sorting and reuse, causing users to feel stressed. Therefore, a system is needed that can easily identify garbage sorting rules for the user's area, determine reusability, and provide notifications that take the user's feelings into consideration.

[1238] 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.

[1239] In this invention, the server includes means for analyzing garbage information and determining the type of garbage, means for inquiring about garbage sorting rules for each region based on the user's residential information and identifying an appropriate sorting method, and means for recognizing the user's emotions and notifying them of reuse suggestions based on the analysis results and emotions. This allows the user to easily understand the garbage sorting rules according to their residential area and receive reuse suggestions without stress.

[1240] "Means for inputting garbage information" refers to the means by which users use devices such as smartphones or computers to input photos of garbage and text information into the system.

[1241] The "means for transmitting the garbage information" is a means for transmitting the garbage information input by the user to the server.

[1242] The "means for analyzing the information about the garbage and determining the type of garbage" refers to a means for analyzing the received garbage information using artificial intelligence or a machine learning model and determining the type and characteristics of the garbage.

[1243] "Means for inquiring about local garbage sorting rules based on the user's residential information and identifying the appropriate sorting method" refers to means for searching a database of garbage sorting rules for the area where the user lives and identifying the correct garbage sorting method for that area.

[1244] The "means for determining the reusability of the waste" is a means for determining whether the waste can be reused based on the analyzed information of the waste.

[1245] "Means for recognizing user emotions and notifying reuse suggestions based on the analysis results and emotions" refers to a means for analyzing user emotions from the user's input data and interactions, and making appropriate reuse suggestions to the user based on the analysis results and garbage information.

[1246] This invention is a system that effectively separates garbage and suggests reuse methods, and is combined with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[1247] Input Method

[1248] Users enter information about the garbage using a device such as a smartphone or computer. Specifically, they must take a photo of the garbage using the smartphone camera or enter text information. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[1249] Transmission method

[1250] The device sends the captured photo or entered text information to the server. Specifically, image data or text data is sent via the Internet, and the server receives the data via API.

[1251] Analysis means

[1252] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, the server might identify a "plastic cup" from a photo and then determine categories such as "clothing" or "broken parts" from the text.

[1253] Inquiry method

[1254] The server queries a database of garbage sorting rules for each region corresponding to the user's residence. This database contains detailed garbage sorting rules for each region. The server then obtains the sorting information corresponding to the analysis results.

[1255] Specific means

[1256] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines that it should be disposed of as "plastic waste."

[1257] Reuse decision method

[1258] Based on the analysis results, the server determines whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1259] Emotion Engine

[1260] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," or if emotions are expressed through voice input, the server recognizes those emotions.

[1261] Notification means

[1262] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[1263] Specific examples

[1264] For plastic products

[1265] 1. User takes a photo of a plastic cup and inputs it into the app:

[1266] A user takes a photo of a plastic cup with their smartphone camera.

[1267] 2. The device sends the photo data to the server:

[1268] The device sends the photo to the server.

[1269] 3. The server analyzes using the AI ​​model:

[1270] The server inputs the photo into an AI model and determines that it is a "plastic product."

[1271] 4. The server queries the local sorting rules:

[1272] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[1273] 5. The server determines whether the item can be reused:

[1274] The server verifies that the cup is still usable and generates a reuse suggestion.

[1275] 6. The server uses the emotion engine to recognize the user's emotion:

[1276] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[1277] 7. The server notifies the user of the result:

[1278] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is too much trouble, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[1279] In the case of clothing

[1280] 1. User enters text information about an old T-shirt:

[1281] A user types "old T-shirt" into the app.

[1282] 2. The device sends the text data to the server:

[1283] The terminal transmits the text information to the server.

[1284] 3. The server analyzes using the AI ​​model:

[1285] The server inputs the text data into an AI model and determines that it is "clothing."

[1286] 4. The server queries the local sorting rules:

[1287] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[1288] 5. The server determines whether the item can be reused:

[1289] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[1290] 6. The server uses the emotion engine to recognize the user's emotion:

[1291] The server checks for sentiment, such as "no longer using."

[1292] 7. The server notifies the user of the result:

[1293] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[1294] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[1295] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1296] Step 1:

[1297] The user inputs information about the garbage.

[1298] Input: The user opens the app on their smartphone and takes a photo of the trash or enters text information.

[1299] Specific action: The user takes a photo of a broken plastic cup with their smartphone camera or types in the text "old T-shirt."

[1300] Output: The input photo data or text data is saved in the device's app.

[1301] Step 2:

[1302] The terminal sends the input data to the server.

[1303] Input: Photo or text data entered by the user.

[1304] Specific operation: The device clicks the send button and sends the input data to the server via the Internet.

[1305] Output: Photo data or text data arrives at the server.

[1306] Step 3:

[1307] The server analyzes the received data using an AI model.

[1308] Input: Photo or text data received by the server.

[1309] Specific operation: The server analyzes the photo data using image recognition technology and determines that it is a "plastic cup." It also uses natural language processing technology to classify the text data as "clothing."

[1310] Output: Analysis results showing the type and characteristics of the waste.

[1311] Step 4:

[1312] The server queries the classification rule database.

[1313] Input: Server analysis results (type and characteristics of garbage) and user's location information.

[1314] Specific operation: The server searches the sorting rule database and obtains the sorting rules for the relevant area.

[1315] Output: How to properly separate waste.

[1316] Step 5:

[1317] The server identifies the appropriate way to dispose of the waste.

[1318] Input: Separation information obtained from the Separation Rules Database.

[1319] Specific behavior: The server classifies the plastic cup as "plastic waste."

[1320] Output: Proper disposal of waste.

[1321] Step 6:

[1322] The server determines whether it can be reused.

[1323] Input: Garbage analysis results.

[1324] Specific operation: The server determines whether the garbage is recyclable and evaluates it as, for example, "Plastic cups can still be used."

[1325] Output: Judgment result on reusability.

[1326] Step 7:

[1327] The server recognizes the user's emotions using an emotion engine.

[1328] Input: User input data and interaction records.

[1329] Specific operation: The server uses an emotion engine to analyze what the user feels is "annoying."

[1330] Output: User sentiment analysis results.

[1331] Step 8:

[1332] The server notifies the user of the results.

[1333] Input: Garbage analysis results, sorting method, reuse decision results, emotion analysis results.

[1334] Specific operation: The server integrates these results and generates a notification message for the user. For example, it may say, "Please separate the item as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." Flexible messages based on emotions may also be included.

[1335] Output: The message that is displayed to the user.

[1336] (Application example 2)

[1337] 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."

[1338] Current technology in waste sorting and reuse suggestion systems provides uniform notifications without considering the user's emotional state, resulting in a decline in user satisfaction and willingness to use the service. Furthermore, there are limitations to the ability of store staff to provide appropriate recycling information and reuse suggestions to customers in real time, which can lead to a decline in service quality. Furthermore, the lack of flexibility in reuse suggestions can result in a cumbersome and inefficient experience for users.

[1339] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1340] In this invention, the server includes: a means for inputting garbage information; a means for transmitting the garbage information; a means for analyzing the garbage information and determining the type of garbage; a means for consulting municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method; a means for determining whether the garbage can be reused; a means for notifying the user of the sorting method and reuse suggestions; an emotion engine for recognizing the user's emotions and adjusting the content of the notification message in accordance with the emotion; and a means for displaying the notification message based on the emotion. This enables flexible notifications based on the user's emotions, thereby improving user satisfaction and willingness to use the service. Furthermore, in brick-and-mortar stores, staff can provide appropriate recycling information and reuse suggestions in real time through the smart glasses, improving service quality and enabling more efficient operations.

[1341] "Garbage information" is data about garbage entered by the user, and includes photo data and text data.

[1342] A "means" is a method or apparatus for performing a particular function or role.

[1343] A "server" is a central computer system that processes and analyzes data over a network.

[1344] "Analysis" is the process of examining data scientifically and technically to arrive at a conclusion.

[1345] "Type of waste" refers to the specific classification of waste, such as plastic, paper, metal, clothing, etc.

[1346] "Residence information" is information about the place where the user lives, and includes local information such as city, ward, town, and village.

[1347] "Garbage sorting rules" are instructions on how to classify and dispose of garbage set by a city, town, village, or other local area.

[1348] "Reusability" is a characteristic that indicates whether or not garbage can be reused.

[1349] "Notification" is the transmission of information from the system to the user.

[1350] An "emotion engine" is a combination of software and algorithms for recognizing and analyzing a user's emotions.

[1351] "Adjusting the content of the notification message" means changing the content of the notification message in accordance with the user's emotions.

[1352] "Smart glasses" are a wearable device in the form of glasses that can display information.

[1353] To implement this invention, a user first inputs information about the garbage using smart glasses. Specifically, the user provides the garbage information to the system by tagging or taking a photo. The image or text data of the garbage captured using smart glasses (e.g., Google Glass) is sent to the server via the terminal.

[1354] The server inputs the received image or text data of the garbage into a generative AI model for analysis. This analysis identifies the type of garbage. Based on the analyzed data, the server uses the user's residential address information to query the city, town, or village's garbage sorting rules from a database. This identifies the appropriate sorting method.

[1355] Next, the server determines whether the trash can be reused. If the trash can be reused, a reuse suggestion is generated. This suggestion may include, for example, a suggestion for reuse on a flea market site or an auction site.

[1356] The server also uses an emotion engine to analyze the text and voice entered by the user and recognize the user's emotions. Based on this result, the content and tone of the notification message are adjusted. For example, if the user feels that something is "troublesome" based on the analysis results of the emotion engine, a message such as "It's easy, so give it a try" will be displayed.

[1357] These notification messages are displayed to users through smart glasses or other display means, allowing users to receive real-time information on appropriate waste sorting methods and reuse suggestions, as well as emotional guidance.

[1358] As a concrete example, suppose a store staff member wearing smart glasses is serving a customer and the customer asks, "How should I dispose of this disposable plastic bottle?" The staff member uses the smart glasses to take an image of the bottle and sends the data to a server. After analysis, a notification is displayed saying, "Simply put this plastic bottle in a collection box. It's easy, so please give it a try."

[1359] Example prompt sentence:

[1360] When a customer brings in a discolored plastic bottle and asks a staff member, "What should I do with this?"

[1361] The hardware used includes smart glasses as an image capture device and a server for data processing and analysis, while the software includes a generative AI model for image recognition and natural language processing, an emotion engine for emotion analysis, and a communication program (such as a requests library) for data transmission.

[1362] By using this system, users can smoothly separate their trash and receive suggestions for reuse, thereby contributing to environmental protection. Furthermore, especially in brick-and-mortar stores, this system improves the quality of service by enabling staff to provide customers with prompt and appropriate answers.

[1363] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1364] Step 1:

[1365] The user uses the smart glasses to input information about the garbage. Specifically, the user can take a photo of the garbage using the smart glasses' camera or input text data about the garbage using voice input. The input data includes information about the type and characteristics of the garbage.

[1366] Input: Photos of garbage or audio text data

[1367] Output: Captured photo data or text data

[1368] Step 2:

[1369] The device sends the captured photo data or text data to the server. The requests library is used for communication, and each piece of data is sent via API.

[1370] Input: Captured photo data or text data

[1371] Output: Photo data or text data sent to the server

[1372] Step 3:

[1373] The server inputs the received photo or text data into a generative AI model for analysis, which uses image recognition and natural language processing techniques to identify the type of garbage.

[1374] Input: Photo data or text data sent to the server

[1375] Output: Information about the types of garbage analyzed

[1376] Step 4:

[1377] Based on the user's residential information, the server queries the database for the city, ward, town, or village's garbage sorting rules and identifies the sorting method that corresponds to the analysis results.

[1378] Input: Information about the type of garbage analyzed, and the user's location

[1379] Output: Information about the identified waste sorting methods

[1380] Step 5:

[1381] The server determines whether the trash can be reused based on the analysis results, and if so, generates a reuse proposal for the trash on a flea market or auction site.

[1382] Input: Information about the type of garbage analyzed

[1383] Output: Judgment of reusability and reuse proposal

[1384] Step 6:

[1385] The server uses an emotion engine to analyze emotions based on user input and interactions. The emotion engine uses natural language processing to identify the emotions the user is feeling (e.g., annoyed, happy).

[1386] Input: User input data and interactions

[1387] Output: Information about the identified user's emotions

[1388] Step 7:

[1389] The server combines the analysis results with the emotion engine results to generate flexible notification messages that respond to the user's emotions. The notifications include garbage sorting methods and reuse suggestions, and the content and tone are adjusted according to the user's emotions.

[1390] Input: Garbage sorting method, reuse suggestions, and user sentiment

[1391] Output: Flexible notification messages that respond to emotions

[1392] Step 8:

[1393] The server generates a notification message and notifies the user through smart glasses or other display means, allowing the user to receive relevant information in real time.

[1394] Input: Flexible notification messages that respond to emotions

[1395] Output: Notification message displayed on the smart glasses

[1396] 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.

[1397] 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.

[1398] 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.

[1399] [Fourth embodiment]

[1400] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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.

[1405] 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).

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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."

[1413] The present invention is a system that includes a series of processes, starting with the user inputting information about the garbage, followed by an analysis by the server and notification of the user of appropriate disposal methods and reuse suggestions. The specific processes and operations of each part of the system are explained below.

[1414] System Overview

[1415] Input Method

[1416] A user uses a smartphone to take a photo of the trash or input text information. For example, a user can take a photo of a broken plastic cup or input "old T-shirt" as text. This information is important data for identifying the characteristics of the trash.

[1417] Transmission method

[1418] The device sends the entered garbage information to a server. This transmission is done via the Internet, and the server receives the data through an API (Application Program Interface).

[1419] Analysis means

[1420] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from text.

[1421] Inquiry method

[1422] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server then retrieves the sorting rules corresponding to the analyzed garbage type.

[1423] Specific means

[1424] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information from a city or ward / ward database that "plastic waste should be collected as plastic resources" and notifies the user of that method.

[1425] Reuse decision method

[1426] Based on the analysis results, the server determines whether the garbage can be reused, for example, by determining whether it is a plastic product that can still be used or clothing that can still be worn, and if so, suggests putting the item up for sale at a flea market or auction service.

[1427] Notification means

[1428] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. Notifications are sent via a smartphone app, allowing users to check the information immediately. For example, a message might appear saying, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[1429] Specific examples

[1430] For plastic products

[1431] 1. User takes a photo of a plastic cup and inputs it into the app:

[1432] A user takes a photo of a plastic cup with their smartphone camera.

[1433] 2. The device sends the photo data to the server:

[1434] The device sends the photo to the server.

[1435] 3. The server analyzes using the AI ​​model:

[1436] The server inputs the photo into an AI model and determines that it is a "plastic product."

[1437] 4. The server queries the city / ward / town / village's sorting rules:

[1438] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[1439] 5. The server determines whether the item can be reused:

[1440] The server verifies that the cup is still usable and generates a reuse suggestion.

[1441] 6. The server notifies the user of the result:

[1442] The notice reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[1443] In the case of clothing

[1444] 1. User enters text information about an old T-shirt:

[1445] A user types "old T-shirt" into the app.

[1446] 2. The device sends the text data to the server:

[1447] The terminal transmits the text information to the server.

[1448] 3. The server analyzes using the generated AI model:

[1449] The server inputs the text data into an AI model and determines that it is "clothing."

[1450] 4. The server queries the city / ward / town / village's sorting rules:

[1451] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[1452] 5. The server determines whether the item can be reused:

[1453] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[1454] 6. The server notifies the user of the result:

[1455] The notice reads, "Please separate it as fabric waste. Also, since it is still usable, we recommend that you sell it on a flea market site."

[1456] The above is an embodiment of the present invention. This system allows users to easily check the appropriate method for separating garbage and whether it can be reused, and allows users to dispose of garbage in accordance with local separation rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective use of resources and reduce environmental impact.

[1457] The processing flow will be explained below.

[1458] Step 1:

[1459] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, a user might take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[1460] Step 2:

[1461] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[1462] Step 3:

[1463] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. Specifically, the server can identify a plastic cup based on photo data, or determine that it is clothing by analyzing text data.

[1464] Step 4:

[1465] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[1466] Step 5:

[1467] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[1468] Step 6:

[1469] The server then checks the analysis results again to determine whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1470] Step 7:

[1471] The server then aggregates all the information and creates a final notification for the user, combining the analysis results and reuse suggestions into a single notification message.

[1472] Step 8:

[1473] The device receives a notification message from the server and displays it to the user. The user can then use their smartphone to view specific sorting methods and reuse suggestions, such as, "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site."

[1474] Example 1

[1475] 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."

[1476] Current waste sorting and reuse systems have the problem that it is difficult for users to accurately identify the type of waste and understand the appropriate disposal method. As a result, potential reuse of waste is overlooked, or incorrect sorting increases the environmental burden. To solve this problem, a system is needed that allows users to easily identify the type of waste and learn the correct sorting and reuse methods.

[1477] 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.

[1478] In this invention, the server includes means for a user to input garbage information, means for transmitting the garbage information from a terminal to the server, means for the server to input the garbage information into a generative AI model and analyze it, means for querying a database of garbage sorting rules for the city, ward, town, or village based on the user's residential information and identifying an appropriate sorting method, means for determining whether the garbage can be reused, and means for notifying the user of the sorting method and reuse suggestions. This enables the user to easily identify the type of garbage and receive accurate sorting methods and reuse suggestions.

[1479] "Garbage information" is data relating to waste input by the user, and includes image data or text data.

[1480] A "terminal" is a device such as a smartphone or tablet operated by a user, which provides a means for inputting garbage information and sending it to a server.

[1481] The "server" is a central computer system that receives garbage information sent from the terminals and performs analysis and query processing.

[1482] A "generative AI model" is an artificial intelligence model that uses image recognition and natural language processing technologies to analyze information about garbage and determine its specific type and characteristics.

[1483] The "database" is an information aggregation system that stores municipal garbage sorting rules and accepts inquiries based on the user's residential address information.

[1484] "Querying" is the process of sending a query to a database to obtain specific information and retrieving the required data.

[1485] "Sorting method" refers to the procedures and rules for separating waste into appropriate categories and following designated collection and disposal methods.

[1486] "Reusability" refers to the criteria or assessment that determines whether waste can be reused or resold.

[1487] "Notification" refers to the act of informing users of the analysis results, sorting methods, and reuse suggestions, and is a message provided via a smartphone app.

[1488] The system of the present invention involves a series of processes in which a user inputs information about the waste, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. The specific configuration and operation for implementing this system are described below.

[1489] System Overview

[1490] Input Method

[1491] Users use a device such as a smartphone to take a photo of the trash or enter text information into the application. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This information becomes important data for the server to analyze the characteristics of the trash.

[1492] Transmission method

[1493] The terminal sends the garbage information entered by the user to a server. This transmission is carried out via the Internet, and the server receives the data through an application program interface (API).

[1494] Analysis means

[1495] The server inputs the received garbage information into a generative AI model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[1496] Inquiry method

[1497] The server queries the municipality's garbage sorting rules database based on the user's residential address information. This database contains detailed garbage sorting rules for each area. The server retrieves the sorting rules corresponding to the analyzed garbage type.

[1498] Specific means

[1499] The server determines the appropriate method for separating the waste based on information obtained from the database. For example, it obtains information such as "plastic waste should be collected as plastic resources" from the municipal waste separation rules database and notifies the user of that method.

[1500] Reuse decision method

[1501] Based on the analysis results, the server determines whether the trash can be reused, for example, by determining whether the plastic products or clothing can still be used, and if so, suggests putting those items up for sale at a flea market or auction service.

[1502] Notification means

[1503] The server integrates all the data and notifies the user of appropriate sorting methods and reuse suggestions. This notification is sent via a smartphone application, allowing the user to immediately check the information. For example, a message such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site" may be displayed.

[1504] Specific examples

[1505] Examples of plastic products

[1506] 1. The user inputs a photo of a plastic cup into the app.

[1507] 2. The device sends the photo data to the server.

[1508] 3. The server inputs the photo into a generative AI model and determines that it is a "plastic cup."

[1509] 4. Based on the user's residential address information, the server queries the city / ward / town / village's database of garbage sorting rules and obtains the information that the item should be collected as a plastic resource.

[1510] 5. The server determines whether the cup is reusable and verifies that it is usable.

[1511] 6. The server notifies the user of the sorting method and reuse suggestions.

[1512] Examples of clothing

[1513] 1. The user enters the text information "old T-shirt" into the app.

[1514] 2. The device sends the text information to the server.

[1515] 3. The server inputs the text data into the generative AI model and determines that it is "clothing."

[1516] 4. The server queries the garbage sorting rules database based on the user's residential address information and obtains the information that the item should be treated as fabric garbage.

[1517] 5. The server determines whether the T-shirt can be reused and verifies that it is usable.

[1518] 6. The server notifies the user of the sorting method and reuse suggestions.

[1519] Examples of prompt statements used:

[1520] Below are some example prompts used as input to generative AI models:

[1521] When inputting a photo

[1522] "Identify the object in this photo and determine what it is made of."

[1523] For text input

[1524] "Based on the text entered, determine which category this item belongs to."

[1525] The above is a specific embodiment for carrying out the invention. This system allows users to easily check the appropriate method for separating waste and the reusability of waste, and allows for appropriate disposal in accordance with local waste separation rules. Furthermore, by properly disposing of reusable items, it is possible to achieve effective use of resources and reduce environmental impact.

[1526] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1527] Step 1:

[1528] The user enters information about the garbage. The system's application is launched, and an input screen is used to take a photo of the garbage or to enter text information. The user takes a photo of the garbage with their smartphone camera and imports it into the app, or enters text about the garbage. This input data is saved on the device as photo data or text data.

[1529] Input: photo of trash or text information

[1530] Output: Garbage photo data or text data

[1531] Step 2:

[1532] The device sends the entered garbage information to the server. When the user taps the "Send" button, the device sends the photo data and text data to the server via API. The API receives this data via the Internet.

[1533] Input: Photo data or text data of garbage sent from the device

[1534] Output: Photo data or text data of the garbage stored on the server side

[1535] Step 3:

[1536] The server inputs the received garbage information into a generative AI model for analysis. The server passes the received data to an analysis engine, which performs image recognition or natural language processing. In the case of photo data, an image recognition algorithm is used to identify the object and distinguish its material and type. In the case of text data, a natural language processing model is used to determine the item category.

[1537] Input: garbage photo data or text data

[1538] Output: Identification of type and material of garbage

[1539] Step 4:

[1540] The server queries the municipality's garbage sorting rules database based on the user's residential address information. The user's residential address information is used as a condition for the database query to retrieve the garbage sorting rules for that area from the database.

[1541] Input: User's residential information, garbage type and material identification results

[1542] Output: Applicable garbage sorting rules

[1543] Step 5:

[1544] The server identifies the appropriate sorting method based on the garbage sorting rules it obtains from the database. It then refers to the analysis results and determines the specific disposal method for that garbage. For example, this data could be, "Plastic garbage should be collected as plastic resources."

[1545] Input: Garbage sorting rules, type and material of garbage

[1546] Output: How to separate garbage

[1547] Step 6:

[1548] The server determines whether the trash can be reused. Based on the trash identification results and its condition (e.g., whether it has scratches or not, and its functionality), it evaluates whether it can be reused. If it can be reused, it generates a proposal to put it up for sale at a flea market or auction.

[1549] Input: type and condition of garbage, sorting method

[1550] Output: Possibility of reuse, reuse proposal

[1551] Step 7:

[1552] The server consolidates all data and notifies the user of appropriate sorting methods and reuse suggestions. The server generates notification messages and sends them to the device via API, which the user receives on their smartphone. The user can check the notifications within the application and take appropriate action.

[1553] Input: Garbage sorting method, reuse suggestions

[1554] Output: A notification message to the user

[1555] The above are the processing steps of the program for this system, and an explanation including the specific operations and data flow at each step.

[1556] (Application example 1)

[1557] 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."

[1558] In modern society, the importance of waste separation and reuse is increasing, but it is difficult for individual citizens to properly determine the correct separation method and the possibility of reuse. In particular, there is a lack of means to quickly learn the appropriate separation and reuse methods for the waste brought to physical stores. In addition, there are technical challenges in efficiently obtaining waste information using smart devices and presenting it in real time.

[1559] 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.

[1560] In this invention, the server includes means for inputting garbage information using a user's terminal, means for transmitting the garbage information to the server via the Internet, means for inputting the garbage information into a generative AI model and determining the type and characteristics of the garbage, means for referencing a database of municipal garbage sorting rules based on the user's residential information and identifying an appropriate sorting method, means for determining the reuse potential of the garbage and generating reuse suggestions, means for notifying the user of the identified sorting method and reuse suggestions, and means for capturing images of the garbage with a camera in a device worn by the user and displaying the analysis results in real time. This allows users to learn appropriate sorting and reuse methods in real time when disposing of their garbage, thereby reducing environmental impact and making effective use of resources.

[1561] A "user-owned terminal" is a hardware device that can be used by any user and has the function of inputting garbage information and transmitting it to a server via the Internet.

[1562] "Via the Internet" refers to a communication method for sending and receiving data based on the Internet Protocol (IP), which realizes data linkage with a server.

[1563] A "generative AI model" is a model trained using artificial intelligence technology that has algorithms for analyzing garbage information and determining its type and characteristics.

[1564] "User's residence information" is information about the district or area where the user lives, and is data for inquiring about the garbage separation rules of the city, ward, town, or village based on that information.

[1565] "Municipal garbage sorting rules" are regulations established by a particular city, town, or village that outline the standards and methods for sorting garbage for each type of garbage.

[1566] A "database" is a system for storing and searching large amounts of data in a structured manner, and manages information including municipal waste sorting rules.

[1567] "Reusability" is a criterion that indicates whether an item classified as garbage can be reused.

[1568] "Reuse suggestions" are recommendations on how items classified as garbage can be reused, and are notified to users.

[1569] "Means for notifying the user" refers to a method for transmitting analysis results and suggestions from the server to the user, and is usually done through the user's terminal.

[1570] A "user-worn device" is a hardware device worn by a user and has the function of capturing images of dust.

[1571] "Means for displaying analysis results in real time" refers to a method for instantly analyzing the acquired garbage information and visually presenting the results to the user.

[1572] Overall system overview

[1573] This system allows users to input information about their waste using their own devices, analyzes that information using a generative AI model, and notifies the user of appropriate sorting methods and reuse suggestions. Furthermore, the system can display the analysis results in real time using the camera on the device worn by the user.

[1574] Hardware and software used

[1575] User devices: Use devices such as smartphones or smart glasses. These devices have a camera, internet connection, and display capabilities.

[1576] Server: A cloud server is used to receive data, analyze it using the generative AI model, query the municipality's garbage sorting rules database, and notify the results.

[1577] Generative AI models: Use TensorFlow and PyTorch for image recognition and natural language processing.

[1578] Database: PostgreSQL is used to store municipal waste sorting rules.

[1579] Program processing

[1580] Entering and sending garbage information

[1581] Users input information about the garbage using their own devices. For example, they can take a photo of the garbage with their smartphone camera and enter supplementary information in text. This information is then sent to a server via the Internet.

[1582] Garbage information analysis

[1583] The server inputs the received garbage information into a generative AI model to analyze the type and characteristics of the garbage. Image recognition technology is used to identify the material and shape of the garbage from the photo, and natural language processing technology is used to determine the category from the text information.

[1584] Identifying sorting methods

[1585] The server queries the municipality's database of waste separation rules based on the user's residential address information and identifies the separation method that corresponds to the analyzed type of waste. For example, plastic items might be classified as "plastic recyclable waste," and glass products might be classified as "glass waste."

[1586] Generating reuse suggestions

[1587] Based on the analysis results, the server determines whether the garbage can be reused. If so, it generates a method and proposal for how to reuse the garbage. For example, if the clothing is still usable, it may suggest selling it on a flea market app.

[1588] Notifications and real-time display

[1589] The server consolidates all data and notifies users of appropriate sorting methods and reuse suggestions. The analysis results are displayed in real time on users' smart devices, allowing them to take appropriate action immediately.

[1590] Specific examples

[1591] As a specific example, consider a scenario in which a user brings a broken mobile phone to an eco-station in a brick-and-mortar store.

[1592] 1. A user brings in a broken phone and puts on smart glasses:

[1593] The camera in the smart glasses captures the mobile phone and obtains the image data.

[1594] 2. The smart glasses send the image data to the server:

[1595] Image data is sent to a server via the Internet.

[1596] 3. The server analyzes using the generated AI model:

[1597] The sent image is input into the AI ​​model and determined to be a "mobile phone."

[1598] 4. The server queries the store's garbage sorting rules:

[1599] The appropriate sorting method is identified based on the analysis results and the user's residential information (location information of the physical store).

[1600] 5. The server determines whether the item can be reused:

[1601] To determine whether a mobile phone is reusable and generate an appropriate reuse proposal.

[1602] 6. Notification of Results:

[1603] The server displays on the smart glasses in real time, "Please put this mobile phone in the electronics recycling box. Also, since there are still usable parts, please consider consulting the reuse corner of a store."

[1604] Example prompt sentence:

[1605] Q: I took a picture of a broken cell phone with my smart glasses. What are the proper ways to separate this item and what are some suggestions for reuse?

[1606] A: Please put this mobile phone in the electronics recycling bin. Also, since there are still usable parts, please consider asking at the reuse corner of a store.

[1607] As a result, users can learn the appropriate methods for separating and reusing waste in real time when disposing of it, which reduces the burden on the environment and makes effective use of resources.

[1608] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1609] Step 1:

[1610] The user inputs information about the garbage using a device they own. Specifically, they wear smart glasses and use their camera to capture images of the garbage. The input data is image data of the captured garbage. This allows the physical characteristics and condition of the garbage to be visually recorded.

[1611] Step 2:

[1612] The device sends the captured image data of the garbage to a server via the Internet. Specifically, the image data is uploaded to the server's API using the HTTP protocol. The input of this step is the image data stored on the user's device, and the output is the image data sent to the server.

[1613] Step 3:

[1614] The server inputs the received image data into a generative AI model to analyze the type and characteristics of the waste. Data processing involves using an image recognition algorithm to read the image and identify the specific type of waste (e.g., plastic, metal, paper, etc.). The input is the received image data, and the output is the type and characteristics of the waste as a result of the analysis.

[1615] Step 4:

[1616] Based on the analysis results, the server compares the user's residential address information and queries the database for the city / ward / town / village's garbage sorting rules. Specifically, it executes a database query to obtain the corresponding sorting method. The input for this step is the type of garbage and the user's residential address information, and the output is the correct sorting method for that garbage.

[1617] Step 5:

[1618] The server determines whether the waste can be reused and generates a reuse proposal. This is an operation that applies an algorithm to evaluate the possibility of reuse based on the analysis results and the sorting rules queried. The input is the sorting method and waste characteristics information, and the output is a reuse proposal.

[1619] Step 6:

[1620] The server notifies the user of the identified sorting method and reuse suggestions. Specifically, the server sends a notification message to the terminal, which receives it and displays it to the user. The input is the sorting method and reuse suggestion data, and the output is a notification message in a format that the user can check.

[1621] Step 7:

[1622] The analysis results and suggestions are displayed in real time on a smart device worn by the user. Specifically, text information and icons are displayed on the glasses' display, visually conveying the results to the user. The input is a notification message from the server, and the output is the analysis results and reuse suggestions displayed in the user's field of vision.

[1623] The above are the specific processing steps of the system that realizes the application example.

[1624] 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.

[1625] This invention combines a system that effectively separates garbage and suggests reuse methods with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[1626] System Overview

[1627] Input Method

[1628] The user uses their smartphone to take a photo of the trash or enter text information about the trash. For example, the user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[1629] Transmission method

[1630] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[1631] Analysis means

[1632] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[1633] Inquiry method

[1634] The server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server obtains the sorting information corresponding to the analysis results obtained.

[1635] Specific means

[1636] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[1637] Reuse decision method

[1638] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1639] Emotion Engine

[1640] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," when inputting, or if emotions are expressed through voice input, the server recognizes those emotions.

[1641] Notification means

[1642] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, the server combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[1643] Specific examples

[1644] For plastic products

[1645] 1. User takes a photo of a plastic cup and inputs it into the app:

[1646] A user takes a photo of a plastic cup with their smartphone camera.

[1647] 2. The device sends the photo data to the server:

[1648] The device sends the photo to the server.

[1649] 3. The server analyzes using the AI ​​model:

[1650] The server inputs the photo into an AI model and determines that it is a "plastic product."

[1651] 4. The server queries the city / ward / town / village's sorting rules:

[1652] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[1653] 5. The server determines whether the item can be reused:

[1654] The server verifies that the cup is still usable and generates a reuse suggestion.

[1655] 6. The server uses the emotion engine to recognize the user's emotion:

[1656] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[1657] 7. The server notifies the user of the result:

[1658] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is too much trouble, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[1659] In the case of clothing

[1660] 1. User enters text information about an old T-shirt:

[1661] A user types "old T-shirt" into the app.

[1662] 2. The device sends the text data to the server:

[1663] The terminal transmits the text information to the server.

[1664] 3. The server analyzes using the generated AI model:

[1665] The server inputs the text data into an AI model and determines that it is "clothing."

[1666] 4. The server queries the city / ward / town / village's sorting rules:

[1667] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[1668] 5. The server determines whether the item can be reused:

[1669] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[1670] 6. The server uses the emotion engine to recognize the user's emotion:

[1671] The server checks for sentiment, such as "no longer using."

[1672] 7. The server notifies the user of the result:

[1673] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[1674] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[1675] The processing flow will be explained below.

[1676] Step 1:

[1677] A user launches a smartphone app. They take a photo of the trash or enter text information about the trash into the app. For example, they can take a photo of a broken plastic cup or enter "old T-shirt" in the text.

[1678] Step 2:

[1679] The device sends the photograph or text information it has entered to the server. Specifically, the image data or text data is sent via the Internet, and the server receives the data via API.

[1680] Step 3:

[1681] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, it can identify a "plastic cup" from a photo and determine categories such as "clothing" or "broken parts" from the text.

[1682] Step 4:

[1683] Based on the analysis results, the server queries the database of garbage sorting rules for the city, town, or village corresponding to the user's place of residence. This database contains detailed garbage sorting rules for each area. The server then obtains the sorting information corresponding to the analysis results.

[1684] Step 5:

[1685] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines how to dispose of it as "plastic waste."

[1686] Step 6:

[1687] Based on the analysis results, the server determines whether the garbage can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1688] Step 7:

[1689] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user enters a keyword that expresses an emotion, such as "annoying," or if an emotion is expressed through voice input, the server recognizes that emotion.

[1690] Step 8:

[1691] The server integrates all the data and creates a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and changes the content and tone of the message depending on the user's emotions.

[1692] Step 9:

[1693] The device receives notification messages from the server and displays them to the user. Through their smartphone, users can check specific sorting methods and reuse suggestions, such as "Please separate this plastic product as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the emotion engine analyzes the user's emotions and determines that the process is "troublesome," the notification message can be flexibly changed to something like "It's easy to do, so give it a try."

[1694] Example 2

[1695] 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."

[1696] Conventional garbage sorting systems often make it difficult for users to understand the garbage sorting rules in their area. Furthermore, they rarely suggest reusability and do not provide notifications that take the user's feelings into consideration. This reduces the efficiency of garbage sorting and reuse, causing users to feel stressed. Therefore, a system is needed that can easily identify garbage sorting rules for the user's area, determine reusability, and provide notifications that take the user's feelings into consideration.

[1697] 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.

[1698] In this invention, the server includes means for analyzing garbage information and determining the type of garbage, means for inquiring about garbage sorting rules for each region based on the user's residential information and identifying an appropriate sorting method, and means for recognizing the user's emotions and notifying them of reuse suggestions based on the analysis results and emotions. This allows the user to easily understand the garbage sorting rules according to their residential area and receive reuse suggestions without stress.

[1699] "Means for inputting garbage information" refers to the means by which users use devices such as smartphones or computers to input photos of garbage and text information into the system.

[1700] The "means for transmitting the garbage information" is a means for transmitting the garbage information input by the user to the server.

[1701] The "means for analyzing the information about the garbage and determining the type of garbage" refers to a means for analyzing the received garbage information using artificial intelligence or a machine learning model and determining the type and characteristics of the garbage.

[1702] "Means for inquiring about local garbage sorting rules based on the user's residential information and identifying the appropriate sorting method" refers to means for searching a database of garbage sorting rules for the area where the user lives and identifying the correct garbage sorting method for that area.

[1703] The "means for determining the reusability of the waste" is a means for determining whether the waste can be reused based on the analyzed information of the waste.

[1704] "Means for recognizing user emotions and notifying reuse suggestions based on the analysis results and emotions" refers to a means for analyzing user emotions from the user's input data and interactions, and making appropriate reuse suggestions to the user based on the analysis results and garbage information.

[1705] This invention is a system that effectively separates garbage and suggests reuse methods, and is combined with an emotion engine that recognizes the user's emotions. This system involves a series of processes in which the user inputs garbage information, the server analyzes it, and notifies the user of appropriate disposal methods and reuse suggestions. Furthermore, by combining the emotion engine, it is possible to provide flexible notifications based on the user's emotions.

[1706] Input Method

[1707] Users enter information about the garbage using a device such as a smartphone or computer. Specifically, they must take a photo of the garbage using the smartphone camera or enter text information. For example, a user can take a photo of a broken plastic cup or enter the text "old T-shirt." This input is done through the application interface.

[1708] Transmission method

[1709] The device sends the captured photo or entered text information to the server. Specifically, image data or text data is sent via the Internet, and the server receives the data via API.

[1710] Analysis means

[1711] The server inputs the received data into an artificial intelligence (AI) model, which uses image recognition and natural language processing techniques to analyze the type and characteristics of the garbage. For example, the server might identify a "plastic cup" from a photo and then determine categories such as "clothing" or "broken parts" from the text.

[1712] Inquiry method

[1713] The server queries a database of garbage sorting rules for each region corresponding to the user's residence. This database contains detailed garbage sorting rules for each region. The server then obtains the sorting information corresponding to the analysis results.

[1714] Specific means

[1715] The server determines the appropriate method of disposal for the waste based on information obtained from the sorting rule database. For example, in the case of a plastic cup, it determines that it should be disposed of as "plastic waste."

[1716] Reuse decision method

[1717] Based on the analysis results, the server determines whether the trash can be reused. For example, it checks whether the plastic products or clothing are still usable. If there is a possibility of reuse, it suggests putting the item up for sale at a flea market or auction site.

[1718] Emotion Engine

[1719] The server uses an emotion engine to analyze the user's emotions based on the user's input data and interactions. For example, if the user includes keywords that express emotions, such as "annoying," or if emotions are expressed through voice input, the server recognizes those emotions.

[1720] Notification means

[1721] The server combines the analysis results and the emotion engine results to create a final notification for the user. Specifically, it combines the analysis results and reuse suggestions into a single notification message, and flexibly changes the content and tone of the message depending on the user's emotions.

[1722] Specific examples

[1723] For plastic products

[1724] 1. User takes a photo of a plastic cup and inputs it into the app:

[1725] A user takes a photo of a plastic cup with their smartphone camera.

[1726] 2. The device sends the photo data to the server:

[1727] The device sends the photo to the server.

[1728] 3. The server analyzes using the AI ​​model:

[1729] The server inputs the photo into an AI model and determines that it is a "plastic product."

[1730] 4. The server queries the local sorting rules:

[1731] The server retrieves sorting rules for plastic waste from a database based on the user's residential address information.

[1732] 5. The server determines whether the item can be reused:

[1733] The server verifies that the cup is still usable and generates a reuse suggestion.

[1734] 6. The server uses the emotion engine to recognize the user's emotion:

[1735] The server runs an emotion engine based on the user's input data and analyzes emotions such as "it's a pain."

[1736] 7. The server notifies the user of the result:

[1737] The message reads, "Please separate it as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." If the user feels that this is too much trouble, the message is changed to something more flexible, such as, "It's easy to do, so give it a try."

[1738] In the case of clothing

[1739] 1. User enters text information about an old T-shirt:

[1740] A user types "old T-shirt" into the app.

[1741] 2. The device sends the text data to the server:

[1742] The terminal transmits the text information to the server.

[1743] 3. The server analyzes using the AI ​​model:

[1744] The server inputs the text data into an AI model and determines that it is "clothing."

[1745] 4. The server queries the local sorting rules:

[1746] The server retrieves sorting rules for old clothes from a database based on the user's residence information.

[1747] 5. The server determines whether the item can be reused:

[1748] The server verifies that the T-shirt is still wearable and generates a reuse suggestion.

[1749] 6. The server uses the emotion engine to recognize the user's emotion:

[1750] The server checks for sentiment, such as "no longer using."

[1751] 7. The server notifies the user of the result:

[1752] The system notifies the user, "Please separate the item as fabric waste. Also, since it is still usable, we recommend selling it on a flea market site." At this time, the system adjusts the message according to the user's emotions, such as "Let's have fun recycling."

[1753] In this way, by combining emotion engines, it is possible to devise ways to make it easier for users to separate and reuse garbage.

[1754] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1755] Step 1:

[1756] The user inputs information about the garbage.

[1757] Input: The user opens the app on their smartphone and takes a photo of the trash or enters text information.

[1758] Specific action: The user takes a photo of a broken plastic cup with their smartphone camera or types in the text "old T-shirt."

[1759] Output: The input photo data or text data is saved in the device's app.

[1760] Step 2:

[1761] The terminal sends the input data to the server.

[1762] Input: Photo or text data entered by the user.

[1763] Specific operation: The device clicks the send button and sends the input data to the server via the Internet.

[1764] Output: Photo data or text data arrives at the server.

[1765] Step 3:

[1766] The server analyzes the received data using an AI model.

[1767] Input: Photo or text data received by the server.

[1768] Specific operation: The server uses image recognition technology to analyze the photo data and determine that it is a "plastic cup." It also uses natural language processing technology to classify the text data as "clothing."

[1769] Output: Analysis results showing the type and characteristics of the waste.

[1770] Step 4:

[1771] The server queries the classification rule database.

[1772] Input: Server analysis results (type and characteristics of garbage) and user's location information.

[1773] Specific operation: The server searches the sorting rule database and obtains the sorting rules for the relevant area.

[1774] Output: How to properly separate waste.

[1775] Step 5:

[1776] The server identifies the appropriate way to dispose of the waste.

[1777] Input: Separation information obtained from the Separation Rules Database.

[1778] Specific behavior: The server classifies the plastic cup as "plastic waste."

[1779] Output: Proper disposal of waste.

[1780] Step 6:

[1781] The server determines whether it can be reused.

[1782] Input: Garbage analysis results.

[1783] Specific operation: The server determines whether the garbage is recyclable and evaluates it as, for example, "Plastic cups can still be used."

[1784] Output: Judgment result on reusability.

[1785] Step 7:

[1786] The server recognizes the user's emotions using an emotion engine.

[1787] Input: User input data and interaction records.

[1788] Specific operation: The server uses an emotion engine to analyze what the user feels is "annoying."

[1789] Output: User sentiment analysis results.

[1790] Step 8:

[1791] The server notifies the user of the results.

[1792] Input: Garbage analysis results, sorting method, reuse decision results, emotion analysis results.

[1793] Specific operation: The server integrates these results and generates a notification message for the user. For example, it may say, "Please separate the item as plastic waste. Also, since it is still usable, please consider selling it on a flea market site." Flexible messages based on emotions may also be included.

[1794] Output: The message that is displayed to the user.

[1795] (Application example 2)

[1796] 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."

[1797] Current technology in waste sorting and reuse suggestion systems provides uniform notifications without considering the user's emotional state, resulting in a decline in user satisfaction and willingness to use the service. Furthermore, there are limitations to the ability of store staff to provide appropriate recycling information and reuse suggestions to customers in real time, which can lead to a decline in service quality. Furthermore, the lack of flexibility in reuse suggestions can result in a cumbersome and inefficient experience for users.

[1798] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1799] In this invention, the server includes: a means for inputting garbage information; a means for transmitting the garbage information; a means for analyzing the garbage information and determining the type of garbage; a means for consulting municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method; a means for determining whether the garbage can be reused; a means for notifying the user of the sorting method and reuse suggestions; an emotion engine for recognizing the user's emotions and adjusting the content of the notification message in accordance with the emotion; and a means for displaying the notification message based on the emotion. This enables flexible notifications based on the user's emotions, thereby improving user satisfaction and willingness to use the service. Furthermore, in brick-and-mortar stores, staff can provide appropriate recycling information and reuse suggestions in real time through the smart glasses, improving service quality and enabling more efficient operations.

[1800] "Garbage information" is data about garbage entered by the user, and includes photo data and text data.

[1801] A "means" is a method or apparatus for performing a particular function or role.

[1802] A "server" is a central computer system that processes and analyzes data over a network.

[1803] "Analysis" is the process of examining data scientifically and technically to arrive at a conclusion.

[1804] "Type of waste" refers to the specific classification of waste, such as plastic, paper, metal, clothing, etc.

[1805] "Residence information" is information about the place where the user lives, and includes local information such as city, ward, town, and village.

[1806] "Garbage sorting rules" are instructions on how to classify and dispose of garbage set by a city, town, village, or other local area.

[1807] "Reusability" is a characteristic that indicates whether or not garbage can be reused.

[1808] "Notification" is the transmission of information from the system to the user.

[1809] An "emotion engine" is a combination of software and algorithms for recognizing and analyzing a user's emotions.

[1810] "Adjusting the content of the notification message" means changing the content of the notification message in accordance with the user's emotions.

[1811] "Smart glasses" are a wearable device in the form of glasses that can display information.

[1812] To implement this invention, a user first inputs information about the garbage using smart glasses. Specifically, the user provides the garbage information to the system by tagging or taking a photo. The image or text data of the garbage captured using smart glasses (e.g., Google Glass) is sent to the server via the terminal.

[1813] The server inputs the received image or text data of the garbage into a generative AI model for analysis. This analysis identifies the type of garbage. Based on the analyzed data, the server uses the user's residential address information to query the city, town, or village's garbage sorting rules from a database. This identifies the appropriate sorting method.

[1814] Next, the server determines whether the trash can be reused. If the trash can be reused, a reuse suggestion is generated. This suggestion may include, for example, a suggestion for reuse on a flea market site or an auction site.

[1815] The server also uses an emotion engine to analyze the text and voice entered by the user and recognize the user's emotions. Based on this result, the content and tone of the notification message are adjusted. For example, if the user feels that something is "troublesome" based on the analysis results of the emotion engine, a message such as "It's easy, so give it a try" will be displayed.

[1816] These notification messages are displayed to users through smart glasses or other display means, allowing users to receive real-time information on appropriate waste sorting methods and reuse suggestions, as well as emotional guidance.

[1817] As a concrete example, suppose a store staff member wearing smart glasses is serving a customer and the customer asks, "How should I dispose of this disposable plastic bottle?" The staff member uses the smart glasses to take an image of the bottle and sends the data to a server. After analysis, a notification is displayed saying, "Simply put this plastic bottle in a collection box. It's easy, so please give it a try."

[1818] Example prompt sentence:

[1819] When a customer brings in a discolored plastic bottle and asks a staff member, "What should I do with this?"

[1820] The hardware used includes smart glasses as an image capture device and a server for data processing and analysis, while the software includes a generative AI model for image recognition and natural language processing, an emotion engine for emotion analysis, and a communication program (such as a requests library) for data transmission.

[1821] By using this system, users can smoothly separate their trash and receive suggestions for reuse, thereby contributing to environmental protection. Furthermore, especially in brick-and-mortar stores, this system improves the quality of service by enabling staff to provide customers with prompt and appropriate answers.

[1822] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1823] Step 1:

[1824] The user uses the smart glasses to input information about the garbage. Specifically, the user takes a photo of the garbage using the smart glasses' camera or inputs text data about the garbage using voice input. The input data includes information about the type and characteristics of the garbage.

[1825] Input: Photos of garbage or audio text data

[1826] Output: Captured photo data or text data

[1827] Step 2:

[1828] The device sends the captured photo data or text data to the server. The requests library is used for communication, and each piece of data is sent via API.

[1829] Input: Captured photo data or text data

[1830] Output: Photo data or text data sent to the server

[1831] Step 3:

[1832] The server inputs the received photo or text data into a generative AI model for analysis, which uses image recognition and natural language processing techniques to identify the type of garbage.

[1833] Input: Photo data or text data sent to the server

[1834] Output: Information about the types of garbage analyzed

[1835] Step 4:

[1836] Based on the user's residential information, the server queries the database for the city, ward, town, or village's garbage sorting rules and identifies the sorting method that corresponds to the analysis results.

[1837] Input: Information about the type of garbage analyzed, and the user's location

[1838] Output: Information about the identified waste sorting methods

[1839] Step 5:

[1840] The server determines whether the trash can be reused based on the analysis results, and if so, generates a reuse proposal for the trash on a flea market or auction site.

[1841] Input: Information about the type of garbage analyzed

[1842] Output: Judgment of reusability and reuse proposal

[1843] Step 6:

[1844] The server uses an emotion engine to analyze emotions based on user input and interactions. The emotion engine uses natural language processing to identify the emotions the user is feeling (e.g., annoyed, happy).

[1845] Input: User input data and interactions

[1846] Output: Information about the identified user's emotions

[1847] Step 7:

[1848] The server combines the analysis results with the emotion engine results to generate flexible notification messages that respond to the user's emotions. The notifications include garbage sorting methods and reuse suggestions, and the content and tone are adjusted according to the user's emotions.

[1849] Input: Garbage sorting method, reuse suggestions, and user sentiment

[1850] Output: Flexible notification messages that respond to emotions

[1851] Step 8:

[1852] The server generates a notification message and notifies the user through smart glasses or other display means, allowing the user to receive relevant information in real time.

[1853] Input: Flexible notification messages that respond to emotions

[1854] Output: Notification message displayed on the smart glasses

[1855] 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.

[1856] 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.

[1857] 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.

[1858] 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.

[1859] 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.

[1860] 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.

[1861] 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).

[1862] 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.

[1863] 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."

[1864] 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.

[1865] 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).

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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.

[1873] 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.

[1874] 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.

[1875] 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.

[1876] The following is further disclosed regarding the above embodiment.

[1877] (Claim 1)

[1878] A means for inputting information about the garbage;

[1879] a means for transmitting information about the garbage;

[1880] means for analyzing the information about the dust and determining the type of dust;

[1881] A means for inquiring about municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method;

[1882] A means for determining whether the waste can be reused;

[1883] means for notifying a user of the sorting method and reuse suggestions;

[1884] A system including:

[1885] (Claim 2)

[1886] 2. The system according to claim 1, wherein the information about the garbage is photographic data or text data.

[1887] (Claim 3)

[1888] 10. The system of claim 1, wherein the system analyzes garbage information using a generative AI model.

[1889] "Example 1"

[1890] (Claim 1)

[1891] a means for a user to input information about the garbage;

[1892] means for transmitting the garbage information from the terminal to a server;

[1893] A means for the server to input information about the garbage into a generative AI model and analyze it;

[1894] A means for searching a database for municipal waste separation rules based on the user's residential address information and identifying an appropriate separation method;

[1895] A means for determining whether the waste can be reused;

[1896] means for notifying a user of the sorting method and reuse suggestions;

[1897] A system including:

[1898] (Claim 2)

[1899] 2. The system according to claim 1, wherein the information about the dust is image data or character data.

[1900] (Claim 3)

[1901] 10. The system of claim 1, wherein the system analyzes garbage information using a generative AI model.

[1902] "Application Example 1"

[1903] (Claim 1)

[1904] A means for inputting information about garbage using a terminal carried by a user;

[1905] means for transmitting the information about the garbage to a server via the Internet;

[1906] a means for inputting information about the waste into a generative AI model and determining the type and characteristics of the waste;

[1907] A means for searching a database for municipal waste separation rules based on the user's residential address information and identifying an appropriate separation method;

[1908] means for determining the reusability of the waste and generating a reuse proposal;

[1909] means for notifying a user of the identified sorting method and reuse suggestions;

[1910] A means for capturing images of the garbage using a camera on a device worn by the user and displaying the analysis results in real time;

[1911] A system including:

[1912] (Claim 2)

[1913] 2. The system according to claim 1, wherein the information about the garbage is photographic data or text data.

[1914] (Claim 3)

[1915] 10. The system of claim 1, wherein the system analyzes garbage information using a generative AI model.

[1916] "Example 2: Combining Emotion Engines"

[1917] (Claim 1)

[1918] A means for inputting information about the garbage;

[1919] a means for transmitting information about the garbage;

[1920] means for analyzing the information about the dust and determining the type of dust;

[1921] A means for inquiring about local garbage sorting rules based on the user's residential location information and identifying an appropriate sorting method;

[1922] A means for determining whether the waste can be reused;

[1923] A means for recognizing a user's emotions and notifying the user of reuse suggestions based on the analysis results and the emotions;

[1924] A system including:

[1925] (Claim 2)

[1926] 2. The system according to claim 1, wherein the information about the garbage is photographic data or text data.

[1927] (Claim 3)

[1928] 10. The system of claim 1, wherein the system analyzes garbage information using a generative AI model.

[1929] "Application example 2 when combining emotion engines"

[1930] (Claim 1)

[1931] A means for inputting information about the garbage;

[1932] a means for transmitting information about the garbage;

[1933] means for analyzing the information about the dust and determining the type of dust;

[1934] A means for inquiring about municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method;

[1935] A means for determining whether the waste can be reused;

[1936] means for notifying a user of the sorting method and reuse suggestions;

[1937] an emotion engine that recognizes the user's emotion and adjusts the content of the notification message according to the emotion;

[1938] means for displaying a notification message based on the emotion;

[1939] A system including:

[1940] (Claim 2)

[1941] 2. The system according to claim 1, wherein the information about the garbage is photographic data or text data.

[1942] (Claim 3)

[1943] 10. The system of claim 1, wherein the system analyzes garbage information using a generative AI model. [Explanation of symbols]

[1944] 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 inputting information about the garbage; a means for transmitting information about the garbage; means for analyzing the information about the dust and determining the type of dust; A means for inquiring about municipal garbage sorting rules based on the user's residential address information and identifying an appropriate sorting method; A means for determining whether the waste can be reused; means for notifying a user of the sorting method and reuse suggestions; A system including:

2. 2. The system according to claim 1, wherein the information about the dust is photographic data or text data.

3. The system of claim 1 , wherein the system analyzes garbage information using a generative AI model.

Images