System

Abstract

A system is provided.SOLUTION: A system comprising: means for a user to scan a recycled good; means for a server to pass the image to a generative AI model to analyze the makeup; means for the server to calculate and add incentive points to the user account based on the analysis; means for the server to record the recycling experience in the block chain; and means for the user to redeem the incentive points.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] Recycling is becoming increasingly important in modern society, yet recycling behavior at the individual level is not being sufficiently practiced. The main reasons for this include the lack of effort and personal benefits associated with recycling. Another problem is that it is difficult for users to see how their recycling activities are contributing to their success, making it difficult to maintain motivation. Furthermore, accurately determining the material composition of recyclable items and properly sorting them is cumbersome, placing a burden on users. There is a need for a system that promotes recycling behavior and visualizes individual achievements, thereby reducing the effort required for recycling and clarifying the personal benefits. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides a recycling platform that utilizes user-participation generative AI. The present invention is a system that includes the following means.

[0006] 1. A way for users to scan their recycling items.

[0007] 2. A means for the server to pass the image to a generative AI model to analyze the material composition.

[0008] 3. A means for the server to calculate incentive points based on the analysis results and add them to the user's account.

[0009] 4. A means for the server to record recycling performance data on the blockchain.

[0010] 5. A means for users to redeem incentive points for cash.

[0011] Furthermore, in a particularly desirable embodiment, the system further encourages recycling behavior by including a means for the server to notify the user terminal of the analysis results of the generated AI model, a means for the server to execute a transfer based on an incentive redemption request received from the user terminal, a means for generating an authentication token for securely managing the user's personal information and recycling activity record and storing it in local storage, and a means for the user to check the material composition of the recyclable item and confirm the incentive rate in advance.

[0012] "Users" refer to individuals or organizations who use the system to scan recyclable items and receive incentives.

[0013] "Server" refers to the computer system that processes data sent by users, analyzes materials using a generative AI model, calculates incentive points based on the results, and manages and records various data.

[0014] "Generative AI model" refers to an artificial intelligence algorithm that analyzes image data and determines the material composition of recycled items.

[0015] "Incentive Points" refers to reward points that are awarded based on the results of users' recycling activities. These points can be redeemed for cash at a later date.

[0016] "Blockchain" refers to a system that uses distributed ledger technology to securely and immutably record recycling performance data.

[0017] "Authentication Token" refers to a digital key used to securely manage user credentials and enhance user identification and security.

[0018] "Recycled goods" refers to waste that is made up of reusable materials (e.g., plastic, paper, glass, etc.).

[0019] "Scanning" refers to the act of a user taking a picture of a recyclable item and sending that image data to the system.

[0020] "Material composition" refers to the types and proportions of various materials that make up a recycled product.

[0021] "Cash-out" refers to the act of exchanging incentive points held by a user for cash or other valuables. [Brief explanation of the drawings]

[0022] [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 illustrating 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

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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."

[0043] The present invention is a user-participation recycling platform that allows users to scan recyclable items and earn incentive points based on the analysis results. This system is operated by exchanging data between users, terminals, and a server. Below, we will explain the program for this system, including specific processing methods and examples.

[0044] System configuration and operation

[0045] User registration and login

[0046] A user installs the app and opens the sign-up screen. The user enters the required information (email address, password, and other required personal information). The device receives the information and sends it to the server. The server stores the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores it in local storage for future authentication.

[0047] Recycled Goods Scan

[0048] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[0049] Material determination by generative AI

[0050] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[0051] Incentive calculation and awarding

[0052] The server calculates incentive points based on the received material composition, for example, "100 points," searches the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the result of the point addition.

[0053] Tracking and Verification

[0054] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0055] Incentive cash redemption

[0056] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount (e.g., "500 points = 1,000 yen") and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[0057] Specific example explanation

[0058] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account. User "A" then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[0059] This system is effective in reducing the effort of recycling and encouraging individual recycling behavior. By combining generative AI models with blockchain technology, users can easily perform recycling activities, track their results, and earn rewards.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] A user installs the app and opens the registration screen, where they enter the required information (email address, password, etc.).

[0063] Step 2:

[0064] The terminal transmits the input information to the server.

[0065] Step 3:

[0066] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[0067] Step 4:

[0068] The server returns an authentication token to the device, which stores the token in local storage.

[0069] Step 5:

[0070] The user launches the app and selects the option to scan for recyclable items.

[0071] Step 6:

[0072] The device activates the camera and the user takes a picture of the recyclable item.

[0073] Step 7:

[0074] The terminal transmits the captured image data to the server.

[0075] Step 8:

[0076] The server passes the received image data to the generative AI model.

[0077] Step 9:

[0078] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[0079] Step 10:

[0080] The server receives the analysis result and calculates incentive points, for example, 100 points.

[0081] Step 11:

[0082] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[0083] Step 12:

[0084] The server notifies the terminal of the result of adding points, and the terminal displays a notification to the user.

[0085] Step 13:

[0086] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[0087] Step 14:

[0088] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[0089] Step 15:

[0090] The server receives the transaction ID from the blockchain network and stores it in a database.

[0091] Step 16:

[0092] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[0093] Step 17:

[0094] The terminal sends the entered number of points and the user ID to the server.

[0095] Step 18:

[0096] The server checks the user's point balance and deducts the specified points.

[0097] Step 19:

[0098] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[0099] Step 20:

[0100] The server checks whether the transfer was successful and notifies the terminal of the result.

[0101] Step 21:

[0102] The device receives the notification and displays the result (success / failure) to the user.

[0103] These are the specific processing steps of the system, which provides an effective means for users to easily carry out recycling activities and receive incentives based on their performance.

[0104] Example 1

[0105] 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."

[0106] There is a need for a system that ensures the efficiency and transparency of recycling activities and encourages users to recycle. However, conventional systems make it difficult for users to accurately identify the materials in recyclable items and easily assign incentive points and convert them into cash. In particular, it is difficult to accurately analyze the material composition of recyclable items and record and manage the performance data in a reliable manner. Furthermore, the process of converting incentive points into cash remains cumbersome.

[0107] 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.

[0108] In this invention, the server includes a means for a user to photograph waste, a means for a terminal to send the photographed image to the server, a means for the server to pass the image to a generative AI model and analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record waste performance data on a distributed ledger, and a means for the user to convert the incentive points into currency. This enables users to easily carry out recycling activities, check the results of their activities in an accurate and reliable manner, and receive appropriate rewards.

[0109] "User" refers to a person who uses the system to carry out recycling activities.

[0110] "Waste" refers to items that can be reused and are subject to recycling.

[0111] "Device" refers to your mobile phone, tablet, or other computing device.

[0112] "Server" refers to a computer that is the central part of the entire system and is a device that processes, stores, analyzes, and communicates data.

[0113] "Images" refers to photographs and visual data of waste taken by users using their device's camera.

[0114] A "generative AI model" refers to a form of artificial intelligence that uses machine learning and deep learning algorithms to analyze and generate information from input data.

[0115] "Material composition" refers to the percentage of materials the waste is made up of (e.g., plastic, paper).

[0116] "Incentive points" refer to reward points that users can earn by scanning recyclable items and based on the material analysis results.

[0117] "Currency" refers to a medium with real value, such as legal tender or electronic money, that users receive when converting incentive points into cash.

[0118] "Database" refers to a system that stores data such as user information and incentive points managed on a server.

[0119] A "distributed ledger" refers to a recording system that uses blockchain technology to increase data transparency and reliability.

[0120] This invention is a system aimed at improving the efficiency of waste management, allowing users to photograph waste, acquire incentive points based on the analysis results, and ultimately convert them into currency. A specific embodiment of this system will be described below.

[0121] System Configuration

[0122] The system consists of the following components:

[0123] User Device: A computing device such as a smartphone or tablet.

[0124] Server: A computer that processes, stores, analyzes, and communicates data.

[0125] Camera: An image capture device built into a user device.

[0126] Database: A system that manages user information and incentive points (e.g., PostgreSQL).

[0127] Generative AI model: An artificial intelligence model that performs image analysis (e.g., TensorFlow).

[0128] Distributed ledger: Blockchain technology (e.g. Ethereum) to ensure data transparency and reliability.

[0129] Program processing overview

[0130] User registration and login

[0131] A user installs the app and opens the new registration screen. The user enters their email address and password. The device sends this information to the server, which stores it in a database and generates a new user ID and authentication token. The authentication token is returned to the device and stored in local storage.

[0132] Recycled Goods Scan

[0133] The user launches the app and selects the option to scan a recyclable item. The camera activates and the user takes a picture of the recyclable item. The image data is then sent from the device to the server.

[0134] Material determination by generative AI

[0135] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. It sends a prompt saying, "Please determine the material composition of the recycled product." The generative AI model analyzes the material composition and returns the results to the server. For example, it may determine that the material composition is "70% plastic, 30% paper."

[0136] Incentive calculation and awarding

[0137] The server calculates incentive points based on the analysis results. For example, 100 points are added to the user's account. The processing results are notified to the terminal.

[0138] Tracking and Verification

[0139] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[0140] Incentive cash redemption

[0141] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. After the transfer is confirmed, the result is notified to the device and displayed to the user.

[0142] Specific examples

[0143] For example, consider a user recycling a plastic bottle. The user launches the app and takes a photo of the bottle with their camera. The device sends the image to a server. The server analyzes the material using a generative AI model and determines that it is "90% plastic, 10% paper." Based on the results, "100 points" are calculated and added to the user's account. The user then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[0144] Prompt Sentence Examples

[0145] "Analyze an image of a recycled PET bottle. Print out the specific material composition, showing the ratio of plastic to paper."

[0146] This system allows users to easily carry out recycling activities, check the results, and receive rewards. The combination of generative AI models and blockchain technology will improve the efficiency and transparency of recycling activities, which is a major feature of the system's implementation.

[0147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0148] Step 1: User Registration and Login

[0149] The user installs the app and opens the new registration screen. The user enters information such as an email address and password. The device sends the entered information to the server via an HTTP POST request. The server receives the information and stores it in a database. This generates a user ID and authentication token. The server returns the generated authentication token to the device, which stores it in local storage.

[0150] Input: User registration information (email address, password),

[0151] Output: Authentication token

[0152] Step 2: Recycled Goods Scan

[0153] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, and the user takes a picture of the recyclable item. The device then sends the image to the server.

[0154] Input: Image of recycled item,

[0155] Output: Image data sent to the server

[0156] Step 3: Material determination by generative AI

[0157] The server passes the received image data to the generative AI model and sends a prompt to analyze the material composition. The prompt sends the message, "Please determine the material composition of the recycled product." The generative AI model analyzes the image data and calculates the proportion of materials. The generative AI model returns the analysis results to the server. For example, it may determine that the material is 70% plastic and 30% paper.

[0158] Input: Image data captured, prompts to analyze,

[0159] Output: Material composition (e.g. 70% plastic, 30% paper)

[0160] Step 4: Incentive calculation and granting

[0161] The server calculates incentive points based on the analysis results it receives. For example, it calculates "100 points" based on the result "70% plastic, 30% paper." The server adds the points to the user's account and notifies the terminal of the processing result.

[0162] Input: Analysis results of material composition,

[0163] Output: Calculated incentive points, notification data

[0164] Step 5: Tracking and verification

[0165] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[0166] Input: Actual data,

[0167] Output: Transaction ID

[0168] Step 6: Incentive Cashing

[0169] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The terminal sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The terminal is notified of the success / failure of the transfer. The terminal displays the transfer result to the user.

[0170] Input: Redemption request (number of points and user ID),

[0171] Output: Transfer result notification

[0172] (Application example 1)

[0173] 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."

[0174] Conventional recycling activities have been time-consuming for users, and the incentives are complicated, making it difficult to promote recycling. There have also been challenges in ensuring the transparency and reliability of recycling results.

[0175] 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.

[0176] In this invention, the server includes a means for users to use their smartphones to earn incentive points based on the results of an analysis of the material composition of recyclable items, a means for users to instantly use the incentive points they earned in physical stores, and a means for the server to verify transactions on the blockchain and perform tracking and certification based on transaction IDs stored in a database. This allows users to easily participate in recycling activities and instantly use the incentives they earned, promoting recycling and ensuring transparent and reliable recycling results.

[0177] A "user" is an individual who scans recycled goods, earns incentive points, and redeems those points in physical stores.

[0178] The "server" refers to a computer system that passes images of recycled items to a generative AI model to analyze their material composition, calculates incentive points based on the analysis results, verifies transactions on the blockchain, and stores recycling performance data in a database.

[0179] A "generative AI model" refers to an artificial intelligence model that analyzes the material composition of recycled products from image data.

[0180] "Incentive Points" refers to reward points that users earn as a result of their recycling activities.

[0181] "Blockchain" refers to a distributed ledger technology that ensures transparency and reliability of recycling performance data.

[0182] "Smartphone" refers to the mobile device used by users to scan recycled items and check and redeem incentive points.

[0183] "Physical store" refers to a physical store where users can immediately use the incentive points they have earned.

[0184] "Transaction ID" refers to a unique identification number used to identify a transaction on a blockchain.

[0185] The present invention is a recycling platform system that allows users to scan recyclable items and earn incentive points based on the analysis results. The system is mainly operated by exchanging data between users, terminals, and a server.

[0186] First, users install a recycling point management app on their smartphone. Using this app, users can take pictures of recyclable items with the device's camera. The images are then sent from the device to a server. The server receives the image data and passes it to a generative AI model to analyze the material composition.

[0187] A generative AI model is an artificial intelligence model that analyzes the material composition of recyclable items from image data, and is built using software such as TensorFlow and PyTorch. This generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. For example, the analysis result may be "80% plastic, 20% paper." Based on this analysis result, the server calculates incentive points and adds them to the user's account.

[0188] Users can instantly use the incentive points they have earned in physical stores using their smartphones. Specifically, users select the point check option in the app to check their point balance, and then use the points in the physical store to receive discounts on products and services.

[0189] Furthermore, the server records recycling performance data (e.g., user ID, material composition, incentive points, timestamp, etc.) on the blockchain. Blockchain is a distributed ledger technology that ensures data transparency and reliability. The server verifies transactions on the blockchain and stores the results in a database. Recycling performance is tracked and verified based on this transaction ID.

[0190] As a concrete example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A"'s account. User "A" can then use the points to purchase products at a discount in a physical store.

[0191] An example of a prompt sentence to input to the generative AI model is as follows:

[0192] "Please analyze the material composition of the image below. The image contains recycled materials (plastic bottles). Please return your analysis results in terms of material types and their percentages. For example, please answer "90% plastic, 10% paper.""

[0193] This system allows users to easily participate in recycling activities and instantly use the incentive points they earn. The use of blockchain technology also improves the transparency and reliability of recycling results.

[0194] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0195] Step 1:

[0196] The user launches the recycling point management app installed on their smartphone. The user selects the option to scan a recyclable item through the app and activates the device's camera. The user takes a picture of the recyclable item, and the device acquires the image data. The input is the image of the recyclable item taken by the user, and the output is the image data acquired by the device's camera.

[0197] Step 2:

[0198] The image data acquired by the device is sent to the server. The input is the image data acquired by the device's camera, and the output is the image data sent to the server. The server receives and stores this image data.

[0199] Step 3:

[0200] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. The input is image data, which is passed to the generative AI model. The generative AI model analyzes the image data and determines the material composition of the recycled product. For example, it may obtain a result such as "80% plastic, 20% paper." This analysis result is then output.

[0201] Step 4:

[0202] The server calculates incentive points based on the analysis results obtained from the generative AI model. For example, a certain point calculation rule is applied to the analysis result of "80% plastic, 20% paper" to calculate 100 points. The input is the analysis result from the generative AI model, and the output is the calculated incentive points.

[0203] Step 5:

[0204] The server adds the calculated incentive points to the user's account. The input is the calculated incentive points and the output is the updated user account data. The server adds the points to the user's account and saves it in the database.

[0205] Step 6:

[0206] The server generates recycling performance data (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network. The input is the recycling performance data, and the output is the data recorded on the blockchain network. The blockchain receives the data, verifies it, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0207] Step 7:

[0208] A user selects the check points option in the app to check their current points balance. The input is the user's account information and the output is the points balance displayed to the user. The device sends a request to the server, which returns the user's current points balance.

[0209] Step 8:

[0210] A user redeems incentive points in a physical store. The user uses their smartphone to select a point redemption option and receive a discount on goods or services in the store. The input is the number of points the user redeems, and the output is the discount offered or the goods or services exchanged. The terminal sends a request to the server, which deducts the specified number of points from the user's account and authorizes the redemption in the physical store.

[0211] This step will make it easier for users to participate in recycling activities, allowing them to immediately use the incentive points they earn, while ensuring the reliability and transparency of recycling results through blockchain technology.

[0212] 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.

[0213] This invention is a system that improves users' motivation to recycle by combining a user-participation recycling platform with an emotion engine that recognizes users' emotions. This system is operated by exchanging data between users, terminals, a server, and the emotion engine. Below, we will explain the program for this system, including specific processing methods and examples.

[0214] System configuration and operation

[0215] User registration and login

[0216] A user installs the app and opens the new registration screen. The user enters the required information (email address, password, etc.). The device sends the entered information to the server. The server saves the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores the token in local storage for future authentication.

[0217] Recycled Goods Scan

[0218] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[0219] Material determination by generative AI

[0220] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[0221] Incentive calculation and awarding

[0222] The server calculates incentive points based on the received material composition, for example, 100 points, searches for the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the point addition result.

[0223] Emotion recognition and incentive adjustment using an emotion engine

[0224] The device uses an emotion engine to analyze the user's facial expressions and voice to determine their emotions. For example, it can detect emotions such as joy, sadness, and surprise. The emotion engine then returns the analysis results to the server.

[0225] The server adjusts and awards incentives according to the user's emotional state based on the results of emotion analysis. For example, if the user is happy, it awards additional bonus points and notifies the user of the analysis results.

[0226] The server also generates feedback messages based on the analysis results and sends them to the device, which then displays the messages to the user, encouraging them to recycle more.

[0227] Tracking and Verification

[0228] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0229] Incentive cash redemption

[0230] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[0231] Specific example explanation

[0232] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which then analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account.

[0233] At this time, the emotion engine analyzes the emotion of user "A" from his / her facial expression and determines it to be "joy." Based on this emotion analysis result, the server awards further bonus points, calculating an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Wonderful!" and sends it to the device. The device then displays the additional points and feedback message to user "A."

[0234] At a later date, User A converts the 300 points into cash, and 3,000 yen is deposited into the designated bank account. This allows User A to contribute to the effective use of resources through recycling activities and also earn personal rewards.

[0235] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, providing an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[0236] The processing flow will be explained below.

[0237] Step 1:

[0238] A user installs the app and opens the registration screen. The user enters the required information (email address, password, etc.).

[0239] Step 2:

[0240] The terminal transmits the input information to the server.

[0241] Step 3:

[0242] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[0243] Step 4:

[0244] The server returns an authentication token to the device, which stores the token in local storage.

[0245] Step 5:

[0246] The user launches the app and selects the option to scan for recyclable items.

[0247] Step 6:

[0248] The device activates the camera and the user takes a picture of the recyclable item.

[0249] Step 7:

[0250] The terminal transmits the captured image data to the server.

[0251] Step 8:

[0252] The server passes the received image data to the generative AI model.

[0253] Step 9:

[0254] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[0255] Step 10:

[0256] The server receives the analysis results and calculates incentive points, for example, 100 points based on the material composition of the recycled product.

[0257] Step 11:

[0258] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[0259] Step 12:

[0260] The server notifies the terminal of the result of the point addition, and the terminal displays a notification to the user.

[0261] Step 13:

[0262] The device uses an emotion engine to analyze the user's emotions, for example, detecting emotions such as "happiness" from the user's facial expressions and voice.

[0263] Step 14:

[0264] The emotion engine sends the analysis results to the server.

[0265] Step 15:

[0266] The server receives the emotion analysis results and adjusts the incentive according to the user's emotional state. For example, if the user is in a "joy" state, an additional 20 points will be added as a bonus.

[0267] Step 16:

[0268] The server stores the adjusted incentive points in a database and notifies the terminal of the result.

[0269] Step 17:

[0270] The server generates a feedback message based on the sentiment analysis results, e.g., "Your efforts are saving the Earth! Great!"

[0271] Step 18:

[0272] The server sends a feedback message to the terminal, which displays it to the user.

[0273] Step 19:

[0274] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[0275] Step 20:

[0276] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[0277] Step 21:

[0278] The server receives the transaction ID from the blockchain network and stores it in a database.

[0279] Step 22:

[0280] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[0281] Step 23:

[0282] The terminal sends the entered number of points and the user ID to the server.

[0283] Step 24:

[0284] The server checks the user's point balance and deducts the specified points.

[0285] Step 25:

[0286] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[0287] Step 26:

[0288] The server checks whether the transfer was successful and notifies the terminal of the result.

[0289] Step 27:

[0290] The device receives the notification and displays the result (success / failure) to the user.

[0291] Example 2

[0292] 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."

[0293] Existing recycling platforms often simply award points without considering users' emotions or motivations. This tends to discourage users from recycling, making it difficult to promote sustainable recycling activities. In addition, the processes for analyzing the materials of recycled products and awarding incentives are generally cumbersome, making efficient system operation necessary.

[0294] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for the user to scan the recyclable item; a means for the terminal to activate the camera and acquire an image; a means for the server to pass the image to a generative AI model and analyze the material composition; a means for the server to calculate incentive points based on the analysis results and add them to the user's account; a means for the terminal to analyze emotions and send the results to the server; a means for the server to adjust and grant incentive points based on the emotion analysis results; a means for the server to record recycling performance data on the blockchain; and a means for the user to redeem the incentive points. This makes it possible to provide incentives that take users' emotions into consideration and increase their motivation to recycle. Furthermore, material composition analysis using a generative AI model and data management using blockchain technology enable an efficient and reliable recycling process.

[0295] "User" refers to an individual or organization that uses the system to carry out recycling activities.

[0296] A "terminal" is a device operated by a user that provides functions such as scanning and display.

[0297] "Server" means a central system that processes, stores, and manages data and interacts with users and devices through various means.

[0298] "Recyclables" refers to items that users choose to recycle, specifically items that are subject to scanning and analysis.

[0299] "Camera" means a device mounted on the Terminal for taking images of recyclable items.

[0300] "Image" refers to visual information obtained by photographing recycled items with a camera.

[0301] A "generative AI model" is an artificial intelligence model that is installed on a server and analyzes the material composition of recycled items from image data.

[0302] "Material composition" refers to the proportion and type of various materials that make up a recycled product, and is analyzed by a generative AI model.

[0303] "Incentive points" are points awarded to users as a reward for recycling activities.

[0304] An "emotion engine" refers to software or hardware that analyzes a user's emotional state from their facial expressions and voice.

[0305] "Emotion analysis results" refers to data indicating the user's emotional state detected by the emotion engine.

[0306] "Blockchain" refers to a distributed ledger technology used to ensure data is trustworthy and immutable.

[0307] "Recycling performance data" refers to data related to recycling activities, such as user ID, material composition, incentive points, and timestamps.

[0308] "Redeem" means the process by which a user redeems incentive points for money or other purposes.

[0309] This invention is a system that improves users' motivation to recycle by combining an emotion engine that recognizes users' emotions in a user-participation recycling platform. This system is operated by exchanging data between users, terminals, a server, and the emotion engine.

[0310] User registration and login

[0311] First, the user installs the app and launches it. The user opens the new registration screen and enters the required information. The device sends this information to the server, which then stores the user information in a database. The server then generates a new user ID and authentication token and returns them to the device. The device then stores the received authentication token in local storage and uses it for future authentication.

[0312] Recycled Goods Scan

[0313] The user launches the app and selects the option to scan a recyclable item. The device's camera activates, and the user takes a picture of the recyclable item and sends it to the server. The server then provides the received image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. The generative AI model then returns the analysis results to the server.

[0314] Incentive calculation and awarding

[0315] The server calculates incentive points based on the received material composition. For example, based on a result such as "70% plastic, 30% paper," it calculates 100 points and adds the points to the database record corresponding to the user's account ID. The server then notifies the terminal of the point addition result.

[0316] Emotion recognition and incentive adjustment using an emotion engine

[0317] The device uses an emotion engine to analyze the user's facial expressions and voice. For example, it detects emotional states such as happiness, sadness, and surprise. The emotion engine returns the results to the server, which then adjusts the incentives according to the user's emotional state based on the analysis results. If the user is happy, the server awards additional bonus points. The server also generates a feedback message based on the analysis results and sends it to the device. The device displays this message to the user, encouraging them to recycle more.

[0318] Tracking and Verification

[0319] The server generates performance data for each recycled item, including user ID, material composition, incentive points, timestamp, etc. The server sends this data to the blockchain network and requests it to be recorded. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in a database.

[0320] Incentive cash redemption

[0321] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends this information and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server then calculates the redemption amount and calls an API to transfer the money to the specified bank account. The server confirms the success of the transfer and notifies the device of the result. The device displays it to the user.

[0322] Specific example explanation

[0323] For example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with his / her camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A's" account. At that time, the emotion engine analyzes user "A's" facial expression and determines the emotion as "joy." Based on this emotion analysis result, the server awards an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Amazing!" and sends it to the device. The device displays the additional points and the feedback message to user "A." Later, user "A" converts the 300 points into cash, and 3,000 yen is deposited into his / her designated bank account.

[0324] Example prompt sentence:

[0325] "Please analyze the material of the plastic bottle."

[0326] "Analyze user sentiment and calculate bonus points."

[0327] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, making it an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[0328] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0329] Step 1: User installs and launches the app.

[0330] Input: The user installs the app and taps to launch it.

[0331] Output: The app starts and the initial screen is displayed.

[0332] Specific operation: The device launches the installed app and displays the initial screen.

[0333] Step 2: The user opens the new registration screen and enters the required information.

[0334] Input: The user enters required information, such as an email address or password.

[0335] Output: The input information is aggregated and stored on the device.

[0336] Specific behavior: The device displays a new registration form, and the user enters information into the form.

[0337] Step 3: The terminal sends the entered information to the server.

[0338] Input: Information entered by the user, such as email address or password.

[0339] Output: The server receives the user information.

[0340] Specific operation: The device sends user information to the server via an HTTP request.

[0341] Step 4: The server saves the user information to the database and generates a new user ID and authentication token.

[0342] Input: User information received by the server from the device.

[0343] Output: A new user ID and authentication token is generated and stored in the database.

[0344] Specific behavior: The server inserts the user information into the database and generates a user ID and authentication token.

[0345] Step 5: The server returns the authentication token to the device, which stores it in local storage.

[0346] Input: The authentication token sent by the server.

[0347] Output: The authentication token is saved on the device.

[0348] Specific operation: The server sends an authentication token in the HTTP response, and the device stores it in local storage.

[0349] Step 6: The user launches the app and selects the option to scan for recyclables.

[0350] Input: User selects the scan option within the app.

[0351] Output: The camera starts.

[0352] Specific behavior: Activates the camera module when the device selects the scan option.

[0353] Step 7: The device activates the camera and the user takes a picture of the item to be recycled.

[0354] Input: User uses camera to take a picture of a recyclable item.

[0355] Output: The captured image data is saved on the device.

[0356] Specific behavior: The device launches the camera app, and the user presses the shutter button to take a picture.

[0357] Step 8: The device sends the captured image to the server.

[0358] Input: Captured image data.

[0359] Output: The image data is transferred to the server.

[0360] Specific operation: The device uploads image data to the server using an HTTP request.

[0361] Step 9: The server provides the received image data to the generative AI model and instructs it to analyze it.

[0362] Input: Image data received by the server.

[0363] Output: The generative AI model begins its analysis.

[0364] Specific operation: The server sends the image data to the API of the generated AI model and issues an analysis request.

[0365] Step 10: The generative AI model analyzes the image data and determines the material composition of the recycled item.

[0366] Input: Image data.

[0367] Output: Material composition analysis (e.g. "70% plastic, 30% paper").

[0368] Specific operation: The generative AI model analyzes the image, calculates the type and proportion of ingredients, and returns the results.

[0369] Step 11: The generative AI model returns the analysis results to the server.

[0370] Input: Material composition analysis results.

[0371] Output: The server receives the analysis results.

[0372] Specific operation: The generative AI model sends the analysis results to the server.

[0373] Step 12: The server calculates incentive points based on the received material configuration.

[0374] Input: Analysis result (e.g. "70% plastic, 30% paper").

[0375] Output: Calculated incentive points (e.g. 100 points).

[0376] What it does: The server calculates points based on the type and percentage of ingredients.

[0377] Step 13: The server adds the calculated points to the database record corresponding to the user's account ID.

[0378] Input: Calculated incentive points, user ID.

[0379] Output: Points are added to the user account.

[0380] Specific behavior: The server updates the database and increases the user's points balance.

[0381] Step 14: The server notifies the terminal of the result of the point addition.

[0382] Input: The result of adding points.

[0383] Output: The device receives the notification.

[0384] Specific operation: The server sends an HTTP response to the terminal to notify it that the points have been added successfully.

[0385] Step 15: The device activates its emotion engine and analyzes the user's facial expressions and voice.

[0386] Input: User's facial expressions and voice data.

[0387] Output: Sentiment analysis result (e.g. "joy").

[0388] How it works: The device uses the camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0389] Step 16: The emotion engine sends the analysis results to the server.

[0390] Input: Sentiment analysis results.

[0391] Output: The server receives the analysis results.

[0392] Specific operation: The device issues the emotion analysis results to the server.

[0393] Step 17: The server adjusts and awards incentive points based on the sentiment analysis results.

[0394] Input: Sentiment analysis results, current incentive points.

[0395] Output: Adjusted incentive points.

[0396] Specific operation: The server increases or decreases points according to the results of the sentiment analysis, recalculates them, and reflects them in the user's account.

[0397] Step 18: The server generates a feedback message based on the analysis result and sends it to the terminal.

[0398] Input: Sentiment analysis results.

[0399] Output: Feedback message.

[0400] Specific operation: The server creates a message based on the emotion analysis results and sends it to the device as an HTTP response.

[0401] Step 19: The terminal displays a feedback message to the user.

[0402] Input: Feedback message.

[0403] Output: The user confirms the message.

[0404] Specific behavior: The device will display a feedback message as a pop-up or notification.

[0405] Step 20: User selects the incentive redemption option within the app and enters the number of points to redeem.

[0406] Input: The number of points entered by the user to redeem.

[0407] Output: The redemption request is saved on the device.

[0408] Specific behavior: The device displays a form for user input and collects input data.

[0409] Step 21: The terminal transmits the input number of points and the user ID to the server.

[0410] Input: Number of points to be redeemed, user ID.

[0411] Output: The server receives the request.

[0412] Specific operation: The terminal sends a cash request to the server using an HTTP request.

[0413] Step 22: The server checks the user's point balance and subtracts the specified points.

[0414] Input: Redemption request, current points balance.

[0415] Output: Updated points balance.

[0416] Specific operation: The server queries the database to check and update the user's point balance.

[0417] Step 23: The server calculates the converted amount and calls an API to instruct the transfer to the specified bank account.

[0418] Input: Number of points to be redeemed, bank account information.

[0419] Output: The result of the transfer instruction.

[0420] Specific operation: The server uses the API to send a transfer instruction to the bank system.

[0421] Step 24: The server checks whether the transfer was successful and notifies the terminal of the result.

[0422] Input: Transfer result from API.

[0423] Output: Transfer result notification.

[0424] Specific operation: The server obtains the transfer result from the API and sends the result to the terminal.

[0425] Step 25: The device receives the notification and displays the result (success / failure) to the user.

[0426] Input: Transfer result notification.

[0427] Output: The resulting information that is displayed to the user.

[0428] Specific operation: The device receives a notification from the server and displays its contents to the user.

[0429] (Application example 2)

[0430] 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."

[0431] In modern society, promoting recycling activities has become an important issue, but sustaining users' motivation is difficult. Conventional systems simply accept recyclable items and award points, but this does not maintain user interest and lacks a means to encourage continued use. Furthermore, to increase the transparency and reliability of recycling activities, it is necessary to prevent fraudulent data collection. There is a need to provide an effective system that can solve these problems and increase users' motivation to recycle.

[0432] 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.

[0433] In this invention, the server includes a means for users to scan recyclable items, a means for the server to pass the image to a generative AI model to analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record recycling performance data on the blockchain, a means for the user to redeem the incentive points, a means for the user's terminal to analyze the user's emotional state using an emotion engine, and a means for the server to adjust incentives based on the emotion analysis results. This allows incentives to be awarded according to the user's emotional state, effectively increasing motivation to participate in recycling activities. Furthermore, recording recycling performance data on the blockchain increases the transparency and reliability of the system.

[0434] "User" means an individual or legal entity that brings in recyclable items and uses the recycling system.

[0435] "Means for scanning recyclable items" refers to a function that uses a device such as a smartphone to capture an image of a recyclable item and send that image to a server.

[0436] A "generative AI model" is a machine learning algorithm that analyzes the material composition of recycled items from images.

[0437] "Means for analyzing material composition" refers to a function that uses a generative AI model to identify the proportion and type of materials contained in image data of recycled products.

[0438] "Incentive points" are points that are given to users as a reward for recycling activities.

[0439] "User Account" means an individual account registered by a User to use the Recycling System.

[0440] "Recycling performance data" refers to information (material composition, points, time, etc.) collected when a user performs recycling activities.

[0441] "Blockchain" is a distributed ledger technology that continuously records transaction data.

[0442] An "emotion engine" is a technology for analyzing emotions from a user's facial expressions, voice, etc.

[0443] The "means for adjusting incentives" is a function that increases or decreases the user's incentive points based on the analysis results of the emotion engine.

[0444] "Means of cashing out" refers to a function that allows users to withdraw the incentive points they have earned by cash, bank transfer, etc.

[0445] This invention is a system that combines a user-participation recycling platform with an emotion engine. Specifically, users scan recyclable items using devices such as smartphones, and the information is sent to a server that analyzes their material composition and provides incentives based on the user's emotional state. Furthermore, to ensure transparency in recycling activities, data is recorded on a blockchain. Below, we explain specific processing methods and examples of this system.

[0446] First, users install the recycling platform's application on their smartphone and log in or register. After logging in, they open the camera to take a picture of the recyclable item. When the user takes a picture of the recyclable item, the device sends the image to a server. The server passes the image to a generative AI model, which analyzes its material composition. This analysis determines what the recyclable item is made of (for example, "70% plastic, 30% paper").

[0447] Next, the server calculates incentive points based on the analysis results and adds them to the user's account. It's worth noting that the system uses an emotion engine. When the user device scans the recyclable item, it simultaneously captures the user's facial expressions and analyzes them with the emotion engine. The emotion engine detects the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns the analysis results to the server.

[0448] The server adjusts incentives based on the results of emotion analysis. For example, if the user is happy, it will give additional bonus points. The server also generates a feedback message for the user based on their emotion and sends it to the device. The device displays the message to the user, thereby increasing their motivation to recycle.

[0449] Next, recycling performance data (such as user ID, material composition, incentive points, timestamp, etc.) is recorded on the blockchain by the server. Blockchain technology ensures data tamper-proofing and transparency.

[0450] Finally, users are also given the option to redeem their incentive points for cash. When a user makes a cash request within the app, the server will check the user's point balance and the cash request, and transfer the funds to the specified bank account. If the transfer is successful, the user will be notified of the result.

[0451] The hardware used includes smartphones, servers, cameras, etc. The software includes generative AI models, sentiment analysis engines (e.g., FER libraries), blockchain APIs, banking APIs, etc. In particular, an example prompt for using a generative AI model is as follows:

[0452] Please analyze the material composition of the image.

[0453] This invention can increase users' motivation to recycle by combining emotion recognition with their recycling activities. In addition, by using blockchain technology, transparency and reliability can be ensured, providing a system that users can use with peace of mind.

[0454] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0455] Step 1:

[0456] This is the step where the user scans the recyclable item.

[0457] The user launches the app on their smartphone and selects the recyclable item scanning function. They then use the smartphone camera to capture an image of the recyclable item. This image becomes the input data, and the device sends the image to the server. The output is the image data of the recyclable item sent to the server.

[0458] Step 2:

[0459] This is the step where the material composition is analyzed using a generative AI model.

[0460] The server passes the received image data of the recycled product to the generative AI model and instructs it to analyze it along with the prompt, "Please analyze the material composition of the image." The generative AI model analyzes the image data and determines the material composition. This determination result (e.g., "70% plastic, 30% paper") is returned to the server. The input is the image data of the recycled product, and the output is the data resulting from the material determination.

[0461] Step 3:

[0462] This is the calculation and addition step of incentive points.

[0463] The server calculates the points assigned to each material in the recycled product based on the material determination results returned by the generative AI model. These calculation results become incentive points and are added to the user's account. The input is the material determination results, and the output is the calculated incentive points.

[0464] Step 4:

[0465] This is the step of analyzing the user's emotional state.

[0466] While scanning recyclable items, the smartphone camera captures the user's facial expressions and sends the images to the emotion engine. The emotion engine performs facial expression analysis to detect the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns it to the server. The input is image data containing the user's facial expressions, and the output is emotional state data.

[0467] Step 5:

[0468] This is a step for adjusting incentives based on the results of sentiment analysis.

[0469] The server adjusts incentive points based on the emotion analysis results from the emotion engine. For example, if the user is happy, additional bonus points are awarded. The adjusted incentive points are recalculated and added to the user's account. The input is the emotion analysis results and existing incentive points, and the output is the adjusted incentive points.

[0470] Step 6:

[0471] A feedback message generation and notification step.

[0472] The server generates a feedback message based on the sentiment analysis results and sends it to the user device. For example, a message such as "Your efforts are saving the Earth! Wonderful!" is generated. The user device displays this message. The input is the sentiment analysis results, and the output is the feedback message.

[0473] Step 7:

[0474] This is the step where recycling performance data is recorded on the blockchain.

[0475] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database. The input is the recycling performance data, and the output is the transaction ID.

[0476] Step 8:

[0477] This is the step of converting incentive points into cash.

[0478] The user selects the cash-out option for incentive points within the app and enters the number of points. The user's device sends the entered number of points and user ID to the server. The server checks the user's point balance and instructs a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the user's device of the result. The input is the cash-out request and number of points, and the output is a notification of the cash-out result.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] [Second embodiment]

[0483] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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."

[0495] The present invention is a user-participation recycling platform that allows users to scan recyclable items and earn incentive points based on the analysis results. This system is operated by exchanging data between users, terminals, and a server. Below, we will explain the program for this system, including specific processing methods and examples.

[0496] System configuration and operation

[0497] User registration and login

[0498] A user installs the app and opens the sign-up screen. The user enters the required information (email address, password, and other required personal information). The device receives the information and sends it to the server. The server stores the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores it in local storage for future authentication.

[0499] Recycled Goods Scan

[0500] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[0501] Material determination by generative AI

[0502] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[0503] Incentive calculation and awarding

[0504] The server calculates incentive points based on the received material composition, for example, "100 points," searches the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the result of the point addition.

[0505] Tracking and Verification

[0506] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0507] Incentive cash redemption

[0508] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount (e.g., "500 points = 1,000 yen") and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[0509] Specific example explanation

[0510] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account. User "A" then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[0511] This system is effective in reducing the effort of recycling and encouraging individual recycling behavior. By combining generative AI models with blockchain technology, users can easily perform recycling activities, track their results, and earn rewards.

[0512] The processing flow will be explained below.

[0513] Step 1:

[0514] A user installs the app and opens the registration screen, where they enter the required information (email address, password, etc.).

[0515] Step 2:

[0516] The terminal transmits the input information to the server.

[0517] Step 3:

[0518] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[0519] Step 4:

[0520] The server returns an authentication token to the device, which stores the token in local storage.

[0521] Step 5:

[0522] The user launches the app and selects the option to scan for recyclable items.

[0523] Step 6:

[0524] The device activates the camera and the user takes a picture of the recyclable item.

[0525] Step 7:

[0526] The terminal transmits the captured image data to the server.

[0527] Step 8:

[0528] The server passes the received image data to the generative AI model.

[0529] Step 9:

[0530] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[0531] Step 10:

[0532] The server receives the analysis result and calculates incentive points, for example, 100 points.

[0533] Step 11:

[0534] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[0535] Step 12:

[0536] The server notifies the terminal of the result of adding points, and the terminal displays a notification to the user.

[0537] Step 13:

[0538] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[0539] Step 14:

[0540] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[0541] Step 15:

[0542] The server receives the transaction ID from the blockchain network and stores it in a database.

[0543] Step 16:

[0544] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[0545] Step 17:

[0546] The terminal sends the entered number of points and the user ID to the server.

[0547] Step 18:

[0548] The server checks the user's point balance and deducts the specified points.

[0549] Step 19:

[0550] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[0551] Step 20:

[0552] The server checks whether the transfer was successful and notifies the terminal of the result.

[0553] Step 21:

[0554] The device receives the notification and displays the result (success / failure) to the user.

[0555] These are the specific processing steps of the system, which provides an effective means for users to easily carry out recycling activities and receive incentives based on their performance.

[0556] Example 1

[0557] 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."

[0558] There is a need for a system that ensures the efficiency and transparency of recycling activities and encourages users to recycle. However, conventional systems make it difficult for users to accurately identify the materials in recyclable items and easily assign incentive points and convert them into cash. In particular, it is difficult to accurately analyze the material composition of recyclable items and record and manage the performance data in a reliable manner. Furthermore, the process of converting incentive points into cash remains cumbersome.

[0559] 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.

[0560] In this invention, the server includes a means for a user to photograph waste, a means for a terminal to send the photographed image to the server, a means for the server to pass the image to a generative AI model and analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record waste performance data on a distributed ledger, and a means for the user to convert the incentive points into currency. This enables users to easily carry out recycling activities, check the results of their activities in an accurate and reliable manner, and receive appropriate rewards.

[0561] "User" refers to a person who uses the system to carry out recycling activities.

[0562] "Waste" refers to items that can be reused and are subject to recycling.

[0563] "Device" refers to your mobile phone, tablet, or other computing device.

[0564] "Server" refers to a computer that is the central part of the entire system and is a device that processes, stores, analyzes, and communicates data.

[0565] "Images" refers to photographs and visual data of waste taken by users using their device's camera.

[0566] A "generative AI model" refers to a form of artificial intelligence that uses machine learning and deep learning algorithms to analyze and generate information from input data.

[0567] "Material composition" refers to the percentage of materials the waste is made up of (e.g., plastic, paper).

[0568] "Incentive points" refer to reward points that users can earn by scanning recyclable items and based on the material analysis results.

[0569] "Currency" refers to a medium with real value, such as legal tender or electronic money, that users receive when converting incentive points into cash.

[0570] "Database" refers to a system that stores data such as user information and incentive points managed on a server.

[0571] A "distributed ledger" refers to a recording system that uses blockchain technology to increase data transparency and reliability.

[0572] This invention is a system aimed at improving the efficiency of waste management, allowing users to photograph waste, acquire incentive points based on the analysis results, and ultimately convert them into currency. A specific embodiment of this system will be described below.

[0573] System Configuration

[0574] The system consists of the following components:

[0575] User Device: A computing device such as a smartphone or tablet.

[0576] Server: A computer that processes, stores, analyzes, and communicates data.

[0577] Camera: An image capture device built into a user device.

[0578] Database: A system that manages user information and incentive points (e.g., PostgreSQL).

[0579] Generative AI model: An artificial intelligence model that performs image analysis (e.g., TensorFlow).

[0580] Distributed ledger: Blockchain technology (e.g. Ethereum) to ensure data transparency and reliability.

[0581] Program processing overview

[0582] User registration and login

[0583] A user installs the app and opens the new registration screen. The user enters their email address and password. The device sends this information to the server, which stores it in a database and generates a new user ID and authentication token. The authentication token is returned to the device and stored in local storage.

[0584] Recycled Goods Scan

[0585] The user launches the app and selects the option to scan a recyclable item. The camera activates and the user takes a picture of the recyclable item. The image data is then sent from the device to the server.

[0586] Material determination by generative AI

[0587] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. It sends a prompt saying, "Please determine the material composition of the recycled product." The generative AI model analyzes the material composition and returns the results to the server. For example, it may determine that the material composition is "70% plastic, 30% paper."

[0588] Incentive calculation and awarding

[0589] The server calculates incentive points based on the analysis results. For example, 100 points are added to the user's account. The processing results are notified to the terminal.

[0590] Tracking and Verification

[0591] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[0592] Incentive cash redemption

[0593] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. After the transfer is confirmed, the result is notified to the device and displayed to the user.

[0594] Specific examples

[0595] For example, consider a user recycling a plastic bottle. The user launches the app and takes a photo of the bottle with their camera. The device sends the image to a server. The server analyzes the material using a generative AI model and determines that it is "90% plastic, 10% paper." Based on the results, "100 points" are calculated and added to the user's account. The user then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[0596] Prompt Sentence Examples

[0597] "Analyze an image of a recycled PET bottle. Print out the specific material composition, showing the ratio of plastic to paper."

[0598] This system allows users to easily carry out recycling activities, check the results, and receive rewards. The combination of generative AI models and blockchain technology will improve the efficiency and transparency of recycling activities, which is a major feature of the system's implementation.

[0599] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0600] Step 1: User Registration and Login

[0601] The user installs the app and opens the new registration screen. The user enters information such as an email address and password. The device sends the entered information to the server via an HTTP POST request. The server receives the information and stores it in a database. This generates a user ID and authentication token. The server returns the generated authentication token to the device, which stores it in local storage.

[0602] Input: User registration information (email address, password),

[0603] Output: Authentication token

[0604] Step 2: Recycled Goods Scan

[0605] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, and the user takes a picture of the recyclable item. The device then sends the image to the server.

[0606] Input: Image of recycled item,

[0607] Output: Image data sent to the server

[0608] Step 3: Material determination by generative AI

[0609] The server passes the received image data to the generative AI model and sends a prompt to analyze the material composition. The prompt sends the message, "Please determine the material composition of the recycled product." The generative AI model analyzes the image data and calculates the proportion of materials. The generative AI model returns the analysis results to the server. For example, it may determine that the material is 70% plastic and 30% paper.

[0610] Input: Image data captured, prompts to analyze,

[0611] Output: Material composition (e.g. 70% plastic, 30% paper)

[0612] Step 4: Incentive calculation and granting

[0613] The server calculates incentive points based on the analysis results it receives. For example, it calculates "100 points" based on the result "70% plastic, 30% paper." The server adds the points to the user's account and notifies the terminal of the processing result.

[0614] Input: Analysis results of material composition,

[0615] Output: Calculated incentive points, notification data

[0616] Step 5: Tracking and verification

[0617] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[0618] Input: Actual data,

[0619] Output: Transaction ID

[0620] Step 6: Incentive Cashing

[0621] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The terminal sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The terminal is notified of the success / failure of the transfer. The terminal displays the transfer result to the user.

[0622] Input: Redemption request (number of points and user ID),

[0623] Output: Transfer result notification

[0624] (Application example 1)

[0625] 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."

[0626] Conventional recycling activities have been time-consuming for users, and the incentives are complicated, making it difficult to promote recycling. There have also been challenges in ensuring the transparency and reliability of recycling results.

[0627] 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.

[0628] In this invention, the server includes a means for users to use their smartphones to earn incentive points based on the results of an analysis of the material composition of recyclable items, a means for users to instantly use the incentive points they earned in physical stores, and a means for the server to verify transactions on the blockchain and perform tracking and certification based on transaction IDs stored in a database. This allows users to easily participate in recycling activities and instantly use the incentives they earned, promoting recycling and ensuring transparent and reliable recycling results.

[0629] A "user" is an individual who scans recycled goods, earns incentive points, and redeems those points in physical stores.

[0630] The "server" refers to a computer system that passes images of recycled items to a generative AI model to analyze their material composition, calculates incentive points based on the analysis results, verifies transactions on the blockchain, and stores recycling performance data in a database.

[0631] A "generative AI model" refers to an artificial intelligence model that analyzes the material composition of recycled products from image data.

[0632] "Incentive Points" refers to reward points that users earn as a result of their recycling activities.

[0633] "Blockchain" refers to a distributed ledger technology that ensures transparency and reliability of recycling performance data.

[0634] "Smartphone" refers to the mobile device used by users to scan recycled items and check and redeem incentive points.

[0635] "Physical store" refers to a physical store where users can immediately use the incentive points they have earned.

[0636] "Transaction ID" refers to a unique identification number used to identify a transaction on a blockchain.

[0637] The present invention is a recycling platform system that allows users to scan recyclable items and earn incentive points based on the analysis results. The system is mainly operated by exchanging data between users, terminals, and a server.

[0638] First, users install a recycling point management app on their smartphone. Using this app, users can take pictures of recyclable items with the device's camera. The images are then sent from the device to a server. The server receives the image data and passes it to a generative AI model to analyze the material composition.

[0639] A generative AI model is an artificial intelligence model that analyzes the material composition of recyclable items from image data, and is built using software such as TensorFlow and PyTorch. This generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. For example, the analysis result may be "80% plastic, 20% paper." Based on this analysis result, the server calculates incentive points and adds them to the user's account.

[0640] Users can instantly use the incentive points they have earned in physical stores using their smartphones. Specifically, users select the point check option in the app to check their point balance, and then use the points in the physical store to receive discounts on products and services.

[0641] Furthermore, the server records recycling performance data (e.g., user ID, material composition, incentive points, timestamp, etc.) on the blockchain. Blockchain is a distributed ledger technology that ensures data transparency and reliability. The server verifies transactions on the blockchain and stores the results in a database. Recycling performance is tracked and verified based on this transaction ID.

[0642] As a concrete example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A"'s account. User "A" can then use the points to purchase products at a discount in a physical store.

[0643] An example of a prompt sentence to input to the generative AI model is as follows:

[0644] "Please analyze the material composition of the image below. The image contains recycled materials (plastic bottles). Please return your analysis results in terms of material types and their percentages. For example, please answer "90% plastic, 10% paper.""

[0645] This system allows users to easily participate in recycling activities and instantly use the incentive points they earn. The use of blockchain technology also improves the transparency and reliability of recycling results.

[0646] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0647] Step 1:

[0648] The user launches the recycling point management app installed on their smartphone. The user selects the option to scan a recyclable item through the app and activates the device's camera. The user takes a picture of the recyclable item, and the device acquires the image data. The input is the image of the recyclable item taken by the user, and the output is the image data acquired by the device's camera.

[0649] Step 2:

[0650] The image data acquired by the device is sent to the server. The input is the image data acquired by the device's camera, and the output is the image data sent to the server. The server receives and stores this image data.

[0651] Step 3:

[0652] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. The input is image data, which is passed to the generative AI model. The generative AI model analyzes the image data and determines the material composition of the recycled product. For example, it may obtain a result such as "80% plastic, 20% paper." This analysis result is then output.

[0653] Step 4:

[0654] The server calculates incentive points based on the analysis results obtained from the generative AI model. For example, a certain point calculation rule is applied to the analysis result of "80% plastic, 20% paper" to calculate 100 points. The input is the analysis result from the generative AI model, and the output is the calculated incentive points.

[0655] Step 5:

[0656] The server adds the calculated incentive points to the user's account. The input is the calculated incentive points and the output is the updated user account data. The server adds the points to the user's account and saves it in the database.

[0657] Step 6:

[0658] The server generates recycling performance data (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network. The input is the recycling performance data, and the output is the data recorded on the blockchain network. The blockchain receives the data, verifies it, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0659] Step 7:

[0660] A user selects the check points option in the app to check their current points balance. The input is the user's account information and the output is the points balance displayed to the user. The device sends a request to the server, which returns the user's current points balance.

[0661] Step 8:

[0662] A user redeems incentive points in a physical store. The user uses their smartphone to select a point redemption option and receive a discount on goods or services in the store. The input is the number of points the user redeems, and the output is the discount offered or the goods or services exchanged. The terminal sends a request to the server, which deducts the specified number of points from the user's account and authorizes the redemption in the physical store.

[0663] This step will make it easier for users to participate in recycling activities, allowing them to immediately use the incentive points they earn, while ensuring the reliability and transparency of recycling results through blockchain technology.

[0664] 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.

[0665] This invention is a system that improves users' motivation to recycle by combining a user-participation recycling platform with an emotion engine that recognizes users' emotions. This system is operated by exchanging data between users, terminals, a server, and the emotion engine. Below, we will explain the program for this system, including specific processing methods and examples.

[0666] System configuration and operation

[0667] User registration and login

[0668] A user installs the app and opens the new registration screen. The user enters the required information (email address, password, etc.). The device sends the entered information to the server. The server saves the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores the token in local storage for future authentication.

[0669] Recycled Goods Scan

[0670] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[0671] Material determination by generative AI

[0672] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[0673] Incentive calculation and awarding

[0674] The server calculates incentive points based on the received material composition, for example, 100 points, searches for the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the point addition result.

[0675] Emotion recognition and incentive adjustment using an emotion engine

[0676] The device uses an emotion engine to analyze the user's facial expressions and voice to determine their emotions. For example, it can detect emotions such as joy, sadness, and surprise. The emotion engine then returns the analysis results to the server.

[0677] The server adjusts and awards incentives according to the user's emotional state based on the results of emotion analysis. For example, if the user is happy, it awards additional bonus points and notifies the user of the analysis results.

[0678] The server also generates feedback messages based on the analysis results and sends them to the device, which then displays the messages to the user, encouraging them to recycle more.

[0679] Tracking and Verification

[0680] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0681] Incentive cash redemption

[0682] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[0683] Specific example explanation

[0684] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which then analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account.

[0685] At this time, the emotion engine analyzes the emotion of user "A" from his / her facial expression and determines it to be "joy." Based on this emotion analysis result, the server awards further bonus points, calculating an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Wonderful!" and sends it to the device. The device then displays the additional points and feedback message to user "A."

[0686] At a later date, User A converts the 300 points into cash, and 3,000 yen is deposited into the designated bank account. This allows User A to contribute to the effective use of resources through recycling activities and also earn personal rewards.

[0687] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, providing an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[0688] The processing flow will be explained below.

[0689] Step 1:

[0690] A user installs the app and opens the registration screen. The user enters the required information (email address, password, etc.).

[0691] Step 2:

[0692] The terminal transmits the input information to the server.

[0693] Step 3:

[0694] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[0695] Step 4:

[0696] The server returns an authentication token to the device, which stores the token in local storage.

[0697] Step 5:

[0698] The user launches the app and selects the option to scan for recyclable items.

[0699] Step 6:

[0700] The device activates the camera and the user takes a picture of the recyclable item.

[0701] Step 7:

[0702] The terminal transmits the captured image data to the server.

[0703] Step 8:

[0704] The server passes the received image data to the generative AI model.

[0705] Step 9:

[0706] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[0707] Step 10:

[0708] The server receives the analysis results and calculates incentive points, for example, 100 points based on the material composition of the recycled product.

[0709] Step 11:

[0710] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[0711] Step 12:

[0712] The server notifies the terminal of the result of the point addition, and the terminal displays a notification to the user.

[0713] Step 13:

[0714] The device uses an emotion engine to analyze the user's emotions, for example, detecting emotions such as "happiness" from the user's facial expressions and voice.

[0715] Step 14:

[0716] The emotion engine sends the analysis results to the server.

[0717] Step 15:

[0718] The server receives the emotion analysis results and adjusts the incentive according to the user's emotional state. For example, if the user is in a "joy" state, an additional 20 points will be added as a bonus.

[0719] Step 16:

[0720] The server stores the adjusted incentive points in a database and notifies the terminal of the result.

[0721] Step 17:

[0722] The server generates a feedback message based on the sentiment analysis results, e.g., "Your efforts are saving the Earth! Great!"

[0723] Step 18:

[0724] The server sends a feedback message to the terminal, which displays it to the user.

[0725] Step 19:

[0726] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[0727] Step 20:

[0728] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[0729] Step 21:

[0730] The server receives the transaction ID from the blockchain network and stores it in a database.

[0731] Step 22:

[0732] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[0733] Step 23:

[0734] The terminal sends the entered number of points and the user ID to the server.

[0735] Step 24:

[0736] The server checks the user's point balance and deducts the specified points.

[0737] Step 25:

[0738] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[0739] Step 26:

[0740] The server checks whether the transfer was successful and notifies the terminal of the result.

[0741] Step 27:

[0742] The device receives the notification and displays the result (success / failure) to the user.

[0743] Example 2

[0744] 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."

[0745] Existing recycling platforms often simply award points without considering users' emotions or motivations. This tends to discourage users from recycling, making it difficult to promote sustainable recycling activities. In addition, the processes for analyzing the materials of recycled products and awarding incentives are generally cumbersome, making efficient system operation necessary.

[0746] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for the user to scan the recyclable item; a means for the terminal to activate the camera and acquire an image; a means for the server to pass the image to a generative AI model and analyze the material composition; a means for the server to calculate incentive points based on the analysis results and add them to the user's account; a means for the terminal to analyze emotions and send the results to the server; a means for the server to adjust and grant incentive points based on the emotion analysis results; a means for the server to record recycling performance data on the blockchain; and a means for the user to redeem the incentive points. This makes it possible to provide incentives that take users' emotions into consideration and increase their motivation to recycle. Furthermore, material composition analysis using a generative AI model and data management using blockchain technology enable an efficient and reliable recycling process.

[0747] "User" refers to an individual or organization that uses the system to carry out recycling activities.

[0748] A "terminal" is a device operated by a user that provides functions such as scanning and display.

[0749] "Server" means a central system that processes, stores, and manages data and interacts with users and devices through various means.

[0750] "Recyclables" refers to items that users choose to recycle, specifically items that are subject to scanning and analysis.

[0751] "Camera" means a device mounted on the Terminal for taking images of recyclable items.

[0752] "Image" refers to visual information obtained by photographing recycled items with a camera.

[0753] A "generative AI model" is an artificial intelligence model that is installed on a server and analyzes the material composition of recycled items from image data.

[0754] "Material composition" refers to the proportion and type of various materials that make up a recycled product, and is analyzed by a generative AI model.

[0755] "Incentive points" are points awarded to users as a reward for recycling activities.

[0756] An "emotion engine" refers to software or hardware that analyzes a user's emotional state from their facial expressions and voice.

[0757] "Emotion analysis results" refers to data indicating the user's emotional state detected by the emotion engine.

[0758] "Blockchain" refers to a distributed ledger technology used to ensure data is trustworthy and immutable.

[0759] "Recycling performance data" refers to data related to recycling activities, such as user ID, material composition, incentive points, and timestamps.

[0760] "Redeem" means the process by which a user redeems incentive points for money or other purposes.

[0761] This invention is a system that improves users' motivation to recycle by combining an emotion engine that recognizes users' emotions in a user-participation recycling platform. This system is operated by exchanging data between users, terminals, a server, and the emotion engine.

[0762] User registration and login

[0763] First, the user installs the app and launches it. The user opens the new registration screen and enters the required information. The device sends this information to the server, which then stores the user information in a database. The server then generates a new user ID and authentication token and returns them to the device. The device then stores the received authentication token in local storage and uses it for future authentication.

[0764] Recycled Goods Scan

[0765] The user launches the app and selects the option to scan a recyclable item. The device's camera activates, and the user takes a picture of the recyclable item and sends it to the server. The server then provides the received image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. The generative AI model then returns the analysis results to the server.

[0766] Incentive calculation and awarding

[0767] The server calculates incentive points based on the received material composition. For example, based on a result such as "70% plastic, 30% paper," it calculates 100 points and adds the points to the database record corresponding to the user's account ID. The server then notifies the terminal of the point addition result.

[0768] Emotion recognition and incentive adjustment using an emotion engine

[0769] The device uses an emotion engine to analyze the user's facial expressions and voice. For example, it detects emotional states such as happiness, sadness, and surprise. The emotion engine returns the results to the server, which then adjusts the incentives according to the user's emotional state based on the analysis results. If the user is happy, the server awards additional bonus points. The server also generates a feedback message based on the analysis results and sends it to the device. The device displays this message to the user, encouraging them to recycle more.

[0770] Tracking and Verification

[0771] The server generates performance data for each recycled item, including user ID, material composition, incentive points, timestamp, etc. The server sends this data to the blockchain network and requests it to be recorded. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in a database.

[0772] Incentive cash redemption

[0773] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends this information and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server then calculates the redemption amount and calls an API to transfer the money to the specified bank account. The server confirms the success of the transfer and notifies the device of the result. The device displays it to the user.

[0774] Specific example explanation

[0775] For example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with his / her camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A's" account. At that time, the emotion engine analyzes user "A's" facial expression and determines the emotion as "joy." Based on this emotion analysis result, the server awards an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Amazing!" and sends it to the device. The device displays the additional points and the feedback message to user "A." Later, user "A" converts the 300 points into cash, and 3,000 yen is deposited into his / her designated bank account.

[0776] Example prompt sentence:

[0777] "Please analyze the material of the plastic bottle."

[0778] "Analyze user sentiment and calculate bonus points."

[0779] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, making it an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[0780] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0781] Step 1: User installs and launches the app.

[0782] Input: The user installs the app and taps to launch it.

[0783] Output: The app starts and the initial screen is displayed.

[0784] Specific operation: The device launches the installed app and displays the initial screen.

[0785] Step 2: The user opens the new registration screen and enters the required information.

[0786] Input: The user enters required information, such as an email address or password.

[0787] Output: The input information is aggregated and stored on the device.

[0788] Specific behavior: The device displays a new registration form, and the user enters information into the form.

[0789] Step 3: The terminal sends the entered information to the server.

[0790] Input: Information entered by the user, such as email address or password.

[0791] Output: The server receives the user information.

[0792] Specific operation: The device sends user information to the server via an HTTP request.

[0793] Step 4: The server saves the user information to the database and generates a new user ID and authentication token.

[0794] Input: User information received by the server from the device.

[0795] Output: A new user ID and authentication token is generated and stored in the database.

[0796] Specific behavior: The server inserts the user information into the database and generates a user ID and authentication token.

[0797] Step 5: The server returns the authentication token to the device, which stores it in local storage.

[0798] Input: The authentication token sent by the server.

[0799] Output: The authentication token is saved on the device.

[0800] Specific operation: The server sends an authentication token in the HTTP response, and the device stores it in local storage.

[0801] Step 6: The user launches the app and selects the option to scan for recyclables.

[0802] Input: User selects the scan option within the app.

[0803] Output: The camera starts.

[0804] Specific behavior: Activates the camera module when the device selects the scan option.

[0805] Step 7: The device activates the camera and the user takes a picture of the item to be recycled.

[0806] Input: User uses camera to take a picture of a recyclable item.

[0807] Output: The captured image data is saved on the device.

[0808] Specific behavior: The device launches the camera app, and the user presses the shutter button to take a picture.

[0809] Step 8: The device sends the captured image to the server.

[0810] Input: Captured image data.

[0811] Output: The image data is transferred to the server.

[0812] Specific operation: The device uploads image data to the server using an HTTP request.

[0813] Step 9: The server provides the received image data to the generative AI model and instructs it to analyze it.

[0814] Input: Image data received by the server.

[0815] Output: The generative AI model begins its analysis.

[0816] Specific operation: The server sends the image data to the API of the generated AI model and issues an analysis request.

[0817] Step 10: The generative AI model analyzes the image data and determines the material composition of the recycled item.

[0818] Input: Image data.

[0819] Output: Material composition analysis (e.g. "70% plastic, 30% paper").

[0820] Specific operation: The generative AI model analyzes the image, calculates the type and proportion of ingredients, and returns the results.

[0821] Step 11: The generative AI model returns the analysis results to the server.

[0822] Input: Material composition analysis results.

[0823] Output: The server receives the analysis results.

[0824] Specific operation: The generative AI model sends the analysis results to the server.

[0825] Step 12: The server calculates incentive points based on the received material configuration.

[0826] Input: Analysis result (e.g. "70% plastic, 30% paper").

[0827] Output: Calculated incentive points (e.g. 100 points).

[0828] What it does: The server calculates points based on the type and percentage of ingredients.

[0829] Step 13: The server adds the calculated points to the database record corresponding to the user's account ID.

[0830] Input: Calculated incentive points, user ID.

[0831] Output: Points are added to the user account.

[0832] Specific behavior: The server updates the database and increases the user's points balance.

[0833] Step 14: The server notifies the terminal of the result of the point addition.

[0834] Input: The result of adding points.

[0835] Output: The device receives the notification.

[0836] Specific operation: The server sends an HTTP response to the terminal to notify it that the points have been added successfully.

[0837] Step 15: The device activates its emotion engine and analyzes the user's facial expressions and voice.

[0838] Input: User's facial expressions and voice data.

[0839] Output: Sentiment analysis result (e.g. "joy").

[0840] How it works: The device uses the camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[0841] Step 16: The emotion engine sends the analysis results to the server.

[0842] Input: Sentiment analysis results.

[0843] Output: The server receives the analysis results.

[0844] Specific operation: The device issues the emotion analysis results to the server.

[0845] Step 17: The server adjusts and awards incentive points based on the sentiment analysis results.

[0846] Input: Sentiment analysis results, current incentive points.

[0847] Output: Adjusted incentive points.

[0848] Specific operation: The server increases or decreases points according to the results of the sentiment analysis, recalculates them, and reflects them in the user's account.

[0849] Step 18: The server generates a feedback message based on the analysis result and sends it to the terminal.

[0850] Input: Sentiment analysis results.

[0851] Output: Feedback message.

[0852] Specific operation: The server creates a message based on the emotion analysis results and sends it to the device as an HTTP response.

[0853] Step 19: The terminal displays a feedback message to the user.

[0854] Input: Feedback message.

[0855] Output: The user confirms the message.

[0856] Specific behavior: The device will display a feedback message as a pop-up or notification.

[0857] Step 20: User selects the incentive redemption option within the app and enters the number of points to redeem.

[0858] Input: The number of points entered by the user to redeem.

[0859] Output: The redemption request is saved on the device.

[0860] Specific behavior: The device displays a form for user input and collects input data.

[0861] Step 21: The terminal transmits the input number of points and the user ID to the server.

[0862] Input: Number of points to be redeemed, user ID.

[0863] Output: The server receives the request.

[0864] Specific operation: The terminal sends a cash request to the server using an HTTP request.

[0865] Step 22: The server checks the user's point balance and subtracts the specified points.

[0866] Input: Redemption request, current points balance.

[0867] Output: Updated points balance.

[0868] Specific operation: The server queries the database to check and update the user's point balance.

[0869] Step 23: The server calculates the converted amount and calls an API to instruct the transfer to the specified bank account.

[0870] Input: Number of points to be redeemed, bank account information.

[0871] Output: The result of the transfer instruction.

[0872] Specific operation: The server uses the API to send a transfer instruction to the bank system.

[0873] Step 24: The server checks whether the transfer was successful and notifies the terminal of the result.

[0874] Input: Transfer result from API.

[0875] Output: Transfer result notification.

[0876] Specific operation: The server obtains the transfer result from the API and sends the result to the terminal.

[0877] Step 25: The device receives the notification and displays the result (success / failure) to the user.

[0878] Input: Transfer result notification.

[0879] Output: The resulting information that is displayed to the user.

[0880] Specific operation: The device receives a notification from the server and displays its contents to the user.

[0881] (Application example 2)

[0882] 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."

[0883] In modern society, promoting recycling activities has become an important issue, but sustaining users' motivation is difficult. Conventional systems simply accept recyclable items and award points, but this does not maintain user interest and lacks a means to encourage continued use. Furthermore, to increase the transparency and reliability of recycling activities, it is necessary to prevent fraudulent data collection. There is a need to provide an effective system that can solve these problems and increase users' motivation to recycle.

[0884] 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.

[0885] In this invention, the server includes a means for users to scan recyclable items, a means for the server to pass the image to a generative AI model to analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record recycling performance data on the blockchain, a means for the user to redeem the incentive points, a means for the user's terminal to analyze the user's emotional state using an emotion engine, and a means for the server to adjust incentives based on the emotion analysis results. This allows incentives to be awarded according to the user's emotional state, effectively increasing motivation to participate in recycling activities. Furthermore, recording recycling performance data on the blockchain increases the transparency and reliability of the system.

[0886] "User" means an individual or legal entity that brings in recyclable items and uses the recycling system.

[0887] "Means for scanning recyclable items" refers to a function that uses a device such as a smartphone to capture an image of a recyclable item and send that image to a server.

[0888] A "generative AI model" is a machine learning algorithm that analyzes the material composition of recycled items from images.

[0889] "Means for analyzing material composition" refers to a function that uses a generative AI model to identify the proportion and type of materials contained in image data of recycled products.

[0890] "Incentive points" are points that are given to users as a reward for recycling activities.

[0891] "User Account" means an individual account registered by a User to use the Recycling System.

[0892] "Recycling performance data" refers to information (material composition, points, time, etc.) collected when a user performs recycling activities.

[0893] "Blockchain" is a distributed ledger technology that continuously records transaction data.

[0894] An "emotion engine" is a technology for analyzing emotions from a user's facial expressions, voice, etc.

[0895] The "means for adjusting incentives" is a function that increases or decreases the user's incentive points based on the analysis results of the emotion engine.

[0896] "Means of cashing out" refers to a function that allows users to withdraw the incentive points they have earned by cash, bank transfer, etc.

[0897] This invention is a system that combines a user-participation recycling platform with an emotion engine. Specifically, users scan recyclable items using devices such as smartphones, and the information is sent to a server that analyzes their material composition and provides incentives based on the user's emotional state. Furthermore, to ensure transparency in recycling activities, data is recorded on a blockchain. Below, we explain specific processing methods and examples of this system.

[0898] First, users install the recycling platform's application on their smartphone and log in or register. After logging in, they open the camera to take a picture of the recyclable item. When the user takes a picture of the recyclable item, the device sends the image to a server. The server passes the image to a generative AI model, which analyzes its material composition. This analysis determines what the recyclable item is made of (for example, "70% plastic, 30% paper").

[0899] Next, the server calculates incentive points based on the analysis results and adds them to the user's account. It's worth noting that the system uses an emotion engine. When the user device scans the recyclable item, it simultaneously captures the user's facial expressions and analyzes them with the emotion engine. The emotion engine detects the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns the analysis results to the server.

[0900] The server adjusts incentives based on the results of emotion analysis. For example, if the user is happy, it will give additional bonus points. The server also generates a feedback message for the user based on their emotion and sends it to the device. The device displays the message to the user, thereby increasing their motivation to recycle.

[0901] Next, recycling performance data (such as user ID, material composition, incentive points, timestamp, etc.) is recorded on the blockchain by the server. Blockchain technology ensures data tamper-proofing and transparency.

[0902] Finally, users are also given the option to redeem their incentive points for cash. When a user makes a cash request within the app, the server will check the user's point balance and the cash request, and transfer the funds to the specified bank account. If the transfer is successful, the user will be notified of the result.

[0903] The hardware used includes smartphones, servers, cameras, etc. The software includes generative AI models, sentiment analysis engines (e.g., FER libraries), blockchain APIs, banking APIs, etc. In particular, an example prompt for using a generative AI model is as follows:

[0904] Please analyze the material composition of the image.

[0905] This invention can increase users' motivation to recycle by combining emotion recognition with their recycling activities. In addition, by using blockchain technology, transparency and reliability can be ensured, providing a system that users can use with peace of mind.

[0906] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0907] Step 1:

[0908] This is the step where the user scans the recyclable item.

[0909] The user launches the app on their smartphone and selects the recyclable item scanning function. They then use the smartphone camera to capture an image of the recyclable item. This image becomes the input data, and the device sends the image to the server. The output is the image data of the recyclable item sent to the server.

[0910] Step 2:

[0911] This is the step where the material composition is analyzed using a generative AI model.

[0912] The server passes the received image data of the recycled product to the generative AI model and instructs it to analyze it along with the prompt, "Please analyze the material composition of the image." The generative AI model analyzes the image data and determines the material composition. This determination result (e.g., "70% plastic, 30% paper") is returned to the server. The input is the image data of the recycled product, and the output is the data resulting from the material determination.

[0913] Step 3:

[0914] This is the calculation and addition step of incentive points.

[0915] The server calculates the points assigned to each material in the recycled product based on the material determination results returned by the generative AI model. These calculation results become incentive points and are added to the user's account. The input is the material determination results, and the output is the calculated incentive points.

[0916] Step 4:

[0917] This is the step of analyzing the user's emotional state.

[0918] While scanning recyclable items, the smartphone camera captures the user's facial expressions and sends the images to the emotion engine. The emotion engine performs facial expression analysis to detect the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns it to the server. The input is image data containing the user's facial expressions, and the output is emotional state data.

[0919] Step 5:

[0920] This is a step for adjusting incentives based on the results of sentiment analysis.

[0921] The server adjusts incentive points based on the emotion analysis results from the emotion engine. For example, if the user is happy, additional bonus points are awarded. The adjusted incentive points are recalculated and added to the user's account. The input is the emotion analysis results and existing incentive points, and the output is the adjusted incentive points.

[0922] Step 6:

[0923] A feedback message generation and notification step.

[0924] The server generates a feedback message based on the sentiment analysis results and sends it to the user device. For example, a message such as "Your efforts are saving the Earth! Wonderful!" is generated. The user device displays this message. The input is the sentiment analysis results, and the output is the feedback message.

[0925] Step 7:

[0926] This is the step where recycling performance data is recorded on the blockchain.

[0927] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database. The input is the recycling performance data, and the output is the transaction ID.

[0928] Step 8:

[0929] This is the step of converting incentive points into cash.

[0930] The user selects the cash-out option for incentive points within the app and enters the number of points. The user's device sends the entered number of points and user ID to the server. The server checks the user's point balance and instructs a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the user's device of the result. The input is the cash-out request and number of points, and the output is a notification of the cash-out result.

[0931] 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.

[0932] 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.

[0933] 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.

[0934] [Third embodiment]

[0935] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0936] 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.

[0937] 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).

[0938] 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.

[0939] 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.

[0940] 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).

[0941] 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.

[0942] 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.

[0943] 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.

[0944] 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.

[0945] 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.

[0946] 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."

[0947] The present invention is a user-participation recycling platform that allows users to scan recyclable items and earn incentive points based on the analysis results. This system is operated by exchanging data between users, terminals, and a server. Below, we will explain the program for this system, including specific processing methods and examples.

[0948] System configuration and operation

[0949] User registration and login

[0950] A user installs the app and opens the sign-up screen. The user enters the required information (email address, password, and other required personal information). The device receives the information and sends it to the server. The server stores the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores it in local storage for future authentication.

[0951] Recycled Goods Scan

[0952] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[0953] Material determination by generative AI

[0954] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[0955] Incentive calculation and awarding

[0956] The server calculates incentive points based on the received material composition, for example, "100 points," searches the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the result of the point addition.

[0957] Tracking and Verification

[0958] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[0959] Incentive cash redemption

[0960] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount (e.g., "500 points = 1,000 yen") and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[0961] Specific example explanation

[0962] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account. User "A" then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[0963] This system is effective in reducing the effort of recycling and encouraging individual recycling behavior. By combining generative AI models with blockchain technology, users can easily perform recycling activities, track their results, and earn rewards.

[0964] The processing flow will be explained below.

[0965] Step 1:

[0966] A user installs the app and opens the registration screen, where they enter the required information (email address, password, etc.).

[0967] Step 2:

[0968] The terminal transmits the input information to the server.

[0969] Step 3:

[0970] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[0971] Step 4:

[0972] The server returns an authentication token to the device, which stores the token in local storage.

[0973] Step 5:

[0974] The user launches the app and selects the option to scan for recyclable items.

[0975] Step 6:

[0976] The device activates the camera and the user takes a picture of the recyclable item.

[0977] Step 7:

[0978] The terminal transmits the captured image data to the server.

[0979] Step 8:

[0980] The server passes the received image data to the generative AI model.

[0981] Step 9:

[0982] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[0983] Step 10:

[0984] The server receives the analysis result and calculates incentive points, for example, 100 points.

[0985] Step 11:

[0986] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[0987] Step 12:

[0988] The server notifies the terminal of the result of adding points, and the terminal displays a notification to the user.

[0989] Step 13:

[0990] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[0991] Step 14:

[0992] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[0993] Step 15:

[0994] The server receives the transaction ID from the blockchain network and stores it in a database.

[0995] Step 16:

[0996] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[0997] Step 17:

[0998] The terminal sends the entered number of points and the user ID to the server.

[0999] Step 18:

[1000] The server checks the user's point balance and deducts the specified points.

[1001] Step 19:

[1002] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[1003] Step 20:

[1004] The server checks whether the transfer was successful and notifies the terminal of the result.

[1005] Step 21:

[1006] The device receives the notification and displays the result (success / failure) to the user.

[1007] These are the specific processing steps of the system, which provides an effective means for users to easily carry out recycling activities and receive incentives based on their performance.

[1008] Example 1

[1009] 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."

[1010] There is a need for a system that ensures the efficiency and transparency of recycling activities and encourages users to recycle. However, conventional systems make it difficult for users to accurately identify the materials in recyclable items and easily assign incentive points and convert them into cash. In particular, it is difficult to accurately analyze the material composition of recyclable items and record and manage the performance data in a reliable manner. Furthermore, the process of converting incentive points into cash remains cumbersome.

[1011] 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.

[1012] In this invention, the server includes a means for a user to photograph waste, a means for a terminal to send the photographed image to the server, a means for the server to pass the image to a generative AI model and analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record waste performance data on a distributed ledger, and a means for the user to convert the incentive points into currency. This enables users to easily carry out recycling activities, check the results of their activities in an accurate and reliable manner, and receive appropriate rewards.

[1013] "User" refers to a person who uses the system to carry out recycling activities.

[1014] "Waste" refers to items that can be reused and are subject to recycling.

[1015] "Device" refers to your mobile phone, tablet, or other computing device.

[1016] "Server" refers to a computer that is the central part of the entire system and is a device that processes, stores, analyzes, and communicates data.

[1017] "Images" refers to photographs and visual data of waste taken by users using their device's camera.

[1018] A "generative AI model" refers to a form of artificial intelligence that uses machine learning and deep learning algorithms to analyze and generate information from input data.

[1019] "Material composition" refers to the percentage of materials the waste is made up of (e.g., plastic, paper).

[1020] "Incentive points" refer to reward points that users can earn by scanning recyclable items and based on the material analysis results.

[1021] "Currency" refers to a medium with real value, such as legal tender or electronic money, that users receive when converting incentive points into cash.

[1022] "Database" refers to a system that stores data such as user information and incentive points managed on a server.

[1023] A "distributed ledger" refers to a recording system that uses blockchain technology to increase data transparency and reliability.

[1024] This invention is a system aimed at improving the efficiency of waste management, allowing users to photograph waste, acquire incentive points based on the analysis results, and ultimately convert them into currency. A specific embodiment of this system will be described below.

[1025] System Configuration

[1026] The system consists of the following components:

[1027] User Device: A computing device such as a smartphone or tablet.

[1028] Server: A computer that processes, stores, analyzes, and communicates data.

[1029] Camera: An image capture device built into a user device.

[1030] Database: A system that manages user information and incentive points (e.g., PostgreSQL).

[1031] Generative AI model: An artificial intelligence model that performs image analysis (e.g., TensorFlow).

[1032] Distributed ledger: Blockchain technology (e.g. Ethereum) to ensure data transparency and reliability.

[1033] Program processing overview

[1034] User registration and login

[1035] A user installs the app and opens the new registration screen. The user enters their email address and password. The device sends this information to the server, which stores it in a database and generates a new user ID and authentication token. The authentication token is returned to the device and stored in local storage.

[1036] Recycled Goods Scan

[1037] The user launches the app and selects the option to scan a recyclable item. The camera activates and the user takes a picture of the recyclable item. The image data is then sent from the device to the server.

[1038] Material determination by generative AI

[1039] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. It sends a prompt saying, "Please determine the material composition of the recycled product." The generative AI model analyzes the material composition and returns the results to the server. For example, it may determine that the material composition is "70% plastic, 30% paper."

[1040] Incentive calculation and awarding

[1041] The server calculates incentive points based on the analysis results. For example, 100 points are added to the user's account. The processing results are notified to the terminal.

[1042] Tracking and Verification

[1043] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[1044] Incentive cash redemption

[1045] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. After the transfer is confirmed, the result is notified to the device and displayed to the user.

[1046] Specific examples

[1047] For example, consider a user recycling a plastic bottle. The user launches the app and takes a photo of the bottle with their camera. The device sends the image to a server. The server analyzes the material using a generative AI model and determines that it is "90% plastic, 10% paper." Based on the results, "100 points" are calculated and added to the user's account. The user then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[1048] Prompt Sentence Examples

[1049] "Analyze an image of a recycled PET bottle. Print out the specific material composition, showing the ratio of plastic to paper."

[1050] This system allows users to easily carry out recycling activities, check the results, and receive rewards. The combination of generative AI models and blockchain technology will improve the efficiency and transparency of recycling activities, which is a major feature of the system's implementation.

[1051] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1052] Step 1: User Registration and Login

[1053] The user installs the app and opens the new registration screen. The user enters information such as an email address and password. The device sends the entered information to the server via an HTTP POST request. The server receives the information and stores it in a database. This generates a user ID and authentication token. The server returns the generated authentication token to the device, which stores it in local storage.

[1054] Input: User registration information (email address, password),

[1055] Output: Authentication token

[1056] Step 2: Recycled Goods Scan

[1057] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, and the user takes a picture of the recyclable item. The device then sends the image to the server.

[1058] Input: Image of recycled item,

[1059] Output: Image data sent to the server

[1060] Step 3: Material determination by generative AI

[1061] The server passes the received image data to the generative AI model and sends a prompt to analyze the material composition. The prompt sends the message, "Please determine the material composition of the recycled product." The generative AI model analyzes the image data and calculates the proportion of materials. The generative AI model returns the analysis results to the server. For example, it may determine that the material is 70% plastic and 30% paper.

[1062] Input: Image data captured, prompts to analyze,

[1063] Output: Material composition (e.g. 70% plastic, 30% paper)

[1064] Step 4: Incentive calculation and granting

[1065] The server calculates incentive points based on the analysis results it receives. For example, it calculates "100 points" based on the result "70% plastic, 30% paper." The server adds the points to the user's account and notifies the terminal of the processing result.

[1066] Input: Analysis results of material composition,

[1067] Output: Calculated incentive points, notification data

[1068] Step 5: Tracking and verification

[1069] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[1070] Input: Actual data,

[1071] Output: Transaction ID

[1072] Step 6: Incentive Cashing

[1073] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The terminal sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The terminal is notified of the success / failure of the transfer. The terminal displays the transfer result to the user.

[1074] Input: Redemption request (number of points and user ID),

[1075] Output: Transfer result notification

[1076] (Application example 1)

[1077] 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."

[1078] Conventional recycling activities have been time-consuming for users, and the incentives are complicated, making it difficult to promote recycling. There have also been challenges in ensuring the transparency and reliability of recycling results.

[1079] 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.

[1080] In this invention, the server includes a means for users to use their smartphones to earn incentive points based on the results of an analysis of the material composition of recyclable items, a means for users to instantly use the incentive points they earned in physical stores, and a means for the server to verify transactions on the blockchain and perform tracking and certification based on transaction IDs stored in a database. This allows users to easily participate in recycling activities and instantly use the incentives they earned, promoting recycling and ensuring transparent and reliable recycling results.

[1081] A "user" is an individual who scans recycled goods, earns incentive points, and redeems those points in physical stores.

[1082] The "server" refers to a computer system that passes images of recycled items to a generative AI model to analyze their material composition, calculates incentive points based on the analysis results, verifies transactions on the blockchain, and stores recycling performance data in a database.

[1083] A "generative AI model" refers to an artificial intelligence model that analyzes the material composition of recycled products from image data.

[1084] "Incentive Points" refers to reward points that users earn as a result of their recycling activities.

[1085] "Blockchain" refers to a distributed ledger technology that ensures transparency and reliability of recycling performance data.

[1086] "Smartphone" refers to the mobile device used by users to scan recycled items and check and redeem incentive points.

[1087] "Physical store" refers to a physical store where users can immediately use the incentive points they have earned.

[1088] "Transaction ID" refers to a unique identification number used to identify a transaction on a blockchain.

[1089] The present invention is a recycling platform system that allows users to scan recyclable items and earn incentive points based on the analysis results. The system is mainly operated by exchanging data between users, terminals, and a server.

[1090] First, users install a recycling point management app on their smartphone. Using this app, users can take pictures of recyclable items with the device's camera. The images are then sent from the device to a server. The server receives the image data and passes it to a generative AI model to analyze the material composition.

[1091] A generative AI model is an artificial intelligence model that analyzes the material composition of recyclable items from image data, and is built using software such as TensorFlow and PyTorch. This generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. For example, the analysis result may be "80% plastic, 20% paper." Based on this analysis result, the server calculates incentive points and adds them to the user's account.

[1092] Users can instantly use the incentive points they have earned in physical stores using their smartphones. Specifically, users select the point check option in the app to check their point balance, and then use the points in the physical store to receive discounts on products and services.

[1093] Furthermore, the server records recycling performance data (e.g., user ID, material composition, incentive points, timestamp, etc.) on the blockchain. Blockchain is a distributed ledger technology that ensures data transparency and reliability. The server verifies transactions on the blockchain and stores the results in a database. Recycling performance is tracked and verified based on this transaction ID.

[1094] As a concrete example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A"'s account. User "A" can then use the points to purchase products at a discount in a physical store.

[1095] An example of a prompt sentence to input to the generative AI model is as follows:

[1096] "Please analyze the material composition of the image below. The image contains recycled materials (plastic bottles). Please return your analysis results in terms of material types and their percentages. For example, please answer "90% plastic, 10% paper.""

[1097] This system allows users to easily participate in recycling activities and instantly use the incentive points they earn. The use of blockchain technology also improves the transparency and reliability of recycling results.

[1098] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1099] Step 1:

[1100] The user launches the recycling point management app installed on their smartphone. The user selects the option to scan a recyclable item through the app and activates the device's camera. The user takes a picture of the recyclable item, and the device acquires the image data. The input is the image of the recyclable item taken by the user, and the output is the image data acquired by the device's camera.

[1101] Step 2:

[1102] The image data acquired by the device is sent to the server. The input is the image data acquired by the device's camera, and the output is the image data sent to the server. The server receives and stores this image data.

[1103] Step 3:

[1104] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. The input is image data, which is passed to the generative AI model. The generative AI model analyzes the image data and determines the material composition of the recycled product. For example, it may obtain a result such as "80% plastic, 20% paper." This analysis result is then output.

[1105] Step 4:

[1106] The server calculates incentive points based on the analysis results obtained from the generative AI model. For example, a certain point calculation rule is applied to the analysis result of "80% plastic, 20% paper" to calculate 100 points. The input is the analysis result from the generative AI model, and the output is the calculated incentive points.

[1107] Step 5:

[1108] The server adds the calculated incentive points to the user's account. The input is the calculated incentive points and the output is the updated user account data. The server adds the points to the user's account and saves it in the database.

[1109] Step 6:

[1110] The server generates recycling performance data (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network. The input is the recycling performance data, and the output is the data recorded on the blockchain network. The blockchain receives the data, verifies it, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[1111] Step 7:

[1112] A user selects the check points option in the app to check their current points balance. The input is the user's account information and the output is the points balance displayed to the user. The device sends a request to the server, which returns the user's current points balance.

[1113] Step 8:

[1114] A user redeems incentive points in a physical store. The user uses their smartphone to select a point redemption option and receive a discount on goods or services in the store. The input is the number of points the user redeems, and the output is the discount offered or the goods or services exchanged. The terminal sends a request to the server, which deducts the specified number of points from the user's account and authorizes the redemption in the physical store.

[1115] This step will make it easier for users to participate in recycling activities, allowing them to immediately use the incentive points they earn, while ensuring the reliability and transparency of recycling results through blockchain technology.

[1116] 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.

[1117] This invention is a system that improves users' motivation to recycle by combining a user-participation recycling platform with an emotion engine that recognizes users' emotions. This system is operated by exchanging data between users, terminals, a server, and the emotion engine. Below, we will explain the program for this system, including specific processing methods and examples.

[1118] System configuration and operation

[1119] User registration and login

[1120] A user installs the app and opens the new registration screen. The user enters the required information (email address, password, etc.). The device sends the entered information to the server. The server saves the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores the token in local storage for future authentication.

[1121] Recycled Goods Scan

[1122] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[1123] Material determination by generative AI

[1124] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[1125] Incentive calculation and awarding

[1126] The server calculates incentive points based on the received material composition, for example, 100 points, searches for the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the point addition result.

[1127] Emotion recognition and incentive adjustment using an emotion engine

[1128] The device uses an emotion engine to analyze the user's facial expressions and voice to determine their emotions. For example, it can detect emotions such as joy, sadness, and surprise. The emotion engine then returns the analysis results to the server.

[1129] The server adjusts and awards incentives according to the user's emotional state based on the results of emotion analysis. For example, if the user is happy, it awards additional bonus points and notifies the user of the analysis results.

[1130] The server also generates feedback messages based on the analysis results and sends them to the device, which then displays the messages to the user, encouraging them to recycle more.

[1131] Tracking and Verification

[1132] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[1133] Incentive cash redemption

[1134] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[1135] Specific example explanation

[1136] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which then analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account.

[1137] At this time, the emotion engine analyzes the emotion of user "A" from his / her facial expression and determines it to be "joy." Based on this emotion analysis result, the server awards further bonus points, calculating an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Wonderful!" and sends it to the device. The device then displays the additional points and feedback message to user "A."

[1138] At a later date, User A converts the 300 points into cash, and 3,000 yen is deposited into the designated bank account. This allows User A to contribute to the effective use of resources through recycling activities and also earn personal rewards.

[1139] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, providing an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[1140] The processing flow will be explained below.

[1141] Step 1:

[1142] A user installs the app and opens the registration screen. The user enters the required information (email address, password, etc.).

[1143] Step 2:

[1144] The terminal transmits the input information to the server.

[1145] Step 3:

[1146] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[1147] Step 4:

[1148] The server returns an authentication token to the device, which stores the token in local storage.

[1149] Step 5:

[1150] The user launches the app and selects the option to scan for recyclable items.

[1151] Step 6:

[1152] The device activates the camera and the user takes a picture of the recyclable item.

[1153] Step 7:

[1154] The terminal transmits the captured image data to the server.

[1155] Step 8:

[1156] The server passes the received image data to the generative AI model.

[1157] Step 9:

[1158] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[1159] Step 10:

[1160] The server receives the analysis results and calculates incentive points, for example, 100 points based on the material composition of the recycled product.

[1161] Step 11:

[1162] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[1163] Step 12:

[1164] The server notifies the terminal of the result of the point addition, and the terminal displays a notification to the user.

[1165] Step 13:

[1166] The device uses an emotion engine to analyze the user's emotions, for example, detecting emotions such as "happiness" from the user's facial expressions and voice.

[1167] Step 14:

[1168] The emotion engine sends the analysis results to the server.

[1169] Step 15:

[1170] The server receives the emotion analysis results and adjusts the incentive according to the user's emotional state. For example, if the user is in a "joy" state, an additional 20 points will be added as a bonus.

[1171] Step 16:

[1172] The server stores the adjusted incentive points in a database and notifies the terminal of the result.

[1173] Step 17:

[1174] The server generates a feedback message based on the sentiment analysis results, e.g., "Your efforts are saving the Earth! Great!"

[1175] Step 18:

[1176] The server sends a feedback message to the terminal, which displays it to the user.

[1177] Step 19:

[1178] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[1179] Step 20:

[1180] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[1181] Step 21:

[1182] The server receives the transaction ID from the blockchain network and stores it in a database.

[1183] Step 22:

[1184] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[1185] Step 23:

[1186] The terminal sends the entered number of points and the user ID to the server.

[1187] Step 24:

[1188] The server checks the user's point balance and deducts the specified points.

[1189] Step 25:

[1190] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[1191] Step 26:

[1192] The server checks whether the transfer was successful and notifies the terminal of the result.

[1193] Step 27:

[1194] The device receives the notification and displays the result (success / failure) to the user.

[1195] Example 2

[1196] 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."

[1197] Existing recycling platforms often simply award points without considering users' emotions or motivations. This tends to discourage users from recycling, making it difficult to promote sustainable recycling activities. In addition, the processes for analyzing the materials of recycled products and awarding incentives are generally cumbersome, making efficient system operation necessary.

[1198] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for the user to scan the recyclable item; a means for the terminal to activate the camera and acquire an image; a means for the server to pass the image to a generative AI model and analyze the material composition; a means for the server to calculate incentive points based on the analysis results and add them to the user's account; a means for the terminal to analyze emotions and send the results to the server; a means for the server to adjust and grant incentive points based on the emotion analysis results; a means for the server to record recycling performance data on the blockchain; and a means for the user to redeem the incentive points. This makes it possible to provide incentives that take users' emotions into consideration and increase their motivation to recycle. Furthermore, material composition analysis using a generative AI model and data management using blockchain technology enable an efficient and reliable recycling process.

[1199] "User" refers to an individual or organization that uses the system to carry out recycling activities.

[1200] A "terminal" is a device operated by a user that provides functions such as scanning and display.

[1201] "Server" means a central system that processes, stores, and manages data and interacts with users and devices through various means.

[1202] "Recyclables" refers to items that users choose to recycle, specifically items that are subject to scanning and analysis.

[1203] "Camera" means a device mounted on the Terminal for taking images of recyclable items.

[1204] "Image" refers to visual information obtained by photographing recycled items with a camera.

[1205] A "generative AI model" is an artificial intelligence model that is installed on a server and analyzes the material composition of recycled items from image data.

[1206] "Material composition" refers to the proportion and type of various materials that make up a recycled product, and is analyzed by a generative AI model.

[1207] "Incentive points" are points awarded to users as a reward for recycling activities.

[1208] An "emotion engine" refers to software or hardware that analyzes a user's emotional state from their facial expressions and voice.

[1209] "Emotion analysis results" refers to data indicating the user's emotional state detected by the emotion engine.

[1210] "Blockchain" refers to a distributed ledger technology used to ensure data is trustworthy and immutable.

[1211] "Recycling performance data" refers to data related to recycling activities, such as user ID, material composition, incentive points, and timestamps.

[1212] "Redeem" means the process by which a user redeems incentive points for money or other purposes.

[1213] This invention is a system that improves users' motivation to recycle by combining an emotion engine that recognizes users' emotions in a user-participation recycling platform. This system is operated by exchanging data between users, terminals, a server, and the emotion engine.

[1214] User registration and login

[1215] First, the user installs the app and launches it. The user opens the new registration screen and enters the required information. The device sends this information to the server, which then stores the user information in a database. The server then generates a new user ID and authentication token and returns them to the device. The device then stores the received authentication token in local storage and uses it for future authentication.

[1216] Recycled Goods Scan

[1217] The user launches the app and selects the option to scan a recyclable item. The device's camera activates, and the user takes a picture of the recyclable item and sends it to the server. The server then provides the received image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. The generative AI model then returns the analysis results to the server.

[1218] Incentive calculation and awarding

[1219] The server calculates incentive points based on the received material composition. For example, based on a result such as "70% plastic, 30% paper," it calculates 100 points and adds the points to the database record corresponding to the user's account ID. The server then notifies the terminal of the point addition result.

[1220] Emotion recognition and incentive adjustment using an emotion engine

[1221] The device uses an emotion engine to analyze the user's facial expressions and voice. For example, it detects emotional states such as happiness, sadness, and surprise. The emotion engine returns the results to the server, which then adjusts the incentives according to the user's emotional state based on the analysis results. If the user is happy, the server awards additional bonus points. The server also generates a feedback message based on the analysis results and sends it to the device. The device displays this message to the user, encouraging them to recycle more.

[1222] Tracking and Verification

[1223] The server generates performance data for each recycled item, including user ID, material composition, incentive points, timestamp, etc. The server sends this data to the blockchain network and requests it to be recorded. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in a database.

[1224] Incentive cash redemption

[1225] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends this information and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server then calculates the redemption amount and calls an API to transfer the money to the specified bank account. The server confirms the success of the transfer and notifies the device of the result. The device displays it to the user.

[1226] Specific example explanation

[1227] For example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with his / her camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A's" account. At that time, the emotion engine analyzes user "A's" facial expression and determines the emotion as "joy." Based on this emotion analysis result, the server awards an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Amazing!" and sends it to the device. The device displays the additional points and the feedback message to user "A." Later, user "A" converts the 300 points into cash, and 3,000 yen is deposited into his / her designated bank account.

[1228] Example prompt sentence:

[1229] "Please analyze the material of the plastic bottle."

[1230] "Analyze user sentiment and calculate bonus points."

[1231] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, making it an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[1232] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1233] Step 1: User installs and launches the app.

[1234] Input: The user installs the app and taps to launch it.

[1235] Output: The app starts and the initial screen is displayed.

[1236] Specific operation: The device launches the installed app and displays the initial screen.

[1237] Step 2: The user opens the new registration screen and enters the required information.

[1238] Input: The user enters required information, such as an email address or password.

[1239] Output: The input information is aggregated and stored on the device.

[1240] Specific behavior: The device displays a new registration form, and the user enters information into the form.

[1241] Step 3: The terminal sends the entered information to the server.

[1242] Input: Information entered by the user, such as email address or password.

[1243] Output: The server receives the user information.

[1244] Specific operation: The device sends user information to the server via an HTTP request.

[1245] Step 4: The server saves the user information to the database and generates a new user ID and authentication token.

[1246] Input: User information received by the server from the device.

[1247] Output: A new user ID and authentication token is generated and stored in the database.

[1248] Specific behavior: The server inserts the user information into the database and generates a user ID and authentication token.

[1249] Step 5: The server returns the authentication token to the device, which stores it in local storage.

[1250] Input: The authentication token sent by the server.

[1251] Output: The authentication token is saved on the device.

[1252] Specific operation: The server sends an authentication token in the HTTP response, and the device stores it in local storage.

[1253] Step 6: The user launches the app and selects the option to scan for recyclables.

[1254] Input: User selects the scan option within the app.

[1255] Output: The camera starts.

[1256] Specific behavior: Activates the camera module when the device selects the scan option.

[1257] Step 7: The device activates the camera and the user takes a picture of the item to be recycled.

[1258] Input: User uses camera to take a picture of a recyclable item.

[1259] Output: The captured image data is saved on the device.

[1260] Specific behavior: The device launches the camera app, and the user presses the shutter button to take a picture.

[1261] Step 8: The device sends the captured image to the server.

[1262] Input: Captured image data.

[1263] Output: The image data is transferred to the server.

[1264] Specific operation: The device uploads image data to the server using an HTTP request.

[1265] Step 9: The server provides the received image data to the generative AI model and instructs it to analyze it.

[1266] Input: Image data received by the server.

[1267] Output: The generative AI model begins its analysis.

[1268] Specific operation: The server sends the image data to the API of the generated AI model and issues an analysis request.

[1269] Step 10: The generative AI model analyzes the image data and determines the material composition of the recycled item.

[1270] Input: Image data.

[1271] Output: Material composition analysis (e.g. "70% plastic, 30% paper").

[1272] Specific operation: The generative AI model analyzes the image, calculates the type and proportion of ingredients, and returns the results.

[1273] Step 11: The generative AI model returns the analysis results to the server.

[1274] Input: Material composition analysis results.

[1275] Output: The server receives the analysis results.

[1276] Specific operation: The generative AI model sends the analysis results to the server.

[1277] Step 12: The server calculates incentive points based on the received material configuration.

[1278] Input: Analysis result (e.g. "70% plastic, 30% paper").

[1279] Output: Calculated incentive points (e.g. 100 points).

[1280] What it does: The server calculates points based on the type and percentage of ingredients.

[1281] Step 13: The server adds the calculated points to the database record corresponding to the user's account ID.

[1282] Input: Calculated incentive points, user ID.

[1283] Output: Points are added to the user account.

[1284] Specific behavior: The server updates the database and increases the user's points balance.

[1285] Step 14: The server notifies the terminal of the result of the point addition.

[1286] Input: The result of adding points.

[1287] Output: The device receives the notification.

[1288] Specific operation: The server sends an HTTP response to the terminal to notify it that the points have been added successfully.

[1289] Step 15: The device activates its emotion engine and analyzes the user's facial expressions and voice.

[1290] Input: User's facial expressions and voice data.

[1291] Output: Sentiment analysis result (e.g. "joy").

[1292] How it works: The device uses the camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1293] Step 16: The emotion engine sends the analysis results to the server.

[1294] Input: Sentiment analysis results.

[1295] Output: The server receives the analysis results.

[1296] Specific operation: The device issues the emotion analysis results to the server.

[1297] Step 17: The server adjusts and awards incentive points based on the sentiment analysis results.

[1298] Input: Sentiment analysis results, current incentive points.

[1299] Output: Adjusted incentive points.

[1300] Specific operation: The server increases or decreases points according to the results of the sentiment analysis, recalculates them, and reflects them in the user's account.

[1301] Step 18: The server generates a feedback message based on the analysis result and sends it to the terminal.

[1302] Input: Sentiment analysis results.

[1303] Output: Feedback message.

[1304] Specific operation: The server creates a message based on the emotion analysis results and sends it to the device as an HTTP response.

[1305] Step 19: The terminal displays a feedback message to the user.

[1306] Input: Feedback message.

[1307] Output: The user confirms the message.

[1308] Specific behavior: The device will display a feedback message as a pop-up or notification.

[1309] Step 20: User selects the incentive redemption option within the app and enters the number of points to redeem.

[1310] Input: The number of points entered by the user to redeem.

[1311] Output: The redemption request is saved on the device.

[1312] Specific behavior: The device displays a form for user input and collects input data.

[1313] Step 21: The terminal transmits the input number of points and the user ID to the server.

[1314] Input: Number of points to be redeemed, user ID.

[1315] Output: The server receives the request.

[1316] Specific operation: The terminal sends a cash request to the server using an HTTP request.

[1317] Step 22: The server checks the user's point balance and subtracts the specified points.

[1318] Input: Redemption request, current points balance.

[1319] Output: Updated points balance.

[1320] Specific operation: The server queries the database to check and update the user's point balance.

[1321] Step 23: The server calculates the converted amount and calls an API to instruct the transfer to the specified bank account.

[1322] Input: Number of points to be redeemed, bank account information.

[1323] Output: The result of the transfer instruction.

[1324] Specific operation: The server uses the API to send a transfer instruction to the bank system.

[1325] Step 24: The server checks whether the transfer was successful and notifies the terminal of the result.

[1326] Input: Transfer result from API.

[1327] Output: Transfer result notification.

[1328] Specific operation: The server obtains the transfer result from the API and sends the result to the terminal.

[1329] Step 25: The device receives the notification and displays the result (success / failure) to the user.

[1330] Input: Transfer result notification.

[1331] Output: The resulting information that is displayed to the user.

[1332] Specific operation: The device receives a notification from the server and displays its contents to the user.

[1333] (Application example 2)

[1334] 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."

[1335] In modern society, promoting recycling activities has become an important issue, but sustaining users' motivation is difficult. Conventional systems simply accept recyclable items and award points, but this does not maintain user interest and lacks a means to encourage continued use. Furthermore, to increase the transparency and reliability of recycling activities, it is necessary to prevent fraudulent data collection. There is a need to provide an effective system that can solve these problems and increase users' motivation to recycle.

[1336] 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.

[1337] In this invention, the server includes a means for users to scan recyclable items, a means for the server to pass the image to a generative AI model to analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record recycling performance data on the blockchain, a means for the user to redeem the incentive points, a means for the user's terminal to analyze the user's emotional state using an emotion engine, and a means for the server to adjust incentives based on the emotion analysis results. This allows incentives to be awarded according to the user's emotional state, effectively increasing motivation to participate in recycling activities. Furthermore, recording recycling performance data on the blockchain increases the transparency and reliability of the system.

[1338] "User" means an individual or legal entity that brings in recyclable items and uses the recycling system.

[1339] "Means for scanning recyclable items" refers to a function that uses a device such as a smartphone to capture an image of a recyclable item and send that image to a server.

[1340] A "generative AI model" is a machine learning algorithm that analyzes the material composition of recycled items from images.

[1341] "Means for analyzing material composition" refers to a function that uses a generative AI model to identify the proportion and type of materials contained in image data of recycled products.

[1342] "Incentive points" are points that are given to users as a reward for recycling activities.

[1343] "User Account" means an individual account registered by a User to use the Recycling System.

[1344] "Recycling performance data" refers to information (material composition, points, time, etc.) collected when a user performs recycling activities.

[1345] "Blockchain" is a distributed ledger technology that continuously records transaction data.

[1346] An "emotion engine" is a technology for analyzing emotions from a user's facial expressions, voice, etc.

[1347] The "means for adjusting incentives" is a function that increases or decreases the user's incentive points based on the analysis results of the emotion engine.

[1348] "Means of cashing out" refers to a function that allows users to withdraw the incentive points they have earned by cash, bank transfer, etc.

[1349] This invention is a system that combines a user-participation recycling platform with an emotion engine. Specifically, users scan recyclable items using devices such as smartphones, and the information is sent to a server that analyzes their material composition and provides incentives based on the user's emotional state. Furthermore, to ensure transparency in recycling activities, data is recorded on a blockchain. Below, we explain specific processing methods and examples of this system.

[1350] First, users install the recycling platform's application on their smartphone and log in or register. After logging in, they open the camera to take a picture of the recyclable item. When the user takes a picture of the recyclable item, the device sends the image to a server. The server passes the image to a generative AI model, which analyzes its material composition. This analysis determines what the recyclable item is made of (for example, "70% plastic, 30% paper").

[1351] Next, the server calculates incentive points based on the analysis results and adds them to the user's account. It's worth noting that the system uses an emotion engine. When the user device scans the recyclable item, it simultaneously captures the user's facial expressions and analyzes them with the emotion engine. The emotion engine detects the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns the analysis results to the server.

[1352] The server adjusts incentives based on the results of emotion analysis. For example, if the user is happy, it will give additional bonus points. The server also generates a feedback message for the user based on their emotion and sends it to the device. The device displays the message to the user, thereby increasing their motivation to recycle.

[1353] Next, recycling performance data (such as user ID, material composition, incentive points, timestamp, etc.) is recorded on the blockchain by the server. Blockchain technology ensures data tamper-proofing and transparency.

[1354] Finally, users are also given the option to redeem their incentive points for cash. When a user makes a cash request within the app, the server will check the user's point balance and the cash request, and transfer the funds to the specified bank account. If the transfer is successful, the user will be notified of the result.

[1355] The hardware used includes smartphones, servers, cameras, etc. The software includes generative AI models, sentiment analysis engines (e.g., FER libraries), blockchain APIs, banking APIs, etc. In particular, an example prompt for using a generative AI model is as follows:

[1356] Please analyze the material composition of the image.

[1357] This invention can increase users' motivation to recycle by combining emotion recognition with their recycling activities. In addition, by using blockchain technology, transparency and reliability can be ensured, providing a system that users can use with peace of mind.

[1358] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1359] Step 1:

[1360] This is the step where the user scans the recyclable item.

[1361] The user launches the app on their smartphone and selects the recyclable item scanning function. They then use the smartphone camera to capture an image of the recyclable item. This image becomes the input data, and the device sends the image to the server. The output is the image data of the recyclable item sent to the server.

[1362] Step 2:

[1363] This is the step where the material composition is analyzed using a generative AI model.

[1364] The server passes the received image data of the recycled product to the generative AI model and instructs it to analyze it along with the prompt, "Please analyze the material composition of the image." The generative AI model analyzes the image data and determines the material composition. This determination result (e.g., "70% plastic, 30% paper") is returned to the server. The input is the image data of the recycled product, and the output is the data resulting from the material determination.

[1365] Step 3:

[1366] This is the calculation and addition step of incentive points.

[1367] The server calculates the points assigned to each material in the recycled product based on the material determination results returned by the generative AI model. These calculation results become incentive points and are added to the user's account. The input is the material determination results, and the output is the calculated incentive points.

[1368] Step 4:

[1369] This is the step of analyzing the user's emotional state.

[1370] While scanning recyclable items, the smartphone camera captures the user's facial expressions and sends the images to the emotion engine. The emotion engine performs facial expression analysis to detect the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns it to the server. The input is image data containing the user's facial expressions, and the output is emotional state data.

[1371] Step 5:

[1372] This is a step for adjusting incentives based on the results of sentiment analysis.

[1373] The server adjusts incentive points based on the emotion analysis results from the emotion engine. For example, if the user is happy, additional bonus points are awarded. The adjusted incentive points are recalculated and added to the user's account. The input is the emotion analysis results and existing incentive points, and the output is the adjusted incentive points.

[1374] Step 6:

[1375] A feedback message generation and notification step.

[1376] The server generates a feedback message based on the sentiment analysis results and sends it to the user device. For example, a message such as "Your efforts are saving the Earth! Wonderful!" is generated. The user device displays this message. The input is the sentiment analysis results, and the output is the feedback message.

[1377] Step 7:

[1378] This is the step where recycling performance data is recorded on the blockchain.

[1379] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database. The input is the recycling performance data, and the output is the transaction ID.

[1380] Step 8:

[1381] This is the step of converting incentive points into cash.

[1382] The user selects the cash-out option for incentive points within the app and enters the number of points. The user's device sends the entered number of points and user ID to the server. The server checks the user's point balance and instructs a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the user's device of the result. The input is the cash-out request and number of points, and the output is a notification of the cash-out result.

[1383] 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.

[1384] 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.

[1385] 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.

[1386] [Fourth embodiment]

[1387] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1388] 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.

[1389] 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).

[1390] 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.

[1391] 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.

[1392] 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).

[1393] 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.

[1394] 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.

[1395] 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.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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."

[1400] The present invention is a user-participation recycling platform that allows users to scan recyclable items and earn incentive points based on the analysis results. This system is operated by exchanging data between users, terminals, and a server. Below, we will explain the program for this system, including specific processing methods and examples.

[1401] System configuration and operation

[1402] User registration and login

[1403] A user installs the app and opens the sign-up screen. The user enters the required information (email address, password, and other required personal information). The device receives the information and sends it to the server. The server stores the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores it in local storage for future authentication.

[1404] Recycled Goods Scan

[1405] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[1406] Material determination by generative AI

[1407] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[1408] Incentive calculation and awarding

[1409] The server calculates incentive points based on the received material composition, for example, "100 points," searches the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the result of the point addition.

[1410] Tracking and Verification

[1411] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[1412] Incentive cash redemption

[1413] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount (e.g., "500 points = 1,000 yen") and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[1414] Specific example explanation

[1415] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account. User "A" then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[1416] This system is effective in reducing the effort of recycling and encouraging individual recycling behavior. By combining generative AI models with blockchain technology, users can easily perform recycling activities, track their results, and earn rewards.

[1417] The processing flow will be explained below.

[1418] Step 1:

[1419] A user installs the app and opens the registration screen, where they enter the required information (email address, password, etc.).

[1420] Step 2:

[1421] The terminal transmits the input information to the server.

[1422] Step 3:

[1423] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[1424] Step 4:

[1425] The server returns an authentication token to the device, which stores the token in local storage.

[1426] Step 5:

[1427] The user launches the app and selects the option to scan for recyclable items.

[1428] Step 6:

[1429] The device activates the camera and the user takes a picture of the recyclable item.

[1430] Step 7:

[1431] The terminal transmits the captured image data to the server.

[1432] Step 8:

[1433] The server passes the received image data to the generative AI model.

[1434] Step 9:

[1435] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[1436] Step 10:

[1437] The server receives the analysis result and calculates incentive points, for example, 100 points.

[1438] Step 11:

[1439] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[1440] Step 12:

[1441] The server notifies the terminal of the result of adding points, and the terminal displays a notification to the user.

[1442] Step 13:

[1443] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[1444] Step 14:

[1445] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[1446] Step 15:

[1447] The server receives the transaction ID from the blockchain network and stores it in a database.

[1448] Step 16:

[1449] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[1450] Step 17:

[1451] The terminal sends the entered number of points and the user ID to the server.

[1452] Step 18:

[1453] The server checks the user's point balance and deducts the specified points.

[1454] Step 19:

[1455] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[1456] Step 20:

[1457] The server checks whether the transfer was successful and notifies the terminal of the result.

[1458] Step 21:

[1459] The device receives the notification and displays the result (success / failure) to the user.

[1460] These are the specific processing steps of the system, which provides an effective means for users to easily carry out recycling activities and receive incentives based on their performance.

[1461] Example 1

[1462] 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."

[1463] There is a need for a system that ensures the efficiency and transparency of recycling activities and encourages users to recycle. However, conventional systems make it difficult for users to accurately identify the materials in recyclable items and easily assign incentive points and convert them into cash. In particular, it is difficult to accurately analyze the material composition of recyclable items and record and manage the performance data in a reliable manner. Furthermore, the process of converting incentive points into cash remains cumbersome.

[1464] 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.

[1465] In this invention, the server includes a means for a user to photograph waste, a means for a terminal to send the photographed image to the server, a means for the server to pass the image to a generative AI model and analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record waste performance data on a distributed ledger, and a means for the user to convert the incentive points into currency. This enables users to easily carry out recycling activities, check the results of their activities in an accurate and reliable manner, and receive appropriate rewards.

[1466] "User" refers to a person who uses the system to carry out recycling activities.

[1467] "Waste" refers to items that can be reused and are subject to recycling.

[1468] "Device" refers to your mobile phone, tablet, or other computing device.

[1469] "Server" refers to a computer that is the central part of the entire system and is a device that processes, stores, analyzes, and communicates data.

[1470] "Images" refers to photographs and visual data of waste taken by users using their device's camera.

[1471] A "generative AI model" refers to a form of artificial intelligence that uses machine learning and deep learning algorithms to analyze and generate information from input data.

[1472] "Material composition" refers to the percentage of materials the waste is made up of (e.g., plastic, paper).

[1473] "Incentive points" refer to reward points that users can earn by scanning recyclable items and based on the material analysis results.

[1474] "Currency" refers to a medium with real value, such as legal tender or electronic money, that users receive when converting incentive points into cash.

[1475] "Database" refers to a system that stores data such as user information and incentive points managed on a server.

[1476] A "distributed ledger" refers to a recording system that uses blockchain technology to increase data transparency and reliability.

[1477] This invention is a system aimed at improving the efficiency of waste management, allowing users to photograph waste, acquire incentive points based on the analysis results, and ultimately convert them into currency. A specific embodiment of this system will be described below.

[1478] System Configuration

[1479] The system consists of the following components:

[1480] User Device: A computing device such as a smartphone or tablet.

[1481] Server: A computer that processes, stores, analyzes, and communicates data.

[1482] Camera: An image capture device built into a user device.

[1483] Database: A system that manages user information and incentive points (e.g., PostgreSQL).

[1484] Generative AI model: An artificial intelligence model that performs image analysis (e.g., TensorFlow).

[1485] Distributed ledger: Blockchain technology (e.g. Ethereum) to ensure data transparency and reliability.

[1486] Program processing overview

[1487] User registration and login

[1488] A user installs the app and opens the new registration screen. The user enters their email address and password. The device sends this information to the server, which stores it in a database and generates a new user ID and authentication token. The authentication token is returned to the device and stored in local storage.

[1489] Recycled Goods Scan

[1490] The user launches the app and selects the option to scan a recyclable item. The camera activates and the user takes a picture of the recyclable item. The image data is then sent from the device to the server.

[1491] Material determination by generative AI

[1492] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. It sends a prompt saying, "Please determine the material composition of the recycled product." The generative AI model analyzes the material composition and returns the results to the server. For example, it may determine that the material composition is "70% plastic, 30% paper."

[1493] Incentive calculation and awarding

[1494] The server calculates incentive points based on the analysis results. For example, 100 points are added to the user's account. The processing results are notified to the terminal.

[1495] Tracking and Verification

[1496] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[1497] Incentive cash redemption

[1498] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. After the transfer is confirmed, the result is notified to the device and displayed to the user.

[1499] Specific examples

[1500] For example, consider a user recycling a plastic bottle. The user launches the app and takes a photo of the bottle with their camera. The device sends the image to a server. The server analyzes the material using a generative AI model and determines that it is "90% plastic, 10% paper." Based on the results, "100 points" are calculated and added to the user's account. The user then converts the points into cash, and 1,000 yen is deposited into their designated bank account.

[1501] Prompt Sentence Examples

[1502] "Analyze an image of a recycled PET bottle. Print out the specific material composition, showing the ratio of plastic to paper."

[1503] This system allows users to easily carry out recycling activities, check the results, and receive rewards. The combination of generative AI models and blockchain technology will improve the efficiency and transparency of recycling activities, which is a major feature of the system's implementation.

[1504] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1505] Step 1: User Registration and Login

[1506] The user installs the app and opens the new registration screen. The user enters information such as an email address and password. The device sends the entered information to the server via an HTTP POST request. The server receives the information and stores it in a database. This generates a user ID and authentication token. The server returns the generated authentication token to the device, which stores it in local storage.

[1507] Input: User registration information (email address, password),

[1508] Output: Authentication token

[1509] Step 2: Recycled Goods Scan

[1510] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, and the user takes a picture of the recyclable item. The device then sends the image to the server.

[1511] Input: Image of recycled item,

[1512] Output: Image data sent to the server

[1513] Step 3: Material determination by generative AI

[1514] The server passes the received image data to the generative AI model and sends a prompt to analyze the material composition. The prompt sends the message, "Please determine the material composition of the recycled product." The generative AI model analyzes the image data and calculates the proportion of materials. The generative AI model returns the analysis results to the server. For example, it may determine that the material is 70% plastic and 30% paper.

[1515] Input: Image data captured, prompts to analyze,

[1516] Output: Material composition (e.g. 70% plastic, 30% paper)

[1517] Step 4: Incentive calculation and granting

[1518] The server calculates incentive points based on the analysis results it receives. For example, it calculates "100 points" based on the result "70% plastic, 30% paper." The server adds the points to the user's account and notifies the terminal of the processing result.

[1519] Input: Analysis results of material composition,

[1520] Output: Calculated incentive points, notification data

[1521] Step 5: Tracking and verification

[1522] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp) and requests that it be recorded in the distributed ledger. The blockchain verifies the transaction and records the data in the ledger. The server receives the transaction ID and stores it in the database.

[1523] Input: Actual data,

[1524] Output: Transaction ID

[1525] Step 6: Incentive Cashing

[1526] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The terminal sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The terminal is notified of the success / failure of the transfer. The terminal displays the transfer result to the user.

[1527] Input: Redemption request (number of points and user ID),

[1528] Output: Transfer result notification

[1529] (Application example 1)

[1530] 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."

[1531] Conventional recycling activities have been time-consuming for users, and the incentives are complicated, making it difficult to promote recycling. There have also been challenges in ensuring the transparency and reliability of recycling results.

[1532] 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.

[1533] In this invention, the server includes a means for users to use their smartphones to earn incentive points based on the results of an analysis of the material composition of recyclable items, a means for users to instantly use the incentive points they earned in physical stores, and a means for the server to verify transactions on the blockchain and perform tracking and certification based on transaction IDs stored in a database. This allows users to easily participate in recycling activities and instantly use the incentives they earned, promoting recycling and ensuring transparent and reliable recycling results.

[1534] A "user" is an individual who scans recycled goods, earns incentive points, and redeems those points in physical stores.

[1535] The "server" refers to a computer system that passes images of recycled items to a generative AI model to analyze their material composition, calculates incentive points based on the analysis results, verifies transactions on the blockchain, and stores recycling performance data in a database.

[1536] A "generative AI model" refers to an artificial intelligence model that analyzes the material composition of recycled products from image data.

[1537] "Incentive Points" refers to reward points that users earn as a result of their recycling activities.

[1538] "Blockchain" refers to a distributed ledger technology that ensures transparency and reliability of recycling performance data.

[1539] "Smartphone" refers to the mobile device used by users to scan recycled items and check and redeem incentive points.

[1540] "Physical store" refers to a physical store where users can immediately use the incentive points they have earned.

[1541] "Transaction ID" refers to a unique identification number used to identify a transaction on a blockchain.

[1542] The present invention is a recycling platform system that allows users to scan recyclable items and earn incentive points based on the analysis results. The system is mainly operated by exchanging data between users, terminals, and a server.

[1543] First, users install a recycling point management app on their smartphone. Using this app, users can take pictures of recyclable items with the device's camera. The images are then sent from the device to a server. The server receives the image data and passes it to a generative AI model to analyze the material composition.

[1544] A generative AI model is an artificial intelligence model that analyzes the material composition of recyclable items from image data, and is built using software such as TensorFlow and PyTorch. This generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. For example, the analysis result may be "80% plastic, 20% paper." Based on this analysis result, the server calculates incentive points and adds them to the user's account.

[1545] Users can instantly use the incentive points they have earned in physical stores using their smartphones. Specifically, users select the point check option in the app to check their point balance, and then use the points in the physical store to receive discounts on products and services.

[1546] Furthermore, the server records recycling performance data (e.g., user ID, material composition, incentive points, timestamp, etc.) on the blockchain. Blockchain is a distributed ledger technology that ensures data transparency and reliability. The server verifies transactions on the blockchain and stores the results in a database. Recycling performance is tracked and verified based on this transaction ID.

[1547] As a concrete example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A"'s account. User "A" can then use the points to purchase products at a discount in a physical store.

[1548] An example of a prompt sentence to input to the generative AI model is as follows:

[1549] "Please analyze the material composition of the image below. The image contains recycled materials (plastic bottles). Please return your analysis results in terms of material types and their percentages. For example, please answer "90% plastic, 10% paper.""

[1550] This system allows users to easily participate in recycling activities and instantly use the incentive points they earn. The use of blockchain technology also improves the transparency and reliability of recycling results.

[1551] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1552] Step 1:

[1553] The user launches the recycling point management app installed on their smartphone. The user selects the option to scan a recyclable item through the app and activates the device's camera. The user takes a picture of the recyclable item, and the device acquires the image data. The input is the image of the recyclable item taken by the user, and the output is the image data acquired by the device's camera.

[1554] Step 2:

[1555] The image data acquired by the device is sent to the server. The input is the image data acquired by the device's camera, and the output is the image data sent to the server. The server receives and stores this image data.

[1556] Step 3:

[1557] The server passes the received image data to the generative AI model and instructs it to analyze the material composition. The input is image data, which is passed to the generative AI model. The generative AI model analyzes the image data and determines the material composition of the recycled product. For example, it may obtain a result such as "80% plastic, 20% paper." This analysis result is then output.

[1558] Step 4:

[1559] The server calculates incentive points based on the analysis results obtained from the generative AI model. For example, a certain point calculation rule is applied to the analysis result of "80% plastic, 20% paper" to calculate 100 points. The input is the analysis result from the generative AI model, and the output is the calculated incentive points.

[1560] Step 5:

[1561] The server adds the calculated incentive points to the user's account. The input is the calculated incentive points and the output is the updated user account data. The server adds the points to the user's account and saves it in the database.

[1562] Step 6:

[1563] The server generates recycling performance data (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network. The input is the recycling performance data, and the output is the data recorded on the blockchain network. The blockchain receives the data, verifies it, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[1564] Step 7:

[1565] A user selects the check points option in the app to check their current points balance. The input is the user's account information and the output is the points balance displayed to the user. The device sends a request to the server, which returns the user's current points balance.

[1566] Step 8:

[1567] A user redeems incentive points in a physical store. The user uses their smartphone to select a point redemption option and receive a discount on goods or services in the store. The input is the number of points the user redeems, and the output is the discount offered or the goods or services exchanged. The terminal sends a request to the server, which deducts the specified number of points from the user's account and authorizes the redemption in the physical store.

[1568] This step will make it easier for users to participate in recycling activities, allowing them to immediately use the incentive points they earn, while ensuring the reliability and transparency of recycling results through blockchain technology.

[1569] 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.

[1570] This invention is a system that improves users' motivation to recycle by combining a user-participation recycling platform with an emotion engine that recognizes users' emotions. This system is operated by exchanging data between users, terminals, a server, and the emotion engine. Below, we will explain the program for this system, including specific processing methods and examples.

[1571] System configuration and operation

[1572] User registration and login

[1573] A user installs the app and opens the new registration screen. The user enters the required information (email address, password, etc.). The device sends the entered information to the server. The server saves the user information in a database and generates a new user ID and authentication token. The authentication token is returned to the device, which stores the token in local storage for future authentication.

[1574] Recycled Goods Scan

[1575] The user launches the app and selects the option to scan a recyclable item. The device activates the camera, allowing the user to take a picture of the recyclable item and send it to the server. The server then passes the image data to the generative AI model.

[1576] Material determination by generative AI

[1577] The server provides the image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recycled product and calculates the proportion of each material. The generative AI model returns the analysis results to the server. For example, it may determine that the product is 70% plastic and 30% paper.

[1578] Incentive calculation and awarding

[1579] The server calculates incentive points based on the received material composition, for example, 100 points, searches for the database record corresponding to the user's account ID, and adds the points. The server then notifies the terminal of the point addition result.

[1580] Emotion recognition and incentive adjustment using an emotion engine

[1581] The device uses an emotion engine to analyze the user's facial expressions and voice to determine their emotions. For example, it can detect emotions such as joy, sadness, and surprise. The emotion engine then returns the analysis results to the server.

[1582] The server adjusts and awards incentives according to the user's emotional state based on the results of emotion analysis. For example, if the user is happy, it awards additional bonus points and notifies the user of the analysis results.

[1583] The server also generates feedback messages based on the analysis results and sends them to the device, which then displays the messages to the user, encouraging them to recycle more.

[1584] Tracking and Verification

[1585] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database.

[1586] Incentive cash redemption

[1587] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends the entered number of points and the user ID to the server. The server checks the user's point balance and deducts the specified points. The server calculates the redemption amount and calls an API to instruct a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the device of the result. The device receives the notification and displays the result (success / failure) to the user.

[1588] Specific example explanation

[1589] For example, consider the case where user "A" wants to recycle a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with their camera. The device sends the image to the server, which then analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. Based on the analysis results, the server calculates "100 points" and adds them to user "A"'s account.

[1590] At this time, the emotion engine analyzes the emotion of user "A" from his / her facial expression and determines it to be "joy." Based on this emotion analysis result, the server awards further bonus points, calculating an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Wonderful!" and sends it to the device. The device then displays the additional points and feedback message to user "A."

[1591] At a later date, User A converts the 300 points into cash, and 3,000 yen is deposited into the designated bank account. This allows User A to contribute to the effective use of resources through recycling activities and also earn personal rewards.

[1592] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, providing an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[1593] The processing flow will be explained below.

[1594] Step 1:

[1595] A user installs the app and opens the registration screen. The user enters the required information (email address, password, etc.).

[1596] Step 2:

[1597] The terminal transmits the input information to the server.

[1598] Step 3:

[1599] The server receives the submitted information, stores it in a database, and generates a new user ID and authentication token.

[1600] Step 4:

[1601] The server returns an authentication token to the device, which stores the token in local storage.

[1602] Step 5:

[1603] The user launches the app and selects the option to scan for recyclable items.

[1604] Step 6:

[1605] The device activates the camera and the user takes a picture of the recyclable item.

[1606] Step 7:

[1607] The terminal transmits the captured image data to the server.

[1608] Step 8:

[1609] The server passes the received image data to the generative AI model.

[1610] Step 9:

[1611] The generative AI model analyzes the image data, determines the material composition of the recycled product, and returns the analysis results (e.g., "70% plastic, 30% paper") to the server.

[1612] Step 10:

[1613] The server receives the analysis results and calculates incentive points, for example, 100 points based on the material composition of the recycled product.

[1614] Step 11:

[1615] The server looks up the database record corresponding to the user's account ID and adds the calculated points.

[1616] Step 12:

[1617] The server notifies the terminal of the result of the point addition, and the terminal displays a notification to the user.

[1618] Step 13:

[1619] The device uses an emotion engine to analyze the user's emotions, for example, detecting emotions such as "happiness" from the user's facial expressions and voice.

[1620] Step 14:

[1621] The emotion engine sends the analysis results to the server.

[1622] Step 15:

[1623] The server receives the emotion analysis results and adjusts the incentive according to the user's emotional state. For example, if the user is in a "joy" state, an additional 20 points will be added as a bonus.

[1624] Step 16:

[1625] The server stores the adjusted incentive points in a database and notifies the terminal of the result.

[1626] Step 17:

[1627] The server generates a feedback message based on the sentiment analysis results, e.g., "Your efforts are saving the Earth! Great!"

[1628] Step 18:

[1629] The server sends a feedback message to the terminal, which displays it to the user.

[1630] Step 19:

[1631] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.), sends it to the blockchain network, and requests that it be recorded.

[1632] Step 20:

[1633] The blockchain network receives the data, verifies the transaction, and records it on a ledger.

[1634] Step 21:

[1635] The server receives the transaction ID from the blockchain network and stores it in a database.

[1636] Step 22:

[1637] The user selects the incentive redemption option within the app and enters the number of points they wish to redeem.

[1638] Step 23:

[1639] The terminal sends the entered number of points and the user ID to the server.

[1640] Step 24:

[1641] The server checks the user's point balance and deducts the specified points.

[1642] Step 25:

[1643] The server calculates the exchange amount and calls an API to instruct the transfer to the specified bank account.

[1644] Step 26:

[1645] The server checks whether the transfer was successful and notifies the terminal of the result.

[1646] Step 27:

[1647] The device receives the notification and displays the result (success / failure) to the user.

[1648] Example 2

[1649] 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."

[1650] Existing recycling platforms often simply award points without considering users' emotions or motivations. This tends to discourage users from recycling, making it difficult to promote sustainable recycling activities. In addition, the processes for analyzing the materials of recycled products and awarding incentives are generally cumbersome, making efficient system operation necessary.

[1651] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for the user to scan the recyclable item; a means for the terminal to activate the camera and acquire an image; a means for the server to pass the image to a generative AI model and analyze the material composition; a means for the server to calculate incentive points based on the analysis results and add them to the user's account; a means for the terminal to analyze emotions and send the results to the server; a means for the server to adjust and grant incentive points based on the emotion analysis results; a means for the server to record recycling performance data on the blockchain; and a means for the user to redeem the incentive points. This makes it possible to provide incentives that take users' emotions into consideration and increase their motivation to recycle. Furthermore, material composition analysis using a generative AI model and data management using blockchain technology enable an efficient and reliable recycling process.

[1652] "User" refers to an individual or organization that uses the system to carry out recycling activities.

[1653] A "terminal" is a device operated by a user that provides functions such as scanning and display.

[1654] "Server" means a central system that processes, stores, and manages data and interacts with users and devices through various means.

[1655] "Recyclables" refers to items that users choose to recycle, specifically items that are subject to scanning and analysis.

[1656] "Camera" means a device mounted on the Terminal for taking images of recyclable items.

[1657] "Image" refers to visual information obtained by photographing recycled items with a camera.

[1658] A "generative AI model" is an artificial intelligence model that is installed on a server and analyzes the material composition of recycled items from image data.

[1659] "Material composition" refers to the proportion and type of various materials that make up a recycled product, and is analyzed by a generative AI model.

[1660] "Incentive points" are points awarded to users as a reward for recycling activities.

[1661] An "emotion engine" refers to software or hardware that analyzes a user's emotional state from their facial expressions and voice.

[1662] "Emotion analysis results" refers to data indicating the user's emotional state detected by the emotion engine.

[1663] "Blockchain" refers to a distributed ledger technology used to ensure data is trustworthy and immutable.

[1664] "Recycling performance data" refers to data related to recycling activities, such as user ID, material composition, incentive points, and timestamps.

[1665] "Redeem" means the process by which a user redeems incentive points for money or other purposes.

[1666] This invention is a system that improves users' motivation to recycle by combining an emotion engine that recognizes users' emotions in a user-participation recycling platform. This system is operated by exchanging data between users, terminals, a server, and the emotion engine.

[1667] User registration and login

[1668] First, the user installs the app and launches it. The user opens the new registration screen and enters the required information. The device sends this information to the server, which then stores the user information in a database. The server then generates a new user ID and authentication token and returns them to the device. The device then stores the received authentication token in local storage and uses it for future authentication.

[1669] Recycled Goods Scan

[1670] The user launches the app and selects the option to scan a recyclable item. The device's camera activates, and the user takes a picture of the recyclable item and sends it to the server. The server then provides the received image data to the generative AI model and instructs it to analyze it. The generative AI model analyzes the material composition of the recyclable item and calculates the proportion of each material. The generative AI model then returns the analysis results to the server.

[1671] Incentive calculation and awarding

[1672] The server calculates incentive points based on the received material composition. For example, based on a result such as "70% plastic, 30% paper," it calculates 100 points and adds the points to the database record corresponding to the user's account ID. The server then notifies the terminal of the point addition result.

[1673] Emotion recognition and incentive adjustment using an emotion engine

[1674] The device uses an emotion engine to analyze the user's facial expressions and voice. For example, it detects emotional states such as happiness, sadness, and surprise. The emotion engine returns the results to the server, which then adjusts the incentives according to the user's emotional state based on the analysis results. If the user is happy, the server awards additional bonus points. The server also generates a feedback message based on the analysis results and sends it to the device. The device displays this message to the user, encouraging them to recycle more.

[1675] Tracking and Verification

[1676] The server generates performance data for each recycled item, including user ID, material composition, incentive points, timestamp, etc. The server sends this data to the blockchain network and requests it to be recorded. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in a database.

[1677] Incentive cash redemption

[1678] The user selects the incentive redemption option within the app and enters the number of points to be redeemed. The device sends this information and the user ID to the server. The server checks the user's point balance and deducts the specified number of points. The server then calculates the redemption amount and calls an API to transfer the money to the specified bank account. The server confirms the success of the transfer and notifies the device of the result. The device displays it to the user.

[1679] Specific example explanation

[1680] For example, consider the case where user "A" is recycling a plastic bottle. User "A" launches the app and takes a photo of the plastic bottle with his / her camera. The device sends the image to the server, which analyzes the material using a generative AI model. The generative AI model determines that the plastic bottle is made of "90% plastic, 10% paper" and returns the result to the server. The server calculates "100 points" based on the analysis results and adds them to user "A's" account. At that time, the emotion engine analyzes user "A's" facial expression and determines the emotion as "joy." Based on this emotion analysis result, the server awards an additional 20 points. The server also generates a feedback message saying, "Your efforts are saving the Earth! Amazing!" and sends it to the device. The device displays the additional points and the feedback message to user "A." Later, user "A" converts the 300 points into cash, and 3,000 yen is deposited into his / her designated bank account.

[1681] Example prompt sentence:

[1682] "Please analyze the material of the plastic bottle."

[1683] "Analyze user sentiment and calculate bonus points."

[1684] This system recognizes users' emotions and adjusts and awards incentives based on those emotions, making it an effective way to motivate them to recycle. By combining generative AI models, blockchain technology, and an emotion engine, users can easily recycle, track their results, and earn rewards.

[1685] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1686] Step 1: User installs and launches the app.

[1687] Input: The user installs the app and taps to launch it.

[1688] Output: The app starts and the initial screen is displayed.

[1689] Specific operation: The device launches the installed app and displays the initial screen.

[1690] Step 2: The user opens the new registration screen and enters the required information.

[1691] Input: The user enters required information, such as an email address or password.

[1692] Output: The input information is aggregated and stored on the device.

[1693] Specific behavior: The device displays a new registration form, and the user enters information into the form.

[1694] Step 3: The terminal sends the entered information to the server.

[1695] Input: Information entered by the user, such as email address or password.

[1696] Output: The server receives the user information.

[1697] Specific operation: The device sends user information to the server via an HTTP request.

[1698] Step 4: The server saves the user information to the database and generates a new user ID and authentication token.

[1699] Input: User information received by the server from the device.

[1700] Output: A new user ID and authentication token is generated and stored in the database.

[1701] Specific behavior: The server inserts the user information into the database and generates a user ID and authentication token.

[1702] Step 5: The server returns the authentication token to the device, which stores it in local storage.

[1703] Input: The authentication token sent by the server.

[1704] Output: The authentication token is saved on the device.

[1705] Specific operation: The server sends an authentication token in the HTTP response, and the device stores it in local storage.

[1706] Step 6: The user launches the app and selects the option to scan for recyclables.

[1707] Input: User selects the scan option within the app.

[1708] Output: The camera starts.

[1709] Specific behavior: Activates the camera module when the device selects the scan option.

[1710] Step 7: The device activates the camera and the user takes a picture of the item to be recycled.

[1711] Input: User uses camera to take a picture of a recyclable item.

[1712] Output: The captured image data is saved on the device.

[1713] Specific behavior: The device launches the camera app, and the user presses the shutter button to take a picture.

[1714] Step 8: The device sends the captured image to the server.

[1715] Input: Captured image data.

[1716] Output: The image data is transferred to the server.

[1717] Specific operation: The device uploads image data to the server using an HTTP request.

[1718] Step 9: The server provides the received image data to the generative AI model and instructs it to analyze it.

[1719] Input: Image data received by the server.

[1720] Output: The generative AI model begins its analysis.

[1721] Specific operation: The server sends the image data to the API of the generated AI model and issues an analysis request.

[1722] Step 10: The generative AI model analyzes the image data and determines the material composition of the recycled item.

[1723] Input: Image data.

[1724] Output: Material composition analysis (e.g. "70% plastic, 30% paper").

[1725] Specific operation: The generative AI model analyzes the image, calculates the type and proportion of ingredients, and returns the results.

[1726] Step 11: The generative AI model returns the analysis results to the server.

[1727] Input: Material composition analysis results.

[1728] Output: The server receives the analysis results.

[1729] Specific operation: The generative AI model sends the analysis results to the server.

[1730] Step 12: The server calculates incentive points based on the received material configuration.

[1731] Input: Analysis result (e.g. "70% plastic, 30% paper").

[1732] Output: Calculated incentive points (e.g. 100 points).

[1733] What it does: The server calculates points based on the type and percentage of ingredients.

[1734] Step 13: The server adds the calculated points to the database record corresponding to the user's account ID.

[1735] Input: Calculated incentive points, user ID.

[1736] Output: Points are added to the user account.

[1737] Specific behavior: The server updates the database and increases the user's points balance.

[1738] Step 14: The server notifies the terminal of the result of the point addition.

[1739] Input: The result of adding points.

[1740] Output: The device receives the notification.

[1741] Specific operation: The server sends an HTTP response to the terminal to notify it that the points have been added successfully.

[1742] Step 15: The device activates its emotion engine and analyzes the user's facial expressions and voice.

[1743] Input: User's facial expressions and voice data.

[1744] Output: Sentiment analysis result (e.g. "joy").

[1745] How it works: The device uses the camera and microphone to capture the user's facial expressions and voice, which are then analyzed by the emotion engine.

[1746] Step 16: The emotion engine sends the analysis results to the server.

[1747] Input: Sentiment analysis results.

[1748] Output: The server receives the analysis results.

[1749] Specific operation: The device issues the emotion analysis results to the server.

[1750] Step 17: The server adjusts and awards incentive points based on the sentiment analysis results.

[1751] Input: Sentiment analysis results, current incentive points.

[1752] Output: Adjusted incentive points.

[1753] Specific operation: The server increases or decreases points according to the results of the sentiment analysis, recalculates them, and reflects them in the user's account.

[1754] Step 18: The server generates a feedback message based on the analysis result and sends it to the terminal.

[1755] Input: Sentiment analysis results.

[1756] Output: Feedback message.

[1757] Specific operation: The server creates a message based on the emotion analysis results and sends it to the device as an HTTP response.

[1758] Step 19: The terminal displays a feedback message to the user.

[1759] Input: Feedback message.

[1760] Output: The user confirms the message.

[1761] Specific behavior: The device will display a feedback message as a pop-up or notification.

[1762] Step 20: User selects the incentive redemption option within the app and enters the number of points to redeem.

[1763] Input: The number of points entered by the user to redeem.

[1764] Output: The redemption request is saved on the device.

[1765] Specific behavior: The device displays a form for user input and collects input data.

[1766] Step 21: The terminal transmits the input number of points and the user ID to the server.

[1767] Input: Number of points to be redeemed, user ID.

[1768] Output: The server receives the request.

[1769] Specific operation: The terminal sends a cash request to the server using an HTTP request.

[1770] Step 22: The server checks the user's point balance and subtracts the specified points.

[1771] Input: Redemption request, current points balance.

[1772] Output: Updated points balance.

[1773] Specific operation: The server queries the database to check and update the user's point balance.

[1774] Step 23: The server calculates the converted amount and calls an API to instruct the transfer to the specified bank account.

[1775] Input: Number of points to be redeemed, bank account information.

[1776] Output: The result of the transfer instruction.

[1777] Specific operation: The server uses the API to send a transfer instruction to the bank system.

[1778] Step 24: The server checks whether the transfer was successful and notifies the terminal of the result.

[1779] Input: Transfer result from API.

[1780] Output: Transfer result notification.

[1781] Specific operation: The server obtains the transfer result from the API and sends the result to the terminal.

[1782] Step 25: The device receives the notification and displays the result (success / failure) to the user.

[1783] Input: Transfer result notification.

[1784] Output: The resulting information that is displayed to the user.

[1785] Specific operation: The device receives a notification from the server and displays its contents to the user.

[1786] (Application example 2)

[1787] 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."

[1788] In modern society, promoting recycling activities has become an important issue, but sustaining users' motivation is difficult. Conventional systems simply accept recyclable items and award points, but this does not maintain user interest and lacks a means to encourage continued use. Furthermore, to increase the transparency and reliability of recycling activities, it is necessary to prevent fraudulent data collection. There is a need to provide an effective system that can solve these problems and increase users' motivation to recycle.

[1789] 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.

[1790] In this invention, the server includes a means for users to scan recyclable items, a means for the server to pass the image to a generative AI model to analyze the material composition, a means for the server to calculate incentive points based on the analysis results and add them to the user's account, a means for the server to record recycling performance data on the blockchain, a means for the user to redeem the incentive points, a means for the user's terminal to analyze the user's emotional state using an emotion engine, and a means for the server to adjust incentives based on the emotion analysis results. This allows incentives to be awarded according to the user's emotional state, effectively increasing motivation to participate in recycling activities. Furthermore, recording recycling performance data on the blockchain increases the transparency and reliability of the system.

[1791] "User" means an individual or legal entity that brings in recyclable items and uses the recycling system.

[1792] "Means for scanning recyclable items" refers to a function that uses a device such as a smartphone to capture an image of a recyclable item and send that image to a server.

[1793] A "generative AI model" is a machine learning algorithm that analyzes the material composition of recycled items from images.

[1794] "Means for analyzing material composition" refers to a function that uses a generative AI model to identify the proportion and type of materials contained in image data of recycled products.

[1795] "Incentive points" are points that are given to users as a reward for recycling activities.

[1796] "User Account" means an individual account registered by a User to use the Recycling System.

[1797] "Recycling performance data" refers to information (material composition, points, time, etc.) collected when a user performs recycling activities.

[1798] "Blockchain" is a distributed ledger technology that continuously records transaction data.

[1799] An "emotion engine" is a technology for analyzing emotions from a user's facial expressions, voice, etc.

[1800] The "means for adjusting incentives" is a function that increases or decreases the user's incentive points based on the analysis results of the emotion engine.

[1801] "Means of cashing out" refers to a function that allows users to withdraw the incentive points they have earned by cash, bank transfer, etc.

[1802] This invention is a system that combines a user-participation recycling platform with an emotion engine. Specifically, users scan recyclable items using devices such as smartphones, and the information is sent to a server that analyzes their material composition and provides incentives based on the user's emotional state. Furthermore, to ensure transparency in recycling activities, data is recorded on a blockchain. Below, we explain specific processing methods and examples of this system.

[1803] First, users install the recycling platform's application on their smartphone and log in or register. After logging in, they open the camera to take a picture of the recyclable item. When the user takes a picture of the recyclable item, the device sends the image to a server. The server passes the image to a generative AI model, which analyzes its material composition. This analysis determines what the recyclable item is made of (for example, "70% plastic, 30% paper").

[1804] Next, the server calculates incentive points based on the analysis results and adds them to the user's account. It's worth noting that the system uses an emotion engine. When the user device scans the recyclable item, it simultaneously captures the user's facial expressions and analyzes them with the emotion engine. The emotion engine detects the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns the analysis results to the server.

[1805] The server adjusts incentives based on the results of emotion analysis. For example, if the user is happy, it will give additional bonus points. The server also generates a feedback message for the user based on their emotion and sends it to the device. The device displays the message to the user, thereby increasing their motivation to recycle.

[1806] Next, recycling performance data (such as user ID, material composition, incentive points, timestamp, etc.) is recorded on the blockchain by the server. Blockchain technology ensures data tamper-proofing and transparency.

[1807] Finally, users are also given the option to redeem their incentive points for cash. When a user makes a cash request within the app, the server will check the user's point balance and the cash request, and transfer the funds to the specified bank account. If the transfer is successful, the user will be notified of the result.

[1808] The hardware used includes smartphones, servers, cameras, etc. The software includes generative AI models, sentiment analysis engines (e.g., FER libraries), blockchain APIs, banking APIs, etc. In particular, an example prompt for using a generative AI model is as follows:

[1809] Please analyze the material composition of the image.

[1810] This invention can increase users' motivation to recycle by combining emotion recognition with their recycling activities. In addition, by using blockchain technology, transparency and reliability can be ensured, providing a system that users can use with peace of mind.

[1811] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1812] Step 1:

[1813] This is the step where the user scans the recyclable item.

[1814] The user launches the app on their smartphone and selects the recyclable item scanning function. They then use the smartphone camera to capture an image of the recyclable item. This image becomes the input data, and the device sends the image to the server. The output is the image data of the recyclable item sent to the server.

[1815] Step 2:

[1816] This is the step where the material composition is analyzed using a generative AI model.

[1817] The server passes the received image data of the recycled product to the generative AI model and instructs it to analyze it along with the prompt, "Please analyze the material composition of the image." The generative AI model analyzes the image data and determines the material composition. This determination result (e.g., "70% plastic, 30% paper") is returned to the server. The input is the image data of the recycled product, and the output is the data resulting from the material determination.

[1818] Step 3:

[1819] This is the calculation and addition step of incentive points.

[1820] The server calculates the points assigned to each material in the recycled product based on the material determination results returned by the generative AI model. These calculation results become incentive points and are added to the user's account. The input is the material determination results, and the output is the calculated incentive points.

[1821] Step 4:

[1822] This is the step of analyzing the user's emotional state.

[1823] While scanning recyclable items, the smartphone camera captures the user's facial expressions and sends the images to the emotion engine. The emotion engine performs facial expression analysis to detect the user's emotional state (e.g., joy, sadness, surprise, etc.) and returns it to the server. The input is image data containing the user's facial expressions, and the output is emotional state data.

[1824] Step 5:

[1825] This is a step for adjusting incentives based on the results of sentiment analysis.

[1826] The server adjusts incentive points based on the emotion analysis results from the emotion engine. For example, if the user is happy, additional bonus points are awarded. The adjusted incentive points are recalculated and added to the user's account. The input is the emotion analysis results and existing incentive points, and the output is the adjusted incentive points.

[1827] Step 6:

[1828] A feedback message generation and notification step.

[1829] The server generates a feedback message based on the sentiment analysis results and sends it to the user device. For example, a message such as "Your efforts are saving the Earth! Wonderful!" is generated. The user device displays this message. The input is the sentiment analysis results, and the output is the feedback message.

[1830] Step 7:

[1831] This is the step where recycling performance data is recorded on the blockchain.

[1832] The server generates performance data for each recycled item (user ID, material composition, incentive points, timestamp, etc.) and sends it to the blockchain network to request recording. The blockchain receives the data, verifies the transaction, and records it in the ledger. The server receives the transaction ID from the blockchain and stores it in the database. The input is the recycling performance data, and the output is the transaction ID.

[1833] Step 8:

[1834] This is the step of converting incentive points into cash.

[1835] The user selects the cash-out option for incentive points within the app and enters the number of points. The user's device sends the entered number of points and user ID to the server. The server checks the user's point balance and instructs a transfer to the specified bank account. The server checks whether the transfer was successful and notifies the user's device of the result. The input is the cash-out request and number of points, and the output is a notification of the cash-out result.

[1836] 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.

[1837] 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.

[1838] 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.

[1839] 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.

[1840] FIG. 9 is a diagram illustrating 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 actions 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.

[1841] 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.

[1842] 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).

[1843] 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.

[1844] 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."

[1845] 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...

Claims

1. A means for users to scan their recycled items; The server passes the image to a generative AI model to analyze the material composition. a means for the server to calculate incentive points based on the analysis results and add them to the user's account; A means for the server to record recycling performance data on the blockchain; A system including a means for users to redeem incentive points for cash.

2. The system according to claim 1, wherein the server includes means for notifying the user terminal of the analysis results by the generated AI model.

3. 2. The system of claim 1, further comprising means for executing a transfer based on an incentive redemption request received from the user terminal by the server.

4. 2. The system according to claim 1, further comprising means for generating an authentication token for securely managing a user's personal information and recycling activity record, and storing the token in a local storage.

5. 2. The system according to claim 1, further comprising means for allowing a user to confirm the material composition of a recyclable product and confirm the incentive rate in advance.

Images