system

Abstract

Provide a system. [Solution] a scanning means for scanning the bulky waste; an analyzing means for analyzing the image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste; a dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means; A system including:

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] In modern society, a large amount of bulky waste is generated, and its disposal is extremely important from the perspective of reducing environmental impact and making effective use of resources. However, conventional methods involve sorting and dismantling bulky waste manually, which is labor-intensive and time-consuming, and the accuracy of sorting is often insufficient. In particular, it is difficult to effectively separate reusable parts of waste. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides the following means. Specifically, the system includes a scanning means for scanning bulky waste, an analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, and a dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means. This enables the efficient and accurate separation and dismantling of bulky waste, thereby achieving effective resource utilization and reducing environmental impact. Furthermore, if the dismantling means further includes a storage means for collecting recyclable waste, the collection and management of recyclable waste after dismantling can be easily performed. Furthermore, the analysis means uses an AI image analysis algorithm to identify each part of the bulky waste with high accuracy, improving the accuracy of sorting.

[0006] The "scanning means" is a device for scanning bulky waste at high resolution and acquiring the image data.

[0007] The "analysis means" is a processing device that identifies each part of the bulky waste based on the image data obtained from the scanning means and classifies them into recyclable waste and non-recyclable waste.

[0008] The "disassembly means" refers to a robot or mechanical device that physically disassembles the bulky waste and separates it into recyclable waste and non-recyclable waste based on the classification results obtained by the analysis means.

[0009] "Storage means" refers to equipment or containers for collecting and properly storing recyclable waste separated by the dismantling means.

[0010] The "AI image analysis algorithm" is an image analysis technology that uses artificial intelligence, and is an algorithm that analyzes image data of scanned bulky waste to identify and specify each part.

[0011] "Bulky waste" refers to large items that cannot be disposed of using standard waste collection methods, including furniture, home appliances, and large plastic products.

[0012] "Recyclable waste" is the part of garbage that can be reused or recycled, and includes metals, plastics, glass, etc.

[0013] "Non-recyclable waste" is the part of the garbage that is difficult to reuse or recycle and must be disposed of. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then uses a robot to physically dismantle the waste based on the classification results.

[0036] System Configuration

[0037] This system consists of a terminal that handles bulky waste, a server that analyzes image data, and a demolition robot that physically dismantles the waste. Each component works together via a network to carry out the process.

[0038] Scanning Method

[0039] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[0040] Image analysis methods

[0041] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[0042] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[0043] Demolition means

[0044] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[0045] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[0046] Storage means

[0047] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0048] Specific examples

[0049] 1. Device: Place an old TV, which is bulky trash, into the scanning device.

[0050] 2. User: Initiates operation of the scanning device.

[0051] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[0052] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[0053] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[0054] 6. Server: Sends dismantling instructions to the dismantling robot.

[0055] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[0056] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[0057] 9. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[0058] 10. User: Check the results of the demolition work and input the next bulky waste into the system.

[0059] As described above, this system can efficiently and accurately separate and dismantle bulky waste, thereby contributing to resource reuse and environmental conservation.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[0063] Step 2:

[0064] The user places bulky trash, such as an old television, in a designated position on the scanner.

[0065] Step 3:

[0066] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[0067] Step 4:

[0068] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[0069] Step 5:

[0070] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[0071] Step 6:

[0072] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[0073] Step 7:

[0074] Based on the analysis results, the server generates disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[0075] Step 8:

[0076] The server transmits the generated dismantling instructions to the dismantling robot.

[0077] Step 9:

[0078] The dismantling robot follows the instructions received from the server and begins the dismantling process. In the case of a television, the robot's arms are first moved to remove the screen.

[0079] Step 10:

[0080] After removing the screen, the dismantling robot then disassembles the plastic case, removing the securing screws and opening the case.

[0081] Step 11:

[0082] The dismantling robot removes the electronic components from inside the case and separates them into recyclable and non-recyclable waste.

[0083] Step 12:

[0084] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0085] Step 13:

[0086] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as needed, and provides feedback to the user.

[0087] Step 14:

[0088] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and, if necessary, reports any problems to the system.

[0089] In this way, each step works in tandem to ensure efficient and accurate sorting and dismantling of bulky waste.

[0090] Example 1

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

[0092] Currently, the separation and dismantling of bulky waste requires a lot of manual labor, is inefficient, and accurate separation is difficult. In particular, the difficulty in accurately separating each part of bulky waste into recyclable and non-recyclable waste reduces the rate of resource reuse and increases the burden on the environment.

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

[0094] In this invention, the server includes image acquisition means for scanning bulky waste, analysis means for analyzing image data obtained from the image acquisition means to identify parts of the bulky waste, classification means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification means, and instruction means for the dismantling means to generate dismantling instructions and send them to the robot. This enables efficient and accurate sorting and dismantling of bulky waste, contributing to resource reuse and environmental conservation.

[0095] The "image acquisition means" is a means for acquiring high-resolution images of the bulky waste using a scanning device.

[0096] The "analysis means" is a means for analyzing the acquired image data and identifying each part of the bulky waste.

[0097] The "classification means" is a means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste.

[0098] "Dismantling means" refers to a means for physically dismantling bulky waste based on the classification means.

[0099] The "instruction means" is a means by which the dismantling means generates dismantling instructions and transmits the instructions to the robot.

[0100] An "artificial intelligence image analysis algorithm" is an algorithm that uses computer vision technology to automatically recognize and classify objects in image data.

[0101] The "storage means" is a means for collecting recyclable waste dismantled by the dismantling means and storing it in a specific container.

[0102] MODE FOR CARRYING OUT THE INVENTION

[0103] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. Specifically, it scans the bulky waste, uses AI image analysis algorithms to identify and classify each part, and then uses robots to physically dismantle the waste based on the classification results.

[0104] System Configuration

[0105] The system consists of the following major hardware and software components:

[0106] 1. Image acquisition means: A device for scanning high-resolution images of bulky waste, such as a high-resolution camera scanner.

[0107] 2. Analysis means: A server that analyzes the scanned image data and performs processing to identify each part of the bulky waste. Specifically, it implements artificial intelligence image analysis algorithms such as TENSORFLOW (registered trademark) and OpenCV.

[0108] 3. Classification means: A server that performs processing to classify each part into recyclable waste and non-recyclable waste based on the analyzed data.

[0109] 4. Dismantling means: A dismantling robot that physically dismantles the bulky waste based on the classification results. The dismantling robot includes servo motors and arms.

[0110] 5. Instruction means: A server for generating dismantling instructions and sending them to the dismantling robot.

[0111] 6. Storage means: A device for collecting disassembled recyclables, including containers for collecting each part.

[0112] System Operation

[0113] The process for disposing of bulky waste is as follows:

[0114] 1. The terminal places the bulky waste item into the scanning device. The user places the bulky waste item, such as an old TV, into the device.

[0115] 2. The user begins operating the scanning device. The process begins by pressing the start button on the scanning device.

[0116] 3. The device scans the bulky waste at high resolution and obtains image data.

[0117] 4. The device sends the acquired image data to the server.

[0118] 5. The server receives and stores the image data from the scanning device.

[0119] 6. The server uses AI image analysis algorithms to analyze the image data, identifying the TV screen, case, electronic components, etc.

[0120] 7. The server sorts each part into recyclable and non-recyclable waste. Identified screens and electronic components are identified as recyclable waste, while plastic casings are identified as non-recyclable waste.

[0121] 8. The server generates specific dismantling instructions for the demolition robot and sends them to the robot via the network.

[0122] 9. The demolition robot follows the instructions received from the server and begins the physical dismantling process: its arms move and carefully remove the TV screen, then unscrew the casing.

[0123] 10. The demolition robot stores the dismantled recyclable waste in a container on the storage means, and sorts the non-recyclable waste into a separate container.

[0124] 11. The server monitors the progress of the demolition and provides feedback to the user as needed. The user then loads the next bulky waste item and repeats the process.

[0125] Specific examples

[0126] Here is an example of a prompt for this system:

[0127] Prompt statement:

[0128] An old TV is placed in the scanning device. Once the scan is complete, the image is sent to the server. The server analyzes the image and identifies and classifies the TV's screen, case, and electronic components. Next, dismantling instructions are sent to a dismantling robot, which dismantles the TV according to the instructions. Finally, the waste is separated into recyclable and non-recyclable waste and stored in designated locations.

[0129] This system enables the efficient and accurate separation and dismantling of bulky waste, contributing to the reuse of resources and environmental conservation.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Step 1:

[0132] The user places the bulky waste in the scanning device.

[0133] What happens: The user places the old TV on the scanning device's stand.

[0134] Input: Bulky waste (e.g. old TV)

[0135] Output: Bulky waste placed in the scanning device

[0136] Step 2:

[0137] The user begins operating the scanning device.

[0138] Specific operation: The user presses the start button on the scanning device.

[0139] Input: Pressing the start button on the scanning device

[0140] Output: Start scanning device

[0141] Step 3:

[0142] The terminal scans the bulky waste at high resolution and captures image data.

[0143] Specific operation: The scanning device's camera scans the entire television and generates image data.

[0144] Input: Bulky waste (set in the scanning device)

[0145] Output: High-resolution image data

[0146] Step 4:

[0147] The terminal transmits the acquired image data to the server.

[0148] Specific operation: The terminal uploads image data to the server via the network.

[0149] Input: High-resolution image data

[0150] Output: Image data sent to the server

[0151] Step 5:

[0152] A server receives and stores image data from the scanning device.

[0153] Specific operation: The server saves the image data in local storage.

[0154] Input: Image data sent from a scanning device

[0155] Output: Saved image data

[0156] Step 6:

[0157] The server analyzes the image data using an AI image analysis algorithm.

[0158] Specific operation: The server uses the TensorFlow library to identify objects in the image data.

[0159] Input: Saved image data

[0160] Output: Identified bulky waste pieces (e.g. screen, case, electronic parts)

[0161] Step 7:

[0162] The server sorts each part into recyclable and non-recyclable.

[0163] Specific operation: The server analyzes the identified parts and classifies each into recyclable and non-recyclable waste.

[0164] Input: Each identified piece of bulky waste

[0165] Output: Classified waste types (recyclable waste, non-recyclable waste)

[0166] Step 8:

[0167] The server generates and transmits specific dismantling instructions to the dismantling robot.

[0168] Specific operation: The server generates an instruction set detailing the dismantling procedure and sends it to the robot via the network.

[0169] Input: Classified garbage type and its location information

[0170] Output: Dismantling instructions to the dismantling robot

[0171] Step 9:

[0172] The demolition robot follows the instructions received from the server and begins the physical demolition work.

[0173] What it does: The robot's arm moves and carefully removes the TV screen, then unscrews the casing to remove the electronic components inside.

[0174] Input: Dismantlement instructions sent from the server

[0175] Output: Dismantled bulky waste

[0176] Step 10:

[0177] The dismantling robot stores the dismantled recyclable waste in a container of the storage means, and sorts the non-recyclable waste into another container.

[0178] Specific operations: The robot places the removed screens and electronic components in designated containers, and moves non-recyclable waste such as cases to a disposal container.

[0179] Input: Dismantled bulky waste parts

[0180] Output: Sorted storage (recyclable and non-recyclable containers)

[0181] Step 11:

[0182] The server monitors the progress of the demolition work and provides feedback to the user as needed.

[0183] Specific operation: The server receives feedback data from the robot in real time and reports the situation to the user by email or UI notification.

[0184] Input: Feedback data from the robot

[0185] Output: Feedback notification to the user

[0186] (Application example 1)

[0187] 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."

[0188] Conventional bulky waste sorting and dismantling systems often lack efficiency and accuracy, particularly due to the lack of real-time monitoring and feedback functions, making it difficult to properly manage the progress of dismantling work. Another issue is the efficiency of manual intervention when dismantling procedures are not performed properly. The present invention aims to solve these issues and provide a more efficient and accurate bulky waste sorting and dismantling system.

[0189] 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.

[0190] In this invention, the server includes scanning means for scanning the bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, monitoring means for monitoring the progress of the dismantling means and manually operating it as necessary, and display means for displaying feedback information from the dismantling means in real time. This allows the progress of the dismantling work to be properly managed, enabling efficient and accurate dismantling and sorting work.

[0191] The "scanning means" is a device that acquires high-resolution image data of bulky waste.

[0192] The "analysis means" is a system that uses image data obtained from the scanning means to identify each part of bulky waste using an AI image analysis algorithm and classify it into recyclable and non-recyclable waste.

[0193] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the analysis means.

[0194] The "monitoring means" is a system or device for monitoring the progress of the dismantling means in real time and for performing manual operations as necessary.

[0195] The "display means" is a device or system that displays feedback information from the demolition means in real time, and visually provides workers with the progress and problems of the demolition work.

[0196] This invention is a system for efficiently and accurately separating and dismantling bulky waste on a factory recycling line. The system scans the bulky waste and uses an AI image analysis algorithm to identify and classify each part. Based on the classification results, a dismantling robot physically dismantles the waste. The system also includes a monitoring means for monitoring the progress of the dismantling process and manually operating it as needed, as well as a display means for displaying feedback information in real time.

[0197] System Configuration

[0198] The structure of this system is as follows:

[0199] Hardware and Software

[0200] Scanning method: A device that captures high-resolution image data of bulky waste (e.g., a high-resolution camera).

[0201] Analysis method: A server that uses TensorFlow and OpenCV to analyze image data and identify each part of bulky waste.

[0202] Dismantling method: A dismantling robot that runs on ROS (Robot Operating System).

[0203] Monitoring method: A system (e.g., smartphone app) to monitor the demolition progress in real time.

[0204] Display means: A device (e.g., a smartphone or tablet) that displays feedback information from the dismantling means.

[0205] Operation overview

[0206] 1. Scanning of bulky waste

[0207] When a user places an item of bulky waste on the scanning device, the terminal scans the item at high resolution and sends the image data to the server.

[0208] 2. Image Analysis

[0209] The server analyzes the image data received from the scanning device and uses AI image analysis algorithms to identify each part of the bulky waste. For example, in the case of an old TV, the screen, case, electronic components, etc. are identified. The analysis results classify each part as recyclable or non-recyclable waste.

[0210] 3. Generating Disassembly Instructions

[0211] The server generates disassembly instructions based on the analysis and sends them to the disassembly robot. For example, in the case of a television, instructions would include first removing the screen, then removing the screws securing the case.

[0212] 4. Monitoring and feedback on demolition progress

[0213] The progress of the dismantling is monitored in real time by the monitoring means, which displays the progress of the dismantling on the display means, and allows manual operation as required.

[0214] Specific examples

[0215] Scanning by device: An old television, which is bulky waste, is placed in a scanning device, and image data of the television is generated and sent to the server.

[0216] AI image analysis: The server receives the image data and uses TensorFlow and OpenCV to identify each part of the TV (screen, case, electronic components, etc.).

[0217] Disassembly instructions: The server generates disassembly instructions and sends them to the disassembly robot. Following the instructions, the robot carefully removes the screen, then removes the screws securing the plastic case, opens it, and removes the internal electronic components.

[0218] Progress and feedback: Monitor demolition progress in real time and view feedback information via a smartphone app.

[0219] Prompt Sentence Examples

[0220] "Dismantling of the old refrigerator has begun. It is recommended that the refrigerator door and shelves be removed first."

[0221] This allows dismantling work on the factory's recycling line to proceed efficiently and accurately, and even when manual operation is required, it can be handled quickly.

[0222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0223] Step 1:

[0224] The user places the bulky waste into the scanning device. The scanning device scans the bulky waste at high resolution and generates image data. The generated image data is sent to the server via the network. The input of this step is the bulky waste, and the output is the scanned high-resolution image data.

[0225] Step 2:

[0226] The server analyzes the image data received from the scanning device. Specifically, it processes the image data using TensorFlow and OpenCV to identify each part of the bulky waste. For example, in the case of a television, the screen, case, electronic components, etc. are identified. The input is the image data, and the output is information about each identified part.

[0227] Step 3:

[0228] The server classifies each part into recyclable and non-recyclable waste based on the analysis results. Based on the classification results of the AI ​​image analysis algorithm, for example, screens and electronic components are classified as recyclable waste, and cases are classified as non-recyclable waste. The input is the information of each identified part, and the output is a list of sorted recyclable and non-recyclable waste.

[0229] Step 4:

[0230] The server generates specific disassembly instructions and sends them to the disassembly robot. For example, instructions include steps such as first removing the screen and then removing the fixing screws of the case. The input is a list of sorted recyclable and non-recyclable waste, and the output is the disassembly instructions.

[0231] Step 5:

[0232] The dismantling robot follows instructions received from the server and begins the physical dismantling process. The robot's arm moves, first carefully removing the screen, then unscrewing the plastic case to extract the electronic components inside. The input is the dismantling instructions, and the output is the dismantled bulky waste.

[0233] Step 6:

[0234] The terminal monitors the progress of the demolition and receives feedback in real time. It displays the feedback information sent from the demolition robot and provides the user with information about the progress and problems of the demolition work. The input is feedback information from the demolition robot, and the output is a real-time display based on that information.

[0235] Step 7:

[0236] The user monitors the progress and takes manual action if necessary. For example, manual intervention is required if the demolition robot encounters an unexpected obstacle. The input is real-time display information, and the output is manual operation instructions.

[0237] 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.

[0238] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, identifies and classifies each part using an AI image analysis algorithm, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, it aims to improve the user experience and increase the efficiency of the system.

[0239] System Configuration

[0240] This system is composed of a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[0241] Scanning Method

[0242] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[0243] Image analysis methods

[0244] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[0245] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[0246] Demolition means

[0247] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[0248] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[0249] Storage means

[0250] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0251] Emotion Engine

[0252] When a user places their bulky waste in the trash, the emotion engine analyzes their voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion engine will detect that emotion and make adjustments such as displaying more detailed system operation instructions.

[0253] The emotion engine has the ability to adjust the system's behavior based on the user's emotion recognition results. For example, if the user is feeling stressed, the system will notify the user of the progress of the process more frequently, improving the user experience.

[0254] Specific examples

[0255] 1. Terminal: Place an old television, which is bulky trash, into the scanner device.

[0256] 2. User: Initiates operation of the scanning device.

[0257] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[0258] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[0259] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[0260] 6. Server: Sends dismantling instructions to the dismantling robot.

[0261] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[0262] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[0263] 9. Emotion Engine: Analyzes the user's voice input and recognizes their emotions. For example, if the user feels anxious, the system will display detailed operating instructions.

[0264] 10. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[0265] 11. User: Checks the results of the demolition work and places the next bulky waste in the scanner. If necessary, reports the problem to the system.

[0266] In this way, combining the emotion engine allows for flexible responses based on the user's emotions, making the system easier to use. This allows for efficient and accurate sorting and dismantling of bulky waste, promoting resource reuse and environmental conservation.

[0267] The processing flow will be explained below.

[0268] Step 1:

[0269] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[0270] Step 2:

[0271] The user places bulky trash, such as an old television, in a designated position on the scanner.

[0272] Step 3:

[0273] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[0274] Step 4:

[0275] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[0276] Step 5:

[0277] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[0278] Step 6:

[0279] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[0280] Step 7:

[0281] Based on the analysis results, the server generates specific disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[0282] Step 8:

[0283] The server transmits the generated dismantling instructions to the dismantling robot.

[0284] Step 9:

[0285] The dismantling robot follows the instructions received from the server and begins the dismantling process. First, it moves its arm to carefully remove the screen.

[0286] Step 10:

[0287] After removing the screen, the dismantling robot then removes the screws that secure the plastic case, opens it, removes the electronic components inside, and separates them into recyclable and non-recyclable waste.

[0288] Step 11:

[0289] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0290] Step 12:

[0291] The emotion engine analyzes voice input to recognize emotions as the user uses the system. For example, if a user is using the system for the first time and emotions such as anxiety or confusion are detected, the system will display more detailed operating instructions.

[0292] Step 13:

[0293] The emotion engine adjusts the system's behavior based on the emotion recognition results. For example, if the user is feeling stressed, the system may notify the user more frequently about the progress of the process or provide additional guidance.

[0294] Step 14:

[0295] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as necessary, and provides feedback on the progress of the demolition work to the user.

[0296] Step 15:

[0297] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and reports any problems to the system if necessary.

[0298] In this way, each step works in coordination to separate and dismantle bulky waste efficiently and accurately. By combining it with an emotion engine, it becomes possible to respond flexibly to the user's emotions, making the system easier to use.

[0299] Example 2

[0300] 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."

[0301] Current systems for separating and dismantling bulky waste rely on manual labor and partial automation, making them inefficient and difficult to accurately classify recyclable and non-recyclable waste. Furthermore, user operations tend to be complicated, resulting in a poor user experience.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes scanning means for scanning bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, sorting means for sorting the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste, and emotion recognition means for recognizing user emotions and adjusting the operation of the system. This enables efficient and accurate sorting and dismantling of bulky waste, and makes user operation simple and intuitive, resulting in an improved user experience.

[0304] "Scanning means" refers to a device and method for scanning bulky waste at high resolution and acquiring image data.

[0305] "Analysis means" refers to software and algorithms that analyze the image data obtained from the scanning means and identify and classify each piece of bulky waste.

[0306] The "dismantling means" refers to mechanical devices and systems for physically dismantling bulky waste based on the classification results obtained by the analysis means.

[0307] The "separation means" refers to an apparatus and method for separating the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste.

[0308] "Emotion recognition means" refers to software and algorithms that recognize a user's emotions through voice input or the like and adjust the system's operation based on that.

[0309] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. This system scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, the system aims to improve the user experience and increase the efficiency of the system.

[0310] The system mainly consists of the following components: a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[0311] Scanning Method

[0312] The terminal includes a scanning device for scanning bulky items at high resolution. For example, when a user places an old television on the scanning device, the terminal fixes it in the correct position and captures the entire image with a high-resolution camera. The captured image data is then sent to a server.

[0313] Image analysis methods

[0314] The server receives the image data sent from the device. To analyze this image data, the server runs an AI image analysis algorithm (e.g., TensorFlow). This algorithm analyzes the image and identifies each part of the TV (screen, plastic case, electronic components, etc.). The analysis results include information to identify each part as recyclable or non-recyclable.

[0315] Demolition means

[0316] Based on the analysis, the server generates dismantling instructions for the robot. These instructions detail which parts to dismantle, in what order, and how. For example, they might include carefully removing the TV screen first, then unscrewing the plastic casing. To send these instructions to the robot, the server uses a control protocol.

[0317] Dismantling and sorting procedures

[0318] The dismantling robot follows instructions received from the server and begins the physical dismantling process. For example, the robot's arm carefully removes the screen, then removes the screws securing the plastic case. The dismantled recyclables are collected in a storage facility, while non-recyclables are sorted into separate containers.

[0319] emotion recognition means

[0320] When a user places their bulky waste in the trash can, the emotion engine analyzes their voice input and recognizes their emotions. For example, if a user expresses anxiety by saying, "Will I be able to do this successfully?", the emotion engine will detect this anxiety and display more detailed operating instructions for the scanning device. The emotion engine uses natural language processing (NLP) technology to analyze voice data in real time.

[0321] Feedback and User Interaction

[0322] The server constantly monitors the progress of the demolition work and provides feedback to the user as needed. For example, it displays a message such as "The demolition work is progressing smoothly." The user can check the results of the demolition work, prepare to load the next bulky waste, and report any problems to the system.

[0323] Based on these processing steps and concrete examples, the system achieves efficient and accurate sorting and dismantling of bulky waste. It also enables flexible responses according to the user's emotions, which is expected to improve the user experience.

[0324] Prompt Sentence Examples

[0325] Below are some example prompts to input to the generative AI model:

[0326] "Please describe a system that efficiently and accurately separates and dismantles old televisions, which are bulky waste. The system uses a scanning device to scan the television, an AI image analysis algorithm to identify each part, and a robot performs the dismantling. It also has an emotion engine that can recognize the user's emotions."

[0327] This prompt allows the generative AI model to generate a detailed description of the system.

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

[0329] Step 1:

[0330] Terminal: Scanning of bulky waste

[0331] Input: A user places an old television, a piece of bulky junk, into the scanning device and begins operation.

[0332] How it works: The device fixes the TV set by the user and captures the entire screen with a high-definition camera.

[0333] Data processing and calculation: Multiple captured images are integrated to generate a single high-resolution image data.

[0334] Output: Send high resolution image data to the server.

[0335] Step 2:

[0336] Server: Receiving and analyzing image data

[0337] Input: High-resolution image data sent from the device.

[0338] Operation: The server receives the image data and runs an AI image analysis algorithm.

[0339] Data processing and calculation: Using an AI image analysis algorithm (e.g., TensorFlow), image data is analyzed and each part of the TV (screen, plastic case, electronic components, etc.) is identified.

[0340] Output: The identification results for each part and the classification of these parts as recyclable or non-recyclable.

[0341] Step 3:

[0342] Server: Generate dismantling instructions

[0343] Input: Image analysis results and classification data.

[0344] Operation: Based on the identification results, the server generates dismantling instructions for the dismantling robot.

[0345] Data processing and calculation: Determine the order and method of disassembly of parts and generate specific instruction data.

[0346] Output: Sends dismantling instruction data to the dismantling robot.

[0347] Step 4:

[0348] Demolition robots: Performing demolition tasks

[0349] Input: Dismantling instruction data sent from the server.

[0350] Behavior: The demolition robot follows instructions and begins the physical dismantling process, carefully removing the television screen, then removing the screws securing the plastic casing.

[0351] Data processing and calculation: Accurate positioning and operation are performed while feeding back information from sensors in real time.

[0352] Output: Disassembled parts.

[0353] Step 5:

[0354] Demolition robot: Separating recyclable and non-recyclable waste

[0355] Input: Dismantling instruction data and dismantled parts.

[0356] How it works: The dismantling robot separates dismantled parts into recyclable and non-recyclable waste and collects them in the appropriate containers. For example, it places the removed screen in a recyclable container and the plastic casing in a non-recyclable container.

[0357] Data processing and calculation: The progress of the sorting work is recorded and sent to the server.

[0358] Output: Sorted recyclable and non-recyclable waste.

[0359] Step 6:

[0360] Emotion Engine: Detecting and responding to user emotions

[0361] Input: User's voice input.

[0362] How it works: The emotion engine analyzes voice data in real time to recognize the user's emotions, for example, when someone expresses anxiety.

[0363] Data processing and calculation: Using natural language processing (NLP) technology, emotions are extracted from voice and appropriate responses are generated.

[0364] Output: Display detailed operation guide in the user interface.

[0365] Step 7:

[0366] Server: User feedback

[0367] Input: Demolition progress and user emotion data.

[0368] How it works: The server constantly monitors the progress of the demolition process and provides feedback to the user as needed.

[0369] Data processing and calculation: Analyzes data from the demolition process and generates messages to report the situation at the appropriate time.

[0370] Output: Display the message "Demolition work is progressing smoothly" to the user.

[0371] Step 8:

[0372] User: Check the disassembly results and input the next item

[0373] Input: Results of demolition work and feedback from the server.

[0374] Actions: The user checks the results of the demolition and prepares to load the next bulky waste. If necessary, the user reports the problem to the system.

[0375] Data processing and calculation: Enter new bulky waste information into the system.

[0376] Output: The next bulky item is ready to be scanned.

[0377] (Application example 2)

[0378] 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."

[0379] Conventional bulky waste sorting and dismantling systems required a lot of manual work, which was inefficient and resulted in long operation times. There was also a lack of support for users who were unsure about operation, which hindered the user experience. Furthermore, proper classification and dismantling of each part of bulky waste was prone to human error.

[0380] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and an action adjustment means. This enables efficient and accurate sorting and dismantling of bulky waste and operational support according to the user's emotions.

[0381] The "image acquisition means" is a device that scans the bulky waste and acquires high-resolution image data thereof.

[0382] The "image analysis means" is a device that analyzes the image data obtained from the image acquisition means, identifies each part of the bulky waste using an AI image analysis algorithm, and classifies it into recyclable waste and non-recyclable waste.

[0383] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the image analysis means.

[0384] The "emotion recognition means" is a device that analyzes the user's voice input and recognizes the user's emotions.

[0385] The "operation adjustment means" is a device for adjusting the operation of the system based on the emotion data obtained by the emotion recognition means, thereby improving the user experience.

[0386] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste in factories. Specifically, it is composed of an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and a behavior adjustment means.

[0387] System Configuration

[0388] 1. Image acquisition method

[0389] The image capture unit scans the bulky waste and generates high-resolution image data. This image data is sent to a server for subsequent analysis. For example, an old television is placed on the scanner, and the image capture unit scans the entire television.

[0390] 2. Image analysis methods

[0391] The server receives the high-resolution image data sent from the image capture means. The server then uses an AI image analysis algorithm to analyze the captured image data and identify each piece of bulky waste. For example, a TV screen, plastic case, or electronic components may be identified. The server then classifies each piece into recyclable or non-recyclable waste.

[0392] 3. Disassembly means

[0393] The server then generates specific disassembly instructions for the disassembly unit, detailing which parts to disassemble, in what order, and how. The instructions are sent to the disassembly unit, which then begins the disassembly process. For example, in the case of a television, the screen would first be carefully removed, followed by the screws securing the plastic case, opening the case, and removing the internal electronic components.

[0394] 4. Emotion recognition means

[0395] When the user places their bulky waste in the trash bin, the emotion recognition unit analyzes the user's voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion recognition unit will detect that emotion and make adjustments such as displaying more detailed operation guides.

[0396] 5. Operation adjustment means

[0397] The system adjusts its behavior based on the emotion data obtained from the emotion recognition means. For example, if the user is feeling stressed, the behavior adjustment means may notify the user of the progress of the process more frequently, thereby improving the user experience.

[0398] Hardware and Software Used

[0399] Hardware: Scanning devices, demolition robots, smartphones

[0400] Software: Python, requests library, PIL (Python Imaging Library)

[0401] Specific examples

[0402] For example, during the dismantling process of an old TV, the dismantling tool monitors the progress status in real time, and a notification such as "Removing the screen" is displayed on the smartphone app. If the user becomes anxious and gives voice commands, detailed operating instructions are displayed, supporting the user to move on to the next step with confidence.

[0403] Prompt Sentence Examples

[0404] Example prompts to input to the AI ​​model:

[0405] "Please explain how to display the current progress of a task to the user based on the results of emotion recognition from speech input. Also, please explain the implementation of providing feedback to the user based on their emotions."

[0406] As described above, this invention enables efficient and accurate sorting and dismantling of bulky waste, and also allows for flexible responses according to the user's feelings.

[0407] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0408] Step 1:

[0409] The terminal places a bulky item (e.g., an old TV) on the scanning device. At this time, the user starts operating the scanning device. As a result, high-resolution image data is generated and sent from the scanning device to the server.

[0410] Step 2:

[0411] The server receives the high-resolution image data sent from the scanning device, and based on this data, the server uses AI image analysis algorithms to analyze the image and identify each piece of bulky waste (e.g., TV screen, plastic case, electronic components, etc.).

[0412] Step 3:

[0413] Based on the results of the image analysis, the server classifies each identified part into recyclable waste and non-recyclable waste. Specifically, it analyzes the characteristics of the image data, determines whether each identified part can be reused as recyclable waste, and assigns an appropriate tag to each.

[0414] Step 4:

[0415] The server generates specific disassembly instructions for the disassembly means, including the order in which each identified part should be disassembled and how the part should be disassembled. These disassembly instructions include the motion pattern of the robot arm and the selection of tools.

[0416] Step 5:

[0417] The dismantling means begins the actual dismantling work based on the dismantling instructions received from the server. For example, in the case of a television, the robotic arm first carefully removes the screen, then removes the screws securing the plastic case, opens the case, and removes the internal electronic components.

[0418] Step 6:

[0419] The dismantling means collects the dismantled recyclable waste in a specific collection means and separates the non-recyclable waste into a separate container, completing the separation and collecting the recyclable resources.

[0420] Step 7:

[0421] An emotion recognition means receives a user's voice input and analyzes the emotion. For example, if the user feels anxious, the voice data is analyzed with an emotion recognition algorithm to identify the specific emotion (e.g., anxiety or stress).

[0422] Step 8:

[0423] The operation adjustment means adjusts the operation of the system based on the emotion data obtained by the emotion recognition means. For example, if the user is feeling stressed, the operation adjustment means may notify the user of the progress of processing more frequently or provide detailed operation guides, thereby improving the user experience.

[0424] Step 9:

[0425] The server monitors the overall progress and provides feedback to the user, such as the current dismantling progress and the next steps to be taken, and displays operation guidance as needed.

[0426] Based on the above steps, the system will be able to achieve efficient and accurate sorting and dismantling of bulky waste, and will also be able to respond flexibly to the user's emotions.

[0427] 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.

[0428] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0429] 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.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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."

[0443] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then uses a robot to physically dismantle the waste based on the classification results.

[0444] System Configuration

[0445] This system consists of a terminal that handles bulky waste, a server that analyzes image data, and a demolition robot that physically dismantles the waste. Each component works together via a network to carry out the process.

[0446] Scanning Method

[0447] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[0448] Image analysis methods

[0449] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[0450] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[0451] Demolition means

[0452] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[0453] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[0454] Storage means

[0455] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0456] Specific examples

[0457] 1. Device: Place an old TV, which is bulky trash, into the scanning device.

[0458] 2. User: Initiates operation of the scanning device.

[0459] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[0460] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[0461] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[0462] 6. Server: Sends dismantling instructions to the dismantling robot.

[0463] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[0464] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[0465] 9. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[0466] 10. User: Check the results of the demolition work and input the next bulky waste into the system.

[0467] As described above, this system can efficiently and accurately separate and dismantle bulky waste, thereby contributing to resource reuse and environmental conservation.

[0468] The processing flow will be explained below.

[0469] Step 1:

[0470] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[0471] Step 2:

[0472] The user places bulky trash, such as an old television, in a designated position on the scanner.

[0473] Step 3:

[0474] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[0475] Step 4:

[0476] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[0477] Step 5:

[0478] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[0479] Step 6:

[0480] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[0481] Step 7:

[0482] Based on the analysis results, the server generates disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[0483] Step 8:

[0484] The server transmits the generated dismantling instructions to the dismantling robot.

[0485] Step 9:

[0486] The dismantling robot follows the instructions received from the server and begins the dismantling process. In the case of a television, the robot's arms are first moved to remove the screen.

[0487] Step 10:

[0488] After removing the screen, the dismantling robot then disassembles the plastic case, removing the securing screws and opening the case.

[0489] Step 11:

[0490] The dismantling robot removes the electronic components from inside the case and separates them into recyclable and non-recyclable waste.

[0491] Step 12:

[0492] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0493] Step 13:

[0494] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as needed, and provides feedback to the user.

[0495] Step 14:

[0496] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and, if necessary, reports any problems to the system.

[0497] In this way, each step works in tandem to ensure efficient and accurate sorting and dismantling of bulky waste.

[0498] Example 1

[0499] 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."

[0500] Currently, the separation and dismantling of bulky waste requires a lot of manual labor, is inefficient, and accurate separation is difficult. In particular, the difficulty in accurately separating each part of bulky waste into recyclable and non-recyclable waste reduces the rate of resource reuse and increases the burden on the environment.

[0501] 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.

[0502] In this invention, the server includes image acquisition means for scanning bulky waste, analysis means for analyzing image data obtained from the image acquisition means to identify parts of the bulky waste, classification means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification means, and instruction means for the dismantling means to generate dismantling instructions and send them to the robot. This enables efficient and accurate sorting and dismantling of bulky waste, contributing to resource reuse and environmental conservation.

[0503] The "image acquisition means" is a means for acquiring high-resolution images of the bulky waste using a scanning device.

[0504] The "analysis means" is a means for analyzing the acquired image data and identifying each part of the bulky waste.

[0505] The "classification means" is a means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste.

[0506] "Dismantling means" refers to a means for physically dismantling bulky waste based on the classification means.

[0507] The "instruction means" is a means by which the dismantling means generates dismantling instructions and transmits the instructions to the robot.

[0508] An "artificial intelligence image analysis algorithm" is an algorithm that uses computer vision technology to automatically recognize and classify objects in image data.

[0509] The "storage means" is a means for collecting recyclable waste dismantled by the dismantling means and storing it in a specific container.

[0510] MODE FOR CARRYING OUT THE INVENTION

[0511] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. Specifically, it scans the bulky waste, uses AI image analysis algorithms to identify and classify each part, and then uses robots to physically dismantle the waste based on the classification results.

[0512] System Configuration

[0513] The system consists of the following major hardware and software components:

[0514] 1. Image acquisition means: A device for scanning high-resolution images of bulky waste, such as a high-resolution camera scanner.

[0515] 2. Analysis method: A server that analyzes the scanned image data and processes it to identify each part of the bulky waste. Specifically, it implements artificial intelligence image analysis algorithms such as TensorFlow and OpenCV.

[0516] 3. Classification means: A server that performs processing to classify each part into recyclable waste and non-recyclable waste based on the analyzed data.

[0517] 4. Dismantling means: A dismantling robot that physically dismantles the bulky waste based on the classification results. The dismantling robot includes servo motors and arms.

[0518] 5. Instruction means: A server for generating dismantling instructions and sending them to the dismantling robot.

[0519] 6. Storage means: A device for collecting disassembled recyclables, including containers for collecting each part.

[0520] System Operation

[0521] The process for disposing of bulky waste is as follows:

[0522] 1. The terminal places the bulky waste item into the scanning device. The user places the bulky waste item, such as an old TV, into the device.

[0523] 2. The user begins operating the scanning device. The process begins by pressing the start button on the scanning device.

[0524] 3. The device scans the bulky waste at high resolution and obtains image data.

[0525] 4. The device sends the acquired image data to the server.

[0526] 5. The server receives and stores the image data from the scanning device.

[0527] 6. The server uses AI image analysis algorithms to analyze the image data, identifying the TV screen, case, electronic components, etc.

[0528] 7. The server sorts each part into recyclable and non-recyclable waste. Identified screens and electronic components are identified as recyclable waste, while plastic casings are identified as non-recyclable waste.

[0529] 8. The server generates specific dismantling instructions for the demolition robot and sends them to the robot via the network.

[0530] 9. The demolition robot follows the instructions received from the server and begins the physical dismantling process: its arms move and carefully remove the TV screen, then unscrew the casing.

[0531] 10. The demolition robot stores the dismantled recyclable waste in a container on the storage means, and sorts the non-recyclable waste into a separate container.

[0532] 11. The server monitors the progress of the demolition and provides feedback to the user as needed. The user then loads the next bulky waste item and repeats the process.

[0533] Specific examples

[0534] Here is an example of a prompt for this system:

[0535] Prompt statement:

[0536] An old TV is placed in the scanning device. Once the scan is complete, the image is sent to the server. The server analyzes the image and identifies and classifies the TV's screen, case, and electronic components. Next, dismantling instructions are sent to a dismantling robot, which dismantles the TV according to the instructions. Finally, the waste is separated into recyclable and non-recyclable waste and stored in designated locations.

[0537] This system enables the efficient and accurate separation and dismantling of bulky waste, contributing to the reuse of resources and environmental conservation.

[0538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0539] Step 1:

[0540] The user places the bulky waste in the scanning device.

[0541] What happens: The user places the old TV on the scanning device's stand.

[0542] Input: Bulky waste (e.g. old TV)

[0543] Output: Bulky waste placed in the scanning device

[0544] Step 2:

[0545] The user begins operating the scanning device.

[0546] Specific operation: The user presses the start button on the scanning device.

[0547] Input: Pressing the start button on the scanning device

[0548] Output: Start scanning device

[0549] Step 3:

[0550] The terminal scans the bulky waste at high resolution and captures image data.

[0551] Specific operation: The scanning device's camera scans the entire television and generates image data.

[0552] Input: Bulky waste (set in the scanning device)

[0553] Output: High-resolution image data

[0554] Step 4:

[0555] The terminal transmits the acquired image data to the server.

[0556] Specific operation: The terminal uploads image data to the server via the network.

[0557] Input: High-resolution image data

[0558] Output: Image data sent to the server

[0559] Step 5:

[0560] A server receives and stores image data from the scanning device.

[0561] Specific operation: The server saves the image data in local storage.

[0562] Input: Image data sent from a scanning device

[0563] Output: Saved image data

[0564] Step 6:

[0565] The server analyzes the image data using an AI image analysis algorithm.

[0566] Specific operation: The server uses the TensorFlow library to identify objects in the image data.

[0567] Input: Saved image data

[0568] Output: Identified bulky waste pieces (e.g. screen, case, electronic parts)

[0569] Step 7:

[0570] The server sorts each part into recyclable and non-recyclable.

[0571] Specific operation: The server analyzes the identified parts and classifies each into recyclable and non-recyclable waste.

[0572] Input: Each identified piece of bulky waste

[0573] Output: Classified waste types (recyclable waste, non-recyclable waste)

[0574] Step 8:

[0575] The server generates and transmits specific dismantling instructions to the dismantling robot.

[0576] Specific operation: The server generates an instruction set detailing the dismantling procedure and sends it to the robot via the network.

[0577] Input: Classified garbage type and its location information

[0578] Output: Dismantling instructions to the dismantling robot

[0579] Step 9:

[0580] The demolition robot follows the instructions received from the server and begins the physical demolition work.

[0581] What it does: The robot's arm moves and carefully removes the TV screen, then unscrews the casing to remove the electronic components inside.

[0582] Input: Dismantlement instructions sent from the server

[0583] Output: Dismantled bulky waste

[0584] Step 10:

[0585] The dismantling robot stores the dismantled recyclable waste in a container of the storage means, and sorts the non-recyclable waste into another container.

[0586] Specific operations: The robot places the removed screens and electronic components in designated containers, and moves non-recyclable waste such as cases to a disposal container.

[0587] Input: Dismantled bulky waste parts

[0588] Output: Sorted storage (recyclable and non-recyclable containers)

[0589] Step 11:

[0590] The server monitors the progress of the demolition work and provides feedback to the user as needed.

[0591] Specific operation: The server receives feedback data from the robot in real time and reports the situation to the user by email or UI notification.

[0592] Input: Feedback data from the robot

[0593] Output: Feedback notification to the user

[0594] (Application example 1)

[0595] 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."

[0596] Conventional bulky waste sorting and dismantling systems often lack efficiency and accuracy, particularly due to the lack of real-time monitoring and feedback functions, making it difficult to properly manage the progress of dismantling work. Another issue is the efficiency of manual intervention when dismantling procedures are not performed properly. The present invention aims to solve these issues and provide a more efficient and accurate bulky waste sorting and dismantling system.

[0597] 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.

[0598] In this invention, the server includes scanning means for scanning the bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, monitoring means for monitoring the progress of the dismantling means and manually operating it as necessary, and display means for displaying feedback information from the dismantling means in real time. This allows the progress of the dismantling work to be properly managed, enabling efficient and accurate dismantling and sorting work.

[0599] The "scanning means" is a device that acquires high-resolution image data of bulky waste.

[0600] The "analysis means" is a system that uses image data obtained from the scanning means to identify each part of bulky waste using an AI image analysis algorithm and classify it into recyclable and non-recyclable waste.

[0601] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the analysis means.

[0602] The "monitoring means" is a system or device for monitoring the progress of the dismantling means in real time and for performing manual operations as necessary.

[0603] The "display means" is a device or system that displays feedback information from the demolition means in real time, and visually provides workers with the progress and problems of the demolition work.

[0604] This invention is a system for efficiently and accurately separating and dismantling bulky waste on a factory recycling line. The system scans the bulky waste and uses an AI image analysis algorithm to identify and classify each part. Based on the classification results, a dismantling robot physically dismantles the waste. The system also includes a monitoring means for monitoring the progress of the dismantling process and manually operating it as needed, as well as a display means for displaying feedback information in real time.

[0605] System Configuration

[0606] The structure of this system is as follows:

[0607] Hardware and Software

[0608] Scanning method: A device that captures high-resolution image data of bulky waste (e.g., a high-resolution camera).

[0609] Analysis method: A server that uses TensorFlow and OpenCV to analyze image data and identify each part of bulky waste.

[0610] Dismantling method: A dismantling robot that runs on ROS (Robot Operating System).

[0611] Monitoring method: A system (e.g., smartphone app) to monitor the demolition progress in real time.

[0612] Display means: A device (e.g., a smartphone or tablet) that displays feedback information from the dismantling means.

[0613] Operation overview

[0614] 1. Scanning of bulky waste

[0615] When a user places an item of bulky waste on the scanning device, the terminal scans the item at high resolution and sends the image data to the server.

[0616] 2. Image Analysis

[0617] The server analyzes the image data received from the scanning device and uses AI image analysis algorithms to identify each part of the bulky waste. For example, in the case of an old TV, the screen, case, electronic components, etc. are identified. The analysis results classify each part as recyclable or non-recyclable waste.

[0618] 3. Generating Disassembly Instructions

[0619] The server generates disassembly instructions based on the analysis and sends them to the disassembly robot. For example, in the case of a television, instructions would include first removing the screen, then removing the screws securing the case.

[0620] 4. Monitoring and feedback on demolition progress

[0621] The progress of the dismantling is monitored in real time by the monitoring means, which displays the progress of the dismantling on the display means, and allows manual operation as required.

[0622] Specific examples

[0623] Scanning by device: An old television, which is bulky waste, is placed in a scanning device, and image data of the television is generated and sent to the server.

[0624] AI image analysis: The server receives the image data and uses TensorFlow and OpenCV to identify each part of the TV (screen, case, electronic components, etc.).

[0625] Disassembly instructions: The server generates disassembly instructions and sends them to the disassembly robot. Following the instructions, the robot carefully removes the screen, then removes the screws securing the plastic case, opens it, and removes the internal electronic components.

[0626] Progress and feedback: Monitor demolition progress in real time and view feedback information via a smartphone app.

[0627] Prompt Sentence Examples

[0628] "Dismantling of the old refrigerator has begun. It is recommended that the refrigerator door and shelves be removed first."

[0629] This allows dismantling work on the factory's recycling line to proceed efficiently and accurately, and even when manual operation is required, it can be handled quickly.

[0630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0631] Step 1:

[0632] The user places the bulky waste into the scanning device. The scanning device scans the bulky waste at high resolution and generates image data. The generated image data is sent to the server via the network. The input of this step is the bulky waste, and the output is the scanned high-resolution image data.

[0633] Step 2:

[0634] The server analyzes the image data received from the scanning device. Specifically, it processes the image data using TensorFlow and OpenCV to identify each part of the bulky waste. For example, in the case of a television, the screen, case, electronic components, etc. are identified. The input is the image data, and the output is information about each identified part.

[0635] Step 3:

[0636] The server classifies each part into recyclable and non-recyclable waste based on the analysis results. Based on the classification results of the AI ​​image analysis algorithm, for example, screens and electronic components are classified as recyclable waste, and cases are classified as non-recyclable waste. The input is the information of each identified part, and the output is a list of sorted recyclable and non-recyclable waste.

[0637] Step 4:

[0638] The server generates specific disassembly instructions and sends them to the disassembly robot. For example, instructions include steps such as first removing the screen and then removing the fixing screws of the case. The input is a list of sorted recyclable and non-recyclable waste, and the output is the disassembly instructions.

[0639] Step 5:

[0640] The dismantling robot follows instructions received from the server and begins the physical dismantling process. The robot's arm moves, first carefully removing the screen, then unscrewing the plastic case to extract the electronic components inside. The input is the dismantling instructions, and the output is the dismantled bulky waste.

[0641] Step 6:

[0642] The terminal monitors the progress of the demolition and receives feedback in real time. It displays the feedback information sent from the demolition robot and provides the user with information about the progress and problems of the demolition work. The input is feedback information from the demolition robot, and the output is a real-time display based on that information.

[0643] Step 7:

[0644] The user monitors the progress and takes manual action if necessary. For example, manual intervention is required if the demolition robot encounters an unexpected obstacle. The input is real-time display information, and the output is manual operation instructions.

[0645] 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.

[0646] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, identifies and classifies each part using an AI image analysis algorithm, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, it aims to improve the user experience and increase the efficiency of the system.

[0647] System Configuration

[0648] This system is composed of a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[0649] Scanning Method

[0650] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[0651] Image analysis methods

[0652] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[0653] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[0654] Demolition means

[0655] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[0656] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[0657] Storage means

[0658] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0659] Emotion Engine

[0660] When a user places their bulky waste in the trash, the emotion engine analyzes their voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion engine will detect that emotion and make adjustments such as displaying more detailed system operation instructions.

[0661] The emotion engine has the ability to adjust the system's behavior based on the user's emotion recognition results. For example, if the user is feeling stressed, the system will notify the user of the progress of the process more frequently, improving the user experience.

[0662] Specific examples

[0663] 1. Terminal: Place an old television, which is bulky trash, into the scanner device.

[0664] 2. User: Initiates operation of the scanning device.

[0665] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[0666] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[0667] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[0668] 6. Server: Sends dismantling instructions to the dismantling robot.

[0669] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[0670] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[0671] 9. Emotion Engine: Analyzes the user's voice input and recognizes their emotions. For example, if the user feels anxious, the system will display detailed operating instructions.

[0672] 10. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[0673] 11. User: Checks the results of the demolition work and places the next bulky waste in the scanner. If necessary, reports the problem to the system.

[0674] In this way, combining the emotion engine allows for flexible responses based on the user's emotions, making the system easier to use. This allows for efficient and accurate sorting and dismantling of bulky waste, promoting resource reuse and environmental conservation.

[0675] The processing flow will be explained below.

[0676] Step 1:

[0677] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[0678] Step 2:

[0679] The user places bulky trash, such as an old television, in a designated position on the scanner.

[0680] Step 3:

[0681] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[0682] Step 4:

[0683] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[0684] Step 5:

[0685] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[0686] Step 6:

[0687] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[0688] Step 7:

[0689] Based on the analysis results, the server generates specific disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[0690] Step 8:

[0691] The server transmits the generated dismantling instructions to the dismantling robot.

[0692] Step 9:

[0693] The dismantling robot follows the instructions received from the server and begins the dismantling process. First, it moves its arm to carefully remove the screen.

[0694] Step 10:

[0695] After removing the screen, the dismantling robot then removes the screws that secure the plastic case, opens it, removes the electronic components inside, and separates them into recyclable and non-recyclable waste.

[0696] Step 11:

[0697] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0698] Step 12:

[0699] The emotion engine analyzes voice input to recognize emotions as the user uses the system. For example, if a user is using the system for the first time and emotions such as anxiety or confusion are detected, the system will display more detailed operating instructions.

[0700] Step 13:

[0701] The emotion engine adjusts the system's behavior based on the emotion recognition results. For example, if the user is feeling stressed, the system may notify the user more frequently about the progress of the process or provide additional guidance.

[0702] Step 14:

[0703] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as necessary, and provides feedback on the progress of the demolition work to the user.

[0704] Step 15:

[0705] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and reports any problems to the system if necessary.

[0706] In this way, each step works in coordination to separate and dismantle bulky waste efficiently and accurately. By combining it with an emotion engine, it becomes possible to respond flexibly to the user's emotions, making the system easier to use.

[0707] Example 2

[0708] 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."

[0709] Current systems for separating and dismantling bulky waste rely on manual labor and partial automation, making them inefficient and difficult to accurately classify recyclable and non-recyclable waste. Furthermore, user operations tend to be complicated, resulting in a poor user experience.

[0710] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0711] In this invention, the server includes scanning means for scanning bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, sorting means for sorting the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste, and emotion recognition means for recognizing user emotions and adjusting the operation of the system. This enables efficient and accurate sorting and dismantling of bulky waste, and makes user operation simple and intuitive, resulting in an improved user experience.

[0712] "Scanning means" refers to a device and method for scanning bulky waste at high resolution and acquiring image data.

[0713] "Analysis means" refers to software and algorithms that analyze the image data obtained from the scanning means and identify and classify each piece of bulky waste.

[0714] The "dismantling means" refers to mechanical devices and systems for physically dismantling bulky waste based on the classification results obtained by the analysis means.

[0715] The "separation means" refers to an apparatus and method for separating the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste.

[0716] "Emotion recognition means" refers to software and algorithms that recognize a user's emotions through voice input or the like and adjust the system's operation based on that.

[0717] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. This system scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, the system aims to improve the user experience and increase the efficiency of the system.

[0718] The system mainly consists of the following components: a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[0719] Scanning Method

[0720] The terminal includes a scanning device for scanning bulky items at high resolution. For example, when a user places an old television on the scanning device, the terminal fixes it in the correct position and captures the entire image with a high-resolution camera. The captured image data is then sent to a server.

[0721] Image analysis methods

[0722] The server receives the image data sent from the device. To analyze this image data, the server runs an AI image analysis algorithm (e.g., TensorFlow). This algorithm analyzes the image and identifies each part of the TV (screen, plastic case, electronic components, etc.). The analysis results include information to identify each part as recyclable or non-recyclable.

[0723] Demolition means

[0724] Based on the analysis, the server generates dismantling instructions for the robot. These instructions detail which parts to dismantle, in what order, and how. For example, they might include carefully removing the TV screen first, then unscrewing the plastic casing. To send these instructions to the robot, the server uses a control protocol.

[0725] Dismantling and sorting procedures

[0726] The dismantling robot follows instructions received from the server and begins the physical dismantling process. For example, the robot's arm carefully removes the screen, then removes the screws securing the plastic case. The dismantled recyclables are collected in a storage facility, while non-recyclables are sorted into separate containers.

[0727] emotion recognition means

[0728] When a user places their bulky waste in the trash can, the emotion engine analyzes their voice input and recognizes their emotions. For example, if a user expresses anxiety by saying, "Will I be able to do this successfully?", the emotion engine will detect this anxiety and display more detailed operating instructions for the scanning device. The emotion engine uses natural language processing (NLP) technology to analyze voice data in real time.

[0729] Feedback and User Interaction

[0730] The server constantly monitors the progress of the demolition work and provides feedback to the user as needed. For example, it displays a message such as "The demolition work is progressing smoothly." The user can check the results of the demolition work, prepare to load the next bulky waste, and report any problems to the system.

[0731] Based on these processing steps and concrete examples, the system achieves efficient and accurate sorting and dismantling of bulky waste. It also enables flexible responses according to the user's emotions, which is expected to improve the user experience.

[0732] Prompt Sentence Examples

[0733] Below are some example prompts to input to the generative AI model:

[0734] "Please describe a system that efficiently and accurately separates and dismantles old televisions, which are bulky waste. The system uses a scanning device to scan the television, an AI image analysis algorithm to identify each part, and a robot performs the dismantling. It also has an emotion engine that can recognize the user's emotions."

[0735] This prompt allows the generative AI model to generate a detailed description of the system.

[0736] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0737] Step 1:

[0738] Terminal: Scanning of bulky waste

[0739] Input: A user places an old television, a piece of bulky junk, into the scanning device and begins operation.

[0740] How it works: The device fixes the TV set by the user and captures the entire screen with a high-definition camera.

[0741] Data processing and calculation: Multiple captured images are integrated to generate a single high-resolution image data.

[0742] Output: Send high resolution image data to the server.

[0743] Step 2:

[0744] Server: Receiving and analyzing image data

[0745] Input: High-resolution image data sent from the device.

[0746] Operation: The server receives the image data and runs an AI image analysis algorithm.

[0747] Data processing and calculation: Using an AI image analysis algorithm (e.g., TensorFlow), image data is analyzed and each part of the TV (screen, plastic case, electronic components, etc.) is identified.

[0748] Output: The identification results for each part and the classification of these parts as recyclable or non-recyclable.

[0749] Step 3:

[0750] Server: Generate dismantling instructions

[0751] Input: Image analysis results and classification data.

[0752] Operation: Based on the identification results, the server generates dismantling instructions for the dismantling robot.

[0753] Data processing and calculation: Determine the order and method of disassembly of parts and generate specific instruction data.

[0754] Output: Sends dismantling instruction data to the dismantling robot.

[0755] Step 4:

[0756] Demolition robots: Performing demolition tasks

[0757] Input: Dismantling instruction data sent from the server.

[0758] Behavior: The demolition robot follows instructions and begins the physical dismantling process, carefully removing the television screen, then removing the screws securing the plastic casing.

[0759] Data processing and calculation: Accurate positioning and operation are performed while feeding back information from sensors in real time.

[0760] Output: Disassembled parts.

[0761] Step 5:

[0762] Demolition robot: Separating recyclable and non-recyclable waste

[0763] Input: Dismantling instruction data and dismantled parts.

[0764] How it works: The dismantling robot separates dismantled parts into recyclable and non-recyclable waste and collects them in the appropriate containers. For example, it places the removed screen in a recyclable container and the plastic casing in a non-recyclable container.

[0765] Data processing and calculation: The progress of the sorting work is recorded and sent to the server.

[0766] Output: Sorted recyclable and non-recyclable waste.

[0767] Step 6:

[0768] Emotion Engine: Detecting and responding to user emotions

[0769] Input: User's voice input.

[0770] How it works: The emotion engine analyzes voice data in real time to recognize the user's emotions, for example, when someone expresses anxiety.

[0771] Data processing and calculation: Using natural language processing (NLP) technology, emotions are extracted from voice and appropriate responses are generated.

[0772] Output: Display detailed operation guide in the user interface.

[0773] Step 7:

[0774] Server: User feedback

[0775] Input: Demolition progress and user emotion data.

[0776] How it works: The server constantly monitors the progress of the demolition process and provides feedback to the user as needed.

[0777] Data processing and calculation: Analyzes data from the demolition process and generates messages to report the situation at the appropriate time.

[0778] Output: Display the message "Demolition work is progressing smoothly" to the user.

[0779] Step 8:

[0780] User: Check the disassembly results and input the next item

[0781] Input: Results of demolition work and feedback from the server.

[0782] Actions: The user checks the results of the demolition and prepares to load the next bulky waste. If necessary, the user reports the problem to the system.

[0783] Data processing and calculation: Enter new bulky waste information into the system.

[0784] Output: The next bulky item is ready to be scanned.

[0785] (Application example 2)

[0786] 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."

[0787] Conventional bulky waste sorting and dismantling systems required a lot of manual work, which was inefficient and resulted in long operation times. There was also a lack of support for users who were unsure about operation, which hindered the user experience. Furthermore, proper classification and dismantling of each part of bulky waste was prone to human error.

[0788] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and an action adjustment means. This enables efficient and accurate sorting and dismantling of bulky waste and operational support according to the user's emotions.

[0789] The "image acquisition means" is a device that scans the bulky waste and acquires high-resolution image data thereof.

[0790] The "image analysis means" is a device that analyzes the image data obtained from the image acquisition means, identifies each part of the bulky waste using an AI image analysis algorithm, and classifies it into recyclable waste and non-recyclable waste.

[0791] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the image analysis means.

[0792] The "emotion recognition means" is a device that analyzes the user's voice input and recognizes the user's emotions.

[0793] The "operation adjustment means" is a device for adjusting the operation of the system based on the emotion data obtained by the emotion recognition means, thereby improving the user experience.

[0794] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste in factories. Specifically, it is composed of an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and a behavior adjustment means.

[0795] System Configuration

[0796] 1. Image acquisition method

[0797] The image capture unit scans the bulky waste and generates high-resolution image data. This image data is sent to a server for subsequent analysis. For example, an old television is placed on the scanner, and the image capture unit scans the entire television.

[0798] 2. Image analysis methods

[0799] The server receives the high-resolution image data sent from the image capture means. The server then uses an AI image analysis algorithm to analyze the captured image data and identify each piece of bulky waste. For example, a TV screen, plastic case, or electronic components may be identified. The server then classifies each piece into recyclable or non-recyclable waste.

[0800] 3. Disassembly means

[0801] The server then generates specific disassembly instructions for the disassembly unit, detailing which parts to disassemble, in what order, and how. The instructions are sent to the disassembly unit, which then begins the disassembly process. For example, in the case of a television, the screen would first be carefully removed, followed by the screws securing the plastic case, opening the case, and removing the internal electronic components.

[0802] 4. Emotion recognition means

[0803] When the user places their bulky waste in the trash bin, the emotion recognition unit analyzes the user's voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion recognition unit will detect that emotion and make adjustments such as displaying more detailed operation guides.

[0804] 5. Operation adjustment means

[0805] The system adjusts its behavior based on the emotion data obtained from the emotion recognition means. For example, if the user is feeling stressed, the behavior adjustment means may notify the user of the progress of the process more frequently, thereby improving the user experience.

[0806] Hardware and Software Used

[0807] Hardware: Scanning devices, demolition robots, smartphones

[0808] Software: Python, requests library, PIL (Python Imaging Library)

[0809] Specific examples

[0810] For example, during the dismantling process of an old TV, the dismantling tool monitors the progress status in real time, and a notification such as "Removing the screen" is displayed on the smartphone app. If the user becomes anxious and gives voice commands, detailed operating instructions are displayed, supporting the user to move on to the next step with confidence.

[0811] Prompt Sentence Examples

[0812] Example prompts to input to the AI ​​model:

[0813] "Please explain how to display the current progress of a task to the user based on the results of emotion recognition from speech input. Also, please explain the implementation of providing feedback to the user based on their emotions."

[0814] As described above, this invention enables efficient and accurate sorting and dismantling of bulky waste, and also allows for flexible responses according to the user's feelings.

[0815] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0816] Step 1:

[0817] The terminal places a bulky item (e.g., an old TV) on the scanning device. At this time, the user starts operating the scanning device. As a result, high-resolution image data is generated and sent from the scanning device to the server.

[0818] Step 2:

[0819] The server receives the high-resolution image data sent from the scanning device, and based on this data, the server uses AI image analysis algorithms to analyze the image and identify each piece of bulky waste (e.g., TV screen, plastic case, electronic components, etc.).

[0820] Step 3:

[0821] Based on the results of the image analysis, the server classifies each identified part into recyclable waste and non-recyclable waste. Specifically, it analyzes the characteristics of the image data, determines whether each identified part can be reused as recyclable waste, and assigns an appropriate tag to each.

[0822] Step 4:

[0823] The server generates specific disassembly instructions for the disassembly means, including the order in which each identified part should be disassembled and how the part should be disassembled. These disassembly instructions include the motion pattern of the robot arm and the selection of tools.

[0824] Step 5:

[0825] The dismantling means begins the actual dismantling work based on the dismantling instructions received from the server. For example, in the case of a television, the robotic arm first carefully removes the screen, then removes the screws securing the plastic case, opens the case, and removes the internal electronic components.

[0826] Step 6:

[0827] The dismantling means collects the dismantled recyclable waste in a specific collection means and separates the non-recyclable waste into a separate container, completing the separation and collecting the recyclable resources.

[0828] Step 7:

[0829] An emotion recognition means receives a user's voice input and analyzes the emotion. For example, if the user feels anxious, the voice data is analyzed with an emotion recognition algorithm to identify the specific emotion (e.g., anxiety or stress).

[0830] Step 8:

[0831] The operation adjustment means adjusts the operation of the system based on the emotion data obtained by the emotion recognition means. For example, if the user is feeling stressed, the operation adjustment means may notify the user of the progress of processing more frequently or provide detailed operation guides, thereby improving the user experience.

[0832] Step 9:

[0833] The server monitors the overall progress and provides feedback to the user, such as the current dismantling progress and the next steps to be taken, and displays operation guidance as needed.

[0834] Based on the above steps, the system will be able to achieve efficient and accurate sorting and dismantling of bulky waste, and will also be able to respond flexibly to the user's emotions.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] [Third embodiment]

[0839] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0840] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0841] 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).

[0842] 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.

[0843] 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.

[0844] 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).

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then uses a robot to physically dismantle the waste based on the classification results.

[0852] System Configuration

[0853] This system consists of a terminal that handles bulky waste, a server that analyzes image data, and a demolition robot that physically dismantles the waste. Each component works together via a network to carry out the process.

[0854] Scanning Method

[0855] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[0856] Image analysis methods

[0857] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[0858] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[0859] Demolition means

[0860] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[0861] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[0862] Storage means

[0863] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0864] Specific examples

[0865] 1. Device: Place an old TV, which is bulky trash, into the scanning device.

[0866] 2. User: Initiates operation of the scanning device.

[0867] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[0868] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[0869] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[0870] 6. Server: Sends dismantling instructions to the dismantling robot.

[0871] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[0872] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[0873] 9. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[0874] 10. User: Check the results of the demolition work and input the next bulky waste into the system.

[0875] As described above, this system can efficiently and accurately separate and dismantle bulky waste, thereby contributing to resource reuse and environmental conservation.

[0876] The processing flow will be explained below.

[0877] Step 1:

[0878] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[0879] Step 2:

[0880] The user places bulky trash, such as an old television, in a designated position on the scanner.

[0881] Step 3:

[0882] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[0883] Step 4:

[0884] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[0885] Step 5:

[0886] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[0887] Step 6:

[0888] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[0889] Step 7:

[0890] Based on the analysis results, the server generates disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[0891] Step 8:

[0892] The server transmits the generated dismantling instructions to the dismantling robot.

[0893] Step 9:

[0894] The dismantling robot follows the instructions received from the server and begins the dismantling process. In the case of a television, the robot's arms are first moved to remove the screen.

[0895] Step 10:

[0896] After removing the screen, the dismantling robot then disassembles the plastic case, removing the securing screws and opening the case.

[0897] Step 11:

[0898] The dismantling robot removes the electronic components from inside the case and separates them into recyclable and non-recyclable waste.

[0899] Step 12:

[0900] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[0901] Step 13:

[0902] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as needed, and provides feedback to the user.

[0903] Step 14:

[0904] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and, if necessary, reports any problems to the system.

[0905] In this way, each step works in tandem to ensure efficient and accurate sorting and dismantling of bulky waste.

[0906] Example 1

[0907] 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."

[0908] Currently, the separation and dismantling of bulky waste requires a lot of manual labor, is inefficient, and accurate separation is difficult. In particular, the difficulty in accurately separating each part of bulky waste into recyclable and non-recyclable waste reduces the rate of resource reuse and increases the burden on the environment.

[0909] 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.

[0910] In this invention, the server includes image acquisition means for scanning bulky waste, analysis means for analyzing image data obtained from the image acquisition means to identify parts of the bulky waste, classification means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification means, and instruction means for the dismantling means to generate dismantling instructions and send them to the robot. This enables efficient and accurate sorting and dismantling of bulky waste, contributing to resource reuse and environmental conservation.

[0911] The "image acquisition means" is a means for acquiring high-resolution images of the bulky waste using a scanning device.

[0912] The "analysis means" is a means for analyzing the acquired image data and identifying each part of the bulky waste.

[0913] The "classification means" is a means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste.

[0914] "Dismantling means" refers to a means for physically dismantling bulky waste based on the classification means.

[0915] The "instruction means" is a means by which the dismantling means generates dismantling instructions and transmits the instructions to the robot.

[0916] An "artificial intelligence image analysis algorithm" is an algorithm that uses computer vision technology to automatically recognize and classify objects in image data.

[0917] The "storage means" is a means for collecting recyclable waste dismantled by the dismantling means and storing it in a specific container.

[0918] MODE FOR CARRYING OUT THE INVENTION

[0919] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. Specifically, it scans the bulky waste, uses AI image analysis algorithms to identify and classify each part, and then uses robots to physically dismantle the waste based on the classification results.

[0920] System Configuration

[0921] The system consists of the following major hardware and software components:

[0922] 1. Image acquisition means: A device for scanning high-resolution images of bulky waste, such as a high-resolution camera scanner.

[0923] 2. Analysis method: A server that analyzes the scanned image data and processes it to identify each part of the bulky waste. Specifically, it implements artificial intelligence image analysis algorithms such as TensorFlow and OpenCV.

[0924] 3. Classification means: A server that performs processing to classify each part into recyclable waste and non-recyclable waste based on the analyzed data.

[0925] 4. Dismantling means: A dismantling robot that physically dismantles the bulky waste based on the classification results. The dismantling robot includes servo motors and arms.

[0926] 5. Instruction means: A server for generating dismantling instructions and sending them to the dismantling robot.

[0927] 6. Storage means: A device for collecting disassembled recyclables, including containers for collecting each part.

[0928] System Operation

[0929] The process for disposing of bulky waste is as follows:

[0930] 1. The terminal places the bulky waste item into the scanning device. The user places the bulky waste item, such as an old TV, into the device.

[0931] 2. The user begins operating the scanning device. The process begins by pressing the start button on the scanning device.

[0932] 3. The device scans the bulky waste at high resolution and obtains image data.

[0933] 4. The device sends the acquired image data to the server.

[0934] 5. The server receives and stores the image data from the scanning device.

[0935] 6. The server uses AI image analysis algorithms to analyze the image data, identifying the TV screen, case, electronic components, etc.

[0936] 7. The server sorts each part into recyclable and non-recyclable waste. Identified screens and electronic components are identified as recyclable waste, while plastic casings are identified as non-recyclable waste.

[0937] 8. The server generates specific dismantling instructions for the demolition robot and sends them to the robot via the network.

[0938] 9. The demolition robot follows the instructions received from the server and begins the physical dismantling process: its arms move and carefully remove the TV screen, then unscrew the casing.

[0939] 10. The demolition robot stores the dismantled recyclable waste in a container on the storage means, and sorts the non-recyclable waste into a separate container.

[0940] 11. The server monitors the progress of the demolition and provides feedback to the user as needed. The user then loads the next bulky waste item and repeats the process.

[0941] Specific examples

[0942] Here is an example of a prompt for this system:

[0943] Prompt statement:

[0944] An old TV is placed in the scanning device. Once the scan is complete, the image is sent to the server. The server analyzes the image and identifies and classifies the TV's screen, case, and electronic components. Next, dismantling instructions are sent to a dismantling robot, which dismantles the TV according to the instructions. Finally, the waste is separated into recyclable and non-recyclable waste and stored in designated locations.

[0945] This system enables the efficient and accurate separation and dismantling of bulky waste, contributing to the reuse of resources and environmental conservation.

[0946] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0947] Step 1:

[0948] The user places the bulky waste in the scanning device.

[0949] What happens: The user places the old TV on the scanning device's stand.

[0950] Input: Bulky waste (e.g. old TV)

[0951] Output: Bulky waste placed in the scanning device

[0952] Step 2:

[0953] The user begins operating the scanning device.

[0954] Specific operation: The user presses the start button on the scanning device.

[0955] Input: Pressing the start button on the scanning device

[0956] Output: Start scanning device

[0957] Step 3:

[0958] The terminal scans the bulky waste at high resolution and captures image data.

[0959] Specific operation: The scanning device's camera scans the entire television and generates image data.

[0960] Input: Bulky waste (set in the scanning device)

[0961] Output: High-resolution image data

[0962] Step 4:

[0963] The terminal transmits the acquired image data to the server.

[0964] Specific operation: The terminal uploads image data to the server via the network.

[0965] Input: High-resolution image data

[0966] Output: Image data sent to the server

[0967] Step 5:

[0968] A server receives and stores image data from the scanning device.

[0969] Specific operation: The server saves the image data in local storage.

[0970] Input: Image data sent from a scanning device

[0971] Output: Saved image data

[0972] Step 6:

[0973] The server analyzes the image data using an AI image analysis algorithm.

[0974] Specific operation: The server uses the TensorFlow library to identify objects in the image data.

[0975] Input: Saved image data

[0976] Output: Identified bulky waste pieces (e.g. screen, case, electronic parts)

[0977] Step 7:

[0978] The server sorts each part into recyclable and non-recyclable.

[0979] Specific operation: The server analyzes the identified parts and classifies each into recyclable and non-recyclable waste.

[0980] Input: Each identified piece of bulky waste

[0981] Output: Classified waste types (recyclable waste, non-recyclable waste)

[0982] Step 8:

[0983] The server generates and transmits specific dismantling instructions to the dismantling robot.

[0984] Specific operation: The server generates an instruction set detailing the dismantling procedure and sends it to the robot via the network.

[0985] Input: Classified garbage type and its location information

[0986] Output: Dismantling instructions to the dismantling robot

[0987] Step 9:

[0988] The demolition robot follows the instructions received from the server and begins the physical demolition work.

[0989] What it does: The robot's arm moves and carefully removes the TV screen, then unscrews the casing to remove the electronic components inside.

[0990] Input: Dismantlement instructions sent from the server

[0991] Output: Dismantled bulky waste

[0992] Step 10:

[0993] The dismantling robot stores the dismantled recyclable waste in a container of the storage means, and sorts the non-recyclable waste into another container.

[0994] Specific operations: The robot places the removed screens and electronic components in designated containers, and moves non-recyclable waste such as cases to a disposal container.

[0995] Input: Dismantled bulky waste parts

[0996] Output: Sorted storage (recyclable and non-recyclable containers)

[0997] Step 11:

[0998] The server monitors the progress of the demolition work and provides feedback to the user as needed.

[0999] Specific operation: The server receives feedback data from the robot in real time and reports the situation to the user by email or UI notification.

[1000] Input: Feedback data from the robot

[1001] Output: Feedback notification to the user

[1002] (Application example 1)

[1003] 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."

[1004] Conventional bulky waste sorting and dismantling systems often lack efficiency and accuracy, particularly due to the lack of real-time monitoring and feedback functions, making it difficult to properly manage the progress of dismantling work. Another issue is the efficiency of manual intervention when dismantling procedures are not performed properly. The present invention aims to solve these issues and provide a more efficient and accurate bulky waste sorting and dismantling system.

[1005] 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.

[1006] In this invention, the server includes scanning means for scanning the bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, monitoring means for monitoring the progress of the dismantling means and manually operating it as necessary, and display means for displaying feedback information from the dismantling means in real time. This allows the progress of the dismantling work to be properly managed, enabling efficient and accurate dismantling and sorting work.

[1007] The "scanning means" is a device that acquires high-resolution image data of bulky waste.

[1008] The "analysis means" is a system that uses image data obtained from the scanning means to identify each part of bulky waste using an AI image analysis algorithm and classify it into recyclable and non-recyclable waste.

[1009] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the analysis means.

[1010] The "monitoring means" is a system or device for monitoring the progress of the dismantling means in real time and for performing manual operations as necessary.

[1011] The "display means" is a device or system that displays feedback information from the demolition means in real time, and visually provides workers with the progress and problems of the demolition work.

[1012] This invention is a system for efficiently and accurately separating and dismantling bulky waste on a factory recycling line. The system scans the bulky waste and uses an AI image analysis algorithm to identify and classify each part. Based on the classification results, a dismantling robot physically dismantles the waste. The system also includes a monitoring means for monitoring the progress of the dismantling process and manually operating it as needed, as well as a display means for displaying feedback information in real time.

[1013] System Configuration

[1014] The structure of this system is as follows:

[1015] Hardware and Software

[1016] Scanning method: A device that captures high-resolution image data of bulky waste (e.g., a high-resolution camera).

[1017] Analysis method: A server that uses TensorFlow and OpenCV to analyze image data and identify each part of bulky waste.

[1018] Dismantling method: A dismantling robot that runs on ROS (Robot Operating System).

[1019] Monitoring method: A system (e.g., smartphone app) to monitor the demolition progress in real time.

[1020] Display means: A device (e.g., a smartphone or tablet) that displays feedback information from the dismantling means.

[1021] Operation overview

[1022] 1. Scanning of bulky waste

[1023] When a user places an item of bulky waste on the scanning device, the terminal scans the item at high resolution and sends the image data to the server.

[1024] 2. Image Analysis

[1025] The server analyzes the image data received from the scanning device and uses AI image analysis algorithms to identify each part of the bulky waste. For example, in the case of an old TV, the screen, case, electronic components, etc. are identified. The analysis results classify each part as recyclable or non-recyclable waste.

[1026] 3. Generating Disassembly Instructions

[1027] The server generates disassembly instructions based on the analysis and sends them to the disassembly robot. For example, in the case of a television, instructions would include first removing the screen, then removing the screws securing the case.

[1028] 4. Monitoring and feedback on demolition progress

[1029] The progress of the dismantling is monitored in real time by the monitoring means, which displays the progress of the dismantling on the display means, and allows manual operation as required.

[1030] Specific examples

[1031] Scanning by device: An old television, which is bulky waste, is placed in a scanning device, and image data of the television is generated and sent to the server.

[1032] AI image analysis: The server receives the image data and uses TensorFlow and OpenCV to identify each part of the TV (screen, case, electronic components, etc.).

[1033] Disassembly instructions: The server generates disassembly instructions and sends them to the disassembly robot. Following the instructions, the robot carefully removes the screen, then removes the screws securing the plastic case, opens it, and removes the internal electronic components.

[1034] Progress and feedback: Monitor demolition progress in real time and view feedback information via a smartphone app.

[1035] Prompt Sentence Examples

[1036] "Dismantling of the old refrigerator has begun. It is recommended that the refrigerator door and shelves be removed first."

[1037] This allows dismantling work on the factory's recycling line to proceed efficiently and accurately, and even when manual operation is required, it can be handled quickly.

[1038] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1039] Step 1:

[1040] The user places the bulky waste into the scanning device. The scanning device scans the bulky waste at high resolution and generates image data. The generated image data is sent to the server via the network. The input of this step is the bulky waste, and the output is the scanned high-resolution image data.

[1041] Step 2:

[1042] The server analyzes the image data received from the scanning device. Specifically, it processes the image data using TensorFlow and OpenCV to identify each part of the bulky waste. For example, in the case of a television, the screen, case, electronic components, etc. are identified. The input is the image data, and the output is information about each identified part.

[1043] Step 3:

[1044] The server classifies each part into recyclable and non-recyclable waste based on the analysis results. Based on the classification results of the AI ​​image analysis algorithm, for example, screens and electronic components are classified as recyclable waste, and cases are classified as non-recyclable waste. The input is the information of each identified part, and the output is a list of sorted recyclable and non-recyclable waste.

[1045] Step 4:

[1046] The server generates specific disassembly instructions and sends them to the disassembly robot. For example, instructions include steps such as first removing the screen and then removing the fixing screws of the case. The input is a list of sorted recyclable and non-recyclable waste, and the output is the disassembly instructions.

[1047] Step 5:

[1048] The dismantling robot follows instructions received from the server and begins the physical dismantling process. The robot's arm moves, first carefully removing the screen, then unscrewing the plastic case to extract the electronic components inside. The input is the dismantling instructions, and the output is the dismantled bulky waste.

[1049] Step 6:

[1050] The terminal monitors the progress of the demolition and receives feedback in real time. It displays the feedback information sent from the demolition robot and provides the user with information about the progress and problems of the demolition work. The input is feedback information from the demolition robot, and the output is a real-time display based on that information.

[1051] Step 7:

[1052] The user monitors the progress and takes manual action if necessary. For example, manual intervention is required if the demolition robot encounters an unexpected obstacle. The input is real-time display information, and the output is manual operation instructions.

[1053] 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.

[1054] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, identifies and classifies each part using an AI image analysis algorithm, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, it aims to improve the user experience and increase the efficiency of the system.

[1055] System Configuration

[1056] This system is composed of a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[1057] Scanning Method

[1058] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[1059] Image analysis methods

[1060] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[1061] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[1062] Demolition means

[1063] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[1064] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[1065] Storage means

[1066] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[1067] Emotion Engine

[1068] When a user places their bulky waste in the trash, the emotion engine analyzes their voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion engine will detect that emotion and make adjustments such as displaying more detailed system operation instructions.

[1069] The emotion engine has the ability to adjust the system's behavior based on the user's emotion recognition results. For example, if the user is feeling stressed, the system will notify the user of the progress of the process more frequently, improving the user experience.

[1070] Specific examples

[1071] 1. Terminal: Place an old television, which is bulky trash, into the scanner device.

[1072] 2. User: Initiates operation of the scanning device.

[1073] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[1074] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[1075] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[1076] 6. Server: Sends dismantling instructions to the dismantling robot.

[1077] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[1078] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[1079] 9. Emotion Engine: Analyzes the user's voice input and recognizes their emotions. For example, if the user feels anxious, the system will display detailed operating instructions.

[1080] 10. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[1081] 11. User: Checks the results of the demolition work and places the next bulky waste in the scanner. If necessary, reports the problem to the system.

[1082] In this way, combining the emotion engine allows for flexible responses based on the user's emotions, making the system easier to use. This allows for efficient and accurate sorting and dismantling of bulky waste, promoting resource reuse and environmental conservation.

[1083] The processing flow will be explained below.

[1084] Step 1:

[1085] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[1086] Step 2:

[1087] The user places bulky trash, such as an old television, in a designated position on the scanner.

[1088] Step 3:

[1089] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[1090] Step 4:

[1091] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[1092] Step 5:

[1093] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[1094] Step 6:

[1095] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[1096] Step 7:

[1097] Based on the analysis results, the server generates specific disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[1098] Step 8:

[1099] The server transmits the generated dismantling instructions to the dismantling robot.

[1100] Step 9:

[1101] The dismantling robot follows the instructions received from the server and begins the dismantling process. First, it moves its arm to carefully remove the screen.

[1102] Step 10:

[1103] After removing the screen, the dismantling robot then removes the screws that secure the plastic case, opens it, removes the electronic components inside, and separates them into recyclable and non-recyclable waste.

[1104] Step 11:

[1105] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[1106] Step 12:

[1107] The emotion engine analyzes voice input to recognize emotions as the user uses the system. For example, if a user is using the system for the first time and emotions such as anxiety or confusion are detected, the system will display more detailed operating instructions.

[1108] Step 13:

[1109] The emotion engine adjusts the system's behavior based on the emotion recognition results. For example, if the user is feeling stressed, the system may notify the user more frequently about the progress of the process or provide additional guidance.

[1110] Step 14:

[1111] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as necessary, and provides feedback on the progress of the demolition work to the user.

[1112] Step 15:

[1113] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and reports any problems to the system if necessary.

[1114] In this way, each step works in coordination to separate and dismantle bulky waste efficiently and accurately. By combining it with an emotion engine, it becomes possible to respond flexibly to the user's emotions, making the system easier to use.

[1115] Example 2

[1116] 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."

[1117] Current systems for separating and dismantling bulky waste rely on manual labor and partial automation, making them inefficient and difficult to accurately classify recyclable and non-recyclable waste. Furthermore, user operations tend to be complicated, resulting in a poor user experience.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1119] In this invention, the server includes scanning means for scanning bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, sorting means for sorting the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste, and emotion recognition means for recognizing user emotions and adjusting the operation of the system. This enables efficient and accurate sorting and dismantling of bulky waste, and makes user operation simple and intuitive, resulting in an improved user experience.

[1120] "Scanning means" refers to a device and method for scanning bulky waste at high resolution and acquiring image data.

[1121] "Analysis means" refers to software and algorithms that analyze the image data obtained from the scanning means and identify and classify each piece of bulky waste.

[1122] The "dismantling means" refers to mechanical devices and systems for physically dismantling bulky waste based on the classification results obtained by the analysis means.

[1123] The "separation means" refers to an apparatus and method for separating the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste.

[1124] "Emotion recognition means" refers to software and algorithms that recognize a user's emotions through voice input or the like and adjust the system's operation based on that.

[1125] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. This system scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, the system aims to improve the user experience and increase the efficiency of the system.

[1126] The system mainly consists of the following components: a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[1127] Scanning Method

[1128] The terminal includes a scanning device for scanning bulky items at high resolution. For example, when a user places an old television on the scanning device, the terminal fixes it in the correct position and captures the entire image with a high-resolution camera. The captured image data is then sent to a server.

[1129] Image analysis methods

[1130] The server receives the image data sent from the device. To analyze this image data, the server runs an AI image analysis algorithm (e.g., TensorFlow). This algorithm analyzes the image and identifies each part of the TV (screen, plastic case, electronic components, etc.). The analysis results include information to identify each part as recyclable or non-recyclable.

[1131] Demolition means

[1132] Based on the analysis, the server generates dismantling instructions for the robot. These instructions detail which parts to dismantle, in what order, and how. For example, they might include carefully removing the TV screen first, then unscrewing the plastic casing. To send these instructions to the robot, the server uses a control protocol.

[1133] Dismantling and sorting procedures

[1134] The dismantling robot follows instructions received from the server and begins the physical dismantling process. For example, the robot's arm carefully removes the screen, then removes the screws securing the plastic case. The dismantled recyclables are collected in a storage facility, while non-recyclables are sorted into separate containers.

[1135] emotion recognition means

[1136] When a user places their bulky waste in the trash can, the emotion engine analyzes their voice input and recognizes their emotions. For example, if a user expresses anxiety by saying, "Will I be able to do this successfully?", the emotion engine will detect this anxiety and display more detailed operating instructions for the scanning device. The emotion engine uses natural language processing (NLP) technology to analyze voice data in real time.

[1137] Feedback and User Interaction

[1138] The server constantly monitors the progress of the demolition work and provides feedback to the user as needed. For example, it displays a message such as "The demolition work is progressing smoothly." The user can check the results of the demolition work, prepare to load the next bulky waste, and report any problems to the system.

[1139] Based on these processing steps and concrete examples, the system achieves efficient and accurate sorting and dismantling of bulky waste. It also enables flexible responses according to the user's emotions, which is expected to improve the user experience.

[1140] Prompt Sentence Examples

[1141] Below are some example prompts to input to the generative AI model:

[1142] "Please describe a system that efficiently and accurately separates and dismantles old televisions, which are bulky waste. The system uses a scanning device to scan the television, an AI image analysis algorithm to identify each part, and a robot performs the dismantling. It also has an emotion engine that can recognize the user's emotions."

[1143] This prompt allows the generative AI model to generate a detailed description of the system.

[1144] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1145] Step 1:

[1146] Terminal: Scanning of bulky waste

[1147] Input: A user places an old television, a piece of bulky junk, into the scanning device and begins operation.

[1148] How it works: The device fixes the TV set by the user and captures the entire screen with a high-definition camera.

[1149] Data processing and calculation: Multiple captured images are integrated to generate a single high-resolution image data.

[1150] Output: Send high resolution image data to the server.

[1151] Step 2:

[1152] Server: Receiving and analyzing image data

[1153] Input: High-resolution image data sent from the device.

[1154] Operation: The server receives the image data and runs an AI image analysis algorithm.

[1155] Data processing and calculation: Using an AI image analysis algorithm (e.g., TensorFlow), image data is analyzed and each part of the TV (screen, plastic case, electronic components, etc.) is identified.

[1156] Output: The identification results for each part and the classification of these parts as recyclable or non-recyclable.

[1157] Step 3:

[1158] Server: Generate dismantling instructions

[1159] Input: Image analysis results and classification data.

[1160] Operation: Based on the identification results, the server generates dismantling instructions for the dismantling robot.

[1161] Data processing and calculation: Determine the order and method of disassembly of parts and generate specific instruction data.

[1162] Output: Sends dismantling instruction data to the dismantling robot.

[1163] Step 4:

[1164] Demolition robots: Performing demolition tasks

[1165] Input: Dismantling instruction data sent from the server.

[1166] Behavior: The demolition robot follows instructions and begins the physical dismantling process, carefully removing the television screen, then removing the screws securing the plastic casing.

[1167] Data processing and calculation: Accurate positioning and operation are performed while feeding back information from sensors in real time.

[1168] Output: Disassembled parts.

[1169] Step 5:

[1170] Demolition robot: Separating recyclable and non-recyclable waste

[1171] Input: Dismantling instruction data and dismantled parts.

[1172] How it works: The dismantling robot separates dismantled parts into recyclable and non-recyclable waste and collects them in the appropriate containers. For example, it places the removed screen in a recyclable container and the plastic casing in a non-recyclable container.

[1173] Data processing and calculation: The progress of the sorting work is recorded and sent to the server.

[1174] Output: Sorted recyclable and non-recyclable waste.

[1175] Step 6:

[1176] Emotion Engine: Detecting and responding to user emotions

[1177] Input: User's voice input.

[1178] How it works: The emotion engine analyzes voice data in real time to recognize the user's emotions, for example, when someone expresses anxiety.

[1179] Data processing and calculation: Using natural language processing (NLP) technology, emotions are extracted from voice and appropriate responses are generated.

[1180] Output: Display detailed operation guide in the user interface.

[1181] Step 7:

[1182] Server: User feedback

[1183] Input: Demolition progress and user emotion data.

[1184] How it works: The server constantly monitors the progress of the demolition process and provides feedback to the user as needed.

[1185] Data processing and calculation: Analyzes data from the demolition process and generates messages to report the situation at the appropriate time.

[1186] Output: Display the message "Demolition work is progressing smoothly" to the user.

[1187] Step 8:

[1188] User: Check the disassembly results and input the next item

[1189] Input: Results of demolition work and feedback from the server.

[1190] Actions: The user checks the results of the demolition and prepares to load the next bulky waste. If necessary, the user reports the problem to the system.

[1191] Data processing and calculation: Enter new bulky waste information into the system.

[1192] Output: The next bulky item is ready to be scanned.

[1193] (Application example 2)

[1194] 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."

[1195] Conventional bulky waste sorting and dismantling systems required a lot of manual work, which was inefficient and resulted in long operation times. There was also a lack of support for users who were unsure about operation, which hindered the user experience. Furthermore, proper classification and dismantling of each part of bulky waste was prone to human error.

[1196] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and an action adjustment means. This enables efficient and accurate sorting and dismantling of bulky waste and operational support according to the user's emotions.

[1197] The "image acquisition means" is a device that scans the bulky waste and acquires high-resolution image data thereof.

[1198] The "image analysis means" is a device that analyzes the image data obtained from the image acquisition means, identifies each part of the bulky waste using an AI image analysis algorithm, and classifies it into recyclable waste and non-recyclable waste.

[1199] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the image analysis means.

[1200] The "emotion recognition means" is a device that analyzes the user's voice input and recognizes the user's emotions.

[1201] The "operation adjustment means" is a device for adjusting the operation of the system based on the emotion data obtained by the emotion recognition means, thereby improving the user experience.

[1202] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste in factories. Specifically, it is composed of an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and a behavior adjustment means.

[1203] System Configuration

[1204] 1. Image acquisition method

[1205] The image capture unit scans the bulky waste and generates high-resolution image data. This image data is sent to a server for subsequent analysis. For example, an old television is placed on the scanner, and the image capture unit scans the entire television.

[1206] 2. Image analysis methods

[1207] The server receives the high-resolution image data sent from the image capture means. The server then uses an AI image analysis algorithm to analyze the captured image data and identify each piece of bulky waste. For example, a TV screen, plastic case, or electronic components may be identified. The server then classifies each piece into recyclable or non-recyclable waste.

[1208] 3. Disassembly means

[1209] The server then generates specific disassembly instructions for the disassembly unit, detailing which parts to disassemble, in what order, and how. The instructions are sent to the disassembly unit, which then begins the disassembly process. For example, in the case of a television, the screen would first be carefully removed, followed by the screws securing the plastic case, opening the case, and removing the internal electronic components.

[1210] 4. Emotion recognition means

[1211] When the user places their bulky waste in the trash bin, the emotion recognition unit analyzes the user's voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion recognition unit will detect that emotion and make adjustments such as displaying more detailed operation guides.

[1212] 5. Operation adjustment means

[1213] The system adjusts its behavior based on the emotion data obtained from the emotion recognition means. For example, if the user is feeling stressed, the behavior adjustment means may notify the user of the progress of the process more frequently, thereby improving the user experience.

[1214] Hardware and Software Used

[1215] Hardware: Scanning devices, demolition robots, smartphones

[1216] Software: Python, requests library, PIL (Python Imaging Library)

[1217] Specific examples

[1218] For example, during the dismantling process of an old TV, the dismantling tool monitors the progress status in real time, and a notification such as "Removing the screen" is displayed on the smartphone app. If the user becomes anxious and gives voice commands, detailed operating instructions are displayed, supporting the user to move on to the next step with confidence.

[1219] Prompt Sentence Examples

[1220] Example prompts to input to the AI ​​model:

[1221] "Please explain how to display the current progress of a task to the user based on the results of emotion recognition from speech input. Also, please explain the implementation of providing feedback to the user based on their emotions."

[1222] As described above, this invention enables efficient and accurate sorting and dismantling of bulky waste, and also allows for flexible responses according to the user's feelings.

[1223] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1224] Step 1:

[1225] The terminal places a bulky item (e.g., an old TV) on the scanning device. At this time, the user starts operating the scanning device. As a result, high-resolution image data is generated and sent from the scanning device to the server.

[1226] Step 2:

[1227] The server receives the high-resolution image data sent from the scanning device, and based on this data, the server uses AI image analysis algorithms to analyze the image and identify each piece of bulky waste (e.g., TV screen, plastic case, electronic components, etc.).

[1228] Step 3:

[1229] Based on the results of the image analysis, the server classifies each identified part into recyclable waste and non-recyclable waste. Specifically, it analyzes the characteristics of the image data, determines whether each identified part can be reused as recyclable waste, and assigns an appropriate tag to each.

[1230] Step 4:

[1231] The server generates specific disassembly instructions for the disassembly means, including the order in which each identified part should be disassembled and how the part should be disassembled. These disassembly instructions include the motion pattern of the robot arm and the selection of tools.

[1232] Step 5:

[1233] The dismantling means begins the actual dismantling work based on the dismantling instructions received from the server. For example, in the case of a television, the robotic arm first carefully removes the screen, then removes the screws securing the plastic case, opens the case, and removes the internal electronic components.

[1234] Step 6:

[1235] The dismantling means collects the dismantled recyclable waste in a specific collection means and separates the non-recyclable waste into a separate container, completing the separation and collecting the recyclable resources.

[1236] Step 7:

[1237] An emotion recognition means receives a user's voice input and analyzes the emotion. For example, if the user feels anxious, the voice data is analyzed with an emotion recognition algorithm to identify the specific emotion (e.g., anxiety or stress).

[1238] Step 8:

[1239] The operation adjustment means adjusts the operation of the system based on the emotion data obtained by the emotion recognition means. For example, if the user is feeling stressed, the operation adjustment means may notify the user of the progress of processing more frequently or provide detailed operation guides, thereby improving the user experience.

[1240] Step 9:

[1241] The server monitors the overall progress and provides feedback to the user, such as the current dismantling progress and the next steps to be taken, and displays operation guidance as needed.

[1242] Based on the above steps, the system will be able to achieve efficient and accurate sorting and dismantling of bulky waste, and will also be able to respond flexibly to the user's emotions.

[1243] 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.

[1244] 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.

[1245] 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.

[1246] [Fourth embodiment]

[1247] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1248] 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.

[1249] 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).

[1250] 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.

[1251] 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.

[1252] 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).

[1253] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1254] 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.

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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."

[1260] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then uses a robot to physically dismantle the waste based on the classification results.

[1261] System Configuration

[1262] This system consists of a terminal that handles bulky waste, a server that analyzes image data, and a demolition robot that physically dismantles the waste. Each component works together via a network to carry out the process.

[1263] Scanning Method

[1264] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[1265] Image analysis methods

[1266] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[1267] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[1268] Demolition means

[1269] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[1270] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[1271] Storage means

[1272] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[1273] Specific examples

[1274] 1. Device: Place an old TV, which is bulky trash, into the scanning device.

[1275] 2. User: Initiates operation of the scanning device.

[1276] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[1277] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[1278] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[1279] 6. Server: Sends dismantling instructions to the dismantling robot.

[1280] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[1281] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[1282] 9. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[1283] 10. User: Check the results of the demolition work and input the next bulky waste into the system.

[1284] As described above, this system can efficiently and accurately separate and dismantle bulky waste, thereby contributing to resource reuse and environmental conservation.

[1285] The processing flow will be explained below.

[1286] Step 1:

[1287] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[1288] Step 2:

[1289] The user places bulky trash, such as an old television, in a designated position on the scanner.

[1290] Step 3:

[1291] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[1292] Step 4:

[1293] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[1294] Step 5:

[1295] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[1296] Step 6:

[1297] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[1298] Step 7:

[1299] Based on the analysis results, the server generates disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[1300] Step 8:

[1301] The server transmits the generated dismantling instructions to the dismantling robot.

[1302] Step 9:

[1303] The dismantling robot follows the instructions received from the server and begins the dismantling process. In the case of a television, the robot's arms are first moved to remove the screen.

[1304] Step 10:

[1305] After removing the screen, the dismantling robot then disassembles the plastic case, removing the securing screws and opening the case.

[1306] Step 11:

[1307] The dismantling robot removes the electronic components from inside the case and separates them into recyclable and non-recyclable waste.

[1308] Step 12:

[1309] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[1310] Step 13:

[1311] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as needed, and provides feedback to the user.

[1312] Step 14:

[1313] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and, if necessary, reports any problems to the system.

[1314] In this way, each step works in tandem to ensure efficient and accurate sorting and dismantling of bulky waste.

[1315] Example 1

[1316] 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."

[1317] Currently, the separation and dismantling of bulky waste requires a lot of manual labor, is inefficient, and accurate separation is difficult. In particular, the difficulty in accurately separating each part of bulky waste into recyclable and non-recyclable waste reduces the rate of resource reuse and increases the burden on the environment.

[1318] 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.

[1319] In this invention, the server includes image acquisition means for scanning bulky waste, analysis means for analyzing image data obtained from the image acquisition means to identify parts of the bulky waste, classification means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification means, and instruction means for the dismantling means to generate dismantling instructions and send them to the robot. This enables efficient and accurate sorting and dismantling of bulky waste, contributing to resource reuse and environmental conservation.

[1320] The "image acquisition means" is a means for acquiring high-resolution images of the bulky waste using a scanning device.

[1321] The "analysis means" is a means for analyzing the acquired image data and identifying each part of the bulky waste.

[1322] The "classification means" is a means for classifying the parts identified by the analysis means into recyclable waste and non-recyclable waste.

[1323] "Dismantling means" refers to a means for physically dismantling bulky waste based on the classification means.

[1324] The "instruction means" is a means by which the dismantling means generates dismantling instructions and transmits the instructions to the robot.

[1325] An "artificial intelligence image analysis algorithm" is an algorithm that uses computer vision technology to automatically recognize and classify objects in image data.

[1326] The "storage means" is a means for collecting recyclable waste dismantled by the dismantling means and storing it in a specific container.

[1327] MODE FOR CARRYING OUT THE INVENTION

[1328] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. Specifically, it scans the bulky waste, uses AI image analysis algorithms to identify and classify each part, and then uses robots to physically dismantle the waste based on the classification results.

[1329] System Configuration

[1330] The system consists of the following major hardware and software components:

[1331] 1. Image acquisition means: A device for scanning high-resolution images of bulky waste, such as a high-resolution camera scanner.

[1332] 2. Analysis method: A server that analyzes the scanned image data and processes it to identify each part of the bulky waste. Specifically, it implements artificial intelligence image analysis algorithms such as TensorFlow and OpenCV.

[1333] 3. Classification means: A server that performs processing to classify each part into recyclable waste and non-recyclable waste based on the analyzed data.

[1334] 4. Dismantling means: A dismantling robot that physically dismantles the bulky waste based on the classification results. The dismantling robot includes servo motors and arms.

[1335] 5. Instruction means: A server for generating dismantling instructions and sending them to the dismantling robot.

[1336] 6. Storage means: A device for collecting disassembled recyclables, including containers for collecting each part.

[1337] System Operation

[1338] The process for disposing of bulky waste is as follows:

[1339] 1. The terminal places the bulky waste item into the scanning device. The user places the bulky waste item, such as an old TV, into the device.

[1340] 2. The user begins operating the scanning device. The process begins by pressing the start button on the scanning device.

[1341] 3. The device scans the bulky waste at high resolution and obtains image data.

[1342] 4. The device sends the acquired image data to the server.

[1343] 5. The server receives and stores the image data from the scanning device.

[1344] 6. The server uses AI image analysis algorithms to analyze the image data, identifying the TV screen, case, electronic components, etc.

[1345] 7. The server sorts each part into recyclable and non-recyclable waste. Identified screens and electronic components are identified as recyclable waste, while plastic casings are identified as non-recyclable waste.

[1346] 8. The server generates specific dismantling instructions for the demolition robot and sends them to the robot via the network.

[1347] 9. The demolition robot follows the instructions received from the server and begins the physical dismantling process. The robot's arms move and carefully remove the TV screen, then unscrew the casing.

[1348] 10. The demolition robot stores the dismantled recyclable waste in a container on the storage means, and sorts the non-recyclable waste into a separate container.

[1349] 11. The server monitors the progress of the demolition and provides feedback to the user as needed. The user then loads the next bulky waste item and repeats the process.

[1350] Specific examples

[1351] Here is an example of a prompt for this system:

[1352] Prompt statement:

[1353] An old TV is placed in the scanning device. Once the scan is complete, the image is sent to the server. The server analyzes the image and identifies and classifies the TV's screen, case, and electronic components. Next, dismantling instructions are sent to a dismantling robot, which dismantles the TV according to the instructions. Finally, the waste is separated into recyclable and non-recyclable waste and stored in designated locations.

[1354] This system enables the efficient and accurate separation and dismantling of bulky waste, contributing to the reuse of resources and environmental conservation.

[1355] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1356] Step 1:

[1357] The user places the bulky waste in the scanning device.

[1358] What happens: The user places the old TV on the scanning device's stand.

[1359] Input: Bulky waste (e.g. old TV)

[1360] Output: Bulky waste placed in the scanning device

[1361] Step 2:

[1362] The user begins operating the scanning device.

[1363] Specific operation: The user presses the start button on the scanning device.

[1364] Input: Pressing the start button on the scanning device

[1365] Output: Start scanning device

[1366] Step 3:

[1367] The device scans the bulky waste at high resolution and captures image data.

[1368] Specific operation: The scanning device's camera scans the entire television and generates image data.

[1369] Input: Bulky waste (set in the scanning device)

[1370] Output: High-resolution image data

[1371] Step 4:

[1372] The terminal transmits the acquired image data to the server.

[1373] Specific operation: The terminal uploads image data to the server via the network.

[1374] Input: High-resolution image data

[1375] Output: Image data sent to the server

[1376] Step 5:

[1377] A server receives and stores image data from the scanning device.

[1378] Specific operation: The server saves the image data in local storage.

[1379] Input: Image data sent from a scanning device

[1380] Output: Saved image data

[1381] Step 6:

[1382] The server analyzes the image data using an AI image analysis algorithm.

[1383] Specific operation: The server uses the TensorFlow library to identify objects in the image data.

[1384] Input: Saved image data

[1385] Output: Identified bulky waste pieces (e.g. screen, case, electronic parts)

[1386] Step 7:

[1387] The server sorts each part into recyclable and non-recyclable.

[1388] Specific operation: The server analyzes the identified parts and classifies each into recyclable and non-recyclable waste.

[1389] Input: Identified pieces of bulky waste

[1390] Output: Classified waste types (recyclable waste, non-recyclable waste)

[1391] Step 8:

[1392] The server generates and transmits specific dismantling instructions to the dismantling robot.

[1393] Specific operation: The server generates an instruction set detailing the dismantling procedure and sends it to the robot via the network.

[1394] Input: Classified garbage type and its location information

[1395] Output: Dismantling instructions to the dismantling robot

[1396] Step 9:

[1397] The demolition robot follows the instructions received from the server and begins the physical demolition work.

[1398] What it does: The robot's arm moves and carefully removes the TV screen, then unscrews the casing to remove the electronic components inside.

[1399] Input: Dismantlement instructions sent from the server

[1400] Output: Dismantled bulky waste

[1401] Step 10:

[1402] The dismantling robot stores the dismantled recyclable waste in a container of the storage means, and sorts the non-recyclable waste into another container.

[1403] Specific operations: The robot places the removed screens and electronic components in designated containers, and moves non-recyclable waste such as cases to a disposal container.

[1404] Input: Dismantled bulky waste parts

[1405] Output: Sorted storage (recyclable and non-recyclable containers)

[1406] Step 11:

[1407] The server monitors the progress of the demolition work and provides feedback to the user as needed.

[1408] Specific operation: The server receives feedback data from the robot in real time and reports the situation to the user by email or UI notification.

[1409] Input: Feedback data from the robot

[1410] Output: Feedback notification to the user

[1411] (Application example 1)

[1412] 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."

[1413] Conventional bulky waste sorting and dismantling systems often lack efficiency and accuracy, particularly due to the lack of real-time monitoring and feedback functions, making it difficult to properly manage the progress of dismantling work. Another issue is the efficiency of manual intervention when dismantling procedures are not performed properly. The present invention aims to solve these issues and provide a more efficient and accurate bulky waste sorting and dismantling system.

[1414] 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.

[1415] In this invention, the server includes scanning means for scanning the bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, monitoring means for monitoring the progress of the dismantling means and manually operating it as necessary, and display means for displaying feedback information from the dismantling means in real time. This allows the progress of the dismantling work to be properly managed, enabling efficient and accurate dismantling and sorting work.

[1416] The "scanning means" is a device that acquires high-resolution image data of bulky waste.

[1417] The "analysis means" is a system that uses image data obtained from the scanning means to identify each part of bulky waste using an AI image analysis algorithm and classify it into recyclable and non-recyclable waste.

[1418] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the analysis means.

[1419] The "monitoring means" is a system or device for monitoring the progress of the dismantling means in real time and for performing manual operations as necessary.

[1420] The "display means" is a device or system that displays feedback information from the dismantling means in real time, and visually provides the worker with the progress and problems of the dismantling work.

[1421] This invention is a system for efficiently and accurately separating and dismantling bulky waste on a factory recycling line. The system scans the bulky waste and uses an AI image analysis algorithm to identify and classify each part. Based on the classification results, a dismantling robot physically dismantles the waste. The system also includes a monitoring means for monitoring the progress of the dismantling process and manually operating it as needed, as well as a display means for displaying feedback information in real time.

[1422] System Configuration

[1423] The structure of this system is as follows:

[1424] Hardware and Software

[1425] Scanning method: A device that captures high-resolution image data of bulky waste (e.g., a high-resolution camera).

[1426] Analysis method: A server that uses TensorFlow and OpenCV to analyze image data and identify each part of bulky waste.

[1427] Dismantling method: A dismantling robot that runs on ROS (Robot Operating System).

[1428] Monitoring method: A system (e.g., smartphone app) to monitor the demolition progress in real time.

[1429] Display means: A device (e.g., a smartphone or tablet) that displays feedback information from the dismantling means.

[1430] Operation overview

[1431] 1. Scanning of bulky waste

[1432] When a user places an item of bulky waste on the scanning device, the terminal scans the item at high resolution and sends the image data to the server.

[1433] 2. Image Analysis

[1434] The server analyzes the image data received from the scanning device and uses AI image analysis algorithms to identify each part of the bulky waste. For example, in the case of an old TV, the screen, case, electronic components, etc. are identified. The analysis results classify each part as recyclable or non-recyclable waste.

[1435] 3. Generating Disassembly Instructions

[1436] The server generates disassembly instructions based on the analysis and sends them to the disassembly robot. For example, in the case of a television, instructions would include first removing the screen, then removing the screws securing the case.

[1437] 4. Monitoring and feedback on demolition progress

[1438] The progress of the dismantling is monitored in real time by the monitoring means, which displays the progress of the dismantling on the display means, and allows manual operation as required.

[1439] Specific examples

[1440] Scanning by device: An old television, which is bulky waste, is placed in a scanning device, and image data of the television is generated and sent to the server.

[1441] AI image analysis: The server receives the image data and uses TensorFlow and OpenCV to identify each part of the TV (screen, case, electronic components, etc.).

[1442] Disassembly instructions: The server generates disassembly instructions and sends them to the disassembly robot. Following the instructions, the robot carefully removes the screen, then removes the screws securing the plastic case, opens it, and removes the internal electronic components.

[1443] Progress and feedback: Monitor demolition progress in real time and view feedback information via a smartphone app.

[1444] Prompt Sentence Examples

[1445] "Dismantling of the old refrigerator has begun. It is recommended that the refrigerator door and shelves be removed first."

[1446] This allows dismantling work on the factory's recycling line to proceed efficiently and accurately, and even when manual operation is required, it can be handled quickly.

[1447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1448] Step 1:

[1449] The user places the bulky waste into the scanning device. The scanning device scans the bulky waste at high resolution and generates image data. The generated image data is sent to the server via the network. The input of this step is the bulky waste, and the output is the scanned high-resolution image data.

[1450] Step 2:

[1451] The server analyzes the image data received from the scanning device. Specifically, it processes the image data using TensorFlow and OpenCV to identify each part of the bulky waste. For example, in the case of a television, the screen, case, electronic components, etc. are identified. The input is the image data, and the output is information about each identified part.

[1452] Step 3:

[1453] The server classifies each part into recyclable and non-recyclable waste based on the analysis results. Based on the classification results of the AI ​​image analysis algorithm, for example, screens and electronic components are classified as recyclable waste, and cases are classified as non-recyclable waste. The input is the information of each identified part, and the output is a list of sorted recyclable and non-recyclable waste.

[1454] Step 4:

[1455] The server generates specific disassembly instructions and sends them to the disassembly robot. For example, instructions include steps such as first removing the screen and then removing the fixing screws of the case. The input is a list of sorted recyclable and non-recyclable waste, and the output is the disassembly instructions.

[1456] Step 5:

[1457] The dismantling robot follows instructions received from the server and begins the physical dismantling process. The robot's arm moves, first carefully removing the screen, then unscrewing the plastic case to extract the electronic components inside. The input is the dismantling instructions, and the output is the dismantled bulky waste.

[1458] Step 6:

[1459] The terminal monitors the progress of the demolition and receives feedback in real time. It displays the feedback information sent from the demolition robot and provides the user with information about the progress and problems of the demolition work. The input is feedback information from the demolition robot, and the output is a real-time display based on that information.

[1460] Step 7:

[1461] The user monitors the progress and takes manual action if necessary. For example, manual intervention is required if the demolition robot encounters an unexpected obstacle. The input is real-time display information, and the output is manual operation instructions.

[1462] 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.

[1463] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. It scans the bulky waste, identifies and classifies each part using an AI image analysis algorithm, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, it aims to improve the user experience and increase the efficiency of the system.

[1464] System Configuration

[1465] This system is composed of a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[1466] Scanning Method

[1467] The bulky waste is first placed in a scanning device, which captures high-resolution images and sends the data to a server. For example, if a user places an old television in the system, the scanning device will scan the entire television and generate image data.

[1468] Image analysis methods

[1469] The server receives the image data from the scanning device and uses AI image analysis algorithms to analyze the image data and identify each piece of bulky waste, such as a TV screen, plastic casing, or electronic components.

[1470] Based on the analysis, the server classifies each part into recyclable and non-recyclable waste. For example, the screen is classified as recyclable, some of the internal electronic components are also classified as recyclable, and the case is identified as non-recyclable waste that should be discarded.

[1471] Demolition means

[1472] The server then generates specific disassembly instructions for the demolition robot, detailing which parts to disassemble, in what order, and how, and sends these instructions to the demolition robot.

[1473] The robot then follows instructions received from the server and begins the physical dismantling process: for example, in the case of a television, the robot's arms first move to carefully remove the screen, then remove the screws securing the plastic casing, open it up, and remove the electronic components inside.

[1474] Storage means

[1475] The demolition robot collects the recyclable waste from the dismantled items into a collection facility. For example, removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[1476] Emotion Engine

[1477] When a user places their bulky waste in the trash, the emotion engine analyzes their voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion engine will detect that emotion and make adjustments such as displaying more detailed system operation instructions.

[1478] The emotion engine has the ability to adjust the system's behavior based on the user's emotion recognition results. For example, if the user is feeling stressed, the system will notify the user of the progress of the process more frequently, improving the user experience.

[1479] Specific examples

[1480] 1. Terminal: Place an old television, which is bulky trash, into the scanner device.

[1481] 2. User: Initiates operation of the scanning device.

[1482] 3. Terminal: Scans the TV in high resolution and sends the image data to the server.

[1483] 4. Server: Receives the image data and uses AI image analysis algorithms to identify each part of the TV (screen, case, electronic components, etc.).

[1484] 5. Server: Classifies each part into recyclable and non-recyclable and generates dismantling instructions based on the results.

[1485] 6. Server: Sends dismantling instructions to the dismantling robot.

[1486] 7. Demolition robot: When instructed, it begins the physical demolition process and separates recyclable and non-recyclable waste.

[1487] 8. Demolition robot: Collects recyclable waste into a storage means and separates non-recyclable waste into a separate container.

[1488] 9. Emotion Engine: Analyzes the user's voice input and recognizes their emotions. For example, if the user feels anxious, the system will display detailed operating instructions.

[1489] 10. Server: Monitors the progress of the demolition work and provides feedback to the user as needed.

[1490] 11. User: Checks the results of the demolition work and places the next bulky waste in the scanner. If necessary, reports the problem to the system.

[1491] In this way, combining the emotion engine allows for flexible responses based on the user's emotions, making the system easier to use. This allows for efficient and accurate sorting and dismantling of bulky waste, promoting resource reuse and environmental conservation.

[1492] The processing flow will be explained below.

[1493] Step 1:

[1494] The terminal displays the operation screen of the bulky waste scanner to the user and instructs the user to place the bulky waste on the scanner.

[1495] Step 2:

[1496] The user places bulky trash, such as an old television, in a designated position on the scanner.

[1497] Step 3:

[1498] The terminal detects that the bulky waste has been placed in the trash can, acquires high-resolution image data, and sends the acquired image data to the server.

[1499] Step 4:

[1500] The server receives the image data sent from the scanning device and prepares it for input into the AI ​​image analysis algorithm.

[1501] Step 5:

[1502] The server runs an image analysis algorithm to analyze the image data and identify each piece of bulky waste (e.g., screen, plastic casing, electronic components, etc.).

[1503] Step 6:

[1504] Based on the analysis results, the server classifies each part into "recyclable waste" and "non-recyclable waste" and stores the classification results in a database.

[1505] Step 7:

[1506] Based on the analysis results, the server generates specific disassembly instructions for the disassembly robot, including the specific disassembly order and method (e.g., first remove the screen, then disassemble the case, and finally remove the electronic components).

[1507] Step 8:

[1508] The server transmits the generated dismantling instructions to the dismantling robot.

[1509] Step 9:

[1510] The dismantling robot follows the instructions received from the server and begins the dismantling process. First, it moves its arm to carefully remove the screen.

[1511] Step 10:

[1512] After removing the screen, the dismantling robot then removes the screws that secure the plastic case, opens it, removes the electronic components inside, and separates them into recyclable and non-recyclable waste.

[1513] Step 11:

[1514] The dismantling robot collects the dismantled recyclables in a storage facility. Removed screens and usable electronic components are placed in a specific container. Non-recyclable waste is sorted into another container.

[1515] Step 12:

[1516] The emotion engine analyzes voice input to recognize emotions as the user uses the system. For example, if a user is using the system for the first time and emotions such as anxiety or confusion are detected, the system will display more detailed operating instructions.

[1517] Step 13:

[1518] The emotion engine adjusts the system's behavior based on the emotion recognition results. For example, if the user is feeling stressed, the system may notify the user more frequently about the progress of the process or provide additional guidance.

[1519] Step 14:

[1520] The server monitors the progress of the demolition robot in real time, corrects the demolition instructions as necessary, and provides feedback on the progress of the demolition work to the user.

[1521] Step 15:

[1522] The user checks the results of the demolition work provided by the server, places the next bulky waste on the scanner, and reports any problems to the system if necessary.

[1523] In this way, each step works in coordination to separate and dismantle bulky waste efficiently and accurately. By combining it with an emotion engine, it becomes possible to respond flexibly to the user's emotions, making the system easier to use.

[1524] Example 2

[1525] 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."

[1526] Current systems for separating and dismantling bulky waste rely on manual labor and partial automation, making them inefficient and difficult to accurately classify recyclable and non-recyclable waste. Furthermore, user operations tend to be complicated, resulting in a poor user experience.

[1527] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1528] In this invention, the server includes scanning means for scanning bulky waste, analysis means for analyzing image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste, dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means, sorting means for sorting the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste, and emotion recognition means for recognizing user emotions and adjusting the operation of the system. This enables efficient and accurate sorting and dismantling of bulky waste, and makes user operation simple and intuitive, resulting in an improved user experience.

[1529] "Scanning means" refers to a device and method for scanning bulky waste at high resolution and acquiring image data.

[1530] "Analysis means" refers to software and algorithms that analyze the image data obtained from the scanning means and identify and classify each piece of bulky waste.

[1531] The "dismantling means" refers to mechanical devices and systems for physically dismantling bulky waste based on the classification results obtained by the analysis means.

[1532] The "separation means" refers to an apparatus and method for separating the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste.

[1533] "Emotion recognition means" refers to software and algorithms that recognize a user's emotions through voice input or the like and adjust the system's operation based on that.

[1534] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste. This system scans the bulky waste, uses an AI image analysis algorithm to identify and classify each part, and then a robot physically dismantles the waste based on the classification results. Furthermore, by adding an emotion engine that recognizes the user's emotions, the system aims to improve the user experience and increase the efficiency of the system.

[1535] The system mainly consists of the following components: a terminal that handles bulky waste, a server that analyzes image data, a demolition robot that physically dismantles the waste, and an emotion engine that recognizes the user's emotions. Each component works together via a network to carry out the process.

[1536] Scanning Method

[1537] The terminal includes a scanning device for scanning bulky items at high resolution. For example, when a user places an old television on the scanning device, the terminal fixes it in the correct position and captures the entire image with a high-resolution camera. The captured image data is then sent to a server.

[1538] Image analysis methods

[1539] The server receives the image data sent from the device. To analyze this image data, the server runs an AI image analysis algorithm (e.g., TensorFlow). This algorithm analyzes the image and identifies each part of the TV (screen, plastic case, electronic components, etc.). The analysis results include information to identify each part as recyclable or non-recyclable.

[1540] Demolition means

[1541] Based on the analysis, the server generates dismantling instructions for the robot. These instructions detail which parts to dismantle, in what order, and how. For example, they might include carefully removing the TV screen first, then unscrewing the plastic casing. To send these instructions to the robot, the server uses a control protocol.

[1542] Dismantling and sorting procedures

[1543] The dismantling robot follows instructions received from the server and begins the physical dismantling process. For example, the robot's arm carefully removes the screen, then removes the screws securing the plastic case. The dismantled recyclables are collected in a storage facility, while non-recyclables are sorted into separate containers.

[1544] emotion recognition means

[1545] When a user places their bulky waste in the trash can, the emotion engine analyzes their voice input and recognizes their emotions. For example, if a user expresses anxiety by saying, "Will I be able to do this successfully?", the emotion engine will detect this anxiety and display more detailed operating instructions for the scanning device. The emotion engine uses natural language processing (NLP) technology to analyze voice data in real time.

[1546] Feedback and User Interaction

[1547] The server constantly monitors the progress of the demolition work and provides feedback to the user as needed. For example, it displays a message such as "The demolition work is progressing smoothly." The user can check the results of the demolition work, prepare to load the next bulky waste, and report any problems to the system.

[1548] Based on these processing steps and concrete examples, the system achieves efficient and accurate sorting and dismantling of bulky waste. It also enables flexible responses according to the user's emotions, which is expected to improve the user experience.

[1549] Prompt Sentence Examples

[1550] Below are some example prompts to input to the generative AI model:

[1551] "Please describe a system that efficiently and accurately separates and dismantles old televisions, which are bulky waste. The system uses a scanning device to scan the television, an AI image analysis algorithm to identify each part, and a robot to dismantle it. It also has an emotion engine that can recognize the user's emotions."

[1552] This prompt allows the generative AI model to generate a detailed description of the system.

[1553] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1554] Step 1:

[1555] Terminal: Scanning of bulky waste

[1556] Input: A user places an old television, a piece of bulky junk, into the scanning device and begins operation.

[1557] How it works: The device fixes the TV set by the user and captures the entire screen with a high-definition camera.

[1558] Data processing and calculation: Multiple captured images are integrated to generate a single high-resolution image data.

[1559] Output: Send high resolution image data to the server.

[1560] Step 2:

[1561] Server: Receiving and analyzing image data

[1562] Input: High-resolution image data sent from the device.

[1563] Operation: The server receives the image data and runs an AI image analysis algorithm.

[1564] Data processing and calculation: Using an AI image analysis algorithm (e.g., TensorFlow), image data is analyzed and each part of the TV (screen, plastic case, electronic components, etc.) is identified.

[1565] Output: The identification results for each part and the classification of these parts as recyclable or non-recyclable.

[1566] Step 3:

[1567] Server: Generate dismantling instructions

[1568] Input: Image analysis results and classification data.

[1569] Operation: Based on the identification results, the server generates dismantling instructions for the dismantling robot.

[1570] Data processing and calculation: Determine the order and method of disassembly of parts and generate specific instruction data.

[1571] Output: Sends dismantling instruction data to the dismantling robot.

[1572] Step 4:

[1573] Demolition robots: Performing demolition tasks

[1574] Input: Dismantling instruction data sent from the server.

[1575] Behavior: The demolition robot follows instructions and begins the physical dismantling process, carefully removing the television screen, then removing the screws securing the plastic casing.

[1576] Data processing and calculation: Accurate positioning and operation are performed while feeding back information from sensors in real time.

[1577] Output: Disassembled parts.

[1578] Step 5:

[1579] Demolition robot: Separating recyclable and non-recyclable waste

[1580] Input: Dismantling instruction data and dismantled parts.

[1581] How it works: The dismantling robot separates dismantled parts into recyclable and non-recyclable waste and collects them in the appropriate containers. For example, it places the removed screen in a recyclable container and the plastic casing in a non-recyclable container.

[1582] Data processing and calculation: The progress of the sorting work is recorded and sent to the server.

[1583] Output: Sorted recyclable and non-recyclable waste.

[1584] Step 6:

[1585] Emotion Engine: Detecting and responding to user emotions

[1586] Input: User's voice input.

[1587] How it works: The emotion engine analyzes voice data in real time to recognize the user's emotions, for example, when someone expresses anxiety.

[1588] Data processing and calculation: Using natural language processing (NLP) technology, emotions are extracted from voice and appropriate responses are generated.

[1589] Output: Display detailed operation guide in the user interface.

[1590] Step 7:

[1591] Server: User feedback

[1592] Input: Demolition progress and user emotion data.

[1593] How it works: The server constantly monitors the progress of the demolition process and provides feedback to the user as needed.

[1594] Data processing and calculation: Analyzes data from the demolition process and generates messages to report the situation at the appropriate time.

[1595] Output: Display the message "Demolition work is progressing smoothly" to the user.

[1596] Step 8:

[1597] User: Check the disassembly results and input the next item

[1598] Input: Results of demolition work and feedback from the server.

[1599] Actions: The user checks the results of the demolition and prepares to load the next bulky waste. If necessary, the user reports the problem to the system.

[1600] Data processing and calculation: Enter new bulky waste information into the system.

[1601] Output: The next bulky item is ready to be scanned.

[1602] (Application example 2)

[1603] 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."

[1604] Conventional bulky waste sorting and dismantling systems required a lot of manual work, which was inefficient and resulted in long operation times. There was also a lack of support for users who were unsure about operation, which hindered the user experience. Furthermore, proper classification and dismantling of each part of bulky waste was prone to human error.

[1605] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and an action adjustment means. This enables efficient and accurate sorting and dismantling of bulky waste and operational support according to the user's emotions.

[1606] The "image acquisition means" is a device that scans the bulky waste and acquires high-resolution image data thereof.

[1607] The "image analysis means" is a device that analyzes the image data obtained from the image acquisition means, identifies each part of the bulky waste using an AI image analysis algorithm, and classifies it into recyclable waste and non-recyclable waste.

[1608] The "dismantling means" is a device that physically dismantles the bulky waste based on the classification results obtained by the image analysis means.

[1609] The "emotion recognition means" is a device that analyzes the user's voice input and recognizes the user's emotions.

[1610] The "operation adjustment means" is a device for adjusting the operation of the system based on the emotion data obtained by the emotion recognition means, thereby improving the user experience.

[1611] This invention relates to a system for efficiently and accurately separating and dismantling bulky waste in factories. Specifically, it is composed of an image acquisition means, an image analysis means, a dismantling means, an emotion recognition means, and a behavior adjustment means.

[1612] System Configuration

[1613] 1. Image acquisition method

[1614] The image capture unit scans the bulky waste and generates high-resolution image data. This image data is sent to a server for subsequent analysis. For example, an old television is placed on the scanner, and the image capture unit scans the entire television.

[1615] 2. Image analysis methods

[1616] The server receives the high-resolution image data sent from the image capture means. The server then uses an AI image analysis algorithm to analyze the captured image data and identify each piece of bulky waste. For example, a TV screen, plastic case, or electronic components may be identified. The server then classifies each piece into recyclable or non-recyclable waste.

[1617] 3. Disassembly means

[1618] The server then generates specific disassembly instructions for the disassembly unit, detailing which parts to disassemble, in what order, and how. The instructions are sent to the disassembly unit, which then begins the disassembly process. For example, in the case of a television, the screen would first be carefully removed, followed by the screws securing the plastic case, opening the case, and removing the internal electronic components.

[1619] 4. Emotion recognition means

[1620] When the user places their bulky waste in the trash bin, the emotion recognition unit analyzes the user's voice input to recognize their emotions. For example, if the user is feeling anxious, the emotion recognition unit will detect that emotion and make adjustments such as displaying more detailed operation guides.

[1621] 5. Operation adjustment means

[1622] The system adjusts its behavior based on the emotion data obtained from the emotion recognition means. For example, if the user is feeling stressed, the behavior adjustment means may notify the user of the progress of the process more frequently, thereby improving the user experience.

[1623] Hardware and Software Used

[1624] Hardware: Scanning devices, demolition robots, smartphones

[1625] Software: Python, requests library, PIL (Python Imaging Library)

[1626] Specific examples

[1627] For example, during the dismantling process of an old TV, the dismantling tool monitors the progress status in real time, and a notification such as "Removing the screen" is displayed on the smartphone app. If the user becomes anxious and gives voice commands, detailed operating instructions are displayed, supporting the user to move on to the next step with confidence.

[1628] Prompt Sentence Examples

[1629] Example prompts to input to the AI ​​model:

[1630] "Please explain how to display the current progress of a task to the user based on the results of emotion recognition from speech input. Also, please explain the implementation of providing feedback to the user based on their emotions."

[1631] As described above, this invention enables efficient and accurate sorting and dismantling of bulky waste, and also allows for flexible responses according to the user's feelings.

[1632] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1633] Step 1:

[1634] The terminal places a bulky item (e.g., an old TV) on the scanning device. At this time, the user starts operating the scanning device. As a result, high-resolution image data is generated and sent from the scanning device to the server.

[1635] Step 2:

[1636] The server receives the high-resolution image data sent from the scanning device, and based on this data, the server uses AI image analysis algorithms to analyze the image and identify each piece of bulky waste (e.g., TV screen, plastic case, electronic components, etc.).

[1637] Step 3:

[1638] Based on the results of the image analysis, the server classifies each identified part into recyclable waste and non-recyclable waste. Specifically, it analyzes the characteristics of the image data, determines whether each identified part can be reused as recyclable waste, and assigns an appropriate tag to each.

[1639] Step 4:

[1640] The server generates specific disassembly instructions for the disassembly means, including the order in which each identified part should be disassembled and how the part should be disassembled. These disassembly instructions include the motion pattern of the robot arm and the selection of tools.

[1641] Step 5:

[1642] The dismantling means begins the actual dismantling work based on the dismantling instructions received from the server. For example, in the case of a television, the robotic arm first carefully removes the screen, then removes the screws securing the plastic case, opens the case, and removes the internal electronic components.

[1643] Step 6:

[1644] The dismantling means collects the dismantled recyclable waste in a specific collection means and separates the non-recyclable waste into a separate container, completing the separation and collecting the recyclable resources.

[1645] Step 7:

[1646] An emotion recognition means receives a user's voice input and analyzes the emotion. For example, if the user feels anxious, the voice data is analyzed with an emotion recognition algorithm to identify the specific emotion (e.g., anxiety or stress).

[1647] Step 8:

[1648] The operation adjustment means adjusts the operation of the system based on the emotion data obtained by the emotion recognition means. For example, if the user is feeling stressed, the operation adjustment means may notify the user of the progress of processing more frequently or provide detailed operation guides, thereby improving the user experience.

[1649] Step 9:

[1650] The server monitors the overall progress and provides feedback to the user, such as the current dismantling progress and the next steps to be taken, and displays operation guidance as needed.

[1651] Based on the above steps, the system will be able to achieve efficient and accurate sorting and dismantling of bulky waste, and will also be able to respond flexibly to the user's emotions.

[1652] 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.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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).

[1659] 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.

[1660] 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."

[1661] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1662] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1663] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1664] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1665] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1666] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1667] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1668] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1669] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1670] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1671] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1672] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1673] The following is further disclosed regarding the above embodiment.

[1674] (Claim 1)

[1675] a scanning means for scanning the bulky waste;

[1676] an analyzing means for analyzing the image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste;

[1677] a dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means;

[1678] A system including:

[1679] (Claim 2)

[1680] 10. The system of claim 1, wherein the demolition means further comprises storage means for collecting recyclable waste.

[1681] (Claim 3)

[1682] The system of claim 1, wherein the analysis means uses an AI image analysis algorithm to identify each piece of bulky waste.

[1683] "Example 1"

[1684] (Claim 1)

[1685] image acquisition means for scanning the bulky waste;

[1686] analysis means for analyzing the image data obtained from the image acquisition means to identify the portion of the bulky waste;

[1687] a classification means for classifying the portion identified by the analysis means into recyclable waste and non-recyclable waste;

[1688] a dismantling means for physically dismantling the bulky waste based on the classification means;

[1689] instruction means for the dismantling means to generate dismantling instructions and transmit them to the robot;

[1690] A system including:

[1691] (Claim 2)

[1692] 10. The system of claim 1, wherein the demolition means further comprises storage means for collecting recyclable waste.

[1693] (Claim 3)

[1694] 2. The system of claim 1, wherein the analysis means uses an artificial intelligence image analysis algorithm to identify each piece of bulky waste.

[1695] "Application Example 1"

[1696] (Claim 1)

[1697] a scanning means for scanning the bulky waste;

[1698] an analyzing means for analyzing the image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste;

[1699] a dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means;

[1700] monitoring means for monitoring the progress of the dismantling means and for manually intervening as necessary;

[1701] display means for displaying feedback information from the disassembly means in real time;

[1702] A system including:

[1703] (Claim 2)

[1704] 10. The system of claim 1, wherein the demolition means further comprises storage means for collecting recyclable waste.

[1705] (Claim 3)

[1706] The system of claim 1, wherein the analysis means uses an AI image analysis algorithm to identify each piece of bulky waste.

[1707] "Example 2: Combining Emotion Engines"

[1708] (Claim 1)

[1709] a scanning means for scanning the bulky waste;

[1710] an analyzing means for analyzing the image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste;

[1711] a dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means;

[1712] a separating means for separating the parts dismantled by the dismantling means into recyclable waste and non-recyclable waste;

[1713] emotion recognition means for recognizing user emotions and adjusting system behavior;

[1714] A system including:

[1715] (Claim 2)

[1716] 10. The system of claim 1, wherein the demolition means further comprises storage means for collecting recyclable waste.

[1717] (Claim 3)

[1718] The system of claim 1, wherein the analysis means uses an AI image analysis algorithm to identify each piece of bulky waste.

[1719] "Application example 2 when combining emotion engines"

[1720] (Claim 1)

[1721] image acquisition means for scanning the bulky waste;

[1722] image analysis means for analyzing image data obtained from the image acquisition means and classifying the bulky waste into recyclable waste and non-recyclable waste;

[1723] Dismantling means for physically dismantling the bulky waste based on the classification results obtained by the image analysis means;

[1724] emotion recognition means for analyzing a user's voice input to recognize emotions;

[1725] an operation adjusting means for adjusting the operation of the system based on the emotion data obtained by the emotion recognition means;

[1726] A system including:

[1727] (Claim 2)

[1728] 10. The system of claim 1, wherein the demolition means further comprises collection means for collecting recyclable waste.

[1729] (Claim 3)

[1730] 2. The system of claim 1, wherein the image analysis means uses an AI image analysis algorithm to identify each piece of bulky waste. [Explanation of symbols]

[1731] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a scanning means for scanning the bulky waste; an analyzing means for analyzing the image data obtained from the scanning means and classifying the bulky waste into recyclable waste and non-recyclable waste; a dismantling means for physically dismantling the bulky waste based on the classification results obtained by the analysis means; A system including:

2. 10. The system of claim 1, wherein the demolition means further comprises storage means for collecting recyclable waste.

3. The system of claim 1, wherein the analysis means uses an AI image analysis algorithm to identify each piece of bulky waste.

Images