system

Abstract

The system according to this embodiment aims to identify the recyclability of waste and classify it efficiently. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a classification unit, and a transmission unit. The acquisition unit acquires images of waste. The analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The classification unit classifies the recyclable waste identified by the analysis unit. The transmission unit sends the waste classified by the classification unit to the recycling process.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to efficiently identify and classify the recyclability of waste.

[0005] The system according to the embodiment aims to identify the recyclability of waste and efficiently classify it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a classification unit, and a transmission unit. The acquisition unit acquires images of waste. The analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The classification unit classifies the recyclable waste identified by the analysis unit. The transmission unit sends the waste classified by the classification unit to the recycling process. [Effects of the Invention]

[0007] The system according to this embodiment can identify the recyclability of waste and classify it efficiently. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) A recycling management system according to an embodiment of the present invention is a system that uses image recognition and classification algorithms to identify recyclable waste and realize an efficient recycling process. This recycling management system acquires images of waste, and AI analyzes these images to identify recyclable waste. Next, the identified recyclable waste is classified and sent to the appropriate recycling process. This system reduces the amount of waste and alleviates the burden on the environment. For example, the recycling management system acquires images of waste. In this case, images of the waste are taken using cameras or sensors. For example, a camera installed at a waste treatment facility acquires images of waste in real time. These images are input to the AI. Next, the recycling management system has the AI ​​analyze the input images. The AI ​​uses image recognition technology to identify the type of waste. For example, it identifies recyclable waste such as plastics, metals, and paper. In this case, the AI ​​analyzes the characteristics of the waste based on data that has been learned in advance. The identified recyclable waste is classified by the recycling management system. The AI ​​classifies the identified waste into the appropriate category. For example, plastics are classified into the plastics category, and metals into the metals category. This classification is important for efficiently carrying out the recycling process. Classified waste is sent to the appropriate recycling process by the recycling management system. For example, plastics are sent to plastic recycling facilities, and metals are sent to metal recycling facilities. This process ensures efficient waste recycling. This system reduces the amount of waste and lessens the environmental burden. Municipalities and waste management companies can sort and process waste more efficiently, improving recycling rates and reducing waste disposal costs and environmental impact. Businesses and industrial facilities can reduce their environmental impact by improving waste management processes and more accurately identifying recyclable waste. Consumers can utilize this technology when properly sorting waste, increasing their awareness of contributing to the environment.Educational institutions can leverage this technology to enhance environmental education programs, enabling students to understand the importance of waste management and learn about the value of recycling. This allows recycling management systems to reduce waste volume and lessen the environmental impact.

[0029] The recycling management system according to this embodiment comprises an acquisition unit, an analysis unit, a classification unit, and a transmission unit. The acquisition unit acquires images of waste. The acquisition unit takes images of waste using, for example, a camera or a sensor. For example, the acquisition unit can acquire images of waste in real time using a camera installed at a waste treatment facility. The acquisition unit can also acquire images of waste using a sensor. For example, the acquisition unit can acquire images of waste using an infrared sensor. Furthermore, the acquisition unit can acquire images of waste using a drone. For example, the acquisition unit can acquire images of waste using a camera mounted on a drone. The analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The analysis unit identifies the type of waste using, for example, image recognition technology. For example, the analysis unit can identify the type of waste using deep learning. Furthermore, the analysis unit can identify the type of waste using pattern recognition technology. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using pattern recognition technology. Furthermore, the analysis unit can also analyze the characteristics of waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of waste using a pre-trained dataset. The classification unit classifies the recyclable waste identified by the analysis unit. The classification unit classifies the identified waste into appropriate categories. For example, the classification unit can classify plastics into the plastics category and metals into the metals category. The classification unit can also further subdivide the recyclable waste. For example, the classification unit can classify plastics by type. The transmission unit sends the waste classified by the classification unit to the recycling process. The transmission unit sends the classified waste to appropriate recycling facilities. For example, the transmission unit can send plastics to plastic recycling facilities and metals to metal recycling facilities. The transmission unit can also send the waste at the appropriate time to ensure the recycling process is efficient. For example, the transmission unit can send the waste considering the operating status of the recycling facilities.As a result, the recycling management system according to this embodiment can achieve an efficient recycling process by acquiring, analyzing, classifying, and transmitting images of waste.

[0030] The acquisition unit acquires images of waste. For example, the acquisition unit takes images of waste using cameras and sensors. Specifically, it can acquire detailed images of waste in real time using high-resolution cameras installed at waste treatment facilities. This allows for accurate capture of features such as the shape, color, and texture of the waste. In addition, by using infrared sensors, it is possible to detect the temperature and internal structure of the waste and acquire information that cannot be confirmed with visible light. Furthermore, by using drones, images of waste over a wide area can be efficiently acquired. Since the camera mounted on the drone can be freely adjusted in altitude and angle, images of waste in places that are difficult to see from the ground or at high altitudes can be easily acquired. As a result, the acquisition unit can acquire images of waste with high accuracy and efficiency using a variety of means and provide them to the subsequent analysis unit.

[0031] The analysis unit analyzes images acquired by the acquisition unit to identify recyclable waste. The analysis utilizes state-of-the-art image recognition technology and deep learning algorithms to accurately identify waste types. Specifically, it uses a model trained on a large dataset of waste images to extract waste features. For example, to identify recyclable waste such as plastic, metal, and paper, it analyzes features such as color, shape, and texture. Furthermore, by combining this with pattern recognition technology, it can detect subtle differences in waste, enabling more detailed classification. The analysis unit processes images in real time, allowing for rapid identification of waste types. In addition, the analysis unit can utilize historical data and statistical information to evaluate waste generation trends and recycling efficiency. This enables the analysis unit to accurately identify waste types and contribute to optimizing the recycling process.

[0032] The classification unit sorts the recyclable waste identified by the analysis unit. The classification unit uses mechanical sorting devices and robotic arms to classify the identified waste into appropriate categories. For example, when classifying into categories such as plastics, metals, and paper, it sets sorting criteria according to the characteristics of each type of waste and performs the classification automatically. Furthermore, the classification unit can further subdivide recyclable waste. For instance, when classifying plastics by type, it identifies different types of plastics such as polyethylene, polypropylene, and polystyrene and classifies them into their respective categories. Similarly, for metal waste, it can identify different metals such as iron, aluminum, and copper and classify them appropriately. This allows the classification unit to classify recyclable waste with high accuracy and improve the efficiency of the recycling process.

[0033] The transmission unit sends the waste classified by the classification unit to the recycling process. The transmission unit uses automated transport systems and conveyor belts to send the classified waste to the appropriate recycling facility. For example, plastic waste can be sent to a plastic recycling facility, and metal waste to a metal recycling facility. The transmission unit can send the waste at the appropriate time, taking into account the operating status and processing capacity of the recycling facility. For example, if a recycling facility is full, the waste can be sent to another facility or temporarily stored. The transmission unit can also monitor the waste transmission status in real time and adjust the transmission route and timing as needed. This allows the transmission unit to efficiently carry out the recycling process and ensure the proper disposal of waste. Furthermore, the transmission unit can record the waste transmission history and ensure traceability of the recycling process. This allows the transmission unit to improve the reliability and efficiency of the entire recycling management system.

[0034] The acquisition unit can capture images of waste using cameras and sensors. For example, the acquisition unit can capture images of waste using a camera. For example, the acquisition unit can capture images of waste using a high-resolution camera. The acquisition unit can also capture images of waste using sensors. For example, the acquisition unit can capture images of waste using an infrared sensor. Furthermore, the acquisition unit can capture images of waste using a drone. For example, the acquisition unit can capture images of waste using a camera mounted on a drone. This allows for accurate acquisition of images of waste using cameras and sensors. Cameras and sensors include, but are not limited to, high-resolution cameras, infrared sensors, and cameras mounted on drones. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the image data of waste captured by the camera into a generating AI and have the generating AI perform image data analysis.

[0035] The analysis unit can identify the type of waste using image recognition technology. The analysis unit can identify the type of waste using, for example, deep learning. For example, the analysis unit can identify the type of waste using a deep learning model. The analysis unit can also identify the type of waste using pattern recognition technology. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using pattern recognition technology. Furthermore, the analysis unit can analyze the characteristics of waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of waste using a pre-trained dataset. This allows for accurate identification of waste types using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and pattern recognition technology. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of waste into a generating AI and have the generating AI perform waste type identification.

[0036] The analysis unit can analyze the characteristics of waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of waste using a pre-trained dataset. For example, the analysis unit can analyze characteristics such as the shape, color, and texture of waste using a pre-trained dataset. The analysis unit can also identify the type of waste using a pre-trained dataset. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using a pre-trained dataset. This allows for an accurate understanding of the characteristics of waste by analyzing based on pre-trained data. Pre-trained data includes, but is not limited to, image datasets of waste and characteristic datasets of waste. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input a pre-trained dataset into a generating AI and have the generating AI perform the analysis of waste characteristics.

[0037] The classification unit can classify identified waste into appropriate categories. For example, the classification unit can classify identified waste into categories such as plastic, metal, and paper. For example, the classification unit can classify plastic into the plastic category and metal into the metal category. The classification unit can also further subdivide recyclable waste. For example, the classification unit can classify plastic by type. By classifying into appropriate categories, the recycling process can be made more efficient. Appropriate categories include, but are not limited to, plastic, metal, and paper. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input data on identified waste into a generating AI and have the generating AI perform the waste classification.

[0038] The transmitting unit can send the classified waste to the appropriate recycling process. For example, the transmitting unit can send the classified waste to the appropriate recycling facility. For example, the transmitting unit can send plastics to a plastic recycling facility and metals to a metal recycling facility. The transmitting unit can also send the waste at the appropriate time to ensure the recycling process is efficient. For example, the transmitting unit can send the waste considering the operating status of the recycling facility. This ensures that the waste is recycled efficiently by sending it to the appropriate recycling process. Appropriate recycling processes include, but are not limited to, plastic recycling facilities and metal recycling facilities. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the classified waste data into a generating AI and have the generating AI perform the transmission to the recycling process.

[0039] The acquisition unit can automatically adjust the camera resolution and shooting angle according to the type and quantity of waste. For example, if there is a large amount of waste, the acquisition unit can use a wide-angle lens to capture the entire area. For example, the acquisition unit can use a wide-angle lens to capture a large amount of waste. Also, if the waste is small and fine, the acquisition unit can use a high-resolution camera to capture details. For example, the acquisition unit can use a high-resolution camera to capture small and fine waste. Furthermore, the acquisition unit can automatically adjust the optimal shooting angle according to the type of waste. For example, the acquisition unit can automatically adjust the optimal shooting angle according to the type of waste. This allows for the acquisition of optimal images by adjusting the camera settings according to the type and quantity of waste. Camera resolution and shooting angle include, but are not limited to, wide-angle lenses, high-resolution cameras, and automatic adjustment of the optimal shooting angle. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the type and quantity of waste into a generating AI and have the generating AI adjust the camera resolution and shooting angle.

[0040] The acquisition unit can simultaneously acquire environmental information such as the temperature and humidity of the waste. For example, the acquisition unit can measure the temperature of the waste with a sensor and record it along with an image. For example, the acquisition unit can measure the temperature of the waste using a temperature sensor and record it along with image data. The acquisition unit can also measure the humidity of the waste with a sensor and record it along with an image. For example, the acquisition unit can measure the humidity of the waste using a humidity sensor and record it along with image data. Furthermore, the acquisition unit can acquire environmental information (temperature, humidity) around the waste and record it along with an image. For example, the acquisition unit can acquire environmental information around the waste using a temperature sensor and a humidity sensor and record it along with image data. This allows for a more accurate understanding of the waste's condition by simultaneously acquiring environmental information. Environmental information includes, but is not limited to, temperature, humidity, and atmospheric pressure. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input temperature and humidity data of the waste into a generating AI and have the generating AI perform the acquisition of environmental information.

[0041] The acquisition unit can acquire location information of waste and propose the optimal placement of recycling facilities. For example, the acquisition unit can acquire location information of waste using GPS and optimize the placement of recycling facilities. For example, the acquisition unit can acquire location information of waste using GPS and optimize the placement of recycling facilities. The acquisition unit can also display the location information of waste on a map and propose the placement of recycling facilities. For example, the acquisition unit can display the location information of waste on a map and propose the placement of recycling facilities. Furthermore, the acquisition unit can acquire location information of waste in real time and optimize the placement of recycling facilities. For example, the acquisition unit can acquire location information of waste in real time and optimize the placement of recycling facilities. In this way, the placement of recycling facilities can be optimized by acquiring location information of waste. Location information includes, but is not limited to, GPS data and location information on a map. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input location information data of waste into a generating AI and have the generating AI perform the optimization of the placement of recycling facilities.

[0042] The acquisition unit can acquire audio information of waste and use it as auxiliary information to identify the type of waste. For example, the acquisition unit can acquire audio information of waste using a microphone and use it for identification. The acquisition unit can also analyze audio information of waste and use it as auxiliary information to identify the type. For example, the acquisition unit can analyze audio information of waste using voice analysis technology and use it for identification. Furthermore, the acquisition unit can acquire audio information of waste in real time and use it for identification. For example, the acquisition unit can acquire audio information of waste in real time and use it for identification. This improves the accuracy of waste identification by utilizing audio information. Audio information includes, but is not limited to, the sounds of waste being crushed or dropped. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input audio information data of waste into a generating AI and have the generating AI perform the analysis of the audio information.

[0043] The analysis unit can analyze not only the shape and color of waste, but also its characteristics such as texture and reflectivity. For example, the analysis unit can analyze the texture of waste and use it for identification. For example, the analysis unit can use texture analysis technology to analyze the texture of waste and use it for identification. The analysis unit can also analyze the reflectivity of waste and use it for identification. For example, the analysis unit can use reflectivity analysis technology to analyze the reflectivity of waste and use it for identification. Furthermore, the analysis unit can comprehensively analyze the shape, color, texture, and reflectivity of waste and use it for identification. For example, the analysis unit can use shape analysis technology, color analysis technology, texture analysis technology, and reflectivity analysis technology to comprehensively analyze the shape, color, texture, and reflectivity of waste and use it for identification. This improves identification accuracy by analyzing characteristics such as texture and reflectivity. Texture and reflectivity include, but are not limited to, the surface texture and light reflectivity of waste. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data on the texture and reflectivity of waste materials into the generating AI, and have the generating AI perform the analysis of texture and reflectivity.

[0044] The analysis unit can display the waste identification results in real time and provide feedback to the user. For example, the analysis unit can display the waste identification results in real time and notify the user. For example, the analysis unit can display the identification results in real time and notify the user. The analysis unit can also display the waste identification results in real time and provide feedback to the user. For example, the analysis unit can display the identification results in real time and provide feedback to the user. Furthermore, the analysis unit can display the waste identification results in real time and provide detailed information to the user. For example, the analysis unit can display the identification results in real time and provide detailed information to the user. This allows for immediate feedback to be provided to the user by displaying the identification results in real time. Real-time display includes, but is not limited to, real-time display of identification results, notification to the user, and provision of detailed information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the identification result data into a generating AI and have the generating AI perform the real-time display.

[0045] The analysis unit can notify the waste identification results by voice, and can also accommodate visually impaired individuals. For example, the analysis unit can notify the waste identification results by voice and provide them to visually impaired individuals. For example, the analysis unit can use speech synthesis technology to notify the waste identification results by voice and provide them to visually impaired individuals. The analysis unit can also notify the waste identification results by voice and provide detailed information to visually impaired individuals. For example, the analysis unit can use speech synthesis technology to notify the waste identification results by voice and provide detailed information to visually impaired individuals. Furthermore, the analysis unit can notify the waste identification results by voice and provide them to visually impaired individuals in real time. For example, the analysis unit can use speech synthesis technology to notify the waste identification results by voice and provide them to visually impaired individuals in real time. This makes it possible to provide identification results to visually impaired individuals by notifying them by voice. Voice notification includes, for example, speech synthesis technology and notification timing, etc., and is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the identification result data into the generating AI, and have the generating AI execute a voice notification.

[0046] The analysis unit can save the waste identification results to the cloud and share them with other devices. For example, the analysis unit can use a cloud storage service to save the waste identification results to the cloud and share them with other devices. The analysis unit can also save the waste identification results to the cloud and provide them to other devices in real time. For example, the analysis unit can use a cloud storage service to save the waste identification results to the cloud and provide them to other devices in real time. Furthermore, the analysis unit can save the waste identification results to the cloud and provide detailed information to other devices. For example, the analysis unit can use a cloud storage service to save the waste identification results to the cloud and provide detailed information to other devices. This allows information to be shared with other devices by saving it to the cloud. Cloud storage includes, but is not limited to, cloud storage services and data storage formats. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the identification result data into the generating AI and have the generating AI perform the task of saving it to the cloud.

[0047] The classification unit can classify waste considering not only its recyclability but also its reusability. For example, the classification unit can classify waste considering its recyclability. For example, the classification unit can classify recyclable waste based on its recyclability. The classification unit can also classify waste considering its reusability. For example, the classification unit can classify reusable waste based on its reusability. Furthermore, the classification unit can classify waste by comprehensively considering both its recyclability and reusability. For example, the classification unit can classify waste by comprehensively considering both its recyclability and reusability. This promotes the effective utilization of waste by considering both recyclability and reusability. Reusability includes, but is not limited to, the conditions and evaluation criteria for waste reuse. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input data on the recyclability and reusability of waste into a generating AI and have the generating AI perform the classification.

[0048] The classification unit can display the waste classification results in real time during classification and provide feedback to the user. For example, the classification unit can display the waste classification results in real time and notify the user. For example, the classification unit can display the classification results in real time and notify the user. The classification unit can also display the waste classification results in real time and provide feedback to the user. For example, the classification unit can display the classification results in real time and provide feedback to the user. Furthermore, the classification unit can display the waste classification results in real time and provide detailed information to the user. For example, the classification unit can display the classification results in real time and provide detailed information to the user. This allows for immediate feedback to be provided to the user by displaying the classification results in real time. Real-time display includes, but is not limited to, real-time display of classification results, notification to the user, and provision of detailed information. Some or all of the above processing in the classification unit may be performed using, for example, AI, or not using AI. For example, the classification unit can input the classification result data into a generating AI and have the generating AI perform the real-time display.

[0049] The classification unit can provide the classification results of waste via voice notification, making it accessible to visually impaired individuals. For example, the classification unit can provide the classification results of waste via voice notification to visually impaired individuals. For example, the classification unit can use speech synthesis technology to provide the classification results of waste via voice notification to visually impaired individuals. Furthermore, the classification unit can provide the classification results of waste via voice notification to visually impaired individuals in real time. For example, the classification unit can use speech synthesis technology to provide the classification results of waste via voice notification to visually impaired individuals in real time. This allows classification results to be provided to visually impaired individuals through voice notification. Voice notification includes, but is not limited to, speech synthesis technology and notification timing. Some or all of the above-described processes in the classification unit may be performed using, for example, AI, or without AI. For example, the classification unit can input the classification result data into the generation AI, and have the generation AI execute a voice notification.

[0050] The classification unit can save the waste classification results to the cloud and share them with other devices. For example, the classification unit can use a cloud storage service to save the waste classification results to the cloud and share them with other devices. The classification unit can also save the waste classification results to the cloud and provide them to other devices in real time. For example, the classification unit can use a cloud storage service to save the waste classification results to the cloud and provide them to other devices in real time. Furthermore, the classification unit can save the waste classification results to the cloud and provide detailed information to other devices. For example, the classification unit can use a cloud storage service to save the waste classification results to the cloud and provide detailed information to other devices. This allows information to be shared with other devices by saving it to the cloud. Cloud storage includes, but is not limited to, cloud storage services and data storage formats. Some or all of the above-described processes in the classification unit may be performed using, for example, AI, or not using AI. For example, the classification unit can input the classification result data into the generation AI and have the generation AI perform the task of saving it to the cloud.

[0051] The transmitting unit can transmit waste while considering the operating status of the recycling facility to which it is sent. For example, the transmitting unit can acquire the operating status of the recycling facility in real time and select the optimal destination. The transmitting unit can also adjust the transmission timing while considering the operating status of the recycling facility. For example, the transmitting unit can adjust the transmission timing while considering the operating status of the recycling facility. Furthermore, the transmitting unit can dynamically change the destination based on the operating status of the recycling facility. For example, the transmitting unit can dynamically change the destination based on the operating status of the recycling facility. This optimizes the selection of the destination by considering the operating status of the recycling facility. Operating status includes, but is not limited to, the operating rate and processing capacity of the recycling facility. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input the operating status data of the recycling facility into a generating AI and have the generating AI perform the selection of the destination.

[0052] The transmitting unit can select a route that minimizes the environmental impact on the recycling facility to which the waste is sent when transmitting it. For example, the transmitting unit can select the shortest route to minimize the environmental impact. The transmitting unit can also select a route with low traffic volume to minimize the environmental impact. Furthermore, the transmitting unit can also select an eco-friendly route to minimize the environmental impact. By selecting a route that minimizes the environmental impact, the impact on the environment can be reduced. Environmental impact includes, but is not limited to, carbon dioxide emissions and energy consumption. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input environmental impact data into a generating AI and have the generating AI perform route selection.

[0053] The transmitting unit can transmit waste while considering the geographical location information of the recycling facility to which the waste is to be sent. For example, the transmitting unit can select the optimal destination based on the geographical location information of the recycling facility. The transmitting unit can also optimize the transmission route while considering the geographical location information of the recycling facility. For example, the transmitting unit can optimize the transmission route while considering the geographical location information of the recycling facility. Furthermore, the transmitting unit can dynamically change the destination based on the geographical location information of the recycling facility. For example, the transmitting unit can dynamically change the destination based on the geographical location information of the recycling facility. This optimizes the selection of the destination by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information on a map. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input geographical location data of the recycling facility into a generating AI and have the generating AI perform the selection of the destination.

[0054] The transmitting unit can transmit waste while considering the processing capacity of the recycling facility to which it is sent. For example, the transmitting unit can acquire the processing capacity of the recycling facility in real time and select the optimal destination. The transmitting unit can also adjust the transmission timing while considering the processing capacity of the recycling facility. For example, the transmitting unit can adjust the transmission timing while considering the processing capacity of the recycling facility. Furthermore, the transmitting unit can dynamically change the destination based on the processing capacity of the recycling facility. For example, the transmitting unit can dynamically change the destination based on the processing capacity of the recycling facility. This optimizes the selection of the destination by considering the processing capacity. Processing capacity includes, but is not limited to, the processing speed and processing volume of the recycling facility. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the processing capacity data of the recycling facility into a generating AI and have the generating AI select the destination.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A recycling management system can include functions to identify the source of waste, in addition to identifying and classifying waste. For example, the acquisition unit can record the location from which the waste originated when it takes images of the waste. This allows for the identification of the waste source and the implementation of measures to reduce waste generation at specific locations. Furthermore, the analysis unit can analyze the type and quantity of waste based on its source and optimize waste management for each source. In addition, the classification unit can classify waste by source and streamline the recycling process for each source. This improves the efficiency of the recycling process by identifying the waste source and optimizing waste management for each source.

[0057] A recycling management system can include functions for evaluating the recyclability of waste, in addition to identifying and classifying it. For example, an analysis unit can evaluate the recyclability based on the type and condition of the waste. This allows for the assessment of the recyclability of waste and prioritize sending recyclable waste to the recycling process. A classification unit can classify waste based on its recyclability and efficiently send recyclable waste to the recycling process. Furthermore, a transmission unit can send waste to appropriate recycling facilities based on its recyclability. This improves the recycling rate by evaluating the recyclability of waste and efficiently sending recyclable waste to the recycling process.

[0058] In addition to identifying and classifying waste, a recycling management system can include functions to support the optimization of the waste recycling process. For example, the analysis unit can propose the optimal recycling process based on the type and quantity of waste. This allows for the selection of the most suitable recycling process according to the type and quantity of waste, thereby improving the efficiency of the recycling process. Furthermore, the classification unit can classify waste based on the optimal recycling process, further streamlining the recycling process. In addition, the transmission unit can send waste to the appropriate recycling facility based on the optimal recycling process. This optimizes the waste recycling process and improves its efficiency.

[0059] In addition to identifying and classifying waste, a recycling management system can include a function to track the waste recycling process. For example, the acquisition unit can record waste tracking information when it takes images of the waste. This allows for the recording of waste tracking information and tracking of the waste recycling process. Furthermore, the analysis unit can analyze the progress of the recycling process based on the waste tracking information. In addition, the classification unit can optimize the recycling process based on the waste tracking information. This improves the efficiency of the recycling process by recording waste tracking information and tracking the recycling process.

[0060] In addition to waste identification and classification, a recycling management system can include a function to predict the waste recycling process. For example, the analysis unit can predict the recycling process based on the type and quantity of waste. This allows for the prediction of the recycling process according to the type and quantity of waste, and the optimization of the recycling process plan. Furthermore, the classification unit can classify the waste based on the recycling process prediction, thereby streamlining the recycling process. In addition, the transmission unit can send the waste to the appropriate recycling facility based on the recycling process prediction. This improves the efficiency of the recycling process by predicting the waste recycling process and optimizing the recycling process plan.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The acquisition unit acquires images of the waste. The acquisition unit takes images of the waste using, for example, a camera or sensor. For example, the acquisition unit can acquire images of the waste in real time using a camera installed at a waste treatment facility. The acquisition unit can also acquire images of the waste using sensors. For example, the acquisition unit can acquire images of the waste using an infrared sensor. Furthermore, the acquisition unit can also acquire images of the waste using a drone. For example, the acquisition unit can acquire images of the waste using a camera mounted on a drone. Step 2: The analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The analysis unit identifies the type of waste using, for example, image recognition technology. For example, the analysis unit can identify the type of waste using deep learning. The analysis unit can also identify the type of waste using pattern recognition technology. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using pattern recognition technology. Furthermore, the analysis unit can also analyze the characteristics of the waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of the waste using a pre-trained dataset. Step 3: The classification unit classifies the recyclable waste identified by the analysis unit. The classification unit classifies the identified waste into appropriate categories, for example. For example, the classification unit can classify plastics into the plastics category and metals into the metals category. The classification unit can also further subdivide the recyclable waste. For example, the classification unit can classify plastics by type. Step 4: The transmission unit sends the waste sorted by the classification unit to the recycling process. The transmission unit sends the sorted waste to the appropriate recycling facility. For example, the transmission unit can send plastics to a plastic recycling facility and metals to a metal recycling facility. The transmission unit can also send the waste at the appropriate time to ensure the recycling process is efficient. For example, the transmission unit can send the waste considering the operating status of the recycling facility.

[0063] (Example of form 2) A recycling management system according to an embodiment of the present invention is a system that uses image recognition and classification algorithms to identify recyclable waste and realize an efficient recycling process. This recycling management system acquires images of waste, and AI analyzes these images to identify recyclable waste. Next, the identified recyclable waste is classified and sent to the appropriate recycling process. This system reduces the amount of waste and alleviates the burden on the environment. For example, the recycling management system acquires images of waste. In this case, images of the waste are taken using cameras or sensors. For example, a camera installed at a waste treatment facility acquires images of waste in real time. These images are input to the AI. Next, the recycling management system has the AI ​​analyze the input images. The AI ​​uses image recognition technology to identify the type of waste. For example, it identifies recyclable waste such as plastics, metals, and paper. In this case, the AI ​​analyzes the characteristics of the waste based on data that has been learned in advance. The identified recyclable waste is classified by the recycling management system. The AI ​​classifies the identified waste into the appropriate category. For example, plastics are classified into the plastics category, and metals into the metals category. This classification is important for efficiently carrying out the recycling process. Classified waste is sent to the appropriate recycling process by the recycling management system. For example, plastics are sent to plastic recycling facilities, and metals are sent to metal recycling facilities. This process ensures efficient waste recycling. This system reduces the amount of waste and lessens the environmental burden. Municipalities and waste management companies can sort and process waste more efficiently, improving recycling rates and reducing waste disposal costs and environmental impact. Businesses and industrial facilities can reduce their environmental impact by improving waste management processes and more accurately identifying recyclable waste. Consumers can utilize this technology when properly sorting waste, increasing their awareness of contributing to the environment.Educational institutions can leverage this technology to enhance environmental education programs, enabling students to understand the importance of waste management and learn about the value of recycling. This allows recycling management systems to reduce waste volume and lessen the environmental impact.

[0064] The recycling management system according to this embodiment comprises an acquisition unit, an analysis unit, a classification unit, and a transmission unit. The acquisition unit acquires images of waste. The acquisition unit takes images of waste using, for example, a camera or a sensor. For example, the acquisition unit can acquire images of waste in real time using a camera installed at a waste treatment facility. The acquisition unit can also acquire images of waste using a sensor. For example, the acquisition unit can acquire images of waste using an infrared sensor. Furthermore, the acquisition unit can acquire images of waste using a drone. For example, the acquisition unit can acquire images of waste using a camera mounted on a drone. The analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The analysis unit identifies the type of waste using, for example, image recognition technology. For example, the analysis unit can identify the type of waste using deep learning. Furthermore, the analysis unit can identify the type of waste using pattern recognition technology. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using pattern recognition technology. Furthermore, the analysis unit can also analyze the characteristics of waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of waste using a pre-trained dataset. The classification unit classifies the recyclable waste identified by the analysis unit. The classification unit classifies the identified waste into appropriate categories. For example, the classification unit can classify plastics into the plastics category and metals into the metals category. The classification unit can also further subdivide the recyclable waste. For example, the classification unit can classify plastics by type. The transmission unit sends the waste classified by the classification unit to the recycling process. The transmission unit sends the classified waste to appropriate recycling facilities. For example, the transmission unit can send plastics to plastic recycling facilities and metals to metal recycling facilities. The transmission unit can also send the waste at the appropriate time to ensure the recycling process is efficient. For example, the transmission unit can send the waste considering the operating status of the recycling facilities.As a result, the recycling management system according to this embodiment can achieve an efficient recycling process by acquiring, analyzing, classifying, and transmitting images of waste.

[0065] The acquisition unit acquires images of waste. For example, the acquisition unit takes images of waste using cameras and sensors. Specifically, it can acquire detailed images of waste in real time using high-resolution cameras installed at waste treatment facilities. This allows for accurate capture of features such as the shape, color, and texture of the waste. In addition, by using infrared sensors, it is possible to detect the temperature and internal structure of the waste and acquire information that cannot be confirmed with visible light. Furthermore, by using drones, images of waste over a wide area can be efficiently acquired. Since the camera mounted on the drone can be freely adjusted in altitude and angle, images of waste in places that are difficult to see from the ground or at high altitudes can be easily acquired. As a result, the acquisition unit can acquire images of waste with high accuracy and efficiency using a variety of means and provide them to the subsequent analysis unit.

[0066] The analysis unit analyzes images acquired by the acquisition unit to identify recyclable waste. The analysis utilizes state-of-the-art image recognition technology and deep learning algorithms to accurately identify waste types. Specifically, it uses a model trained on a large dataset of waste images to extract waste features. For example, to identify recyclable waste such as plastic, metal, and paper, it analyzes features such as color, shape, and texture. Furthermore, by combining this with pattern recognition technology, it can detect subtle differences in waste, enabling more detailed classification. The analysis unit processes images in real time, allowing for rapid identification of waste types. In addition, the analysis unit can utilize historical data and statistical information to evaluate waste generation trends and recycling efficiency. This enables the analysis unit to accurately identify waste types and contribute to optimizing the recycling process.

[0067] The classification unit sorts the recyclable waste identified by the analysis unit. The classification unit uses mechanical sorting devices and robotic arms to classify the identified waste into appropriate categories. For example, when classifying into categories such as plastics, metals, and paper, it sets sorting criteria according to the characteristics of each type of waste and performs the classification automatically. Furthermore, the classification unit can further subdivide recyclable waste. For instance, when classifying plastics by type, it identifies different types of plastics such as polyethylene, polypropylene, and polystyrene and classifies them into their respective categories. Similarly, for metal waste, it can identify different metals such as iron, aluminum, and copper and classify them appropriately. This allows the classification unit to classify recyclable waste with high accuracy and improve the efficiency of the recycling process.

[0068] The transmission unit sends the waste classified by the classification unit to the recycling process. The transmission unit uses automated transport systems and conveyor belts to send the classified waste to the appropriate recycling facility. For example, plastic waste can be sent to a plastic recycling facility, and metal waste to a metal recycling facility. The transmission unit can send the waste at the appropriate time, taking into account the operating status and processing capacity of the recycling facility. For example, if a recycling facility is full, the waste can be sent to another facility or temporarily stored. The transmission unit can also monitor the waste transmission status in real time and adjust the transmission route and timing as needed. This allows the transmission unit to efficiently carry out the recycling process and ensure the proper disposal of waste. Furthermore, the transmission unit can record the waste transmission history and ensure traceability of the recycling process. This allows the transmission unit to improve the reliability and efficiency of the entire recycling management system.

[0069] The acquisition unit can capture images of waste using cameras and sensors. For example, the acquisition unit can capture images of waste using a camera. For example, the acquisition unit can capture images of waste using a high-resolution camera. The acquisition unit can also capture images of waste using sensors. For example, the acquisition unit can capture images of waste using an infrared sensor. Furthermore, the acquisition unit can capture images of waste using a drone. For example, the acquisition unit can capture images of waste using a camera mounted on a drone. This allows for accurate acquisition of images of waste using cameras and sensors. Cameras and sensors include, but are not limited to, high-resolution cameras, infrared sensors, and cameras mounted on drones. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the image data of waste captured by the camera into a generating AI and have the generating AI perform image data analysis.

[0070] The analysis unit can identify the type of waste using image recognition technology. The analysis unit can identify the type of waste using, for example, deep learning. For example, the analysis unit can identify the type of waste using a deep learning model. The analysis unit can also identify the type of waste using pattern recognition technology. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using pattern recognition technology. Furthermore, the analysis unit can analyze the characteristics of waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of waste using a pre-trained dataset. This allows for accurate identification of waste types using image recognition technology. Image recognition technology includes, but is not limited to, deep learning and pattern recognition technology. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of waste into a generating AI and have the generating AI perform waste type identification.

[0071] The analysis unit can analyze the characteristics of waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of waste using a pre-trained dataset. For example, the analysis unit can analyze characteristics such as the shape, color, and texture of waste using a pre-trained dataset. The analysis unit can also identify the type of waste using a pre-trained dataset. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using a pre-trained dataset. This allows for an accurate understanding of the characteristics of waste by analyzing based on pre-trained data. Pre-trained data includes, but is not limited to, image datasets of waste and characteristic datasets of waste. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input a pre-trained dataset into a generating AI and have the generating AI perform the analysis of waste characteristics.

[0072] The classification unit can classify identified waste into appropriate categories. For example, the classification unit can classify identified waste into categories such as plastic, metal, and paper. For example, the classification unit can classify plastic into the plastic category and metal into the metal category. The classification unit can also further subdivide recyclable waste. For example, the classification unit can classify plastic by type. By classifying into appropriate categories, the recycling process can be made more efficient. Appropriate categories include, but are not limited to, plastic, metal, and paper. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input data on identified waste into a generating AI and have the generating AI perform the waste classification.

[0073] The transmitting unit can send the classified waste to the appropriate recycling process. For example, the transmitting unit can send the classified waste to the appropriate recycling facility. For example, the transmitting unit can send plastics to a plastic recycling facility and metals to a metal recycling facility. The transmitting unit can also send the waste at the appropriate time to ensure the recycling process is efficient. For example, the transmitting unit can send the waste considering the operating status of the recycling facility. This ensures that the waste is recycled efficiently by sending it to the appropriate recycling process. Appropriate recycling processes include, but are not limited to, plastic recycling facilities and metal recycling facilities. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the classified waste data into a generating AI and have the generating AI perform the transmission to the recycling process.

[0074] The acquisition unit can estimate the user's emotions and adjust the timing of waste image acquisition based on the estimated emotions. For example, if the user is feeling stressed, the acquisition unit can delay the timing of waste image acquisition. For example, the acquisition unit can estimate the user's emotions and delay the timing of image acquisition if the user is feeling stressed. The acquisition unit can also speed up the timing of waste image acquisition if the user is relaxed. For example, the acquisition unit can estimate the user's emotions and speed up the timing of image acquisition if the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can instantly acquire the waste image. For example, the acquisition unit can estimate the user's emotions and instantly acquire the image if the user is in a hurry. In this way, the burden on the user can be reduced by adjusting the timing of image acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without using AI. For example, the acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0075] The acquisition unit can automatically adjust the camera resolution and shooting angle according to the type and quantity of waste. For example, if there is a large amount of waste, the acquisition unit can use a wide-angle lens to capture the entire area. For example, the acquisition unit can use a wide-angle lens to capture a large amount of waste. Also, if the waste is small and fine, the acquisition unit can use a high-resolution camera to capture details. For example, the acquisition unit can use a high-resolution camera to capture small and fine waste. Furthermore, the acquisition unit can automatically adjust the optimal shooting angle according to the type of waste. For example, the acquisition unit can automatically adjust the optimal shooting angle according to the type of waste. This allows for the acquisition of optimal images by adjusting the camera settings according to the type and quantity of waste. Camera resolution and shooting angle include, but are not limited to, wide-angle lenses, high-resolution cameras, and automatic adjustment of the optimal shooting angle. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the type and quantity of waste into a generating AI and have the generating AI adjust the camera resolution and shooting angle.

[0076] The acquisition unit can simultaneously acquire environmental information such as the temperature and humidity of the waste. For example, the acquisition unit can measure the temperature of the waste with a sensor and record it along with an image. For example, the acquisition unit can measure the temperature of the waste using a temperature sensor and record it along with image data. The acquisition unit can also measure the humidity of the waste with a sensor and record it along with an image. For example, the acquisition unit can measure the humidity of the waste using a humidity sensor and record it along with image data. Furthermore, the acquisition unit can acquire environmental information (temperature, humidity) around the waste and record it along with an image. For example, the acquisition unit can acquire environmental information around the waste using a temperature sensor and a humidity sensor and record it along with image data. This allows for a more accurate understanding of the waste's condition by simultaneously acquiring environmental information. Environmental information includes, but is not limited to, temperature, humidity, and atmospheric pressure. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input temperature and humidity data of the waste into a generating AI and have the generating AI perform the acquisition of environmental information.

[0077] The acquisition unit can estimate the user's emotions and adjust the frequency of waste image acquisition based on the estimated emotions. For example, if the user is feeling stressed, the acquisition unit can reduce the image acquisition frequency. For example, if the acquisition unit estimates the user's emotions and reduces the image acquisition frequency if the user is feeling stressed. The acquisition unit can also increase the image acquisition frequency if the user is relaxed. For example, if the acquisition unit estimates the user's emotions and increases the image acquisition frequency if the user is relaxed. Furthermore, if the acquisition unit is in a hurry, it can instantly adjust the image acquisition frequency. For example, if the acquisition unit estimates the user's emotions and instantly adjusts the image acquisition frequency if the user is in a hurry. In this way, the burden on the user can be reduced by adjusting the image acquisition frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0078] The acquisition unit can acquire location information of waste and propose the optimal placement of recycling facilities. For example, the acquisition unit can acquire location information of waste using GPS and optimize the placement of recycling facilities. For example, the acquisition unit can acquire location information of waste using GPS and optimize the placement of recycling facilities. The acquisition unit can also display the location information of waste on a map and propose the placement of recycling facilities. For example, the acquisition unit can display the location information of waste on a map and propose the placement of recycling facilities. Furthermore, the acquisition unit can acquire location information of waste in real time and optimize the placement of recycling facilities. For example, the acquisition unit can acquire location information of waste in real time and optimize the placement of recycling facilities. In this way, the placement of recycling facilities can be optimized by acquiring location information of waste. Location information includes, but is not limited to, GPS data and location information on a map. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input location information data of waste into a generating AI and have the generating AI perform the optimization of the placement of recycling facilities.

[0079] The acquisition unit can acquire audio information of waste and use it as auxiliary information to identify the type of waste. For example, the acquisition unit can acquire audio information of waste using a microphone and use it for identification. The acquisition unit can also analyze audio information of waste and use it as auxiliary information to identify the type. For example, the acquisition unit can analyze audio information of waste using voice analysis technology and use it for identification. Furthermore, the acquisition unit can acquire audio information of waste in real time and use it for identification. For example, the acquisition unit can acquire audio information of waste in real time and use it for identification. This improves the accuracy of waste identification by utilizing audio information. Audio information includes, but is not limited to, the sounds of waste being crushed or dropped. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input audio information data of waste into a generating AI and have the generating AI perform the analysis of the audio information.

[0080] The analysis unit can estimate the user's emotions and adjust the accuracy of waste identification based on the estimated emotions. For example, the analysis unit can increase the identification accuracy if the user is stressed. For example, the analysis unit can estimate the user's emotions and increase the identification accuracy if the user is stressed. The analysis unit can also maintain normal identification accuracy if the user is relaxed. For example, the analysis unit can estimate the user's emotions and maintain normal identification accuracy if the user is relaxed. Furthermore, the analysis unit can quickly adjust the identification accuracy if the user is in a hurry. For example, the analysis unit can estimate the user's emotions and quickly adjust the identification accuracy if the user is in a hurry. This improves the accuracy of identification by adjusting the identification accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0081] The analysis unit can analyze not only the shape and color of waste, but also its characteristics such as texture and reflectivity. For example, the analysis unit can analyze the texture of waste and use it for identification. For example, the analysis unit can use texture analysis technology to analyze the texture of waste and use it for identification. The analysis unit can also analyze the reflectivity of waste and use it for identification. For example, the analysis unit can use reflectivity analysis technology to analyze the reflectivity of waste and use it for identification. Furthermore, the analysis unit can comprehensively analyze the shape, color, texture, and reflectivity of waste and use it for identification. For example, the analysis unit can use shape analysis technology, color analysis technology, texture analysis technology, and reflectivity analysis technology to comprehensively analyze the shape, color, texture, and reflectivity of waste and use it for identification. This improves identification accuracy by analyzing characteristics such as texture and reflectivity. Texture and reflectivity include, but are not limited to, the surface texture and light reflectivity of waste. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input data on the texture and reflectivity of waste materials into the generating AI, and have the generating AI perform the analysis of texture and reflectivity.

[0082] The analysis unit can display the waste identification results in real time and provide feedback to the user. For example, the analysis unit can display the waste identification results in real time and notify the user. For example, the analysis unit can display the identification results in real time and notify the user. The analysis unit can also display the waste identification results in real time and provide feedback to the user. For example, the analysis unit can display the identification results in real time and provide feedback to the user. Furthermore, the analysis unit can display the waste identification results in real time and provide detailed information to the user. For example, the analysis unit can display the identification results in real time and provide detailed information to the user. This allows for immediate feedback to be provided to the user by displaying the identification results in real time. Real-time display includes, but is not limited to, real-time display of identification results, notification to the user, and provision of detailed information. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the identification result data into a generating AI and have the generating AI perform the real-time display.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the waste identification results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is tense, the analysis unit can estimate the user's emotions and provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the analysis unit estimates the user's emotions and provides a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the analysis unit estimates the user's emotions and provides a display method that gets straight to the point if the user is in a hurry. By adjusting the display method according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0084] The analysis unit can notify the waste identification results by voice, and can also accommodate visually impaired individuals. For example, the analysis unit can notify the waste identification results by voice and provide them to visually impaired individuals. For example, the analysis unit can use speech synthesis technology to notify the waste identification results by voice and provide them to visually impaired individuals. The analysis unit can also notify the waste identification results by voice and provide detailed information to visually impaired individuals. For example, the analysis unit can use speech synthesis technology to notify the waste identification results by voice and provide detailed information to visually impaired individuals. Furthermore, the analysis unit can notify the waste identification results by voice and provide them to visually impaired individuals in real time. For example, the analysis unit can use speech synthesis technology to notify the waste identification results by voice and provide them to visually impaired individuals in real time. This makes it possible to provide identification results to visually impaired individuals by notifying them by voice. Voice notification includes, for example, speech synthesis technology and notification timing, etc., and is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the identification result data into the generating AI, and have the generating AI execute a voice notification.

[0085] The analysis unit can save the waste identification results to the cloud and share them with other devices. For example, the analysis unit can use a cloud storage service to save the waste identification results to the cloud and share them with other devices. The analysis unit can also save the waste identification results to the cloud and provide them to other devices in real time. For example, the analysis unit can use a cloud storage service to save the waste identification results to the cloud and provide them to other devices in real time. Furthermore, the analysis unit can save the waste identification results to the cloud and provide detailed information to other devices. For example, the analysis unit can use a cloud storage service to save the waste identification results to the cloud and provide detailed information to other devices. This allows information to be shared with other devices by saving it to the cloud. Cloud storage includes, but is not limited to, cloud storage services and data storage formats. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the identification result data into the generating AI and have the generating AI perform the task of saving it to the cloud.

[0086] The classification unit can estimate the user's emotions and adjust the waste classification criteria based on the estimated emotions. For example, if the user is stressed, the classification unit can relax the classification criteria. For example, if the user is stressed, the classification unit can estimate the user's emotions and relax the classification criteria. The classification unit can also maintain normal classification criteria if the user is relaxed. For example, if the user is relaxed, the classification unit can maintain normal classification criteria. Furthermore, if the user is in a hurry, the classification unit can quickly adjust the classification criteria. For example, if the classification unit is stressed, the classification unit can quickly adjust the classification criteria. By adjusting the classification criteria according to the user's emotions, the accuracy of classification is improved. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The classification unit can classify waste considering not only its recyclability but also its reusability. For example, the classification unit can classify waste considering its recyclability. For example, the classification unit can classify recyclable waste based on its recyclability. The classification unit can also classify waste considering its reusability. For example, the classification unit can classify reusable waste based on its reusability. Furthermore, the classification unit can classify waste by comprehensively considering both its recyclability and reusability. For example, the classification unit can classify waste by comprehensively considering both its recyclability and reusability. This promotes the effective utilization of waste by considering both recyclability and reusability. Reusability includes, but is not limited to, the conditions and evaluation criteria for waste reuse. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input data on the recyclability and reusability of waste into a generating AI and have the generating AI perform the classification.

[0088] The classification unit can display the waste classification results in real time during classification and provide feedback to the user. For example, the classification unit can display the waste classification results in real time and notify the user. For example, the classification unit can display the classification results in real time and notify the user. The classification unit can also display the waste classification results in real time and provide feedback to the user. For example, the classification unit can display the classification results in real time and provide feedback to the user. Furthermore, the classification unit can display the waste classification results in real time and provide detailed information to the user. For example, the classification unit can display the classification results in real time and provide detailed information to the user. This allows for immediate feedback to be provided to the user by displaying the classification results in real time. Real-time display includes, but is not limited to, real-time display of classification results, notification to the user, and provision of detailed information. Some or all of the above processing in the classification unit may be performed using, for example, AI, or not using AI. For example, the classification unit can input the classification result data into a generating AI and have the generating AI perform the real-time display.

[0089] The classification unit can estimate the user's emotions and adjust the display method of the waste classification results based on the estimated user emotions. For example, if the user is tense, the classification unit can provide a simple and highly visible display method. For example, if the user is tense, the classification unit can estimate the user's emotions and provide a simple and highly visible display method. Also, if the user is relaxed, the classification unit can provide a display method that includes detailed information. For example, if the user is relaxed, the classification unit can estimate the user's emotions and provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the classification unit can provide a display method that gets to the point. For example, if the classification unit estimates the user's emotions and provides a display method that gets to the point. This makes it possible to provide a user-friendly display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0090] The classification unit can provide the classification results of waste via voice notification, making it accessible to visually impaired individuals. For example, the classification unit can provide the classification results of waste via voice notification to visually impaired individuals. For example, the classification unit can use speech synthesis technology to provide the classification results of waste via voice notification to visually impaired individuals. Furthermore, the classification unit can provide the classification results of waste via voice notification to visually impaired individuals in real time. For example, the classification unit can use speech synthesis technology to provide the classification results of waste via voice notification to visually impaired individuals in real time. This allows classification results to be provided to visually impaired individuals through voice notification. Voice notification includes, but is not limited to, speech synthesis technology and notification timing. Some or all of the above-described processes in the classification unit may be performed using, for example, AI, or without AI. For example, the classification unit can input the classification result data into the generation AI, and have the generation AI execute a voice notification.

[0091] The classification unit can save the waste classification results to the cloud and share them with other devices. For example, the classification unit can use a cloud storage service to save the waste classification results to the cloud and share them with other devices. The classification unit can also save the waste classification results to the cloud and provide them to other devices in real time. For example, the classification unit can use a cloud storage service to save the waste classification results to the cloud and provide them to other devices in real time. Furthermore, the classification unit can save the waste classification results to the cloud and provide detailed information to other devices. For example, the classification unit can use a cloud storage service to save the waste classification results to the cloud and provide detailed information to other devices. This allows information to be shared with other devices by saving it to the cloud. Cloud storage includes, but is not limited to, cloud storage services and data storage formats. Some or all of the above-described processes in the classification unit may be performed using, for example, AI, or not using AI. For example, the classification unit can input the classification result data into the generation AI and have the generation AI perform the task of saving it to the cloud.

[0092] The transmission unit can estimate the user's emotions and adjust the timing of waste transmission based on the estimated emotions. For example, if the user is feeling stressed, the transmission unit can delay the transmission timing. For example, the transmission unit can estimate the user's emotions and delay the transmission timing if the user is feeling stressed. The transmission unit can also speed up the transmission timing if the user is relaxed. For example, the transmission unit can estimate the user's emotions and speed up the transmission timing if the user is relaxed. Furthermore, if the user is in a hurry, the transmission unit can transmit immediately. For example, the transmission unit can estimate the user's emotions and transmit immediately if the user is in a hurry. This reduces the burden on the user by adjusting the transmission timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0093] The transmitting unit can transmit waste while considering the operating status of the recycling facility to which it is sent. For example, the transmitting unit can acquire the operating status of the recycling facility in real time and select the optimal destination. The transmitting unit can also adjust the transmission timing while considering the operating status of the recycling facility. For example, the transmitting unit can adjust the transmission timing while considering the operating status of the recycling facility. Furthermore, the transmitting unit can dynamically change the destination based on the operating status of the recycling facility. For example, the transmitting unit can dynamically change the destination based on the operating status of the recycling facility. This optimizes the selection of the destination by considering the operating status of the recycling facility. Operating status includes, but is not limited to, the operating rate and processing capacity of the recycling facility. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input the operating status data of the recycling facility into a generating AI and have the generating AI perform the selection of the destination.

[0094] The transmitting unit can select a route that minimizes the environmental impact on the recycling facility to which the waste is sent when transmitting it. For example, the transmitting unit can select the shortest route to minimize the environmental impact. The transmitting unit can also select a route with low traffic volume to minimize the environmental impact. Furthermore, the transmitting unit can also select an eco-friendly route to minimize the environmental impact. By selecting a route that minimizes the environmental impact, the impact on the environment can be reduced. Environmental impact includes, but is not limited to, carbon dioxide emissions and energy consumption. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input environmental impact data into a generating AI and have the generating AI perform route selection.

[0095] The transmission unit can estimate the user's emotions and adjust the frequency of waste transmission based on the estimated emotions. For example, if the user is stressed, the transmission unit can reduce the transmission frequency. For example, the transmission unit can estimate the user's emotions and reduce the transmission frequency if the user is stressed. The transmission unit can also increase the transmission frequency if the user is relaxed. For example, the transmission unit can estimate the user's emotions and increase the transmission frequency if the user is relaxed. Furthermore, if the user is in a hurry, the transmission unit can instantly adjust the transmission frequency. For example, the transmission unit can estimate the user's emotions and instantly adjust the transmission frequency if the user is in a hurry. This reduces the burden on the user by adjusting the transmission frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0096] The transmitting unit can transmit waste while considering the geographical location information of the recycling facility to which the waste is to be sent. For example, the transmitting unit can select the optimal destination based on the geographical location information of the recycling facility. The transmitting unit can also optimize the transmission route while considering the geographical location information of the recycling facility. For example, the transmitting unit can optimize the transmission route while considering the geographical location information of the recycling facility. Furthermore, the transmitting unit can dynamically change the destination based on the geographical location information of the recycling facility. For example, the transmitting unit can dynamically change the destination based on the geographical location information of the recycling facility. This optimizes the selection of the destination by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and location information on a map. Some or all of the above processing in the transmitting unit may be performed using, for example, AI, or not using AI. For example, the transmitting unit can input geographical location data of the recycling facility into a generating AI and have the generating AI perform the selection of the destination.

[0097] The transmitting unit can transmit waste while considering the processing capacity of the recycling facility to which it is sent. For example, the transmitting unit can acquire the processing capacity of the recycling facility in real time and select the optimal destination. The transmitting unit can also adjust the transmission timing while considering the processing capacity of the recycling facility. For example, the transmitting unit can adjust the transmission timing while considering the processing capacity of the recycling facility. Furthermore, the transmitting unit can dynamically change the destination based on the processing capacity of the recycling facility. For example, the transmitting unit can dynamically change the destination based on the processing capacity of the recycling facility. This optimizes the selection of the destination by considering the processing capacity. Processing capacity includes, but is not limited to, the processing speed and processing volume of the recycling facility. Some or all of the above processing in the transmitting unit may be performed using AI, for example, or without AI. For example, the transmitting unit can input the processing capacity data of the recycling facility into a generating AI and have the generating AI select the destination.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] A recycling management system can include functions to identify the source of waste, in addition to identifying and classifying waste. For example, the acquisition unit can record the location from which the waste originated when it takes images of the waste. This allows for the identification of the waste source and the implementation of measures to reduce waste generation at specific locations. Furthermore, the analysis unit can analyze the type and quantity of waste based on its source and optimize waste management for each source. In addition, the classification unit can classify waste by source and streamline the recycling process for each source. This improves the efficiency of the recycling process by identifying the waste source and optimizing waste management for each source.

[0100] A recycling management system can include functions for evaluating the recyclability of waste, in addition to identifying and classifying it. For example, an analysis unit can evaluate the recyclability based on the type and condition of the waste. This allows for the assessment of the recyclability of waste and prioritize sending recyclable waste to the recycling process. A classification unit can classify waste based on its recyclability and efficiently send recyclable waste to the recycling process. Furthermore, a transmission unit can send waste to appropriate recycling facilities based on its recyclability. This improves the recycling rate by evaluating the recyclability of waste and efficiently sending recyclable waste to the recycling process.

[0101] In addition to identifying and classifying waste, a recycling management system can include functions to support the optimization of the waste recycling process. For example, the analysis unit can propose the optimal recycling process based on the type and quantity of waste. This allows for the selection of the most suitable recycling process according to the type and quantity of waste, thereby improving the efficiency of the recycling process. Furthermore, the classification unit can classify waste based on the optimal recycling process, further streamlining the recycling process. In addition, the transmission unit can send waste to the appropriate recycling facility based on the optimal recycling process. This optimizes the waste recycling process and improves its efficiency.

[0102] In addition to identifying and classifying waste, a recycling management system can include a function to track the waste recycling process. For example, the acquisition unit can record waste tracking information when it takes images of the waste. This allows for the recording of waste tracking information and tracking of the waste recycling process. Furthermore, the analysis unit can analyze the progress of the recycling process based on the waste tracking information. In addition, the classification unit can optimize the recycling process based on the waste tracking information. This improves the efficiency of the recycling process by recording waste tracking information and tracking the recycling process.

[0103] In addition to waste identification and classification, a recycling management system can include a function to predict the waste recycling process. For example, the analysis unit can predict the recycling process based on the type and quantity of waste. This allows for the prediction of the recycling process according to the type and quantity of waste, and the optimization of the recycling process plan. Furthermore, the classification unit can classify the waste based on the recycling process prediction, thereby streamlining the recycling process. In addition, the transmission unit can send the waste to the appropriate recycling facility based on the recycling process prediction. This improves the efficiency of the recycling process by predicting the waste recycling process and optimizing the recycling process plan.

[0104] The recycling management system can estimate the user's emotions and adjust the waste recycling process based on those emotions. For example, the analysis unit can simplify the recycling process if the user is stressed. This reduces the user's burden and allows the recycling process to proceed smoothly. The classification unit can provide a detailed recycling process if the user is relaxed. Furthermore, the transmission unit can expedite the recycling process if the user is in a hurry. By adjusting the recycling process according to the user's emotions, the system can reduce the user's burden and improve the efficiency of the recycling process.

[0105] A recycling management system can estimate a user's emotions and provide feedback on the waste recycling process based on those emotions. For example, the analysis unit can provide simple, easy-to-understand feedback if the user is stressed, making the recycling process easier for the user to understand. The classification unit can provide detailed feedback if the user is relaxed. Furthermore, the transmission unit can provide concise feedback if the user is in a hurry. By providing feedback according to the user's emotions, the system can make the recycling process easier for the user to understand and improve its efficiency.

[0106] The recycling management system can estimate the user's emotions and adjust the notification method for the waste recycling process based on those emotions. For example, the analysis unit can provide a simple and highly visible notification method if the user is feeling stressed, making it easier for the user to understand the recycling process. The classification unit can provide a detailed notification method if the user is relaxed. Furthermore, the transmission unit can provide a concise notification method if the user is in a hurry. By adjusting the notification method according to the user's emotions, the system can make it easier for users to understand the recycling process and improve its efficiency.

[0107] The recycling management system can estimate the user's emotions and adjust the interface of the waste recycling process based on those emotions. For example, the analysis unit can provide a simple and highly visible interface if the user is stressed, making it easier for the user to understand the recycling process. The classification unit can provide a detailed interface if the user is relaxed. Furthermore, the transmission unit can provide a concise interface if the user is in a hurry. By adjusting the interface according to the user's emotions, the system can make the recycling process easier for the user to understand and improve its efficiency.

[0108] The recycling management system can estimate the user's emotions and provide support for the waste recycling process based on those emotions. For example, the analysis unit can enhance support for the recycling process if the user is feeling stressed, allowing the user to proceed smoothly. The classification unit can provide detailed support if the user is relaxed. Furthermore, the transmission unit can provide rapid support if the user is in a hurry. By providing support according to the user's emotions, the system can help the user proceed smoothly with the recycling process and improve its efficiency.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The acquisition unit acquires images of the waste. The acquisition unit takes images of the waste using, for example, a camera or sensor. For example, the acquisition unit can acquire images of the waste in real time using a camera installed at a waste treatment facility. The acquisition unit can also acquire images of the waste using sensors. For example, the acquisition unit can acquire images of the waste using an infrared sensor. Furthermore, the acquisition unit can also acquire images of the waste using a drone. For example, the acquisition unit can acquire images of the waste using a camera mounted on a drone. Step 2: The analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The analysis unit identifies the type of waste using, for example, image recognition technology. For example, the analysis unit can identify the type of waste using deep learning. The analysis unit can also identify the type of waste using pattern recognition technology. For example, the analysis unit can identify recyclable waste such as plastic, metal, and paper using pattern recognition technology. Furthermore, the analysis unit can also analyze the characteristics of the waste based on pre-trained data. For example, the analysis unit can analyze the characteristics of the waste using a pre-trained dataset. Step 3: The classification unit classifies the recyclable waste identified by the analysis unit. The classification unit classifies the identified waste into appropriate categories, for example. For example, the classification unit can classify plastics into the plastics category and metals into the metals category. The classification unit can also further subdivide the recyclable waste. For example, the classification unit can classify plastics by type. Step 4: The transmission unit sends the waste sorted by the classification unit to the recycling process. The transmission unit sends the sorted waste to the appropriate recycling facility. For example, the transmission unit can send plastics to a plastic recycling facility and metals to a metal recycling facility. The transmission unit can also send the waste at the appropriate time to ensure the recycling process is efficient. For example, the transmission unit can send the waste considering the operating status of the recycling facility.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the acquisition unit, analysis unit, classification unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 and sensors of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the acquired images to identify recyclable waste. The classification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and classifies the identified waste into appropriate categories. The transmission unit is implemented in the control unit 46A of the smart device 14, for example, and can send the classified waste to an appropriate recycling facility. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the acquisition unit, analysis unit, classification unit, and transmission unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which analyzes the acquired images to identify recyclable waste. The classification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, which classifies the identified waste into the appropriate category. The transmission unit is implemented, for example, in the control unit 46A of the smart glasses 214, which can send the classified waste to an appropriate recycling facility. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the acquisition unit, analysis unit, classification unit, and transmission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and analyzes the acquired images to identify recyclable waste. The classification unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and classifies the identified waste into appropriate categories. The transmission unit is implemented in the control unit 46A of the headset terminal 314, for example, and can send the classified waste to an appropriate recycling facility. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the acquisition unit, analysis unit, classification unit, and transmission unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire images of waste using the camera 42 and sensors of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired images to identify recyclable waste. The classification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which classifies the identified waste into appropriate categories. The transmission unit is implemented, for example, by the control unit 46A of the robot 414, which can send the classified waste to an appropriate recycling facility. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) An acquisition unit that acquires images of waste, An analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste, A classification unit that classifies the recyclable waste identified by the analysis unit, The system includes a transmission unit that sends the waste classified by the classification unit to the recycling process. A system characterized by the following features. (Note 2) The acquisition unit is, Use cameras and sensors to take images of waste. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Identifying waste types using image recognition technology The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, The characteristics of the waste are analyzed based on pre-trained data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned classification unit is Identify the waste and classify it into the appropriate category. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned transmitting unit Send the sorted waste to the appropriate recycling process. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of waste image acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The camera's resolution and shooting angle are automatically adjusted according to the type and quantity of waste. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, The system also acquires environmental information such as the temperature and humidity of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of waste image acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, We acquire location information for waste and propose the optimal placement of recycling facilities. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, Audio information about waste is acquired and used as auxiliary information to identify the type of waste. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the accuracy of waste identification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The analysis will examine not only the shape and color of the waste, but also its characteristics such as texture and reflectivity. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system displays waste identification results in real time and provides feedback to the user. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and adjusts how waste identification results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system provides voice notifications of waste identification results, making it accessible to visually impaired users. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Save waste identification results to the cloud and share them with other devices. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned classification unit is The system estimates user sentiment and adjusts waste classification criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned classification unit is Classify waste considering not only its recyclability but also its reusability. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned classification unit is During classification, the waste classification results are displayed in real time, and feedback is provided to the user. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned classification unit is The system estimates the user's emotions and adjusts how waste classification results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned classification unit is The system provides voice notifications of waste classification results, making it accessible to visually impaired users. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned classification unit is Save waste classification results to the cloud and share them with other devices. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned transmitting unit The system estimates the user's emotions and adjusts the timing of waste transmission based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned transmitting unit When sending waste, the system takes into account the operating status of the recycling facility to which the waste is being sent. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transmitting unit When transmitting waste, select a route that minimizes the environmental impact on the recycling facility to which the waste is sent. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transmitting unit The system estimates the user's emotions and adjusts the frequency of waste transmission based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transmitting unit When sending the waste, the geographical location information of the recycling facility to which the waste is being sent will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transmitting unit When sending waste, the processing capacity of the recycling facility at the destination is taken into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires images of waste, An analysis unit analyzes the images acquired by the acquisition unit and identifies recyclable waste, A classification unit that classifies the recyclable waste identified by the analysis unit, The system includes a transmission unit that sends the waste classified by the classification unit to the recycling process. A system characterized by the following features.

2. The acquisition unit is, Use cameras and sensors to take images of waste. The system according to feature 1.

3. The aforementioned analysis unit, Identifying waste types using image recognition technology The system according to feature 1.

4. The aforementioned analysis unit, The characteristics of the waste are analyzed based on pre-trained data. The system according to feature 1.

5. The aforementioned classification unit is Identify the waste and classify it into the appropriate category. The system according to feature 1.

6. The aforementioned transmitting unit Send the sorted waste to the appropriate recycling process. The system according to feature 1.

7. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of waste image acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, The camera's resolution and shooting angle are automatically adjusted according to the type and quantity of waste. The system according to feature 1.

9. The acquisition unit is, The system also acquires environmental information such as the temperature and humidity of the waste. The system according to feature 1.

10. The acquisition unit is, The system estimates the user's emotions and adjusts the frequency of waste image acquisition based on the estimated emotions. The system according to feature 1.

Images