system

Abstract

The system according to this embodiment aims to efficiently identify and appropriately classify recyclable waste. [Solution] The system according to the embodiment comprises an acquisition unit, an identification unit, a classification unit, and a presentation unit. The acquisition unit acquires images of waste. The identification unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The classification unit classifies the waste identified by the identification unit into recycling methods using a classification algorithm. The presentation unit presents the recycling methods classified by the classification unit to the user.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

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

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an identification unit, a classification unit, and a presentation unit. The acquisition unit acquires images of waste. The identification unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The classification unit classifies the waste identified by the identification unit into recycling methods using a classification algorithm. The presentation unit presents the recycling methods classified by the classification unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently identify and appropriately classify recyclable waste. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple 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) The recycling 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 system acquires images of waste and identifies recyclable waste using an image recognition algorithm. Next, the identified waste is classified into an appropriate recycling method using a classification algorithm. This reduces the amount of waste and alleviates the burden on the environment. For example, it can automatically identify waste of different materials such as plastic, metal, and paper, and propose a suitable recycling method for each. This system can be used for waste management in recycling facilities and homes. First, cameras or sensors for acquiring images of waste are installed in recycling facilities or household trash cans. Next, an image recognition algorithm analyzes the acquired images, identifying recyclable waste by analyzing its shape, color, material, etc. Furthermore, the identified waste is classified into an appropriate recycling method using a classification algorithm. For example, it proposes specific recycling methods such as melting and reusing plastic, melting and reusing metal, and pulping and reusing paper. The proposed recycling methods are presented visually in an easy-to-understand manner through a display or smartphone app. As a result, the recycling system can reduce the amount of waste and alleviate the burden on the environment.

[0029] The recycling system according to the embodiment comprises an acquisition unit, an identification unit, a classification unit, and a presentation unit. The acquisition unit acquires images of waste. Images of waste include, but are not limited to, still images, videos, and resolution. The acquisition unit acquires images of waste using, for example, cameras or sensors installed in recycling facilities or household trash cans. Cameras and sensors include CCD cameras and CMOS sensors. The identification unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The identification unit analyzes, for example, the shape, color, and material of the waste. This includes shape feature quantities, RGB values ​​of color, and material identification methods. The identification unit performs the analysis using image processing algorithms and feature extraction methods. The classification unit classifies the waste identified by the identification unit according to the recycling method using a classification algorithm. The classification unit classifies waste of different materials, such as plastic, metal, and paper. Classification algorithms include machine learning algorithms and rule-based classification methods. The classification unit proposes specific recycling methods, such as melting and reusing plastics, dissolving and reusing metals, and pulping and reusing paper. The presentation unit presents the recycling methods classified by the classification unit to the user. The presentation unit presents the recycling methods visually and clearly, for example, through a display or smartphone app. The presentation unit devises interface design and information display format to make it easy for the user to understand the recycling methods. As a result, the recycling system according to the embodiment can achieve an efficient recycling process by acquiring images of waste, identifying recyclable waste, classifying it to the appropriate recycling method, and presenting it to the user.

[0030] The acquisition unit includes a camera or sensor installed in a recycling facility or household trash can. The acquisition unit acquires images of waste using, for example, a camera or sensor installed in a recycling facility or household trash can. Cameras and sensors include CCD cameras and CMOS sensors. For example, a CCD camera can acquire high-resolution images, and a CMOS sensor can acquire high-sensitivity images with low power consumption. The acquisition unit efficiently acquires images of waste by optimizing the installation position of the camera or sensor. For example, a camera can be installed on top of the trash can to acquire images when waste is put in. Alternatively, a sensor can be installed inside the trash can to detect the amount and type of waste. This allows for efficient acquisition of images of waste using cameras and sensors installed in recycling facilities or household trash cans.

[0031] The identification unit can analyze the shape, color, and material of waste. For example, the identification unit can analyze the shape of the waste. Shape features include, for example, the number of edges, angles, and area. The identification unit identifies waste based on these shape features. The identification unit can also analyze the color of the waste. It identifies waste based on the RGB values, hue, saturation, etc. Furthermore, the identification unit can analyze the material of the waste. Methods for identifying material include, for example, light reflectance, transmittance, and density. As a result, the identification unit can accurately identify recyclable waste by analyzing its shape, color, and material.

[0032] The classification unit can classify waste made of different materials, such as plastics, metals, and paper. For example, the classification unit classifies waste made of different materials, such as plastics, metals, and paper. Plastic classification includes types such as polyethylene, polypropylene, and polystyrene. Metal classification includes types such as iron, aluminum, and copper. Paper classification includes types such as newspaper, cardboard, and copy paper. The classification unit classifies waste based on the characteristics of these materials. For example, it proposes specific recycling methods, such as melting and recycling plastics, melting and recycling metals, and pulping and recycling paper. Thus, by classifying waste made of different materials, the classification unit can propose appropriate recycling methods.

[0033] The classification unit can propose methods for recycling plastics by melting them down, metals by dissolving them, and paper by pulping it. For example, the classification unit can propose a method for recycling plastics by melting them down. Based on factors such as melting temperature and recycling process, it proposes a method for recycling plastics. The classification unit can also propose a method for recycling metals by dissolving them. Based on factors such as melting temperature and recycling process, it proposes a method for recycling metals. Furthermore, the classification unit can also propose a method for recycling paper by pulping it. Based on factors such as pulping process and recycling method, it proposes a method for recycling paper. In this way, the classification unit can make the recycling process more efficient by proposing specific recycling methods.

[0034] The display unit can visually present recycling methods in an easy-to-understand manner through a display or smartphone app. For example, the display unit can visually present recycling methods in an easy-to-understand manner using a display. The display will show the steps and precautions for recycling. The display unit can also present recycling methods using a smartphone app. The smartphone app will show videos, images, and text about recycling methods. The display unit will make it easy for users to understand recycling methods by designing the interface and the format of the information. For example, it will display the recycling procedure step by step so that users can recycle in the correct order. It will also highlight precautions and key points of the recycling method so that users do not miss important information. In this way, the display unit can make it easy for users to understand recycling methods by visually presenting them in an easy-to-understand manner.

[0035] The acquisition unit can analyze the user's past waste disposal history and select an acquisition method when acquiring images of waste. For example, the acquisition unit analyzes the user's past waste disposal history. This history includes the types, quantities, and methods of waste that the user has processed in the past. Based on this history, the acquisition unit selects the optimal image acquisition method. For example, it prioritizes acquiring images of types of waste that the user has frequently processed in the past. It can also acquire images at specific time periods based on the user's past processing history. Furthermore, it can analyze the user's past processing patterns and automatically adjust the optimal camera settings. In this way, the acquisition unit can select the optimal image acquisition method by analyzing the user's past waste disposal history.

[0036] The acquisition unit can automatically adjust camera or sensor settings according to the type or quantity of waste when acquiring images of waste. For example, the acquisition unit automatically adjusts camera and sensor settings according to the type and quantity of waste. Specific methods for determining the type and quantity of waste include weight sensors and image analysis. For example, if there is a large amount of waste, a wide-angle lens is used to capture the entire area. Also, if there is a large amount of a particular type of waste, the unit can set the optimal sensor settings for that type. Furthermore, if the amount of waste is small, the zoom function can be used to acquire detailed images. In this way, the acquisition unit enables efficient image acquisition by automatically adjusting camera and sensor settings according to the type and quantity of waste.

[0037] The acquisition unit can prioritize the acquisition of highly relevant waste based on the user's geographical location information when acquiring images of waste. For example, the acquisition unit prioritizes the acquisition of highly relevant waste by considering the user's geographical location information. Specific methods for acquiring geographical location information include GPS data and location information services. For example, if the user is in a specific area, the acquisition unit prioritizes the acquisition of waste commonly found in that area. Also, if the user is on the move, the acquisition unit can acquire the most suitable waste based on their current location. Furthermore, if the user is in a specific facility, the acquisition unit can prioritize the acquisition of waste commonly found in that facility. In this way, the acquisition unit can prioritize the acquisition of highly relevant waste by considering the user's geographical location information.

[0038] The acquisition unit can analyze the user's social media activity when acquiring images of waste and acquire related waste. For example, the acquisition unit analyzes the user's social media activity. Specific methods for analyzing social media activity include analyzing the content of posts and analyzing the user's interests. For example, if a user mentions a particular type of waste on social media, that waste will be prioritized for acquisition. It can also estimate and acquire waste of interest based on the user's social media activity. Furthermore, it can acquire waste related to events the user is participating in. In this way, the acquisition unit can efficiently acquire relevant waste by analyzing the user's social media activity.

[0039] The identification unit can analyze the odor and sound of waste in addition to its shape, color, and material when identifying waste. For example, the identification unit can analyze the shape of the waste. Shape features include, for example, the number of edges, angles, and area. The identification unit identifies waste based on these shape features. The identification unit can also analyze the color of the waste. It identifies waste based on the RGB values, hue, saturation, etc. Furthermore, the identification unit can also analyze the material of the waste. Material identification methods include, for example, light reflectance, transmittance, and density. Furthermore, the identification unit can also analyze the odor and sound of the waste. Using odor sensors and sound analysis algorithms, it analyzes the odor and sound of the waste to identify specific waste. In this way, the identification unit can improve its identification accuracy by analyzing the odor and sound of waste in addition to its shape, color, and material.

[0040] The identification unit can optimize its identification algorithm based on past identification data when identifying waste. For example, the identification unit optimizes the identification algorithm by referring to past identification data. Past identification data includes the type and quantity of waste previously identified by the user, as well as the identification results. The identification unit updates the identification algorithm based on past identification data. For example, it can learn patterns of misrecognition from past identification data and improve accuracy. It can also refer to past identification data to help identify new waste. In this way, the identification unit can optimize its identification algorithm and improve identification accuracy by referring to past identification data.

[0041] The identification unit can perform waste identification based on its geographical distribution. For example, the identification unit considers the geographical distribution of the waste when performing identification. Specific methods for obtaining geographical distribution include analysis of geographic information systems (GIS) and location data. For example, it can prioritize the identification of waste commonly found in a particular area. It can also adjust the identification algorithm based on geographical distribution. Furthermore, it can learn the characteristics of waste in each region to improve identification accuracy. In this way, the identification unit can improve its identification accuracy by considering the geographical distribution of the waste.

[0042] The identification unit can improve the accuracy of waste identification based on relevant literature. For example, the identification unit improves identification accuracy by referring to relevant literature. Specific methods for obtaining relevant literature include literature databases and literature analysis methods. For example, the identification algorithm can be updated based on relevant literature. Furthermore, the identification unit can learn specific waste identification methods from relevant literature. It can also refer to relevant literature to help identify new types of waste. In this way, the identification unit can improve its identification accuracy by referring to relevant literature on waste.

[0043] The classification unit can improve the accuracy of waste classification based on the interrelationships of the waste materials. For example, the classification unit analyzes the interrelationships of the waste materials. Specific methods for analyzing the interrelationships of waste materials include analysis of co-occurrence relationships and analysis of correlation relationships. For example, by analyzing the interrelationships of waste materials, appropriate classification can be performed. Furthermore, the classification algorithm can be optimized by considering the interrelationships of the waste materials. In addition, the classification unit can learn the interrelationships of the waste materials and improve classification accuracy. In this way, the classification unit can improve classification accuracy by considering the interrelationships of the waste materials.

[0044] The classification unit can classify waste based on the attribute information of the waste submitter. For example, the classification unit considers the attribute information of the waste submitter when classifying waste. Specific methods for obtaining the submitter's attribute information include age, gender, occupation, etc. For example, it performs appropriate classification based on the submitter's attribute information. It can also optimize the classification algorithm by considering the submitter's attribute information. Furthermore, it can learn from the submitter's attribute information to improve classification accuracy. In this way, the classification unit can improve classification accuracy by considering the submitter's attribute information.

[0045] The classification unit can classify waste based on its geographical distribution. For example, it can classify waste considering its geographical distribution. Specific methods for obtaining geographical distribution include analysis of geographic information systems (GIS) and location data. For instance, it can prioritize the classification of waste commonly found in specific areas. Furthermore, the classification algorithm can be adjusted based on geographical distribution. It can also learn the characteristics of waste in each region to improve classification accuracy. Thus, the classification unit can improve classification accuracy by considering the geographical distribution of waste.

[0046] The classification unit can improve the accuracy of waste classification by referring to relevant literature on waste. For example, the classification unit can improve classification accuracy by referring to relevant literature on waste. Specific methods for obtaining relevant literature include literature databases and literature analysis methods. For example, the classification algorithm can be updated based on relevant waste literature. Furthermore, classification methods for specific types of waste can be learned from relevant literature. Additionally, relevant literature can be used to help classify new types of waste. In this way, the classification unit can improve classification accuracy by referring to relevant waste literature.

[0047] The display unit can select a display method based on the user's past recycling history when presenting recycling methods. For example, the display unit can select a display method by referring to the user's past recycling history. Past recycling history includes the recycling methods the user has used in the past and their results. Based on the past recycling history, the display unit proposes the optimal display method. For example, it proposes the optimal display method based on the recycling methods the user has used in the past. It can also select a highly visible display method from the user's past recycling history. Furthermore, it can analyze the user's past recycling history and provide the most effective display method. In this way, the display unit can select the optimal display method by referring to the user's past recycling history.

[0048] The display unit can customize the content displayed when presenting recycling methods according to the user's current lifestyle. For example, the display unit customizes the content by considering the user's current lifestyle. Specific methods for obtaining the current lifestyle include analyzing lifestyle patterns and understanding current activity levels. For example, if the user is busy, a concise and to-the-point recycling method can be presented. Conversely, if the user is relaxed, a detailed recycling method can be presented. Furthermore, the display unit can customize and present the most suitable recycling method according to the user's lifestyle. In this way, the display unit can present the most suitable recycling method for the user by customizing the content based on the user's current lifestyle.

[0049] The display unit can present recycling methods based on the user's geographical location information. For example, the display unit can present the optimal recycling method by considering the user's geographical location information. Specific methods for obtaining geographical location information include GPS data and location-based services. For example, if the user is in a specific area, the display unit can present recycling methods available in that area. It can also present the optimal recycling method based on the user's current location. Furthermore, if the user is on the move, it can present recycling methods based on their current location. In this way, the display unit can present the optimal recycling method by considering the user's geographical location information.

[0050] The presentation unit can analyze the user's social media activity to suggest recycling methods. For example, the presentation unit analyzes the user's social media activity. Specific methods for analyzing social media activity include analyzing the content of posts and analyzing the user's interests. For example, if a user mentions a particular recycling method on social media, that method will be presented preferentially. It can also estimate and present recycling methods of interest based on the user's social media activity. Furthermore, it can present recycling methods related to events the user is participating in. In this way, the presentation unit can propose the most suitable recycling method for the user by analyzing their social media activity.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The acquisition unit can also measure the temperature of waste when acquiring images of it. For example, it can use a thermographic camera to acquire the temperature distribution of the waste and identify the type of waste based on the temperature information. Furthermore, it can use the temperature information to optimize the waste disposal method. For example, since high-temperature waste may require cooling, it can suggest an appropriate disposal method. In this way, the acquisition unit can improve the identification accuracy by utilizing the temperature information of the waste.

[0053] The identification unit can analyze not only the shape, color, and material of the waste, but also its surface texture. For example, it can analyze surface irregularities and patterns to identify specific waste. Furthermore, it can use surface texture information to estimate the state of deterioration and usage of the waste. As a result, the identification unit can improve its identification accuracy by analyzing the surface texture of the waste.

[0054] The classification unit can also analyze the chemical composition of waste during the classification process. For example, it can use spectroscopic analysis techniques to identify the chemical components of waste and propose appropriate recycling methods. Furthermore, it can evaluate the hazardous nature and reusability of waste based on the chemical composition information. In this way, the classification unit can improve the accuracy of its classification by analyzing the chemical composition of waste.

[0055] The display unit can customize the content shown based on the user's health condition when presenting recycling methods. For example, if the user has allergies, the display will highlight the disposal methods for waste containing allergens. Similarly, if the user is elderly, the display can prioritize simple and safe recycling methods. This allows the display unit to present the most suitable recycling method for the user by customizing the content based on their health condition.

[0056] The display unit can customize the content displayed when presenting recycling methods based on the user's past recycling history. For example, it can suggest the most suitable display method based on the recycling methods the user has used in the past. It can also select a highly visible display method from the user's past recycling history. Furthermore, it can analyze the user's past recycling history and provide the most effective display method. In this way, the display unit can select the optimal display method by referring to the user's past recycling history.

[0057] The display unit can customize the content shown when presenting recycling methods according to the user's current lifestyle. For example, if the user is busy, it can present a concise and to-the-point recycling method. If the user is relaxed, it can also present a detailed recycling method. Furthermore, it can customize and present the most suitable recycling method according to the user's lifestyle. In this way, the display unit can present the most suitable recycling method for the user by customizing the content shown based on the user's current lifestyle.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The acquisition unit acquires images of the waste. These images include still images, videos, and resolution. The acquisition unit uses cameras and sensors installed in recycling facilities and household trash cans to acquire images of the waste. These cameras and sensors include CCD cameras and CMOS sensors. Step 2: The identification unit analyzes the images acquired by the acquisition unit to identify recyclable waste. The identification unit analyzes the shape, color, and material of the waste, using shape features, RGB values ​​of the color, and material identification methods. The identification unit performs the analysis using image processing algorithms and feature extraction methods. Step 3: The classification unit classifies the waste identified by the identification unit into recycling methods using a classification algorithm. The classification unit classifies waste of different materials such as plastic, metal, and paper, using machine learning algorithms and rule-based classification methods. The classification unit proposes specific recycling methods, such as melting and recycling plastic, melting and recycling metal, and pulping and recycling paper. Step 4: The presentation unit presents the recycling methods classified by the classification unit to the user. The presentation unit visually presents the recycling methods in an easy-to-understand manner through a display or smartphone app, and the interface design and information display format are carefully designed to make it easy for the user to understand the recycling methods.

[0060] (Example of form 2) The recycling 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 system acquires images of waste and identifies recyclable waste using an image recognition algorithm. Next, the identified waste is classified into an appropriate recycling method using a classification algorithm. This reduces the amount of waste and alleviates the burden on the environment. For example, it can automatically identify waste of different materials such as plastic, metal, and paper, and propose a suitable recycling method for each. This system can be used for waste management in recycling facilities and homes. First, cameras or sensors for acquiring images of waste are installed in recycling facilities or household trash cans. Next, an image recognition algorithm analyzes the acquired images, identifying recyclable waste by analyzing its shape, color, material, etc. Furthermore, the identified waste is classified into an appropriate recycling method using a classification algorithm. For example, it proposes specific recycling methods such as melting and reusing plastic, melting and reusing metal, and pulping and reusing paper. The proposed recycling methods are presented visually in an easy-to-understand manner through a display or smartphone app. As a result, the recycling system can reduce the amount of waste and alleviate the burden on the environment.

[0061] The recycling system according to the embodiment comprises an acquisition unit, an identification unit, a classification unit, and a presentation unit. The acquisition unit acquires images of waste. Images of waste include, but are not limited to, still images, videos, and resolution. The acquisition unit acquires images of waste using, for example, cameras or sensors installed in recycling facilities or household trash cans. Cameras and sensors include CCD cameras and CMOS sensors. The identification unit analyzes the images acquired by the acquisition unit and identifies recyclable waste. The identification unit analyzes, for example, the shape, color, and material of the waste. This includes shape feature quantities, RGB values ​​of color, and material identification methods. The identification unit performs the analysis using image processing algorithms and feature extraction methods. The classification unit classifies the waste identified by the identification unit according to the recycling method using a classification algorithm. The classification unit classifies waste of different materials, such as plastic, metal, and paper. Classification algorithms include machine learning algorithms and rule-based classification methods. The classification unit proposes specific recycling methods, such as melting and reusing plastics, dissolving and reusing metals, and pulping and reusing paper. The presentation unit presents the recycling methods classified by the classification unit to the user. The presentation unit presents the recycling methods visually and clearly, for example, through a display or smartphone app. The presentation unit devises interface design and information display format to make it easy for the user to understand the recycling methods. As a result, the recycling system according to the embodiment can achieve an efficient recycling process by acquiring images of waste, identifying recyclable waste, classifying it to the appropriate recycling method, and presenting it to the user.

[0062] The acquisition unit includes a camera or sensor installed in a recycling facility or household trash can. The acquisition unit acquires images of waste using, for example, a camera or sensor installed in a recycling facility or household trash can. Cameras and sensors include CCD cameras and CMOS sensors. For example, a CCD camera can acquire high-resolution images, and a CMOS sensor can acquire high-sensitivity images with low power consumption. The acquisition unit efficiently acquires images of waste by optimizing the installation position of the camera or sensor. For example, a camera can be installed on top of the trash can to acquire images when waste is put in. Alternatively, a sensor can be installed inside the trash can to detect the amount and type of waste. This allows for efficient acquisition of images of waste using cameras and sensors installed in recycling facilities or household trash cans.

[0063] The identification unit can analyze the shape, color, and material of waste. For example, the identification unit can analyze the shape of the waste. Shape features include, for example, the number of edges, angles, and area. The identification unit identifies waste based on these shape features. The identification unit can also analyze the color of the waste. It identifies waste based on the RGB values, hue, saturation, etc. Furthermore, the identification unit can analyze the material of the waste. Methods for identifying material include, for example, light reflectance, transmittance, and density. As a result, the identification unit can accurately identify recyclable waste by analyzing its shape, color, and material.

[0064] The classification unit can classify waste made of different materials, such as plastics, metals, and paper. For example, the classification unit classifies waste made of different materials, such as plastics, metals, and paper. Plastic classification includes types such as polyethylene, polypropylene, and polystyrene. Metal classification includes types such as iron, aluminum, and copper. Paper classification includes types such as newspaper, cardboard, and copy paper. The classification unit classifies waste based on the characteristics of these materials. For example, it proposes specific recycling methods, such as melting and recycling plastics, melting and recycling metals, and pulping and recycling paper. Thus, by classifying waste made of different materials, the classification unit can propose appropriate recycling methods.

[0065] The classification unit can propose methods for recycling plastics by melting them down, metals by dissolving them, and paper by pulping it. For example, the classification unit can propose a method for recycling plastics by melting them down. Based on factors such as melting temperature and recycling process, it proposes a method for recycling plastics. The classification unit can also propose a method for recycling metals by dissolving them. Based on factors such as melting temperature and recycling process, it proposes a method for recycling metals. Furthermore, the classification unit can also propose a method for recycling paper by pulping it. Based on factors such as pulping process and recycling method, it proposes a method for recycling paper. In this way, the classification unit can make the recycling process more efficient by proposing specific recycling methods.

[0066] The display unit can visually present recycling methods in an easy-to-understand manner through a display or smartphone app. For example, the display unit can visually present recycling methods in an easy-to-understand manner using a display. The display will show the steps and precautions for recycling. The display unit can also present recycling methods using a smartphone app. The smartphone app will show videos, images, and text about recycling methods. The display unit will make it easy for users to understand recycling methods by designing the interface and the format of the information. For example, it will display the recycling procedure step by step so that users can recycle in the correct order. It will also highlight precautions and key points of the recycling method so that users do not miss important information. In this way, the display unit can make it easy for users to understand recycling methods by visually presenting them in an easy-to-understand manner.

[0067] The acquisition unit can estimate the user's emotions and adjust the timing of waste image acquisition according to the user's emotions. For example, the acquisition unit can estimate the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the acquisition unit adjusts the timing of waste image acquisition. For example, if the user is feeling stressed, image acquisition can be performed automatically to reduce the user's effort. If the user is relaxed, an option for manual image acquisition can be provided. Furthermore, if the user is in a hurry, a high-speed mode can be provided for rapid image acquisition. In this way, the acquisition unit can reduce the burden on the user by adjusting the image acquisition timing according to the user's emotions.

[0068] The acquisition unit can analyze the user's past waste disposal history and select an acquisition method when acquiring images of waste. For example, the acquisition unit analyzes the user's past waste disposal history. This history includes the types, quantities, and methods of waste that the user has processed in the past. Based on this history, the acquisition unit selects the optimal image acquisition method. For example, it prioritizes acquiring images of types of waste that the user has frequently processed in the past. It can also acquire images at specific time periods based on the user's past processing history. Furthermore, it can analyze the user's past processing patterns and automatically adjust the optimal camera settings. In this way, the acquisition unit can select the optimal image acquisition method by analyzing the user's past waste disposal history.

[0069] The acquisition unit can automatically adjust camera or sensor settings according to the type or quantity of waste when acquiring images of waste. For example, the acquisition unit automatically adjusts camera and sensor settings according to the type and quantity of waste. Specific methods for determining the type and quantity of waste include weight sensors and image analysis. For example, if there is a large amount of waste, a wide-angle lens is used to capture the entire area. Also, if there is a large amount of a particular type of waste, the unit can set the optimal sensor settings for that type. Furthermore, if the amount of waste is small, the zoom function can be used to acquire detailed images. In this way, the acquisition unit enables efficient image acquisition by automatically adjusting camera and sensor settings according to the type and quantity of waste.

[0070] The acquisition unit can estimate the user's emotions and determine the priority of waste to acquire according to the user's emotions. For example, the acquisition unit estimates the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the acquisition unit determines the priority of waste to acquire. For example, if the user is stressed, it will prioritize acquiring waste that can be easily processed. If the user is relaxed, it can also acquire complex waste. Furthermore, if the user is in a hurry, it can prioritize acquiring waste that can be processed quickly. In this way, the acquisition unit can reduce the user's burden by determining the priority of waste according to the user's emotions.

[0071] The acquisition unit can prioritize the acquisition of highly relevant waste based on the user's geographical location information when acquiring images of waste. For example, the acquisition unit prioritizes the acquisition of highly relevant waste by considering the user's geographical location information. Specific methods for acquiring geographical location information include GPS data and location information services. For example, if the user is in a specific area, the acquisition unit prioritizes the acquisition of waste commonly found in that area. Also, if the user is on the move, the acquisition unit can acquire the most suitable waste based on their current location. Furthermore, if the user is in a specific facility, the acquisition unit can prioritize the acquisition of waste commonly found in that facility. In this way, the acquisition unit can prioritize the acquisition of highly relevant waste by considering the user's geographical location information.

[0072] The acquisition unit can analyze the user's social media activity when acquiring images of waste and acquire related waste. For example, the acquisition unit analyzes the user's social media activity. Specific methods for analyzing social media activity include analyzing the content of posts and analyzing the user's interests. For example, if a user mentions a particular type of waste on social media, that waste will be prioritized for acquisition. It can also estimate and acquire waste of interest based on the user's social media activity. Furthermore, it can acquire waste related to events the user is participating in. In this way, the acquisition unit can efficiently acquire relevant waste by analyzing the user's social media activity.

[0073] The identification unit can estimate the user's emotions and adjust the accuracy of waste identification according to the user's emotions. For example, the identification unit estimates the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the identification unit adjusts the accuracy of waste identification. For example, if the user is stressed, the identification accuracy is increased to reduce misrecognition. If the user is relaxed, processing can be done with normal identification accuracy. Furthermore, if the user is in a hurry, the accuracy can be adjusted to perform identification quickly. In this way, the identification unit can reduce misrecognition by adjusting the identification accuracy according to the user's emotions.

[0074] The identification unit can analyze the odor and sound of waste in addition to its shape, color, and material when identifying waste. For example, the identification unit can analyze the shape of the waste. Shape features include, for example, the number of edges, angles, and area. The identification unit identifies waste based on these shape features. The identification unit can also analyze the color of the waste. It identifies waste based on the RGB values, hue, saturation, etc. Furthermore, the identification unit can also analyze the material of the waste. Material identification methods include, for example, light reflectance, transmittance, and density. Furthermore, the identification unit can also analyze the odor and sound of the waste. Using odor sensors and sound analysis algorithms, it analyzes the odor and sound of the waste to identify specific waste. In this way, the identification unit can improve its identification accuracy by analyzing the odor and sound of waste in addition to its shape, color, and material.

[0075] The identification unit can optimize its identification algorithm based on past identification data when identifying waste. For example, the identification unit optimizes the identification algorithm by referring to past identification data. Past identification data includes the type and quantity of waste previously identified by the user, as well as the identification results. The identification unit updates the identification algorithm based on past identification data. For example, it can learn patterns of misrecognition from past identification data and improve accuracy. It can also refer to past identification data to help identify new waste. In this way, the identification unit can optimize its identification algorithm and improve identification accuracy by referring to past identification data.

[0076] The identification unit can estimate the user's emotions and adjust the display method of the identification results according to the user's emotions. For example, the identification unit estimates the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the identification unit adjusts the display method of the identification results. For example, if the user is nervous, a simple and highly visible display method is provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. In this way, the identification unit can make it easier for the user to understand the results by adjusting the display method of the identification results according to the user's emotions.

[0077] The identification unit can perform waste identification based on its geographical distribution. For example, the identification unit considers the geographical distribution of the waste when performing identification. Specific methods for obtaining geographical distribution include analysis of geographic information systems (GIS) and location data. For example, it can prioritize the identification of waste commonly found in a particular area. It can also adjust the identification algorithm based on geographical distribution. Furthermore, it can learn the characteristics of waste in each region to improve identification accuracy. In this way, the identification unit can improve its identification accuracy by considering the geographical distribution of the waste.

[0078] The identification unit can improve the accuracy of waste identification based on relevant literature. For example, the identification unit improves identification accuracy by referring to relevant literature. Specific methods for obtaining relevant literature include literature databases and literature analysis methods. For example, the identification algorithm can be updated based on relevant literature. Furthermore, the identification unit can learn specific waste identification methods from relevant literature. It can also refer to relevant literature to help identify new types of waste. In this way, the identification unit can improve its identification accuracy by referring to relevant literature on waste.

[0079] The classification unit can estimate the user's emotions and adjust the classification criteria accordingly. For example, the classification unit can estimate the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For instance, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. The classification unit adjusts the classification criteria based on the estimated user emotions. For example, if the user is stressed, a simple classification criterion is used. If the user is relaxed, a more detailed classification criterion can be used. Furthermore, if the user is in a hurry, a criterion that allows for quick classification can be used. This allows the classification unit to reduce the user's burden by adjusting the classification criteria according to the user's emotions.

[0080] The classification unit can improve the accuracy of waste classification based on the interrelationships of the waste materials. For example, the classification unit analyzes the interrelationships of the waste materials. Specific methods for analyzing the interrelationships of waste materials include analysis of co-occurrence relationships and analysis of correlation relationships. For example, by analyzing the interrelationships of waste materials, appropriate classification can be performed. Furthermore, the classification algorithm can be optimized by considering the interrelationships of the waste materials. In addition, the classification unit can learn the interrelationships of the waste materials and improve classification accuracy. In this way, the classification unit can improve classification accuracy by considering the interrelationships of the waste materials.

[0081] The classification unit can classify waste based on the attribute information of the waste submitter. For example, the classification unit considers the attribute information of the waste submitter when classifying waste. Specific methods for obtaining the submitter's attribute information include age, gender, occupation, etc. For example, it performs appropriate classification based on the submitter's attribute information. It can also optimize the classification algorithm by considering the submitter's attribute information. Furthermore, it can learn from the submitter's attribute information to improve classification accuracy. In this way, the classification unit can improve classification accuracy by considering the submitter's attribute information.

[0082] The classification unit can estimate the user's emotions and adjust the display order of the classification results according to the user's emotions. For example, the classification unit estimates the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the classification unit adjusts the display order of the classification results. For example, if the user is nervous, important classification results will be displayed preferentially. If the user is relaxed, detailed classification results may be displayed. Furthermore, if the user is in a hurry, concise classification results may be displayed. In this way, the classification unit can make it easier for the user to understand the results by adjusting the display order of the classification results according to the user's emotions.

[0083] The classification unit can classify waste based on its geographical distribution. For example, it can classify waste considering its geographical distribution. Specific methods for obtaining geographical distribution include analysis of geographic information systems (GIS) and location data. For instance, it can prioritize the classification of waste commonly found in specific areas. Furthermore, the classification algorithm can be adjusted based on geographical distribution. It can also learn the characteristics of waste in each region to improve classification accuracy. Thus, the classification unit can improve classification accuracy by considering the geographical distribution of waste.

[0084] The classification unit can improve the accuracy of waste classification by referring to relevant literature on waste. For example, the classification unit can improve classification accuracy by referring to relevant literature on waste. Specific methods for obtaining relevant literature include literature databases and literature analysis methods. For example, the classification algorithm can be updated based on relevant waste literature. Furthermore, classification methods for specific types of waste can be learned from relevant literature. Additionally, relevant literature can be used to help classify new types of waste. In this way, the classification unit can improve classification accuracy by referring to relevant waste literature.

[0085] The display unit can estimate the user's emotions and adjust the display method for recycling instructions according to the user's emotions. For example, the display unit can estimate the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the display unit adjusts the display method for recycling instructions. For example, if the user is nervous, it provides a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, the display unit can make it easier for the user to understand recycling instructions by adjusting the display method according to the user's emotions.

[0086] The display unit can select a display method based on the user's past recycling history when presenting recycling methods. For example, the display unit can select a display method by referring to the user's past recycling history. Past recycling history includes the recycling methods the user has used in the past and their results. Based on the past recycling history, the display unit proposes the optimal display method. For example, it proposes the optimal display method based on the recycling methods the user has used in the past. It can also select a highly visible display method from the user's past recycling history. Furthermore, it can analyze the user's past recycling history and provide the most effective display method. In this way, the display unit can select the optimal display method by referring to the user's past recycling history.

[0087] The display unit can customize the content displayed when presenting recycling methods according to the user's current lifestyle. For example, the display unit customizes the content by considering the user's current lifestyle. Specific methods for obtaining the current lifestyle include analyzing lifestyle patterns and understanding current activity levels. For example, if the user is busy, a concise and to-the-point recycling method can be presented. Conversely, if the user is relaxed, a detailed recycling method can be presented. Furthermore, the display unit can customize and present the most suitable recycling method according to the user's lifestyle. In this way, the display unit can present the most suitable recycling method for the user by customizing the content based on the user's current lifestyle.

[0088] The display unit can estimate the user's emotions and prioritize recycling methods according to those emotions. For example, the display unit can estimate the user's emotions. Methods for estimating user emotions include facial recognition, voice analysis, and behavioral analysis. For example, facial recognition technology can be used to analyze the user's facial expressions and estimate their emotions. Voice analysis technology can also be used to analyze the tone and speed of the user's voice and estimate their emotions. Furthermore, behavioral analysis technology can be used to analyze the user's movements and behavioral patterns and estimate their emotions. Based on the estimated user emotions, the display unit determines the priority of recycling methods. For example, if the user is stressed, it will prioritize presenting recycling methods that are easy to perform. If the user is relaxed, it may also prioritize presenting more detailed recycling methods. Furthermore, if the user is in a hurry, it may also prioritize presenting recycling methods that can be performed quickly. In this way, the display unit can reduce the burden on the user by prioritizing recycling methods according to the user's emotions.

[0089] The display unit can present recycling methods based on the user's geographical location information. For example, the display unit can present the optimal recycling method by considering the user's geographical location information. Specific methods for obtaining geographical location information include GPS data and location-based services. For example, if the user is in a specific area, the display unit can present recycling methods available in that area. It can also present the optimal recycling method based on the user's current location. Furthermore, if the user is on the move, it can present recycling methods based on their current location. In this way, the display unit can present the optimal recycling method by considering the user's geographical location information.

[0090] The presentation unit can analyze the user's social media activity to suggest recycling methods. For example, the presentation unit analyzes the user's social media activity. Specific methods for analyzing social media activity include analyzing the content of posts and analyzing the user's interests. For example, if a user mentions a particular recycling method on social media, that method will be presented preferentially. It can also estimate and present recycling methods of interest based on the user's social media activity. Furthermore, it can present recycling methods related to events the user is participating in. In this way, the presentation unit can propose the most suitable recycling method for the user by analyzing their social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, identification unit, classification unit, and presentation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires images of waste using the camera 42 and sensors of the smart device 14, and these images are analyzed by the identification processing unit 290 of the data processing unit 12. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the waste using an image recognition algorithm. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies the identified waste into an appropriate recycling method. The presentation unit presents the recycling method to the user through the display 40A of the smart device 14 or a smartphone application. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, identification unit, classification unit, and presentation 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 acquires images of waste using the camera 42 and sensors of the smart glasses 214 and analyzes them with the identification processing unit 290 of the data processing unit 12. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the waste using an image recognition algorithm. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies the identified waste into an appropriate recycling method. The presentation unit presents the recycling method to the user through the display of the smart glasses 214 or a smartphone application. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, identification unit, classification unit, and presentation unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires images of waste using the camera 42 and sensors of the headset terminal 314 and analyzes them with the identification processing unit 290 of the data processing unit 12. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the waste using an image recognition algorithm. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies the identified waste into an appropriate recycling method. The presentation unit presents the recycling method to the user through the display of the headset terminal 314 or a smartphone application. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, identification unit, classification unit, and presentation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires images of waste using the camera 42 and sensors of the robot 414, and these images are analyzed by the identification processing unit 290 of the data processing unit 12. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the waste using an image recognition algorithm. The classification unit is implemented by the identification processing unit 290 of the data processing unit 12 and classifies the identified waste into an appropriate recycling method. The presentation unit presents the recycling method to the user through the display of the robot 414 or a smartphone application.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The acquisition unit can also measure the temperature of waste when acquiring images of it. For example, it can use a thermographic camera to acquire the temperature distribution of the waste and identify the type of waste based on the temperature information. Furthermore, it can use the temperature information to optimize the waste disposal method. For example, since high-temperature waste may require cooling, it can suggest an appropriate disposal method. In this way, the acquisition unit can improve the identification accuracy by utilizing the temperature information of the waste.

[0093] The identification unit can analyze not only the shape, color, and material of the waste, but also its surface texture. For example, it can analyze surface irregularities and patterns to identify specific waste. Furthermore, it can use surface texture information to estimate the state of deterioration and usage of the waste. As a result, the identification unit can improve its identification accuracy by analyzing the surface texture of the waste.

[0094] The classification unit can also analyze the chemical composition of waste during the classification process. For example, it can use spectroscopic analysis techniques to identify the chemical components of waste and propose appropriate recycling methods. Furthermore, it can evaluate the hazardous nature and reusability of waste based on the chemical composition information. In this way, the classification unit can improve the accuracy of its classification by analyzing the chemical composition of waste.

[0095] The display unit can customize the content shown based on the user's health condition when presenting recycling methods. For example, if the user has allergies, the display will highlight the disposal methods for waste containing allergens. Similarly, if the user is elderly, the display can prioritize simple and safe recycling methods. This allows the display unit to present the most suitable recycling method for the user by customizing the content based on their health condition.

[0096] The acquisition unit can estimate the user's emotions and customize the waste image acquisition method according to the user's emotions. For example, if the user is stressed, image acquisition can be automated to reduce the user's effort. If the user is relaxed, an option for manual image acquisition can be provided. Furthermore, if the user is in a hurry, a high-speed mode can be provided for rapid image acquisition. In this way, the acquisition unit can reduce the burden on the user by customizing the image acquisition method according to the user's emotions.

[0097] The identification unit can estimate the user's emotions and adjust the parameters of the identification algorithm according to the user's emotions. For example, if the user is stressed, the identification accuracy can be increased to reduce misrecognition. Conversely, if the user is relaxed, processing can be performed with normal identification accuracy. Furthermore, if the user is in a hurry, the accuracy can be adjusted to perform identification quickly. In this way, the identification unit can reduce misrecognition by adjusting the parameters of the identification algorithm according to the user's emotions.

[0098] The classification unit can estimate the user's emotions and adjust how the classification results are displayed accordingly. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. In this way, the classification unit can make it easier for the user to understand the results by adjusting how the classification results are displayed according to the user's emotions.

[0099] The display unit can estimate the user's emotions and adjust the display order of recycling methods according to the user's emotions. For example, if the user is stressed, it will prioritize displaying important recycling methods. If the user is relaxed, it can also display detailed recycling methods. Furthermore, if the user is in a hurry, it can display recycling methods that are to the essentials. In this way, the display unit can make it easier for the user to understand recycling methods by adjusting the display order according to the user's emotions.

[0100] The display unit can customize the content displayed when presenting recycling methods based on the user's past recycling history. For example, it can suggest the most suitable display method based on the recycling methods the user has used in the past. It can also select a highly visible display method from the user's past recycling history. Furthermore, it can analyze the user's past recycling history and provide the most effective display method. In this way, the display unit can select the optimal display method by referring to the user's past recycling history.

[0101] The display unit can customize the content shown when presenting recycling methods according to the user's current lifestyle. For example, if the user is busy, it can present a concise and to-the-point recycling method. If the user is relaxed, it can also present a detailed recycling method. Furthermore, it can customize and present the most suitable recycling method according to the user's lifestyle. In this way, the display unit can present the most suitable recycling method for the user by customizing the content shown based on the user's current lifestyle.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The acquisition unit acquires images of the waste. These images include still images, videos, and resolution. The acquisition unit uses cameras and sensors installed in recycling facilities and household trash cans to acquire images of the waste. These cameras and sensors include CCD cameras and CMOS sensors. Step 2: The identification unit analyzes the images acquired by the acquisition unit to identify recyclable waste. The identification unit analyzes the shape, color, and material of the waste, using shape features, RGB values ​​of the color, and material identification methods. The identification unit performs the analysis using image processing algorithms and feature extraction methods. Step 3: The classification unit classifies the waste identified by the identification unit into recycling methods using a classification algorithm. The classification unit classifies waste of different materials such as plastic, metal, and paper, using machine learning algorithms and rule-based classification methods. The classification unit proposes specific recycling methods, such as melting and recycling plastic, melting and recycling metal, and pulping and recycling paper. Step 4: The presentation unit presents the recycling methods classified by the classification unit to the user. The presentation unit visually presents the recycling methods in an easy-to-understand manner through a display or smartphone app, and the interface design and information display format are carefully designed to make it easy for the user to understand the recycling methods.

[0104] 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.

[0105] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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. 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.

[0122] 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.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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. 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.

[0138] 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.

[0139] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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. 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.

[0155] 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.

[0156] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 identification unit analyzes the image acquired by the acquisition unit and identifies recyclable waste, A classification unit that classifies the waste identified by the identification unit into a recycling method using a classification algorithm, The system includes a display unit that presents the recycling methods classified by the classification unit to the user. A system characterized by the following features.

2. The acquisition unit is, Equipped with cameras or sensors installed in recycling facilities or household trash cans. The system according to feature 1.

3. The aforementioned identification unit is Analyze the shape, color, and material of the waste. The system according to feature 1.

4. The aforementioned classification unit is Sorting waste made from different materials such as plastic, metal, and paper. The system according to feature 1.

5. The aforementioned classification unit is We propose methods for recycling plastics by melting them down, metals by dissolving them, and paper by pulping it. The system according to feature 1.

6. The aforementioned display unit is, The recycling method is presented visually and clearly through a display or smartphone app. 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 according to the user's emotions. The system according to feature 1.

8. The acquisition unit is, When acquiring images of waste, the system analyzes the user's past waste disposal history to select the acquisition method. The system according to feature 1.

Images