system

Abstract

The system according to this embodiment aims to enable general consumers to easily identify and accurately sort plastic waste. [Solution] The system according to the embodiment comprises a shooting unit, an analysis unit, and a presentation unit. The shooting unit takes a photograph of the trash. The analysis unit analyzes the photograph taken by the shooting unit and determines whether the trash is plastic or not. The presentation unit presents the results determined by the analysis unit to the user.

Description

Technical Field

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[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, and includes 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] <​​​​​​​​​​​​​​​​​​​​​​​​​​​

[0007] The system according to this embodiment allows ordinary consumers to easily identify and accurately sort plastic 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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 ۳۲ 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 waste sorting support system according to an embodiment of the present invention provides a new sorting method using AI technology to solve the problem that general consumers find it difficult to identify plastics, in light of the new Plastics Law which requires household waste sorting. The waste sorting support system is developed as a home AI application that determines in real time whether a waste is plastic by taking a picture of it with a smartphone. This application makes it easy for consumers to sort their waste and promotes accurate recycling. For example, with the waste sorting support system, the user takes a picture of the waste with their smartphone. At this time, the user does not need to do any special operation, and simply points the camera at the waste and takes a picture. For example, they can take a picture of a PET bottle or a plastic container. This information is input into the AI. Next, the waste sorting support system uses the AI ​​to analyze the input waste picture. The AI ​​uses image recognition technology to determine whether the waste is plastic. For example, the AI ​​analyzes the shape and material of a PET bottle and determines that it is plastic. This analysis is performed in real time, and the user can check the results immediately. Based on the analysis results, the waste sorting support system presents the user with a method for sorting the waste. For example, if an item is identified as plastic, the waste sorting support system will display a message such as "Please separate this as plastic waste." This allows users to sort their waste accurately. This system supports consumers in sorting their waste and promotes accurate recycling. For instance, accurate sorting of plastic waste improves the recycling rate and reduces the environmental burden. Furthermore, because waste sorting is easy for users, it becomes easier for them to participate in recycling activities in their daily lives. In the future, it will also be possible to recover valuable resources such as rare metals using AI technology. For example, the waste sorting support system could efficiently recover valuable resources by having users take pictures of electronic devices with their smartphones and having the AI ​​identify the rare metals contained within them. In this way, utilizing AI technology can make resource recovery within households more efficient and contribute to the realization of a circular economy. Thus, the waste sorting support system can support consumers in sorting their waste and promote accurate recycling.

[0029] The waste sorting support system according to this embodiment comprises a shooting unit, an analysis unit, and a display unit. The shooting unit takes pictures of the waste. The shooting unit takes pictures of the waste using, for example, a smartphone camera. The shooting unit does not require any special operation from the user; the user simply needs to point the camera at the waste and take a picture. For example, it takes pictures of PET bottles or plastic containers. The analysis unit analyzes the pictures taken by the shooting unit and determines whether the waste is plastic. The analysis unit determines whether the waste is plastic using, for example, image recognition technology. For example, AI analyzes the shape and material of a PET bottle and determines that it is plastic. The analysis unit performs the analysis in real time, and the user can check the results immediately. The display unit presents the results determined by the analysis unit to the user. For example, if the display unit determines that the waste is plastic, it displays "Please separate it as plastic waste." The display unit presents the user with a method for sorting the waste. For example, accurate sorting of plastic waste improves the recycling rate and reduces the environmental burden. As a result, the waste sorting support system according to the embodiment allows users to easily sort their waste. Some or all of the above-described processes in the shooting unit, analysis unit, and presentation unit may be performed using AI, for example, or without AI. For example, the shooting unit can input image data captured by a smartphone camera into a generation AI and cause the generation AI to perform analysis to determine the type of waste from the image data. The analysis unit can determine the type of waste using the generation AI. The presentation unit can use the generation AI to present waste sorting methods to the user.

[0030] The camera unit takes pictures of the trash. For example, it uses a smartphone camera to take pictures of the trash. The user doesn't need to perform any special operations; they simply point the camera at the trash and take a picture. For example, it can photograph plastic bottles or plastic containers. The camera unit can capture high-resolution images of the trash through the smartphone's camera application. Furthermore, the camera unit has a function to automatically correct image blur and focus errors, allowing users to easily take high-quality images. For example, the smartphone's camera application has shooting modes tailored to different types of trash, and the optimal settings are automatically applied when photographing plastic bottles or containers. This allows the camera unit to easily take pictures of trash and provide high-quality image data to the analysis unit. The camera unit can also take multiple images in sequence to provide more information to the analysis unit. For example, taking images of the trash from different angles allows the analysis unit to more accurately identify the type of trash. This allows the camera unit to easily take pictures of trash and provide high-quality image data to the analysis unit.

[0031] The analysis unit analyzes photos taken by the camera unit to determine whether the waste is plastic. For example, the analysis unit uses image recognition technology to determine if the waste is plastic. Specifically, the AI ​​analyzes the shape and material of a plastic bottle to determine that it is plastic. The analysis unit performs the analysis in real time, allowing users to see the results immediately. The analysis unit employs a deep learning-based image recognition algorithm to analyze features such as the shape, color, and texture of the waste with high accuracy. For example, it uses a convolutional neural network (CNN) to extract features from images of waste and use them to determine the type of waste. Furthermore, the analysis unit can improve its discrimination accuracy by learning from past data. For example, it can train an AI model using image data of waste analyzed in the past, enabling high-accuracy discrimination of new images of waste. The analysis unit can also send images taken by the user to a cloud server for analysis on the cloud. This allows for fast and highly accurate analysis, independent of the smartphone's processing power. The analysis unit can not only determine the type of waste but also analyze its condition and degree of soiling. For example, it can determine whether a plastic bottle has been washed and suggest the appropriate sorting method. This allows the analysis unit to easily identify the type of waste and learn the appropriate sorting method.

[0032] The display unit presents the results determined by the analysis unit to the user. For example, if the display unit determines that an item is plastic, it will display "Please separate this as plastic waste." The display unit also provides the user with instructions on how to separate waste. Specifically, it can not only display the separation method on the smartphone screen but also use voice guidance and vibration notifications to alert the user. For example, accurate separation of plastic waste improves the recycling rate and reduces the environmental burden. The display unit provides a visual interface to make it easy for users to understand how to separate waste. For example, it can intuitively show the separation method using different colors and icons for each type of waste. The display unit can also record the user's past separation history and provide suggestions for improvement and advice on separation methods. For example, if a user has separated waste incorrectly in the past, it will analyze the cause and provide points to note for the next separation. Furthermore, the display unit is compatible with regional separation rules and can provide information based on the separation rules of the area where the user lives. This allows users to separate waste accurately according to local rules. The display unit reduces the stress users experience when sorting waste and supports them in performing the sorting process smoothly. For example, it not only provides a concise explanation of sorting methods but also details specific procedures and points to note, allowing users to sort their waste without confusion. As a result, the display unit enables users to easily sort their waste, contributing to improved recycling rates and reduced environmental impact.

[0033] The analysis unit can determine whether trash is plastic using image recognition technology. For example, the analysis unit can analyze images of trash using a convolutional neural network (CNN) to determine if it is plastic. For example, the analysis unit can analyze the shape and material of the trash to determine if it is plastic. The analysis unit can also analyze images of trash using a support vector machine (SVM) to determine if it is plastic. For example, the analysis unit can extract features from the trash and determine if it is plastic based on those features. Furthermore, the analysis unit can analyze images of trash using deep learning technology to determine if it is plastic. For example, the analysis unit can learn from a large amount of trash image data to perform highly accurate discrimination. This allows for accurate determination of whether trash is plastic using image recognition technology. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trash image data into a generating AI, which can then determine the type of trash.

[0034] The display unit can present the user with instructions on how to sort waste. For example, the display unit can show the user how to sort plastic waste. For example, the display unit might display, "Please sort this as plastic waste." The display unit can also present the user with instructions on how to sort metal waste. For example, the display unit might display, "Please sort this as metal waste." Furthermore, the display unit can also present the user with instructions on how to sort paper waste. For example, the display unit might display, "Please sort this as paper waste." By presenting the user with instructions on how to sort waste, the user can sort their waste accurately. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the waste sorting method into a generating AI, and the generating AI can present the sorting method to the user.

[0035] The shooting unit can take pictures of trash using a smartphone camera. The shooting unit can, for example, take high-resolution photos using a smartphone camera. For example, the shooting unit can take pictures of trash using the autofocus function. The shooting unit can also shoot videos using a smartphone camera. For example, the shooting unit shoots a video of trash and sends it to the analysis unit. Furthermore, the shooting unit can also take panoramic photos using a smartphone camera. For example, the shooting unit captures an overall view of the trash and sends it to the analysis unit. This allows users to easily take pictures of trash using a smartphone camera. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input image data captured by a smartphone camera into a generating AI, which can then analyze the image data.

[0036] The analysis unit can identify valuable resources such as rare metals. For example, the analysis unit can analyze photographs of electronic devices and identify the rare metals contained within them. For instance, it can analyze photographs of smartphones and identify rare metals such as lithium and cobalt. The analysis unit can also analyze photographs of personal computers and identify rare metals such as nickel and gold. For example, it can analyze the internal structure of a personal computer and identify the presence of rare metals. Furthermore, the analysis unit can analyze photographs of home appliances and identify rare metals. For example, it can analyze photographs of refrigerators and washing machines and identify rare metals. This allows for the efficient recovery of valuable resources by identifying rare metals and other valuable resources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of electronic devices into a generating AI, which can then identify rare metals.

[0037] The display unit can present users with methods for recovering valuable resources. For example, the display unit can present users with methods for recovering rare metals. For example, the display unit may display, "Please take it to a recycling center." The display unit can also present users with methods for recovering electronic devices. For example, the display unit may display, "Please have it collected at an electronics retailer." Furthermore, the display unit can also present users with methods for recovering home appliances. For example, the display unit may display, "Please put it out on your local government's collection day." By presenting users with methods for recovering valuable resources, efficient resource recovery becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input methods for recovering valuable resources into a generating AI, and the generating AI can present the recovery methods to the user.

[0038] The camera unit can automatically adjust the optimal shooting angle and distance according to the type of waste. For example, in the case of a plastic bottle, the AI ​​automatically sets the optimal angle and distance and takes a picture. For example, the camera unit analyzes the shape of the plastic bottle and sets the optimal angle and distance. In the case of a plastic container, the AI ​​can also set the optimal shooting settings according to the shape of the container. For example, the camera unit analyzes the shape of the plastic container and sets the optimal shooting settings. Furthermore, the AI ​​can calculate the appropriate distance according to the size of the waste and take a picture. For example, the camera unit analyzes the size of the waste, calculates the appropriate distance and takes a picture. This improves the accuracy of the analysis by automatically adjusting the optimal shooting angle and distance according to the type of waste. Some or all of the above processing in the camera unit may be performed using AI, or not using AI. For example, the camera unit can input shooting settings according to the type of waste into a generating AI, and the generating AI can perform the optimal shooting settings.

[0039] The image capture unit can automatically remove the background of the debris, improving the accuracy of the analysis. For example, the image capture unit can use AI to analyze the background of the debris in real time and remove unnecessary parts. The image capture unit can also use AI to automatically blur the background and emphasize the shape of the debris during shooting. Furthermore, the image capture unit can identify the color of the debris and the background, and the AI ​​can remove the background and extract only the debris. This improves the accuracy of the analysis by automatically removing the background of the debris. Some or all of the above processing in the image capture unit may be performed using AI, or not using AI. For example, the image capture unit can input the process of removing the background of the debris into a generating AI, and the generating AI can perform background removal.

[0040] The camera unit can take into account the user's geographical location and follow local waste sorting rules. For example, the camera unit can automatically apply local waste sorting rules based on the user's location using AI. The camera unit can also retrieve local waste sorting rules from a database, and the AI ​​can perform appropriate shooting settings. Furthermore, if the user moves to a different region, the camera unit can perform shooting according to the sorting rules of the new region using AI. This ensures accurate sorting by following local waste sorting rules. Some or all of the above processing in the camera unit may be performed using AI, or not. For example, the camera unit can input the user's geographical location into a generating AI, which can then apply local waste sorting rules.

[0041] The shooting unit can suggest optimal shooting settings by referring to the user's past shooting history. For example, the shooting unit can suggest optimal shooting settings based on the types of trash the user has photographed in the past. The shooting unit can also have the AI ​​select the shooting settings with the highest success rate from the user's past shooting history. For example, the shooting unit can select the shooting settings with the highest success rate from the user's past shooting history. Furthermore, the shooting unit can have the AI ​​provide customized shooting settings depending on the types of trash the user frequently photographs. For example, the shooting unit can provide customized shooting settings depending on the types of trash the user frequently photographs. This allows the optimal shooting settings to be suggested by referring to past shooting history. Some or all of the above processing in the shooting unit may be performed using AI, or not using AI. For example, the shooting unit can input the user's past shooting history into a generating AI, which can then suggest optimal shooting settings.

[0042] The analysis unit can perform detailed classification based on the material and shape of the waste. For example, the analysis unit can use AI to analyze the material of the waste and classify it into plastic, metal, paper, etc. The analysis unit can also use AI to analyze the shape of the waste and classify it into bottles, containers, bags, etc. Furthermore, the analysis unit can combine the material and shape of the waste to perform detailed classification using AI. This allows for accurate sorting by performing detailed classification based on the material and shape of the waste. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input data on the material and shape of the waste into a generating AI, which can then perform detailed classification.

[0043] The analysis unit can propose sorting methods considering the degree of contamination of the waste. For example, if the waste is dirty, the AI ​​may suggest that it needs to be cleaned. The analysis unit can also suggest that if the waste is clean, it can be sorted as is. Furthermore, the analysis unit can suggest an appropriate sorting method depending on the degree of contamination of the waste. In this way, an appropriate sorting method is proposed by considering the degree of contamination of the waste. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input data on the degree of contamination of the waste into a generating AI, and the generating AI can suggest an appropriate sorting method.

[0044] The analysis unit can propose region-specific recycling methods by considering the user's geographical location information. For example, the analysis unit can use AI to propose regional recycling methods based on the user's location information. The analysis unit can also retrieve regional recycling rules from a database and have the AI ​​propose an appropriate method. Furthermore, if the user moves to a different region, the analysis unit can have the AI ​​propose a recycling method for the new region. This allows for accurate recycling by proposing region-specific recycling methods. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then propose region-specific recycling methods.

[0045] The analysis unit can improve analysis accuracy by referring to the user's past analysis history. For example, the analysis unit improves analysis accuracy based on the waste data that the user has previously analyzed. The analysis unit can also select the analysis method with the highest success rate from the user's past analysis history. For example, the analysis unit selects the analysis method with the highest success rate from the user's past analysis history. Furthermore, the analysis unit can provide a customized analysis method based on the type of waste that the user frequently analyzes. For example, the analysis unit provides a customized analysis method based on the type of waste that the user frequently analyzes. This improves analysis accuracy by referring to past analysis history. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's past analysis history into a generating AI, which can then improve analysis accuracy.

[0046] The display unit can provide detailed explanations of the optimal sorting methods depending on the type of waste. For example, in the case of plastic waste, the AI ​​will provide detailed sorting instructions. The display unit can also provide appropriate sorting instructions for metal waste. Furthermore, the display unit can provide detailed sorting instructions for paper waste. By providing detailed explanations of the optimal sorting methods depending on the type of waste, accurate sorting can be achieved. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input sorting methods according to the type of waste into a generating AI, which can then provide detailed sorting instructions.

[0047] The presentation unit can provide personalized advice by referring to the user's past sorting history. For example, the presentation unit can use AI to provide personalized advice based on data of waste sorted by the user in the past. The presentation unit can also use AI to suggest the sorting method with the highest success rate based on the user's past sorting history. Furthermore, the presentation unit can use AI to provide customized advice depending on the types of waste the user frequently sorts. In this way, personalized advice is provided by referring to past sorting history. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input the user's past sorting history into a generating AI, which can then provide personalized advice.

[0048] The display unit can display region-specific classification rules, taking into account the user's geographical location information. For example, the display unit can have an AI display region classification rules based on the user's location information. The display unit can also retrieve region-specific classification rules from a database and have an AI display the appropriate method. Furthermore, if the user moves to a different region, the display unit can have an AI display the classification rules for the new region. This allows for accurate classification by displaying region-specific classification rules. Some or all of the above processing in the display unit may be performed using an AI, or not. For example, the display unit can input the user's geographical location information into a generating AI, which can then display region-specific classification rules.

[0049] The presentation unit can analyze a user's social media activity and provide relevant classification information. For example, the presentation unit can use AI to provide relevant classification information based on the content of a user's social media posts. The presentation unit can also use AI to suggest appropriate classification methods based on information shared by the user on social media. Furthermore, the presentation unit can use AI to provide customized classification information based on the user's social media activity. In this way, relevant classification information is provided by analyzing social media activity. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input the user's social media activity into a generating AI, and the generating AI can provide relevant classification information.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The waste sorting support system can improve sorting accuracy by referring to the user's past sorting history. For example, the AI ​​can improve sorting accuracy based on data of waste sorted by the user in the past. For instance, it can analyze the types and frequency of waste sorted by the user in the past and suggest the most appropriate sorting method. The AI ​​can also select the sorting method with the highest success rate based on the user's past sorting history. For example, it can provide a customized sorting method depending on the types of waste the user frequently sorts. Furthermore, the AI ​​can provide advice to prevent errors based on data of waste that the user has sorted incorrectly in the past. For example, it can display specific instructions such as, "Last time you sorted this waste as plastic waste, but this time please sort it as metal waste." By referring to past sorting history, sorting accuracy is improved and accurate recycling is promoted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past sorting history into a generating AI, which can then improve sorting accuracy.

[0052] The waste sorting support system can apply region-specific waste sorting rules by taking into account the user's geographical location. For example, the AI ​​can automatically apply regional waste sorting rules based on the user's location. For example, if the user moves to a different region, the system can take photos according to the sorting rules of the new region. For example, when a user sorts waste while traveling, the system will display the sorting rules of that region and suggest appropriate sorting methods. The system can also retrieve regional waste sorting rules from a database, and the AI ​​can perform appropriate shooting settings. For example, it can automatically adjust the shooting angle and distance of the waste according to the sorting rules of the region where the user lives. Furthermore, if the user moves to a different region, the AI ​​can take photos according to the sorting rules of the new region. This ensures accurate sorting by following region-specific waste sorting rules. Some or all of the above processing in the shooting unit may be performed using AI, or not. For example, the shooting unit can input the user's geographical location information into a generating AI, which can then apply region-specific waste sorting rules.

[0053] The waste sorting support system can analyze a user's social media activity and provide relevant sorting information. For example, the AI ​​can provide relevant sorting information based on the user's social media posts. For instance, if a user uses keywords such as "recycling" or "waste sorting" on social media, the system will suggest appropriate sorting methods based on those posts. The AI ​​can also suggest appropriate sorting methods based on information shared by the user on social media. For example, if a user posts that they "don't know how to sort plastic waste," the system will suggest specific sorting methods. Furthermore, the AI ​​can provide customized sorting information based on the user's social media activity. For example, if a user frequently posts about "recycling," the system will provide that user with detailed recycling information. In this way, by analyzing social media activity, relevant sorting information is provided, supporting the user's sorting work. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's social media activity into a generating AI, which can then provide relevant sorting information.

[0054] The waste sorting support system can apply region-specific waste sorting rules by taking into account the user's geographical location. For example, the AI ​​can automatically apply regional waste sorting rules based on the user's location. For example, if the user moves to a different region, the system can take photos according to the sorting rules of the new region. For example, when a user sorts waste while traveling, the system will display the sorting rules of that region and suggest appropriate sorting methods. The system can also retrieve regional waste sorting rules from a database, and the AI ​​can perform appropriate shooting settings. For example, it can automatically adjust the shooting angle and distance of the waste according to the sorting rules of the region where the user lives. Furthermore, if the user moves to a different region, the AI ​​can take photos according to the sorting rules of the new region. This ensures accurate sorting by following region-specific waste sorting rules. Some or all of the above processing in the shooting unit may be performed using AI, or not. For example, the shooting unit can input the user's geographical location information into a generating AI, which can then apply region-specific waste sorting rules.

[0055] The waste sorting support system can improve sorting accuracy by referring to the user's past sorting history. For example, the AI ​​can improve sorting accuracy based on data of waste sorted by the user in the past. For instance, it can analyze the types and frequency of waste sorted by the user in the past and suggest the most appropriate sorting method. The AI ​​can also select the sorting method with the highest success rate based on the user's past sorting history. For example, it can provide a customized sorting method depending on the types of waste the user frequently sorts. Furthermore, the AI ​​can provide advice to prevent errors based on data of waste that the user has sorted incorrectly in the past. For example, it can display specific instructions such as, "Last time you sorted this waste as plastic waste, but this time please sort it as metal waste." By referring to past sorting history, sorting accuracy is improved and accurate recycling is promoted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past sorting history into a generating AI, which can then improve sorting accuracy.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The camera unit takes a picture of the trash. For example, it uses a smartphone camera to take a picture of the trash. The user does not need to do anything special; they simply need to point the camera at the trash and take a picture. For example, they might photograph plastic bottles or plastic containers. Step 2: The analysis unit analyzes the photos taken by the shooting unit to determine whether the waste is plastic. For example, it uses image recognition technology to determine if the waste is plastic. AI analyzes the shape and material of the PET bottle and determines that it is plastic. The analysis unit performs the analysis in real time, and the user can check the results immediately. Step 3: The display unit presents the results determined by the analysis unit to the user. For example, if it is determined to be plastic, it will display "Please separate it as plastic waste." The display unit then presents the user with instructions on how to separate the waste. For example, accurate separation of plastic waste improves the recycling rate and reduces the environmental burden.

[0058] (Example of form 2) The waste sorting support system according to an embodiment of the present invention provides a new sorting method using AI technology to solve the problem that general consumers find it difficult to identify plastics, in light of the new Plastics Law which requires household waste sorting. The waste sorting support system is developed as a home AI application that determines in real time whether a waste is plastic by taking a picture of it with a smartphone. This application makes it easy for consumers to sort their waste and promotes accurate recycling. For example, with the waste sorting support system, the user takes a picture of the waste with their smartphone. At this time, the user does not need to do any special operation, and simply points the camera at the waste and takes a picture. For example, they can take a picture of a PET bottle or a plastic container. This information is input into the AI. Next, the waste sorting support system uses the AI ​​to analyze the input waste picture. The AI ​​uses image recognition technology to determine whether the waste is plastic. For example, the AI ​​analyzes the shape and material of a PET bottle and determines that it is plastic. This analysis is performed in real time, and the user can check the results immediately. Based on the analysis results, the waste sorting support system presents the user with a method for sorting the waste. For example, if an item is identified as plastic, the waste sorting support system will display a message such as "Please separate this as plastic waste." This allows users to sort their waste accurately. This system supports consumers in sorting their waste and promotes accurate recycling. For instance, accurate sorting of plastic waste improves the recycling rate and reduces the environmental burden. Furthermore, because waste sorting is easy for users, it becomes easier for them to participate in recycling activities in their daily lives. In the future, it will also be possible to recover valuable resources such as rare metals using AI technology. For example, the waste sorting support system could efficiently recover valuable resources by having users take pictures of electronic devices with their smartphones and having the AI ​​identify the rare metals contained within them. In this way, utilizing AI technology can make resource recovery within households more efficient and contribute to the realization of a circular economy. Thus, the waste sorting support system can support consumers in sorting their waste and promote accurate recycling.

[0059] The waste sorting support system according to this embodiment comprises a shooting unit, an analysis unit, and a display unit. The shooting unit takes pictures of the waste. The shooting unit takes pictures of the waste using, for example, a smartphone camera. The shooting unit does not require any special operation from the user; the user simply needs to point the camera at the waste and take a picture. For example, it takes pictures of PET bottles or plastic containers. The analysis unit analyzes the pictures taken by the shooting unit and determines whether the waste is plastic. The analysis unit determines whether the waste is plastic using, for example, image recognition technology. For example, AI analyzes the shape and material of a PET bottle and determines that it is plastic. The analysis unit performs the analysis in real time, and the user can check the results immediately. The display unit presents the results determined by the analysis unit to the user. For example, if the display unit determines that the waste is plastic, it displays "Please separate it as plastic waste." The display unit presents the user with a method for sorting the waste. For example, accurate sorting of plastic waste improves the recycling rate and reduces the environmental burden. As a result, the waste sorting support system according to the embodiment allows users to easily sort their waste. Some or all of the above-described processes in the shooting unit, analysis unit, and presentation unit may be performed using AI, for example, or without AI. For example, the shooting unit can input image data captured by a smartphone camera into a generation AI and cause the generation AI to perform analysis to determine the type of waste from the image data. The analysis unit can determine the type of waste using the generation AI. The presentation unit can use the generation AI to present waste sorting methods to the user.

[0060] The camera unit takes pictures of the trash. For example, it uses a smartphone camera to take pictures of the trash. The user doesn't need to perform any special operations; they simply point the camera at the trash and take a picture. For example, it can photograph plastic bottles or plastic containers. The camera unit can capture high-resolution images of the trash through the smartphone's camera application. Furthermore, the camera unit has a function to automatically correct image blur and focus errors, allowing users to easily take high-quality images. For example, the smartphone's camera application has shooting modes tailored to different types of trash, and the optimal settings are automatically applied when photographing plastic bottles or containers. This allows the camera unit to easily take pictures of trash and provide high-quality image data to the analysis unit. The camera unit can also take multiple images in sequence to provide more information to the analysis unit. For example, taking images of the trash from different angles allows the analysis unit to more accurately identify the type of trash. This allows the camera unit to easily take pictures of trash and provide high-quality image data to the analysis unit.

[0061] The analysis unit analyzes photos taken by the camera unit to determine whether the waste is plastic. For example, the analysis unit uses image recognition technology to determine if the waste is plastic. Specifically, the AI ​​analyzes the shape and material of a plastic bottle to determine that it is plastic. The analysis unit performs the analysis in real time, allowing users to see the results immediately. The analysis unit employs a deep learning-based image recognition algorithm to analyze features such as the shape, color, and texture of the waste with high accuracy. For example, it uses a convolutional neural network (CNN) to extract features from images of waste and use them to determine the type of waste. Furthermore, the analysis unit can improve its discrimination accuracy by learning from past data. For example, it can train an AI model using image data of waste analyzed in the past, enabling high-accuracy discrimination of new images of waste. The analysis unit can also send images taken by the user to a cloud server for analysis on the cloud. This allows for fast and highly accurate analysis, independent of the smartphone's processing power. The analysis unit can not only determine the type of waste but also analyze its condition and degree of soiling. For example, it can determine whether a plastic bottle has been washed and suggest the appropriate sorting method. This allows the analysis unit to easily identify the type of waste and learn the appropriate sorting method.

[0062] The display unit presents the results determined by the analysis unit to the user. For example, if the display unit determines that an item is plastic, it will display "Please separate this as plastic waste." The display unit also provides the user with instructions on how to separate waste. Specifically, it can not only display the separation method on the smartphone screen but also use voice guidance and vibration notifications to alert the user. For example, accurate separation of plastic waste improves the recycling rate and reduces the environmental burden. The display unit provides a visual interface to make it easy for users to understand how to separate waste. For example, it can intuitively show the separation method using different colors and icons for each type of waste. The display unit can also record the user's past separation history and provide suggestions for improvement and advice on separation methods. For example, if a user has separated waste incorrectly in the past, it will analyze the cause and provide points to note for the next separation. Furthermore, the display unit is compatible with regional separation rules and can provide information based on the separation rules of the area where the user lives. This allows users to separate waste accurately according to local rules. The display unit reduces the stress users experience when sorting waste and supports them in performing the sorting process smoothly. For example, it not only provides a concise explanation of sorting methods but also details specific procedures and points to note, allowing users to sort their waste without confusion. As a result, the display unit enables users to easily sort their waste, contributing to improved recycling rates and reduced environmental impact.

[0063] The analysis unit can determine whether trash is plastic using image recognition technology. For example, the analysis unit can analyze images of trash using a convolutional neural network (CNN) to determine if it is plastic. For example, the analysis unit can analyze the shape and material of the trash to determine if it is plastic. The analysis unit can also analyze images of trash using a support vector machine (SVM) to determine if it is plastic. For example, the analysis unit can extract features from the trash and determine if it is plastic based on those features. Furthermore, the analysis unit can analyze images of trash using deep learning technology to determine if it is plastic. For example, the analysis unit can learn from a large amount of trash image data to perform highly accurate discrimination. This allows for accurate determination of whether trash is plastic using image recognition technology. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input trash image data into a generating AI, which can then determine the type of trash.

[0064] The display unit can present the user with instructions on how to sort waste. For example, the display unit can show the user how to sort plastic waste. For example, the display unit might display, "Please sort this as plastic waste." The display unit can also present the user with instructions on how to sort metal waste. For example, the display unit might display, "Please sort this as metal waste." Furthermore, the display unit can also present the user with instructions on how to sort paper waste. For example, the display unit might display, "Please sort this as paper waste." By presenting the user with instructions on how to sort waste, the user can sort their waste accurately. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the waste sorting method into a generating AI, and the generating AI can present the sorting method to the user.

[0065] The shooting unit can take pictures of trash using a smartphone camera. The shooting unit can, for example, take high-resolution photos using a smartphone camera. For example, the shooting unit can take pictures of trash using the autofocus function. The shooting unit can also shoot videos using a smartphone camera. For example, the shooting unit shoots a video of trash and sends it to the analysis unit. Furthermore, the shooting unit can also take panoramic photos using a smartphone camera. For example, the shooting unit captures an overall view of the trash and sends it to the analysis unit. This allows users to easily take pictures of trash using a smartphone camera. Some or all of the above processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input image data captured by a smartphone camera into a generating AI, which can then analyze the image data.

[0066] The analysis unit can identify valuable resources such as rare metals. For example, the analysis unit can analyze photographs of electronic devices and identify the rare metals contained within them. For instance, it can analyze photographs of smartphones and identify rare metals such as lithium and cobalt. The analysis unit can also analyze photographs of personal computers and identify rare metals such as nickel and gold. For example, it can analyze the internal structure of a personal computer and identify the presence of rare metals. Furthermore, the analysis unit can analyze photographs of home appliances and identify rare metals. For example, it can analyze photographs of refrigerators and washing machines and identify rare metals. This allows for the efficient recovery of valuable resources by identifying rare metals and other valuable resources. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of electronic devices into a generating AI, which can then identify rare metals.

[0067] The display unit can present users with methods for recovering valuable resources. For example, the display unit can present users with methods for recovering rare metals. For example, the display unit may display, "Please take it to a recycling center." The display unit can also present users with methods for recovering electronic devices. For example, the display unit may display, "Please have it collected at an electronics retailer." Furthermore, the display unit can also present users with methods for recovering home appliances. For example, the display unit may display, "Please put it out on your local government's collection day." By presenting users with methods for recovering valuable resources, efficient resource recovery becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input methods for recovering valuable resources into a generating AI, and the generating AI can present the recovery methods to the user.

[0068] The camera unit can estimate the user's emotions and adjust the timing of the photo shoot based on those emotions. For example, if the user is feeling stressed, the AI ​​can automatically take a photo at the optimal time. For instance, the camera unit can analyze the user's facial expressions and, if it determines the user is stressed, take a photo at the optimal time. The camera unit can also allow the user to manually choose when to take a photo if the user is relaxed. For example, the camera unit can analyze the user's voice and, if it determines the user is relaxed, allow the user to manually choose when to take a photo. Furthermore, if the user is in a hurry, the AI ​​can quickly take a photo and immediately analyze the results. For example, the camera unit can analyze the user's behavior and, if it determines the user is in a hurry, quickly take a photo and immediately analyze the results. This allows for optimal timing of the photo shoot by adjusting the timing based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the shooting unit may be performed using AI, for example, or without AI. For example, the shooting unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0069] The camera unit can automatically adjust the optimal shooting angle and distance according to the type of waste. For example, in the case of a plastic bottle, the AI ​​automatically sets the optimal angle and distance and takes a picture. For example, the camera unit analyzes the shape of the plastic bottle and sets the optimal angle and distance. In the case of a plastic container, the AI ​​can also set the optimal shooting settings according to the shape of the container. For example, the camera unit analyzes the shape of the plastic container and sets the optimal shooting settings. Furthermore, the AI ​​can calculate the appropriate distance according to the size of the waste and take a picture. For example, the camera unit analyzes the size of the waste, calculates the appropriate distance and takes a picture. This improves the accuracy of the analysis by automatically adjusting the optimal shooting angle and distance according to the type of waste. Some or all of the above processing in the camera unit may be performed using AI, or not using AI. For example, the camera unit can input shooting settings according to the type of waste into a generating AI, and the generating AI can perform the optimal shooting settings.

[0070] The image capture unit can automatically remove the background of the debris, improving the accuracy of the analysis. For example, the image capture unit can use AI to analyze the background of the debris in real time and remove unnecessary parts. The image capture unit can also use AI to automatically blur the background and emphasize the shape of the debris during shooting. Furthermore, the image capture unit can identify the color of the debris and the background, and the AI ​​can remove the background and extract only the debris. This improves the accuracy of the analysis by automatically removing the background of the debris. Some or all of the above processing in the image capture unit may be performed using AI, or not using AI. For example, the image capture unit can input the process of removing the background of the debris into a generating AI, and the generating AI can perform background removal.

[0071] The camera unit can estimate the user's emotions and prioritize which trash to photograph based on those emotions. For example, if the user is stressed, the camera unit will prioritize photographing trash that the AI ​​can easily identify. For instance, the camera unit analyzes the user's facial expressions and, if it determines the user is stressed, will prioritize photographing easily identifiable trash. Furthermore, if the user is relaxed, the camera unit can prioritize photographing trash with complex shapes. For example, if the camera unit analyzes the user's voice and, if it determines the user is relaxed, will prioritize photographing trash with complex shapes. Additionally, if the user is in a hurry, the camera unit can prioritize photographing trash that the AI ​​can quickly identify. For example, if the camera unit analyzes the user's behavior and, if it determines the user is in a hurry, will prioritize photographing trash that can be quickly identified. This allows for efficient photography by prioritizing trash based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the shooting unit may be performed using AI, or not using AI. For example, the shooting unit can input user facial expression data into the generation AI, which can then estimate emotions.

[0072] The camera unit can take into account the user's geographical location and follow local waste sorting rules. For example, the camera unit can automatically apply local waste sorting rules based on the user's location using AI. The camera unit can also retrieve local waste sorting rules from a database, and the AI ​​can perform appropriate shooting settings. Furthermore, if the user moves to a different region, the camera unit can perform shooting according to the sorting rules of the new region using AI. This ensures accurate sorting by following local waste sorting rules. Some or all of the above processing in the camera unit may be performed using AI, or not. For example, the camera unit can input the user's geographical location into a generating AI, which can then apply local waste sorting rules.

[0073] The shooting unit can suggest optimal shooting settings by referring to the user's past shooting history. For example, the shooting unit can suggest optimal shooting settings based on the types of trash the user has photographed in the past. The shooting unit can also have the AI ​​select the shooting settings with the highest success rate from the user's past shooting history. For example, the shooting unit can select the shooting settings with the highest success rate from the user's past shooting history. Furthermore, the shooting unit can have the AI ​​provide customized shooting settings depending on the types of trash the user frequently photographs. For example, the shooting unit can provide customized shooting settings depending on the types of trash the user frequently photographs. This allows the optimal shooting settings to be suggested by referring to past shooting history. Some or all of the above processing in the shooting unit may be performed using AI, or not using AI. For example, the shooting unit can input the user's past shooting history into a generating AI, which can then suggest optimal shooting settings.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can display concise and easy-to-understand analysis results. For example, if the analysis unit analyzes the user's facial expressions and determines that the user is stressed, it can display concise and easy-to-understand analysis results. The analysis unit can also display detailed analysis results if the user is relaxed. For example, if the analysis unit analyzes the user's voice and determines that the user is relaxed, it can display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display analysis results that emphasize only the essential points. For example, if the analysis unit analyzes the user's behavior and determines that the user is in a hurry, it can display analysis results that emphasize only the essential points. In this way, by adjusting the presentation of the analysis results based on the user's emotions, results that are easy for the user to understand are provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0075] The analysis unit can perform detailed classification based on the material and shape of the waste. For example, the analysis unit can use AI to analyze the material of the waste and classify it into plastic, metal, paper, etc. The analysis unit can also use AI to analyze the shape of the waste and classify it into bottles, containers, bags, etc. Furthermore, the analysis unit can combine the material and shape of the waste to perform detailed classification using AI. This allows for accurate sorting by performing detailed classification based on the material and shape of the waste. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input data on the material and shape of the waste into a generating AI, which can then perform detailed classification.

[0076] The analysis unit can propose sorting methods considering the degree of contamination of the waste. For example, if the waste is dirty, the AI ​​may suggest that it needs to be cleaned. The analysis unit can also suggest that if the waste is clean, it can be sorted as is. Furthermore, the analysis unit can suggest an appropriate sorting method depending on the degree of contamination of the waste. In this way, an appropriate sorting method is proposed by considering the degree of contamination of the waste. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input data on the degree of contamination of the waste into a generating AI, and the generating AI can suggest an appropriate sorting method.

[0077] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important analysis results. For example, if the analysis unit analyzes the user's facial expressions and determines that the user is stressed, it will prioritize displaying important analysis results. The analysis unit can also sequentially display detailed analysis results if the user is relaxed. For example, if the analysis unit analyzes the user's voice and determines that the user is relaxed, it will sequentially display detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can display the most important analysis results first. For example, if the analysis unit analyzes the user's behavior and determines that the user is in a hurry, it will display the most important analysis results first. In this way, important information is provided preferentially by prioritizing the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0078] The analysis unit can propose region-specific recycling methods by considering the user's geographical location information. For example, the analysis unit can use AI to propose regional recycling methods based on the user's location information. The analysis unit can also retrieve regional recycling rules from a database and have the AI ​​propose an appropriate method. Furthermore, if the user moves to a different region, the analysis unit can have the AI ​​propose a recycling method for the new region. This allows for accurate recycling by proposing region-specific recycling methods. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the user's geographical location information into a generating AI, which can then propose region-specific recycling methods.

[0079] The analysis unit can improve analysis accuracy by referring to the user's past analysis history. For example, the analysis unit improves analysis accuracy based on the waste data that the user has previously analyzed. The analysis unit can also select the analysis method with the highest success rate from the user's past analysis history. For example, the analysis unit selects the analysis method with the highest success rate from the user's past analysis history. Furthermore, the analysis unit can provide a customized analysis method based on the type of waste that the user frequently analyzes. For example, the analysis unit provides a customized analysis method based on the type of waste that the user frequently analyzes. This improves analysis accuracy by referring to past analysis history. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's past analysis history into a generating AI, which can then improve analysis accuracy.

[0080] The presentation unit can estimate the user's emotions and adjust the way the presentation content is presented based on the estimated emotions. For example, if the user is stressed, the presentation unit can display concise and easy-to-understand content. For example, if the presentation unit analyzes the user's facial expressions and determines that the user is stressed, it can display concise and easy-to-understand content. The presentation unit can also display detailed content if the user is relaxed. For example, if the presentation unit analyzes the user's voice and determines that the user is relaxed, it can display detailed content. Furthermore, if the user is in a hurry, the presentation unit can display content that emphasizes only the essentials. For example, if the presentation unit analyzes the user's behavior and determines that the user is in a hurry, it can display content that emphasizes only the essentials. In this way, by adjusting the way the presentation content is presented based on the user's emotions, information that is easy for the user to understand is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0081] The display unit can provide detailed explanations of the optimal sorting methods depending on the type of waste. For example, in the case of plastic waste, the AI ​​will provide detailed sorting instructions. The display unit can also provide appropriate sorting instructions for metal waste. Furthermore, the display unit can provide detailed sorting instructions for paper waste. By providing detailed explanations of the optimal sorting methods depending on the type of waste, accurate sorting can be achieved. Some or all of the above processing in the display unit may be performed using AI, or without AI. For example, the display unit can input sorting methods according to the type of waste into a generating AI, which can then provide detailed sorting instructions.

[0082] The presentation unit can provide personalized advice by referring to the user's past sorting history. For example, the presentation unit can use AI to provide personalized advice based on data of waste sorted by the user in the past. The presentation unit can also use AI to suggest the sorting method with the highest success rate based on the user's past sorting history. Furthermore, the presentation unit can use AI to provide customized advice depending on the types of waste the user frequently sorts. In this way, personalized advice is provided by referring to past sorting history. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input the user's past sorting history into a generating AI, which can then provide personalized advice.

[0083] The presentation unit can estimate the user's emotions and determine the priority of the presented content based on the estimated emotions. For example, if the user is stressed, the presentation unit will prioritize displaying important information. For example, if the presentation unit analyzes the user's facial expressions and determines that the user is stressed, it will prioritize displaying important information. The presentation unit can also sequentially display detailed information if the user is relaxed. For example, if the presentation unit analyzes the user's voice and determines that the user is relaxed, it will sequentially display detailed information. Furthermore, if the user is in a hurry, the presentation unit can display the most important information first. For example, if the presentation unit analyzes the user's behavior and determines that the user is in a hurry, it will display the most important information first. In this way, by prioritizing the presented content based on the user's emotions, important information is provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0084] The display unit can display region-specific classification rules, taking into account the user's geographical location information. For example, the display unit can have an AI display region classification rules based on the user's location information. The display unit can also retrieve region-specific classification rules from a database and have an AI display the appropriate method. Furthermore, if the user moves to a different region, the display unit can have an AI display the classification rules for the new region. This allows for accurate classification by displaying region-specific classification rules. Some or all of the above processing in the display unit may be performed using an AI, or not. For example, the display unit can input the user's geographical location information into a generating AI, which can then display region-specific classification rules.

[0085] The presentation unit can analyze a user's social media activity and provide relevant classification information. For example, the presentation unit can use AI to provide relevant classification information based on the content of a user's social media posts. The presentation unit can also use AI to suggest appropriate classification methods based on information shared by the user on social media. Furthermore, the presentation unit can use AI to provide customized classification information based on the user's social media activity. In this way, relevant classification information is provided by analyzing social media activity. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input the user's social media activity into a generating AI, and the generating AI can provide relevant classification information.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The waste sorting support system can estimate the user's emotions and customize waste sorting advice based on those emotions. For example, if the user is stressed, the system provides concise and intuitive advice, such as displaying a simple instruction like, "Please sort this waste as plastic waste." If the user is relaxed, the system can also provide detailed explanations, such as displaying specific instructions like, "This waste is made of plastic and is recyclable. Please take it to the recycling center." Furthermore, if the user is in a hurry, the system can prioritize displaying the most important information, such as displaying an urgent instruction like, "This waste is plastic waste. Please sort it immediately." This provides appropriate advice tailored to the user's emotions, improving the efficiency of waste sorting. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not. For example, the presentation unit can input the user's facial expression data into the generative AI, which can then estimate emotions.

[0088] The waste sorting support system can improve sorting accuracy by referring to the user's past sorting history. For example, the AI ​​can improve sorting accuracy based on data of waste sorted by the user in the past. For instance, it can analyze the types and frequency of waste sorted by the user in the past and suggest the most appropriate sorting method. The AI ​​can also select the sorting method with the highest success rate based on the user's past sorting history. For example, it can provide a customized sorting method depending on the types of waste the user frequently sorts. Furthermore, the AI ​​can provide advice to prevent errors based on data of waste that the user has sorted incorrectly in the past. For example, it can display specific instructions such as, "Last time you sorted this waste as plastic waste, but this time please sort it as metal waste." By referring to past sorting history, sorting accuracy is improved and accurate recycling is promoted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past sorting history into a generating AI, which can then improve sorting accuracy.

[0089] The waste sorting support system can apply region-specific waste sorting rules by taking into account the user's geographical location. For example, the AI ​​can automatically apply regional waste sorting rules based on the user's location. For example, if the user moves to a different region, the system can take photos according to the sorting rules of the new region. For example, when a user sorts waste while traveling, the system will display the sorting rules of that region and suggest appropriate sorting methods. The system can also retrieve regional waste sorting rules from a database, and the AI ​​can perform appropriate shooting settings. For example, it can automatically adjust the shooting angle and distance of the waste according to the sorting rules of the region where the user lives. Furthermore, if the user moves to a different region, the AI ​​can take photos according to the sorting rules of the new region. This ensures accurate sorting by following region-specific waste sorting rules. Some or all of the above processing in the shooting unit may be performed using AI, or not. For example, the shooting unit can input the user's geographical location information into a generating AI, which can then apply region-specific waste sorting rules.

[0090] The waste sorting support system can estimate the user's emotions and suggest waste sorting methods based on those emotions. For example, if the user is stressed, the system can suggest a simple and intuitive sorting method. For instance, it might display a simple instruction such as, "Please sort this waste as plastic waste." If the user is relaxed, the system can also provide a detailed explanation. For example, it might display a specific instruction such as, "This waste is made of plastic and is recyclable. Please take it to a recycling center." Furthermore, if the user is in a hurry, the system can prioritize and display the most important information. For example, it might display an urgent instruction such as, "This waste is plastic waste. Please sort it immediately." This improves the efficiency of waste sorting by suggesting appropriate sorting methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation section may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0091] The waste sorting support system can analyze a user's social media activity and provide relevant sorting information. For example, the AI ​​can provide relevant sorting information based on the user's social media posts. For instance, if a user uses keywords such as "recycling" or "waste sorting" on social media, the system will suggest appropriate sorting methods based on those posts. The AI ​​can also suggest appropriate sorting methods based on information shared by the user on social media. For example, if a user posts that they "don't know how to sort plastic waste," the system will suggest specific sorting methods. Furthermore, the AI ​​can provide customized sorting information based on the user's social media activity. For example, if a user frequently posts about "recycling," the system will provide that user with detailed recycling information. In this way, by analyzing social media activity, relevant sorting information is provided, supporting the user's sorting work. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's social media activity into a generating AI, which can then provide relevant sorting information.

[0092] The waste sorting support system can estimate the user's emotions and suggest waste sorting methods based on those emotions. For example, if the user is stressed, the system can suggest a simple and intuitive sorting method. For instance, it might display a simple instruction such as, "Please sort this waste as plastic waste." If the user is relaxed, the system can also provide a detailed explanation. For example, it might display a specific instruction such as, "This waste is made of plastic and is recyclable. Please take it to a recycling center." Furthermore, if the user is in a hurry, the system can prioritize and display the most important information. For example, it might display an urgent instruction such as, "This waste is plastic waste. Please sort it immediately." This improves the efficiency of waste sorting by suggesting appropriate sorting methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation section may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0093] The waste sorting support system can estimate the user's emotions and suggest waste sorting methods based on those emotions. For example, if the user is stressed, the system can suggest a simple and intuitive sorting method. For instance, it might display a simple instruction such as, "Please sort this waste as plastic waste." If the user is relaxed, the system can also provide a detailed explanation. For example, it might display a specific instruction such as, "This waste is made of plastic and is recyclable. Please take it to a recycling center." Furthermore, if the user is in a hurry, the system can prioritize and display the most important information. For example, it might display an urgent instruction such as, "This waste is plastic waste. Please sort it immediately." This improves the efficiency of waste sorting by suggesting appropriate sorting methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation section may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0094] The waste sorting support system can estimate the user's emotions and suggest waste sorting methods based on those emotions. For example, if the user is stressed, the system can suggest a simple and intuitive sorting method. For instance, it might display a simple instruction such as, "Please sort this waste as plastic waste." If the user is relaxed, the system can also provide a detailed explanation. For example, it might display a specific instruction such as, "This waste is made of plastic and is recyclable. Please take it to a recycling center." Furthermore, if the user is in a hurry, the system can prioritize and display the most important information. For example, it might display an urgent instruction such as, "This waste is plastic waste. Please sort it immediately." This improves the efficiency of waste sorting by suggesting appropriate sorting methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation section may be performed using AI, for example, or without AI. For example, the display unit can input user facial expression data into a generating AI, which can then estimate emotions.

[0095] The waste sorting support system can apply region-specific waste sorting rules by taking into account the user's geographical location. For example, the AI ​​can automatically apply regional waste sorting rules based on the user's location. For example, if the user moves to a different region, the system can take photos according to the sorting rules of the new region. For example, when a user sorts waste while traveling, the system will display the sorting rules of that region and suggest appropriate sorting methods. The system can also retrieve regional waste sorting rules from a database, and the AI ​​can perform appropriate shooting settings. For example, it can automatically adjust the shooting angle and distance of the waste according to the sorting rules of the region where the user lives. Furthermore, if the user moves to a different region, the AI ​​can take photos according to the sorting rules of the new region. This ensures accurate sorting by following region-specific waste sorting rules. Some or all of the above processing in the shooting unit may be performed using AI, or not. For example, the shooting unit can input the user's geographical location information into a generating AI, which can then apply region-specific waste sorting rules.

[0096] The waste sorting support system can improve sorting accuracy by referring to the user's past sorting history. For example, the AI ​​can improve sorting accuracy based on data of waste sorted by the user in the past. For instance, it can analyze the types and frequency of waste sorted by the user in the past and suggest the most appropriate sorting method. The AI ​​can also select the sorting method with the highest success rate based on the user's past sorting history. For example, it can provide a customized sorting method depending on the types of waste the user frequently sorts. Furthermore, the AI ​​can provide advice to prevent errors based on data of waste that the user has sorted incorrectly in the past. For example, it can display specific instructions such as, "Last time you sorted this waste as plastic waste, but this time please sort it as metal waste." By referring to past sorting history, sorting accuracy is improved and accurate recycling is promoted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past sorting history into a generating AI, which can then improve sorting accuracy.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The camera unit takes a picture of the trash. For example, it uses a smartphone camera to take a picture of the trash. The user does not need to do anything special; they simply need to point the camera at the trash and take a picture. For example, they might photograph plastic bottles or plastic containers. Step 2: The analysis unit analyzes the photos taken by the shooting unit to determine whether the waste is plastic. For example, it uses image recognition technology to determine if the waste is plastic. AI analyzes the shape and material of the PET bottle and determines that it is plastic. The analysis unit performs the analysis in real time, and the user can check the results immediately. Step 3: The display unit presents the results determined by the analysis unit to the user. For example, if it is determined to be plastic, it will display "Please separate it as plastic waste." The display unit then presents the user with instructions on how to separate the waste. For example, accurate separation of plastic waste improves the recycling rate and reduces the environmental burden.

[0099] 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] 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.

[0102] Each of the multiple elements described above, including the imaging unit, analysis unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the imaging unit takes a photograph of the waste using the camera 42 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses AI to determine whether the waste is plastic. The presentation unit presents the user with a method for sorting the waste using the display 40A of the smart device 14. The analysis unit may be implemented in the control unit 46A of the smart device 14, for example, and the presentation unit may be implemented in the identification processing unit 290 of the data processing unit 12, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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.

[0118] Each of the multiple elements described above, including the imaging unit, analysis 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 imaging unit takes a picture of the trash using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses AI to determine whether the trash is plastic. The presentation unit presents the user with trash sorting methods using the display of the smart glasses 214. The analysis unit may be implemented, for example, by the control unit 46A of the smart glasses 214, and the presentation unit may be implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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.

[0134] Each of the multiple elements described above, including the imaging unit, analysis unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the imaging unit takes a photograph of the waste using the camera 42 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, for example, and uses AI to determine whether the waste is plastic. The presentation unit presents the user with a method for sorting the waste using the display 343 of the headset terminal 314. The analysis unit may be implemented in the control unit 46A of the headset terminal 314, for example, and the presentation unit may be implemented in the identification processing unit 290 of the data processing unit 12, for example. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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.

[0151] Each of the multiple elements described above, including the imaging unit, analysis unit, and presentation unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the imaging unit takes a photograph of the waste using the camera 42 of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and uses AI to determine whether the waste is plastic. The presentation unit presents the user with a method for sorting the waste using the display of the robot 414. The analysis unit may be implemented, for example, by the control unit 46A of the robot 414, and the presentation unit may be implemented, for example, by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) The photography team takes pictures of the trash, An analysis unit analyzes the photograph taken by the aforementioned imaging unit to determine whether the waste is plastic, The system includes a presentation unit that presents the results determined by the analysis unit to the user. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Image recognition technology is used to determine whether the waste is made of plastic. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, Show users how to sort their waste. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned imaging unit is Take a picture of the trash using your smartphone camera. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Identifying valuable resources such as rare metals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Present users methods for recovering valuable resources. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is The system automatically adjusts the optimal shooting angle and distance depending on the type of dust. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is Automatically removes background clutter and improves analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is It estimates the user's emotions and determines the priority of what trash to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is The system takes the user's geographical location into account and follows local waste sorting rules. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is The system suggests optimal shooting settings by referencing the user's past shooting history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Detailed classification is performed based on the material and shape of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We propose sorting methods considering the degree of soiling of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, We propose region-specific recycling methods that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Improve analysis accuracy by referring to the user's past analysis history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, It estimates the user's emotions and adjusts the way the presented content is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, This explains in detail the optimal sorting method for each type of waste. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, Provide personalized advice by referring to the user's past sorting history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, It estimates the user's emotions and prioritizes the content presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, Display region-specific sorting rules, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, Analyze users' social media activity and provide relevant classified information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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. The photography team takes pictures of garbage, An analysis unit analyzes the photograph taken by the aforementioned imaging unit to determine whether the waste is plastic, The system includes a presentation unit that presents the results determined by the analysis unit to the user. A system characterized by the following features.

2. The aforementioned analysis unit, Image recognition technology is used to determine whether the waste is made of plastic. The system according to feature 1.

3. The aforementioned display unit is, Show users how to sort their waste. The system according to feature 1.

4. The aforementioned imaging unit is Take a picture of the trash using your smartphone camera. The system according to feature 1.

5. The aforementioned analysis unit, Identifying valuable resources such as rare metals The system according to feature 1.

6. The aforementioned display unit is, Present users methods for recovering valuable resources. The system according to feature 1.

7. The aforementioned imaging unit is It estimates the user's emotions and adjusts the timing of the photo shoot based on those emotions. The system according to feature 1.

8. The aforementioned imaging unit is The system automatically adjusts the optimal shooting angle and distance depending on the type of dust. The system according to feature 1.

Images