System

Abstract

An object of a system according to an embodiment is to enable a user to intuitively perform waste sorting.SOLUTION: A system according to an embodiment includes an image recognition unit, a sorting presentation unit, and a multilingual handling unit. The image recognition unit analyzes an image of an unnecessary item. The sorting presentation unit presents a sorting destination according to the waste separation rule of each area based on the type of the unnecessary item analyzed by the image recognition unit. The multi-language support unit displays the information presented by the sorting presentation unit based on the language setting of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for users to properly separate their garbage because garbage sorting rules vary from region to region.

[0005] The system according to the embodiment aims to enable users to intuitively separate garbage. [Means for solving the problem]

[0006] The system according to the embodiment includes an image recognition unit, a sorting and presentation unit, and a multilingual support unit. The image recognition unit analyzes images of unwanted items. The sorting and presentation unit presents sorting destinations in accordance with local waste sorting rules based on the types of unwanted items analyzed by the image recognition unit. The multilingual support unit displays the information presented by the sorting and presentation unit based on the user's language setting. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to intuitively separate garbage. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The garbage sorting app according to the embodiment of the present invention is an app that scans unwanted items with a smartphone and tells them where to sort them in accordance with local garbage sorting rules. This allows users to intuitively sort unwanted items and dispose of them appropriately in accordance with local garbage sorting rules.

[0029] A garbage sorting app according to an embodiment includes an image recognition unit, a sorting presentation unit, and a multilingual support unit. The image recognition unit analyzes images of unwanted items. For example, it analyzes images taken by a user with a smartphone camera and identifies the type of item. The image recognition unit can also use machine learning algorithms to extract and classify item features. The image recognition unit can also use computer vision technology to analyze the shape and color of items. The sorting presentation unit presents sorting destinations in accordance with local garbage sorting rules based on the type of unwanted item analyzed by the image recognition unit. For example, the sorting presentation unit displays sorting destinations according to local rules, such as "plastic waste" for PET bottles and "paper waste" for paper. The sorting presentation unit also stores a database of local garbage sorting rules and can apply appropriate rules based on the user's location information and set regional information. The sorting presentation unit can automatically update the latest sorting rules to always provide the latest information. The multilingual support unit displays the information presented by the sorting presentation unit based on the user's language setting. For example, the app provides information on garbage sorting in multiple languages, including English, Chinese, and Korean. The multilingual support unit can also display information in the appropriate language based on the user's language settings. Furthermore, the multilingual support unit can automatically translate and provide regional garbage sorting rules using machine translation technology. This allows the app to recognize unwanted items through image recognition and suggest sorting locations according to each region's garbage sorting rules. Multilingual support also makes the app accessible to foreign users. For example, when a user takes a photo of a plastic bottle with their smartphone camera, the app automatically identifies it as "plastic garbage" and displays the sorting location. The user can then follow the instructions to sort their garbage. Multilingual support also allows foreign users to use the app without getting lost.

[0030] The image recognition unit can analyze the usage status and degree of dirt of an item and determine whether it is recyclable. For example, the image recognition unit uses a generative AI to analyze the degree of dirt on the surface of an item and determine whether it is recyclable. For example, it detects dirt and the presence or absence of labels on the surface of a plastic bottle and evaluates whether it is recyclable. The image recognition unit also analyzes the usage status of an item and determines whether it is recyclable. For example, it analyzes the frequency of use and period of use and evaluates whether it is recyclable. The image recognition unit can also quantify the degree of dirt and determine whether it meets the recyclability criteria. This makes it possible to analyze the usage status and degree of dirt of an item and determine whether it is recyclable.

[0031] The image recognition unit can identify the material of an item and present the optimal sorting method for each material. For example, the image recognition unit uses a generative AI to identify the material of an item and present the optimal sorting method for each material. For example, it can identify glass, metal, plastic, etc., and present the appropriate sorting method for each material. The image recognition unit can also use machine learning algorithms to identify materials. For example, it can analyze the reflective properties and texture of the item's surface to identify the material. The image recognition unit also has a database of sorting methods for each material, and can present the appropriate sorting method based on the identified material. This allows the material of an item to be identified and the optimal sorting method to be presented.

[0032] The image recognition unit can analyze the shape and size of an item and suggest an appropriate sorting method. For example, the image recognition unit uses a generative AI to analyze the shape of an item and suggest an appropriate sorting method. For example, it identifies a cylindrical item as a can and displays how to sort it as can waste. The image recognition unit can also analyze the size of an item and suggest an appropriate sorting method. For example, it can identify a large item as bulky waste and display how to sort it as bulky waste. The image recognition unit can also use computer vision technology to analyze shape and size. For example, it can extract the outline of an item and identify its shape. The image recognition unit can also use image processing technology to measure size. For example, it can measure the length and width of an item and identify its size. This allows the shape and size of an item to be analyzed and an appropriate sorting method to be suggested.

[0033] The image recognition unit can identify the brand and manufacturer of an item and provide a sorting method for each specific brand. For example, the image recognition unit uses a generative AI to identify the brand of an item and provide a sorting method for each specific brand. For example, it can identify plastic bottles from a specific beverage manufacturer and display sorting methods in accordance with that manufacturer's recycling program. The image recognition unit can also identify the manufacturer of an item and provide a sorting method for each specific manufacturer. For example, it can identify products from a specific home appliance manufacturer and display sorting methods in accordance with that manufacturer's recycling program. The image recognition unit can also use logo recognition technology to identify the brand and manufacturer. For example, it can analyze a logo printed on the surface of an item to identify the brand and manufacturer. This makes it possible to identify the brand and manufacturer of an item and provide a sorting method for each specific brand.

[0034] The sorting presentation unit can automatically update the sorting rules for each region and provide the latest information. For example, the generation AI in the sorting presentation unit can automatically update the sorting rules for each region and provide the latest information. For example, the latest sorting rules can be periodically obtained from the official website of a local government and reflected in the app. The sorting presentation unit can also update the database via the Internet and always provide the latest sorting rules. For example, the sorting presentation unit can periodically collect information and add the latest sorting rules to the database. The sorting presentation unit can also apply the latest sorting rules for each region based on the user's location information. This allows the sorting rules for each region to be automatically updated and the latest information to be provided.

[0035] The sorting presentation unit can learn the user's past sorting history and present an individually optimized sorting method. For example, the sorting presentation unit uses a generation AI to learn the user's past sorting history and present an individually optimized sorting method. For example, the sorting presentation unit analyzes the user's sorting tendencies based on the history of items sorted in the past and displays the optimal sorting method. The sorting presentation unit can also use a database to store the sorting history. For example, the sorting history for each user is stored in a database and analyzed using a learning algorithm. The sorting presentation unit can also use a machine learning algorithm to learn the sorting history. For example, the sorting presentation unit predicts the user's sorting tendencies based on past sorting data and presents the optimal sorting method. This makes it possible to learn the user's past sorting history and present an individually optimized sorting method.

[0036] The multilingual support unit can automatically translate and provide regional sorting rules based on the user's language settings. The multilingual support unit, for example, uses a generation AI to automatically translate and provide regional sorting rules based on the user's language settings. For example, Japanese sorting rules are translated into English and displayed to an English-speaking user. The multilingual support unit can also automatically translate the sorting rules using machine translation technology. For example, a translation memory is used to perform translation based on previously translated data. The multilingual support unit can also display the sorting rules in an appropriate language based on the user's language settings. This allows regional sorting rules to be automatically translated and provided based on the user's language settings.

[0037] The multilingual support unit can provide a detailed explanation of the sorting method based on the user's language setting. For example, the generation AI of the multilingual support unit provides a detailed explanation of the sorting method based on the user's language setting. For example, a detailed explanation of the sorting method is displayed in English for an English-speaking user. The multilingual support unit can also use a text explanation for the detailed explanation. For example, the steps of the sorting method are displayed in text. The multilingual support unit can also use images or videos for the detailed explanation. For example, an image or video showing the sorting method is displayed. This makes it possible to provide a detailed explanation of the sorting method based on the user's language setting.

[0038] The multilingual support unit can provide information on recycling facilities for each region based on the user's language settings. For example, the generation AI provides information on recycling facilities for each region based on the user's language settings. For example, for an English-speaking user, information on the nearest recycling center is displayed in English. The multilingual support unit can also include the location of the facility in the information on recycling facilities. For example, the address and map of the recycling center are displayed. The multilingual support unit can also include the types of items that can be accepted in the information on recycling facilities. For example, a list of items that can be accepted at the recycling center is displayed. This makes it possible to provide information on recycling facilities for each region based on the user's language settings.

[0039] The multilingual support unit can support sorting actions based on the user's language settings. For example, the generation AI supports sorting actions based on the user's language settings. For example, if an English-speaking user has a confused expression, the multilingual support unit can display detailed sorting instructions in English. The multilingual support unit can also present specific procedures to support sorting actions. For example, it can display step-by-step procedures for sorting methods. The multilingual support unit can also include explanations of points to note when supporting sorting actions. For example, it can display points to note when sorting and common mistakes. This makes it possible to support sorting actions based on the user's language settings.

[0040] The sorting and presentation unit can provide information on recycling facilities for each region, making it easier for users to recycle. For example, the generation AI can provide information on recycling facilities for each region, making it easier for users to recycle. For example, the app can display the location and business hours of the nearest recycling center. The sorting and presentation unit can also include the types of items that can be accepted in the information on recycling facilities. For example, it can display a list of items that can be accepted at the recycling center. The sorting and presentation unit can also include contact information for the facility in the information on recycling facilities. For example, it can display the phone number and email address of the recycling center. This provides information on recycling facilities for each region, making it easier for users to recycle.

[0041] The sorting and presentation unit can provide information on local events and campaigns to promote waste separation behavior. For example, the generation AI of the sorting and presentation unit provides information on local events and campaigns to promote waste separation behavior. For example, the app displays information on recycling campaigns and garbage sorting events. The sorting and presentation unit can also include the date and time of the event in the event information. For example, it displays the date and time of the recycling event. The sorting and presentation unit can also include the location of the event in the event information. For example, it displays the location of the recycling event on a map. The sorting and presentation unit can also include the duration of the campaign in the campaign information. For example, it displays the start and end dates of the recycling campaign. This allows the provision of information on local events and campaigns to promote waste separation behavior.

[0042] The sorting and presentation unit can provide detailed information about recyclable items to help users understand the importance of recycling. For example, the generative AI can provide detailed information about recyclable items to help users understand the importance of recycling. For example, the sorting and presentation unit can explain the recycling process for PET bottles and their impact on the environment. The sorting and presentation unit can also display a list of types of recyclable items. For example, it can display a list of recyclable items such as plastic, metal, and paper. The sorting and presentation unit can also display text and images explaining the importance of recycling. For example, it can explain the benefits of recycling and the importance of environmental protection. This provides detailed information about recyclable items to help users understand the importance of recycling.

[0043] The sorting and presentation unit can evaluate the environmental impact of an item and provide that information. For example, the generation AI in the sorting and presentation unit evaluates the environmental impact of an item and provides that information. For example, it explains the time it takes for a plastic product to decompose and its impact on the environment. The sorting and presentation unit can also include carbon dioxide emissions in the environmental impact evaluation. For example, it evaluates and displays the carbon dioxide emissions associated with the manufacture and disposal of an item. The sorting and presentation unit can also evaluate recyclability and provide that information. For example, it evaluates the recyclability of an item and explains the benefits of recycling. This makes it possible to evaluate the environmental impact of an item and provide that information.

[0044] The multilingual support unit can provide information on recycling facilities for each region based on the user's language settings. For example, the generation AI provides information on recycling facilities for each region based on the user's language settings. For example, for an English-speaking user, information on the nearest recycling center is displayed in English. The multilingual support unit can also include the location of the facility in the information on recycling facilities. For example, the address and map of the recycling center are displayed. The multilingual support unit can also include the types of items that can be accepted in the information on recycling facilities. For example, a list of items that can be accepted at the recycling center is displayed. This makes it possible to provide information on recycling facilities for each region based on the user's language settings.

[0045] The multilingual support unit can provide regional event and campaign information based on the user's language settings. For example, the generation AI provides regional event and campaign information based on the user's language settings. For example, for an English-speaking user, it displays recycling campaign information in English. The multilingual support unit can also include the date and time of the event in the event information. For example, it displays the date and time of the recycling event. The multilingual support unit can also include the location of the event in the event information. For example, it displays the location of the recycling event on a map. The multilingual support unit can also include the duration of the campaign in the campaign information. For example, it displays the start and end dates of the recycling campaign. This makes it possible to provide regional event and campaign information based on the user's language settings.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The garbage sorting app can also monitor the user's health and suggest appropriate methods for sorting. For example, if the user has allergies, it can advise them to avoid certain types of garbage. It can also show caution when carrying heavy garbage if the user is elderly. Furthermore, it can make suggestions to make the sorting process easier based on the user's health.

[0048] Garbage sorting apps can also gamify users' sorting behavior, allowing them to have fun while sorting. For example, they can introduce a system where users earn points for each sort they make and receive rewards when they reach a certain number of points. They can also add a ranking function that allows users to compete for sorting accuracy and speed. They can also provide a function that allows users to share their sorting results with friends and family and compete against each other.

[0049] Garbage sorting apps can record users' sorting behavior and visualize their contribution to the environment. For example, they can record the amount and type of garbage sorted and display the amount of carbon dioxide reduction achieved through recycling. They can also display graphs and charts showing how much the user is contributing to the environment. Users can also compare their sorting behavior with other users to see how good it is.

[0050] A waste sorting app can also suggest ways to reuse recyclable items based on the user's sorting behavior. For example, it can provide ideas for DIY projects using plastic bottles, introduce craft methods using recycled paper, and even suggest eco-friendly living using recyclable items.

[0051] A waste sorting app can also provide information about local recycling events and workshops based on a user's sorting behavior. For example, it can display the date, time, and location of nearby recycling events, provide instructions on how to participate in recycling workshops, and introduce the benefits of participating in local recycling activities.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The image recognition unit analyzes images of unwanted items. For example, it analyzes images taken by a user with a smartphone camera and identifies the type of item. The image recognition unit can also use machine learning algorithms to extract and classify the features of the items. Furthermore, the image recognition unit can also use computer vision technology to analyze the shape and color of the items. Step 2: The sorting presentation unit presents sorting destinations in accordance with local waste sorting rules based on the type of unwanted item analyzed by the image recognition unit. For example, sorting destinations are displayed according to local rules, such as "plastic waste" for PET bottles and "paper waste" for paper. The sorting presentation unit also has a database of local waste sorting rules, and can apply appropriate rules based on the user's location information and set area information. Furthermore, the sorting presentation unit can automatically update the latest sorting rules, always providing the latest information. Step 3: The multilingual support unit displays the information presented by the sorting presentation unit based on the user's language setting. For example, it provides garbage sorting information in multiple languages, such as English, Chinese, and Korean. The multilingual support unit can also display information in an appropriate language based on the user's language setting. Furthermore, the multilingual support unit can use machine translation technology to automatically translate and provide regional sorting rules.

[0054] (Example 2) The garbage sorting app according to the embodiment of the present invention is an app that scans unwanted items with a smartphone and tells them where to sort them in accordance with local garbage sorting rules. This allows users to intuitively sort unwanted items and dispose of them appropriately in accordance with local garbage sorting rules.

[0055] A garbage sorting app according to an embodiment includes an image recognition unit, a sorting presentation unit, and a multilingual support unit. The image recognition unit analyzes images of unwanted items. For example, it analyzes images taken by a user with a smartphone camera and identifies the type of item. The image recognition unit can also use machine learning algorithms to extract and classify item features. The image recognition unit can also use computer vision technology to analyze the shape and color of items. The sorting presentation unit presents sorting destinations in accordance with local garbage sorting rules based on the type of unwanted item analyzed by the image recognition unit. For example, the sorting presentation unit displays sorting destinations according to local rules, such as "plastic waste" for PET bottles and "paper waste" for paper. The sorting presentation unit also stores a database of local garbage sorting rules and can apply appropriate rules based on the user's location information and set regional information. The sorting presentation unit can automatically update the latest sorting rules to always provide the latest information. The multilingual support unit displays the information presented by the sorting presentation unit based on the user's language setting. For example, the app provides information on garbage sorting in multiple languages, including English, Chinese, and Korean. The multilingual support unit can also display information in the appropriate language based on the user's language settings. Furthermore, the multilingual support unit can automatically translate and provide regional garbage sorting rules using machine translation technology. This allows the app to recognize unwanted items through image recognition and suggest sorting locations according to each region's garbage sorting rules. Multilingual support also makes the app accessible to foreign users. For example, when a user takes a photo of a plastic bottle with their smartphone camera, the app automatically identifies it as "plastic garbage" and displays the sorting location. The user can then follow the instructions to sort their garbage. Multilingual support also allows foreign users to use the app without getting lost.

[0056] The image recognition unit can analyze the usage status and degree of dirt of an item and determine whether it is recyclable. For example, the image recognition unit uses a generative AI to analyze the degree of dirt on the surface of an item and determine whether it is recyclable. For example, it detects dirt and the presence or absence of labels on the surface of a plastic bottle and evaluates whether it is recyclable. The image recognition unit also analyzes the usage status of an item and determines whether it is recyclable. For example, it analyzes the frequency of use and period of use and evaluates whether it is recyclable. The image recognition unit can also quantify the degree of dirt and determine whether it meets the recyclability criteria. This makes it possible to analyze the usage status and degree of dirt of an item and determine whether it is recyclable.

[0057] The image recognition unit can identify the material of an item and present the optimal sorting method for each material. For example, the image recognition unit uses a generative AI to identify the material of an item and present the optimal sorting method for each material. For example, it can identify glass, metal, plastic, etc., and present the appropriate sorting method for each material. The image recognition unit can also use machine learning algorithms to identify materials. For example, it can analyze the reflective properties and texture of the item's surface to identify the material. The image recognition unit also has a database of sorting methods for each material, and can present the appropriate sorting method based on the identified material. This allows the material of an item to be identified and the optimal sorting method to be presented.

[0058] The image recognition unit can analyze the shape and size of an item and suggest an appropriate sorting method. For example, the image recognition unit uses a generative AI to analyze the shape of an item and suggest an appropriate sorting method. For example, it identifies a cylindrical item as a can and displays how to sort it as can waste. The image recognition unit can also analyze the size of an item and suggest an appropriate sorting method. For example, it can identify a large item as bulky waste and display how to sort it as bulky waste. The image recognition unit can also use computer vision technology to analyze shape and size. For example, it can extract the outline of an item and identify its shape. The image recognition unit can also use image processing technology to measure size. For example, it can measure the length and width of an item and identify its size. This allows the shape and size of an item to be analyzed and an appropriate sorting method to be suggested.

[0059] The image recognition unit can identify the brand and manufacturer of an item and provide a sorting method for each specific brand. For example, the image recognition unit uses a generative AI to identify the brand of an item and provide a sorting method for each specific brand. For example, it can identify plastic bottles from a specific beverage manufacturer and display sorting methods in accordance with that manufacturer's recycling program. The image recognition unit can also identify the manufacturer of an item and provide a sorting method for each specific manufacturer. For example, it can identify products from a specific home appliance manufacturer and display sorting methods in accordance with that manufacturer's recycling program. The image recognition unit can also use logo recognition technology to identify the brand and manufacturer. For example, it can analyze a logo printed on the surface of an item to identify the brand and manufacturer. This makes it possible to identify the brand and manufacturer of an item and provide a sorting method for each specific brand.

[0060] The sorting presentation unit can automatically update the sorting rules for each region and provide the latest information. For example, the generation AI in the sorting presentation unit can automatically update the sorting rules for each region and provide the latest information. For example, the latest sorting rules can be periodically obtained from the official website of a local government and reflected in the app. The sorting presentation unit can also update the database via the Internet and always provide the latest sorting rules. For example, the sorting presentation unit can periodically collect information and add the latest sorting rules to the database. The sorting presentation unit can also apply the latest sorting rules for each region based on the user's location information. This allows the sorting rules for each region to be automatically updated and the latest information to be provided.

[0061] The sorting presentation unit can learn the user's past sorting history and present an individually optimized sorting method. For example, the sorting presentation unit uses a generation AI to learn the user's past sorting history and present an individually optimized sorting method. For example, the sorting presentation unit analyzes the user's sorting tendencies based on the history of items sorted in the past and displays the optimal sorting method. The sorting presentation unit can also use a database to store the sorting history. For example, the sorting history for each user is stored in a database and analyzed using a learning algorithm. The sorting presentation unit can also use a machine learning algorithm to learn the sorting history. For example, the sorting presentation unit predicts the user's sorting tendencies based on past sorting data and presents the optimal sorting method. This makes it possible to learn the user's past sorting history and present an individually optimized sorting method.

[0062] The multilingual support unit can automatically translate and provide regional sorting rules based on the user's language settings. The multilingual support unit, for example, uses a generation AI to automatically translate and provide regional sorting rules based on the user's language settings. For example, Japanese sorting rules are translated into English and displayed to an English-speaking user. The multilingual support unit can also automatically translate the sorting rules using machine translation technology. For example, a translation memory is used to perform translation based on previously translated data. The multilingual support unit can also display the sorting rules in an appropriate language based on the user's language settings. This allows regional sorting rules to be automatically translated and provided based on the user's language settings.

[0063] The multilingual support unit can provide a detailed explanation of the sorting method based on the user's language setting. For example, the generation AI of the multilingual support unit provides a detailed explanation of the sorting method based on the user's language setting. For example, a detailed explanation of the sorting method is displayed in English for an English-speaking user. The multilingual support unit can also use a text explanation for the detailed explanation. For example, the steps of the sorting method are displayed in text. The multilingual support unit can also use images or videos for the detailed explanation. For example, an image or video showing the sorting method is displayed. This makes it possible to provide a detailed explanation of the sorting method based on the user's language setting.

[0064] The multilingual support unit can provide information on recycling facilities for each region based on the user's language settings. For example, the generation AI provides information on recycling facilities for each region based on the user's language settings. For example, for an English-speaking user, information on the nearest recycling center is displayed in English. The multilingual support unit can also include the location of the facility in the information on recycling facilities. For example, the address and map of the recycling center are displayed. The multilingual support unit can also include the types of items that can be accepted in the information on recycling facilities. For example, a list of items that can be accepted at the recycling center is displayed. This makes it possible to provide information on recycling facilities for each region based on the user's language settings.

[0065] The multilingual support unit can support sorting actions based on the user's language settings. For example, the generation AI supports sorting actions based on the user's language settings. For example, if an English-speaking user has a confused expression, the multilingual support unit can display detailed sorting instructions in English. The multilingual support unit can also present specific procedures to support sorting actions. For example, it can display step-by-step procedures for sorting methods. The multilingual support unit can also include explanations of points to note when supporting sorting actions. For example, it can display points to note when sorting and common mistakes. This makes it possible to support sorting actions based on the user's language settings.

[0066] The sorting and presentation unit can provide information on recycling facilities for each region, making it easier for users to recycle. For example, the generation AI can provide information on recycling facilities for each region, making it easier for users to recycle. For example, the app can display the location and business hours of the nearest recycling center. The sorting and presentation unit can also include the types of items that can be accepted in the information on recycling facilities. For example, it can display a list of items that can be accepted at the recycling center. The sorting and presentation unit can also include contact information for the facility in the information on recycling facilities. For example, it can display the phone number and email address of the recycling center. This provides information on recycling facilities for each region, making it easier for users to recycle.

[0067] The sorting and presentation unit can provide information on local events and campaigns to promote waste separation behavior. For example, the generation AI of the sorting and presentation unit provides information on local events and campaigns to promote waste separation behavior. For example, the app displays information on recycling campaigns and garbage sorting events. The sorting and presentation unit can also include the date and time of the event in the event information. For example, it displays the date and time of the recycling event. The sorting and presentation unit can also include the location of the event in the event information. For example, it displays the location of the recycling event on a map. The sorting and presentation unit can also include the duration of the campaign in the campaign information. For example, it displays the start and end dates of the recycling campaign. This allows the provision of information on local events and campaigns to promote waste separation behavior.

[0068] The sorting and presentation unit can provide detailed information about recyclable items to help users understand the importance of recycling. For example, the generative AI can provide detailed information about recyclable items to help users understand the importance of recycling. For example, the sorting and presentation unit can explain the recycling process for PET bottles and their impact on the environment. The sorting and presentation unit can also display a list of types of recyclable items. For example, it can display a list of recyclable items such as plastic, metal, and paper. The sorting and presentation unit can also display text and images explaining the importance of recycling. For example, it can explain the benefits of recycling and the importance of environmental protection. This provides detailed information about recyclable items to help users understand the importance of recycling.

[0069] The sorting and presentation unit can evaluate the environmental impact of an item and provide that information. For example, the generation AI in the sorting and presentation unit evaluates the environmental impact of an item and provides that information. For example, it explains the time it takes for a plastic product to decompose and its impact on the environment. The sorting and presentation unit can also include carbon dioxide emissions in the environmental impact evaluation. For example, it evaluates and displays the carbon dioxide emissions associated with the manufacture and disposal of an item. The sorting and presentation unit can also evaluate recyclability and provide that information. For example, it evaluates the recyclability of an item and explains the benefits of recycling. This makes it possible to evaluate the environmental impact of an item and provide that information.

[0070] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotions regarding the waste sorting behavior and provide positive feedback, thereby promoting the waste sorting behavior. The sorting and presentation unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the waste sorting behavior and provide positive feedback. For example, when the user successfully sorts their trash, it displays an encouraging message. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, it analyzes the user's facial expression to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, it analyzes the tone and speed of the user's voice to estimate the emotion. The sorting and presentation unit can also include compliments and awarding points as positive feedback. For example, it displays compliments and awards points to a user who successfully sorts their trash. In this way, the emotion estimation function can be used to analyze the user's emotions regarding the waste sorting behavior and provide positive feedback, thereby promoting the waste sorting behavior.

[0071] The multilingual support unit can promote trash sorting behavior by using the emotion estimation function to provide positive feedback based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to provide positive feedback based on the user's language setting. For example, an encouraging message in English is displayed for an English-speaking user. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the emotion is estimated by analyzing the user's facial expression. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the emotion is estimated by analyzing the tone and speed of the user's voice. The multilingual support unit can also include compliments and point awards in the positive feedback. For example, a compliment is displayed and points are awarded to a user who successfully sorts trash. In this way, trash sorting behavior is promoted by using the emotion estimation function to provide positive feedback based on the user's language setting.

[0072] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0073] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0074] The multilingual support unit can provide information on recycling facilities for each region based on the user's language settings. For example, the generation AI provides information on recycling facilities for each region based on the user's language settings. For example, for an English-speaking user, information on the nearest recycling center is displayed in English. The multilingual support unit can also include the location of the facility in the information on recycling facilities. For example, the address and map of the recycling center are displayed. The multilingual support unit can also include the types of items that can be accepted in the information on recycling facilities. For example, a list of items that can be accepted at the recycling center is displayed. This makes it possible to provide information on recycling facilities for each region based on the user's language settings.

[0075] The multilingual support unit can provide regional event and campaign information based on the user's language settings. For example, the generation AI provides regional event and campaign information based on the user's language settings. For example, for an English-speaking user, it displays recycling campaign information in English. The multilingual support unit can also include the date and time of the event in the event information. For example, it displays the date and time of the recycling event. The multilingual support unit can also include the location of the event in the event information. For example, it displays the location of the recycling event on a map. The multilingual support unit can also include the duration of the campaign in the campaign information. For example, it displays the start and end dates of the recycling campaign. This makes it possible to provide regional event and campaign information based on the user's language settings.

[0076] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0077] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0078] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0079] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0080] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0081] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0082] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0083] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0084] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0085] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0086] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0087] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0088] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0089] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0090] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0091] The sorting and presentation unit can use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. The sorting and presentation unit can, for example, use the emotion estimation function to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior. For example, if the user has a confused expression, a detailed sorting method is displayed. The sorting and presentation unit can also use facial expression recognition technology for the emotion estimation function. For example, the user's facial expression is analyzed to estimate the emotion. The sorting and presentation unit can also use voice analysis technology for the emotion estimation function. For example, the tone and speed of the user's voice are analyzed to estimate the emotion. The sorting and presentation unit can also analyze the emotion in real time and provide immediate feedback. For example, if the user has a confused expression, a detailed sorting method is immediately displayed. In this way, the emotion estimation function can be used to analyze the user's emotion regarding the waste sorting behavior in real time and support the waste sorting behavior.

[0092] The multilingual support unit can use the emotion estimation function to support sorting actions based on the user's language setting. The multilingual support unit, for example, uses the emotion estimation function to support sorting actions based on the user's language setting. For example, if an English-speaking user has a confused expression, the multilingual support unit displays detailed sorting instructions in English. The multilingual support unit can also use facial expression recognition technology for the emotion estimation function. For example, the multilingual support unit can analyze the user's facial expression to estimate their emotion. The multilingual support unit can also use voice analysis technology for the emotion estimation function. For example, the multilingual support unit can analyze the tone and speed of the user's voice to estimate their emotion. The multilingual support unit can also present specific procedures for supporting sorting actions. For example, the multilingual support unit can display step-by-step instructions for the sorting method. In this way, the emotion estimation function can be used to support sorting actions based on the user's language setting.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The garbage sorting app can also monitor the user's health and suggest appropriate methods for sorting. For example, if the user has allergies, it can advise them to avoid certain types of garbage. It can also show caution when carrying heavy garbage if the user is elderly. Furthermore, it can make suggestions to make the sorting process easier based on the user's health.

[0095] Garbage sorting apps can also gamify users' sorting behavior, allowing them to have fun while sorting. For example, they can introduce a system where users earn points for each sort they make and receive rewards when they reach a certain number of points. They can also add a ranking function that allows users to compete for sorting accuracy and speed. They can also provide a function that allows users to share their sorting results with friends and family and compete against each other.

[0096] Garbage sorting apps can record users' sorting behavior and visualize their contribution to the environment. For example, they can record the amount and type of garbage sorted and display the amount of carbon dioxide reduction achieved through recycling. They can also display graphs and charts showing how much the user is contributing to the environment. Users can also compare their sorting behavior with other users to see how good it is.

[0097] A waste sorting app can also suggest ways to reuse recyclable items based on the user's sorting behavior. For example, it can provide ideas for DIY projects using plastic bottles, introduce craft methods using recycled paper, and even suggest eco-friendly living using recyclable items.

[0098] A waste sorting app can also provide information about local recycling events and workshops based on a user's sorting behavior. For example, it can display the date, time, and location of nearby recycling events, provide instructions on how to participate in recycling workshops, and introduce the benefits of participating in local recycling activities.

[0099] The sorting display unit can estimate the user's emotions and provide feedback to increase motivation for sorting. For example, if the user feels anxious about sorting, it can display an encouraging message. Also, if the user successfully sorts, it can display a message that praises the user or gives them a sense of accomplishment. Furthermore, it can provide advice to encourage them to continue sorting, depending on the user's emotions.

[0100] The sorting presentation unit can estimate the user's emotions and provide support to reduce stress associated with the waste sorting task. For example, if the user feels stressed about sorting, it can display advice to help them relax. If the user has positive feelings about sorting, it can also provide feedback to help them maintain those feelings. Furthermore, it can make suggestions to make the waste sorting task more enjoyable, depending on the user's emotions.

[0101] The sorting and presentation unit can estimate the user's emotions and customize the feedback for the waste sorting behavior. For example, if the user is interested in waste sorting, it can provide detailed information and trivia. If the user is not interested in waste sorting, it can provide simple, easy-to-understand information. Furthermore, it can adjust the content and format of the feedback depending on the user's emotions.

[0102] The sorting and display unit can estimate the user's emotions and provide real-time feedback on the user's sorting behavior. For example, if the user is confused about sorting, it can instantly display detailed sorting instructions. Furthermore, if the user has positive feelings about sorting, it can provide feedback to reinforce those feelings. Furthermore, it can adjust the timing and content of the feedback depending on the user's emotions.

[0103] The sorting and presentation unit can estimate the user's emotions and provide personalized feedback for the waste sorting behavior. For example, if the user has negative emotions toward waste sorting, the unit can provide feedback to alleviate those emotions. Alternatively, if the user has positive emotions toward waste sorting, the unit can provide feedback to maintain those emotions. Furthermore, the content and format of the feedback can be individually adjusted depending on the user's emotions.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The image recognition unit analyzes images of unwanted items. For example, it analyzes images taken by a user with a smartphone camera and identifies the type of item. The image recognition unit can also use machine learning algorithms to extract and classify the features of the items. Furthermore, the image recognition unit can also use computer vision technology to analyze the shape and color of the items. Step 2: The sorting presentation unit presents sorting destinations in accordance with local waste sorting rules based on the type of unwanted item analyzed by the image recognition unit. For example, sorting destinations are displayed according to local rules, such as "plastic waste" for PET bottles and "paper waste" for paper. The sorting presentation unit also has a database of local waste sorting rules, and can apply appropriate rules based on the user's location information and set area information. Furthermore, the sorting presentation unit can automatically update the latest sorting rules, always providing the latest information. Step 3: The multilingual support unit displays the information presented by the sorting presentation unit based on the user's language setting. For example, it provides garbage sorting information in multiple languages, such as English, Chinese, and Korean. The multilingual support unit can also display information in an appropriate language based on the user's language setting. Furthermore, the multilingual support unit can use machine translation technology to automatically translate and provide regional sorting rules.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0150] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image recognition unit that analyzes images of unwanted items; a sorting presentation unit that presents a sorting destination in accordance with local garbage sorting rules based on the type of the unwanted item analyzed by the image recognition unit; a multilingual support unit that displays the information presented by the sorting and presentation unit based on a language setting of a user. A system characterized by:

2. The image recognition unit Analyze the item's usage and soiling to determine whether it can be recycled.

2. The system of claim 1.

3. The sorting and presenting unit Automatically updates regional sorting rules to provide the latest information 2. The system of claim 1.

4. The multilingual support unit Automatically translating and providing the sorting rules for each region based on the language setting of the user 2. The system of claim 1.

5. The sorting and presenting unit Analyzing the user's feelings about their waste management behavior and providing positive feedback to encourage their waste management behavior.

2. The system of claim 1.

Images