System

Abstract

An object of a system according to an embodiment is to realize efficient waste separation.SOLUTION: A system includes a waste discrimination part, an installation place optimization part, and a sorting automation part. The dust discrimination unit discriminates the type of dust. The installation location optimization unit optimizes an installation location of the trash can based on the type of the trash identified by the trash identification unit. The sorting automating section automatically sorts the waste identified by the waste identifying section.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 faced issues with securing space and labor costs when sorting garbage, making it difficult to sort garbage efficiently.

[0005] The system according to the embodiment aims to achieve efficient waste separation. [Means for solving the problem]

[0006] A system according to an embodiment includes a trash identification unit, an installation location optimization unit, and an automated sorting unit. The trash identification unit identifies the type of trash. The installation location optimization unit optimizes the installation locations of trash bins based on the type of trash identified by the trash identification unit. The automated sorting unit automatically sorts the trash identified by the trash identification unit. [Effects of the Invention]

[0007] The system according to the embodiment can achieve efficient waste separation. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) A waste sorting system according to an embodiment of the present invention utilizes AI technology to efficiently separate waste and contribute to the Sustainable Development Goals (SDGs). This system automatically identifies the type of waste and separates it appropriately. This allows the waste sorting system to efficiently separate waste and contribute to the Sustainable Development Goals (SDGs).

[0029] A waste sorting system according to an embodiment includes a waste identification unit, an installation location optimization unit, and an automated sorting unit. The waste identification unit identifies the type of waste. For example, the waste identification unit uses a camera or sensor to collect image data and sensor information about the waste, which is then analyzed by a generation AI to identify the type of waste. The installation location optimization unit optimizes the installation locations of waste bins based on the type of waste identified by the waste identification unit. For example, the installation location optimization unit analyzes the population density and waste generation volume of an area to propose optimal locations for installing waste bins. The automated sorting unit automatically sorts the waste identified by the waste identification unit. For example, the automated sorting unit sorts waste into appropriate waste bins depending on the type of waste. This enables the waste sorting system to efficiently separate waste and contribute to the Sustainable Development Goals (SDGs).

[0030] The garbage identification unit can identify the type of garbage by analyzing smell and sound in addition to the shape and material of the garbage. For example, the garbage identification unit uses an odor sensor to analyze the smell of the garbage, and the generation AI uses that information to identify the type of garbage. For example, it can detect the smell of food waste and identify it as organic garbage. It can also collect the sound made when garbage is put in with a microphone, and the generation AI can analyze that sound to identify the type of garbage. For example, it can detect the sound of glass breaking and identify it as glass garbage. It can also scan the surface of the garbage with a tactile sensor, and the generation AI can analyze the tactile data to identify the type of garbage. For example, it can detect the texture of paper and identify it as paper garbage. This improves the accuracy of garbage identification.

[0031] The garbage identification unit can provide real-time feedback to the user on the results of garbage identification, allowing them to learn how to properly separate garbage. For example, when garbage is placed in the garbage identification unit, the generation AI displays the identification results on a display in real time, teaching the user how to properly separate garbage. For example, it displays a message such as, "This garbage is plastic. Please dispose of it in the plastic trash bin." It also works in conjunction with a smartphone app to notify the user of the garbage identification results on their smartphone. For example, it sends a push notification saying, "This garbage is paper. Please dispose of it in the paper trash bin." The trash bin can also be equipped with a voice assistant that communicates the garbage identification results to the user aloud. For example, it may say, "This garbage is glass. Please dispose of it in the glass trash bin." This allows the user to learn how to properly separate garbage.

[0032] The waste identification unit can analyze waste trends in each region based on the waste identification results and generate region-specific waste sorting guidelines. For example, the waste identification unit stores the waste identification results in a database and analyzes the type and amount of waste in each region. For example, if there is a lot of plastic waste in a particular region, it generates sorting guidelines specific to that region. The generation AI also analyzes waste trends in each region and automatically updates the sorting guidelines. For example, it adjusts the guidelines according to the type of waste that changes with the seasons. It also distributes sorting guidelines based on waste trends to local residents. For example, it notifies region-specific sorting guidelines via a smartphone app. This makes it possible to generate region-specific waste sorting guidelines.

[0033] The waste identification unit can link the waste identification data with other smart city systems to optimize waste management throughout the city. For example, the waste identification unit sends the waste identification data to a smart city's central management system to optimize waste management throughout the city. For example, it uses the data to make waste collection routes more efficient. It can also link with other smart city systems (for example, traffic management systems) to optimize the operation of garbage collection vehicles. For example, it can suggest routes that avoid traffic congestion. It can also link the waste identification data with an energy management system to optimize energy recovery from recyclable waste. For example, it can adjust the operating schedules of recycling facilities. This makes it possible to optimize waste management throughout the city.

[0034] When optimizing trash can installation locations, the installation location optimization unit can analyze local traffic volume and pedestrian flow to identify locations that are most likely to be used. The installation location optimization unit, for example, collects local traffic volume data, and the generation AI optimizes trash can installation locations. For example, installing trash cans in locations with high traffic volume increases usage rates. In addition, to analyze pedestrian flow, data is collected using cameras and sensors, and the generation AI identifies the optimal trash can installation locations. For example, trash cans are installed at the entrances of commercial facilities and parks. In addition, local event information is analyzed, and the generation AI suggests the optimal trash can installation locations when an event is held. For example, temporary trash cans are installed around the event venue. This increases the usage rate of trash cans.

[0035] The installation location optimization unit will introduce a system that can dynamically change the installation locations of trash cans, allowing the generation AI to suggest the optimal locations in real time. The installation location optimization unit will, for example, build a system in which trash cans are installed on movable installation stands and the generation AI will suggest the optimal locations in real time. For example, trash cans are moved to locations with a high amount of trash. In addition, a system will be developed that uses drones to dynamically change the installation locations of trash cans. For example, drones will place trash cans in optimal locations when an event is held. In addition, a system will be built in which the installation locations of trash cans are monitored in real time and the generation AI will suggest the optimal locations. For example, the usage status of trash cans will be monitored and they will be moved from locations with low usage to locations with high usage. This will allow the installation locations of trash cans to be changed dynamically.

[0036] The location optimization unit can take into account local event information and seasonal fluctuations in waste generation when optimizing trash can locations. For example, the location optimization unit collects local event information, and the generation AI proposes the optimal trash can locations when an event is held. For example, temporary trash cans are installed around the event venue. The generation AI also analyzes seasonal fluctuations in waste generation, and proposes the optimal trash can locations. For example, more trash cans for beverage containers are installed in the summer, as beverage container waste increases. The generation AI also analyzes the local event calendar, and builds a system in which the generation AI dynamically changes the optimal trash can locations when an event is held. For example, the trash cans are returned to their normal locations after the event ends. This allows the trash can locations to be optimized.

[0037] The installation location optimization unit can use drones to monitor trash can usage in real time in order to optimize trash can installation locations. The installation location optimization unit, for example, builds a system that equips drones with cameras and monitors trash can usage in real time. For example, the drone sends a notification when a trash can is full. The unit also uses drones to regularly patrol trash can installation locations and monitor usage. For example, it identifies locations where trash cans are underutilized and changes their locations. The unit also develops a system that equips drones with sensors and analyzes trash can usage in real time. For example, it measures how full a trash can is and notifies garbage collection trucks. This makes it possible to monitor trash can usage in real time.

[0038] The automated sorting unit can automatically adjust the capacity of trash bins according to the amount and type of trash. For example, the automated sorting unit will build a system in which sensors measure the amount of trash and the generation AI automatically adjusts the capacity of the trash bin. For example, if there is a lot of trash, the capacity of the trash bin will be increased. In addition, a function will be added to identify the type of trash and the generation AI will automatically adjust the capacity of the trash bin. For example, if there is a lot of recyclable trash, the capacity of the recycling trash bin will be increased. In addition, a system will be developed in which the amount of trash generated is monitored in real time and the generation AI will dynamically adjust the capacity of the trash bin. For example, if there is a sudden increase in trash generation, the capacity of the trash bin will be increased. This will allow the capacity of the trash bin to be adjusted automatically.

[0039] The automated sorting unit can monitor the garbage sorting status and issue an alert if an abnormality occurs. For example, the automated sorting unit will build a system that monitors the garbage sorting status with a camera and issues an alert if the generation AI detects an abnormality. For example, an alert will be issued if different types of garbage are mixed together. In addition, a function will be added that monitors the garbage sorting status with a sensor and issues an alert if the generation AI detects an abnormality. For example, an alert will be issued if a garbage bin becomes full. In addition, a system will be developed that monitors the garbage sorting status in real time and issues an alert if the generation AI detects an abnormality. For example, an alert will be issued if the amount of garbage generated suddenly increases. This will enable abnormalities in garbage sorting to be detected and responded to quickly.

[0040] The automated sorting unit can link the automated waste sorting system with other facility management systems to provide comprehensive management. For example, the automated sorting unit can link the automated waste sorting system with a building's cleaning management system to build a system for comprehensive management. For example, it can adjust cleaning schedules based on the amount of waste generated. It can also link the automated waste sorting system with an energy management system to optimize energy recovery from recyclable waste. For example, it can adjust the operating schedule of recycling facilities. It can also link the automated waste sorting system with a security management system to develop a system to prevent illegal waste dumping. For example, it can link surveillance cameras for trash cans with the security system. This makes comprehensive facility management possible.

[0041] The automated sorting unit can analyze the garbage sorting situation and suggest efficient garbage collection routes. For example, the automated sorting unit will build a system that stores the garbage sorting situation in a database and has the generation AI suggest efficient garbage collection routes. For example, it will suggest routes that prioritize collection from areas with high garbage output. In addition, a function will be added that monitors the garbage sorting situation in real time and has the generation AI suggest efficient garbage collection routes. For example, it will suggest routes that prioritize collection from areas where garbage bins are full. In addition, a system will be developed that analyzes the garbage sorting situation and has the generation AI suggest garbage collection routes by season. For example, since beverage container garbage increases in the summer, it will suggest routes that prioritize collection of beverage container bins. This will enable efficient garbage collection.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The waste identification unit can analyze waste trends in each region based on the waste identification results and generate region-specific waste sorting guidelines. For example, the waste identification unit stores the waste identification results in a database and analyzes the type and amount of waste in each region. If there is a lot of plastic waste in a particular region, it generates sorting guidelines specific to that region. The generation AI also analyzes waste trends in each region and automatically updates the sorting guidelines. The guidelines can also be adjusted according to the type of waste that changes with the seasons. Furthermore, sorting guidelines based on waste trends are distributed to local residents. It is also possible to notify region-specific sorting guidelines via a smartphone app. This makes it possible to generate region-specific waste sorting guidelines.

[0044] The waste identification unit can link the waste identification data with other smart city systems to optimize waste management throughout the city. For example, the waste identification unit can send the waste identification data to a smart city's central management system to optimize waste management throughout the city. The data can be used to make waste collection routes more efficient. It can also link with other smart city systems (e.g., traffic management systems) to optimize the operation of garbage collection vehicles. It can also suggest routes that avoid traffic congestion. Furthermore, the waste identification data can be linked with an energy management system to optimize energy recovery from recyclable waste. It can also adjust the operating schedules of recycling facilities. This makes it possible to optimize waste management throughout the city.

[0045] When optimizing trash can placement locations, the location optimization unit analyzes local traffic volume and pedestrian flow to identify locations that are most likely to be used. For example, the location optimization unit collects local traffic volume data, and the generation AI optimizes trash can placement locations. By placing trash cans in locations with heavy traffic, utilization rates can be increased. In addition, cameras and sensors are used to collect data to analyze pedestrian flow, and the generation AI identifies the optimal trash can placement locations. It is also possible to place trash cans at the entrances to commercial facilities or parks. Furthermore, by analyzing local event information, the generation AI suggests the optimal trash can placement locations when an event is held. Temporary trash cans can also be placed around the event venue. This can increase trash can utilization rates.

[0046] The installation location optimization unit will introduce a system that can dynamically change the installation locations of trash cans, allowing the generation AI to suggest the optimal locations in real time. For example, the installation location optimization unit will build a system in which trash cans are placed on movable installation stands and the generation AI will suggest the optimal locations in real time. Trash cans can be moved to locations with high waste generation. A system will also be developed that uses drones to dynamically change the installation locations of trash cans. Drones can also place trash cans in optimal locations when events are held. Furthermore, a system will be built in which the installation locations of trash cans are monitored in real time and the generation AI will suggest the optimal locations. It will also be possible to monitor the usage of trash cans and move them from locations with low usage to locations with high usage. This will allow the installation locations of trash cans to be changed dynamically.

[0047] The automated sorting unit can automatically adjust the capacity of trash bins according to the amount and type of trash. For example, the automated sorting unit will build a system in which sensors measure the amount of trash and the generation AI automatically adjusts the trash bin capacity. This will allow the trash bin capacity to be increased if there is a lot of trash. In addition, a function will be added to identify the type of trash and have the generation AI automatically adjust the trash bin capacity. It will also be possible to increase the capacity of recycling trash bins if there is a lot of recyclable trash. Furthermore, a system will be developed in which the amount of trash generated is monitored in real time and the generation AI dynamically adjusts the trash bin capacity. The trash bin capacity can also be increased if there is a sudden increase in trash generation. This will allow the trash bin capacity to be automatically adjusted.

[0048] The automated sorting unit can monitor the garbage sorting status and issue an alert if an abnormality occurs. For example, the automated sorting unit will build a system that monitors the garbage sorting status with a camera and issues an alert if the generation AI detects an abnormality. An alert can be issued if different types of garbage are mixed together. In addition, a function will be added that monitors the garbage sorting status with a sensor and issues an alert if the generation AI detects an abnormality. It will also be possible to issue an alert if the garbage bin is full. Furthermore, a system will be developed that monitors the garbage sorting status in real time and issues an alert if the generation AI detects an abnormality. An alert can also be issued if there is a sudden increase in garbage output. This will enable abnormalities in garbage sorting to be detected and responded to quickly.

[0049] The automated sorting unit can analyze the garbage sorting situation and suggest efficient garbage collection routes. For example, the automated sorting unit will build a system in which the garbage sorting situation is stored in a database and the generation AI suggests efficient garbage collection routes. It can suggest routes that prioritize collection of areas with high garbage output. In addition, a function will be added that monitors the garbage sorting situation in real time and the generation AI suggests efficient garbage collection routes. It will also be possible to suggest routes that prioritize collection of areas where garbage bins are full. Furthermore, a system will be developed in which the generation AI analyzes the garbage sorting situation and suggests garbage collection routes for each season. As beverage container garbage increases in the summer, it can also suggest routes that prioritize collection of beverage container bins. This will enable efficient garbage collection.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The dust identification unit identifies the type of dust. For example, the dust identification unit uses a camera or sensor to collect image data and sensor information about the dust, which the generation AI analyzes to identify the type of dust. Step 2: The location optimization unit optimizes the locations of trash cans based on the type of trash identified by the trash identification unit. For example, the location optimization unit analyzes the population density and trash volume of the area and proposes optimal locations for trash cans. Step 3: The automated sorting unit automatically sorts the trash identified by the trash identification unit. For example, the automated sorting unit sorts the trash into appropriate trash bins depending on the type of trash.

[0052] (Example 2) A waste sorting system according to an embodiment of the present invention utilizes AI technology to efficiently separate waste and contribute to the Sustainable Development Goals (SDGs). This system automatically identifies the type of waste and separates it appropriately. This allows the waste sorting system to efficiently separate waste and contribute to the Sustainable Development Goals (SDGs).

[0053] A waste sorting system according to an embodiment includes a waste identification unit, an installation location optimization unit, and an automated sorting unit. The waste identification unit identifies the type of waste. For example, the waste identification unit uses a camera or sensor to collect image data and sensor information about the waste, which is then analyzed by a generation AI to identify the type of waste. The installation location optimization unit optimizes the installation locations of waste bins based on the type of waste identified by the waste identification unit. For example, the installation location optimization unit analyzes the population density and waste generation volume of an area to propose optimal locations for installing waste bins. The automated sorting unit automatically sorts the waste identified by the waste identification unit. For example, the automated sorting unit sorts waste into appropriate waste bins depending on the type of waste. This enables the waste sorting system to efficiently separate waste and contribute to the Sustainable Development Goals (SDGs).

[0054] The garbage identification unit can identify the type of garbage by analyzing smell and sound in addition to the shape and material of the garbage. For example, the garbage identification unit uses an odor sensor to analyze the smell of the garbage, and the generation AI uses that information to identify the type of garbage. For example, it can detect the smell of food waste and identify it as organic garbage. It can also collect the sound made when garbage is put in with a microphone, and the generation AI can analyze that sound to identify the type of garbage. For example, it can detect the sound of glass breaking and identify it as glass garbage. It can also scan the surface of the garbage with a tactile sensor, and the generation AI can analyze the tactile data to identify the type of garbage. For example, it can detect the texture of paper and identify it as paper garbage. This improves the accuracy of garbage identification.

[0055] The garbage identification unit can provide real-time feedback to the user on the results of garbage identification, allowing them to learn how to properly separate garbage. For example, when garbage is placed in the garbage identification unit, the generation AI displays the identification results on a display in real time, teaching the user how to properly separate garbage. For example, it displays a message such as, "This garbage is plastic. Please dispose of it in the plastic trash bin." It also works in conjunction with a smartphone app to notify the user of the garbage identification results on their smartphone. For example, it sends a push notification saying, "This garbage is paper. Please dispose of it in the paper trash bin." The trash bin can also be equipped with a voice assistant that communicates the garbage identification results to the user aloud. For example, it may say, "This garbage is glass. Please dispose of it in the glass trash bin." This allows the user to learn how to properly separate garbage.

[0056] The garbage identification unit uses an emotion estimation function to analyze the user's emotions when throwing away garbage and can display a message designed to elicit positive emotions. For example, when throwing away garbage, the garbage identification unit uses a camera to analyze the user's facial expression and the emotion estimation function to identify the user's emotions. For example, if a smile is detected, the unit displays the message "Great! Thank you for sorting your garbage correctly." The garbage identification unit also includes a microphone installed in the trash can to analyze the tone of the user's voice to estimate emotions. For example, if a positive tone is detected, the unit will announce the message "Great! Thank you for helping protect the environment." The unit also works in conjunction with a smartphone app to collect and analyze the user's emotional data. For example, if a positive emotion is detected, the unit will send a push notification saying "Great! Please continue to sort your garbage correctly." This can elicit positive emotions from the user.

[0057] The waste identification unit can analyze waste trends in each region based on the waste identification results and generate region-specific waste sorting guidelines. For example, the waste identification unit stores the waste identification results in a database and analyzes the type and amount of waste in each region. For example, if there is a lot of plastic waste in a particular region, it generates sorting guidelines specific to that region. The generation AI also analyzes waste trends in each region and automatically updates the sorting guidelines. For example, it adjusts the guidelines according to the type of waste that changes with the seasons. It also distributes sorting guidelines based on waste trends to local residents. For example, it notifies region-specific sorting guidelines via a smartphone app. This makes it possible to generate region-specific waste sorting guidelines.

[0058] The waste identification unit can link the waste identification data with other smart city systems to optimize waste management throughout the city. For example, the waste identification unit sends the waste identification data to a smart city's central management system to optimize waste management throughout the city. For example, it uses the data to make waste collection routes more efficient. It can also link with other smart city systems (for example, traffic management systems) to optimize the operation of garbage collection vehicles. For example, it can suggest routes that avoid traffic congestion. It can also link the waste identification data with an energy management system to optimize energy recovery from recyclable waste. For example, it can adjust the operating schedules of recycling facilities. This makes it possible to optimize waste management throughout the city.

[0059] The garbage classification unit can use the emotion estimation function to introduce an incentive system to increase the user's motivation for garbage sorting. For example, the garbage classification unit uses the emotion estimation function to provide positive feedback to users who sort their garbage correctly. For example, it could display a message such as, "Great! Please continue to sort your garbage correctly." It also uses the emotion estimation function to analyze the user's emotions during garbage sorting and provide incentives to elicit positive emotions. For example, it could introduce a point system and award points to users who sort their garbage correctly. It can also link with a smartphone app to send messages to increase the user's motivation based on the emotion estimation data. For example, it could send a push notification saying, "Great! Thank you for helping protect the environment." This can increase the user's motivation.

[0060] When optimizing trash can installation locations, the installation location optimization unit can analyze local traffic volume and pedestrian flow to identify locations that are most likely to be used. The installation location optimization unit, for example, collects local traffic volume data, and the generation AI optimizes trash can installation locations. For example, installing trash cans in locations with high traffic volume increases usage rates. In addition, to analyze pedestrian flow, data is collected using cameras and sensors, and the generation AI identifies the optimal trash can installation locations. For example, trash cans are installed at the entrances of commercial facilities and parks. In addition, local event information is analyzed, and the generation AI suggests the optimal trash can installation locations when an event is held. For example, temporary trash cans are installed around the event venue. This increases the usage rate of trash cans.

[0061] The installation location optimization unit will introduce a system that can dynamically change the installation locations of trash cans, allowing the generation AI to suggest the optimal locations in real time. The installation location optimization unit will, for example, build a system in which trash cans are installed on movable installation stands and the generation AI will suggest the optimal locations in real time. For example, trash cans are moved to locations with a high amount of trash. In addition, a system will be developed that uses drones to dynamically change the installation locations of trash cans. For example, drones will place trash cans in optimal locations when an event is held. In addition, a system will be built in which the installation locations of trash cans are monitored in real time and the generation AI will suggest the optimal locations. For example, the usage status of trash cans will be monitored and they will be moved from locations with low usage to locations with high usage. This will allow the installation locations of trash cans to be changed dynamically.

[0062] The installation location optimization unit uses the emotion estimation function to analyze residents' emotions regarding trash can installation locations and installs trash cans in locations that satisfy residents. The installation location optimization unit, for example, analyzes residents' emotions regarding trash can installation locations using a camera and measures resident satisfaction using the emotion estimation function. For example, it installs trash cans in locations where there are many smiling faces. It also collects resident feedback through a smartphone app and analyzes resident emotions using the emotion estimation function. For example, it installs trash cans in locations where there is a lot of positive feedback. It also measures resident emotions regarding trash can installation locations using voice analysis and evaluates resident satisfaction using the emotion estimation function. For example, it installs trash cans in locations where there are many positive voice tones. This makes it possible to increase resident satisfaction.

[0063] The location optimization unit can take into account local event information and seasonal fluctuations in waste generation when optimizing trash can locations. For example, the location optimization unit collects local event information, and the generation AI proposes the optimal trash can locations when an event is held. For example, temporary trash cans are installed around the event venue. The generation AI also analyzes seasonal fluctuations in waste generation, and proposes the optimal trash can locations. For example, more trash cans for beverage containers are installed in the summer, as beverage container waste increases. The generation AI also analyzes the local event calendar, and builds a system in which the generation AI dynamically changes the optimal trash can locations when an event is held. For example, the trash cans are returned to their normal locations after the event ends. This allows the trash can locations to be optimized.

[0064] The installation location optimization unit can use drones to monitor trash can usage in real time in order to optimize trash can installation locations. The installation location optimization unit, for example, builds a system that equips drones with cameras and monitors trash can usage in real time. For example, the drone sends a notification when a trash can is full. The unit also uses drones to regularly patrol trash can installation locations and monitor usage. For example, it identifies locations where trash cans are underutilized and changes their locations. The unit also develops a system that equips drones with sensors and analyzes trash can usage in real time. For example, it measures how full a trash can is and notifies garbage collection trucks. This makes it possible to monitor trash can usage in real time.

[0065] The installation location optimization unit can use the emotion estimation function to collect resident feedback regarding trash can installation locations and reflect this in optimizing the installation locations. The installation location optimization unit collects resident feedback, for example, through a smartphone app and analyzes resident emotions using the emotion estimation function. For example, trash cans are installed in locations with a lot of positive feedback. Furthermore, the unit analyzes resident emotions regarding trash can installation locations using a camera and measures resident satisfaction using the emotion estimation function. For example, trash cans are installed in locations with a lot of smiling faces. Furthermore, the unit measures resident emotions regarding trash can installation locations using voice analysis and evaluates resident satisfaction using the emotion estimation function. For example, trash cans are installed in locations with a lot of positive voice tones. In this way, the installation locations of trash cans can be optimized by reflecting resident feedback.

[0066] The automated sorting unit can automatically adjust the capacity of trash bins according to the amount and type of trash. For example, the automated sorting unit will build a system in which sensors measure the amount of trash and the generation AI automatically adjusts the capacity of the trash bin. For example, if there is a lot of trash, the capacity of the trash bin will be increased. In addition, a function will be added to identify the type of trash and the generation AI will automatically adjust the capacity of the trash bin. For example, if there is a lot of recyclable trash, the capacity of the recycling trash bin will be increased. In addition, a system will be developed in which the amount of trash generated is monitored in real time and the generation AI will dynamically adjust the capacity of the trash bin. For example, if there is a sudden increase in trash generation, the capacity of the trash bin will be increased. This will allow the capacity of the trash bin to be adjusted automatically.

[0067] The automated sorting unit can monitor the garbage sorting status and issue an alert if an abnormality occurs. For example, the automated sorting unit will build a system that monitors the garbage sorting status with a camera and issues an alert if the generation AI detects an abnormality. For example, an alert will be issued if different types of garbage are mixed together. In addition, a function will be added that monitors the garbage sorting status with a sensor and issues an alert if the generation AI detects an abnormality. For example, an alert will be issued if a garbage bin becomes full. In addition, a system will be developed that monitors the garbage sorting status in real time and issues an alert if the generation AI detects an abnormality. For example, an alert will be issued if the amount of garbage generated suddenly increases. This will enable abnormalities in garbage sorting to be detected and responded to quickly.

[0068] The automated sorting unit can use the emotion estimation function to introduce a support system to reduce employee stress caused by waste sorting work. For example, the automated sorting unit analyzes the facial expressions of employees while sorting waste using a camera and measures their stress levels using the emotion estimation function. For example, if stress is high, it plays relaxing music. It also analyzes the tone of employees' voices while sorting waste and measures their stress levels using the emotion estimation function. For example, if stress is high, it displays a message encouraging employees to take a break. It also collects employee emotion data through a smartphone app and measures their stress levels using the emotion estimation function. For example, it provides advice on how to relax when stress is high. This reduces employee stress.

[0069] The automated sorting unit can link the automated waste sorting system with other facility management systems to provide comprehensive management. For example, the automated sorting unit can link the automated waste sorting system with a building's cleaning management system to build a system for comprehensive management. For example, it can adjust cleaning schedules based on the amount of waste generated. It can also link the automated waste sorting system with an energy management system to optimize energy recovery from recyclable waste. For example, it can adjust the operating schedule of recycling facilities. It can also link the automated waste sorting system with a security management system to develop a system to prevent illegal waste dumping. For example, it can link surveillance cameras for trash cans with the security system. This makes comprehensive facility management possible.

[0070] The automated sorting unit can analyze the garbage sorting situation and suggest efficient garbage collection routes. For example, the automated sorting unit will build a system that stores the garbage sorting situation in a database and has the generation AI suggest efficient garbage collection routes. For example, it will suggest routes that prioritize collection from areas with high garbage output. In addition, a function will be added that monitors the garbage sorting situation in real time and has the generation AI suggest efficient garbage collection routes. For example, it will suggest routes that prioritize collection from areas where garbage bins are full. In addition, a system will be developed that analyzes the garbage sorting situation and has the generation AI suggest garbage collection routes by season. For example, since beverage container garbage increases in the summer, it will suggest routes that prioritize collection of beverage container bins. This will enable efficient garbage collection.

[0071] The automated sorting unit can use the emotion estimation function to introduce an incentive program to improve employee motivation for sorting garbage. For example, the automated sorting unit analyzes the facial expressions of employees while sorting garbage using a camera and measures their motivation using the emotion estimation function. For example, if there are many positive emotions, an incentive is given. The automated sorting unit also analyzes the tone of employees' voices while sorting garbage and measures their motivation using the emotion estimation function. For example, if there are many positive tones, an incentive is given. The automated sorting unit also collects employee emotion data through a smartphone app and measures their motivation using the emotion estimation function. For example, if there are many positive emotions, an incentive is given. This can improve employee motivation.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The waste identification unit can analyze waste trends in each region based on the waste identification results and generate region-specific waste sorting guidelines. For example, the waste identification unit stores the waste identification results in a database and analyzes the type and amount of waste in each region. If there is a lot of plastic waste in a particular region, it generates sorting guidelines specific to that region. The generation AI also analyzes waste trends in each region and automatically updates the sorting guidelines. The guidelines can also be adjusted according to the type of waste that changes with the seasons. Furthermore, sorting guidelines based on waste trends are distributed to local residents. It is also possible to notify region-specific sorting guidelines via a smartphone app. This makes it possible to generate region-specific waste sorting guidelines.

[0074] The waste identification unit can link the waste identification data with other smart city systems to optimize waste management throughout the city. For example, the waste identification unit can send the waste identification data to a smart city's central management system to optimize waste management throughout the city. The data can be used to make waste collection routes more efficient. It can also link with other smart city systems (e.g., traffic management systems) to optimize the operation of garbage collection vehicles. It can also suggest routes that avoid traffic congestion. Furthermore, the waste identification data can be linked with an energy management system to optimize energy recovery from recyclable waste. It can also adjust the operating schedules of recycling facilities. This makes it possible to optimize waste management throughout the city.

[0075] The garbage classification unit can use the emotion estimation function to introduce an incentive system to increase user motivation for garbage sorting. For example, the garbage classification unit can use the emotion estimation function to provide positive feedback to users who sort their garbage correctly. For example, it can display a message such as, "Great! Please continue to sort your garbage correctly." It can also use the emotion estimation function to analyze the user's emotions during garbage sorting and provide incentives to elicit positive emotions. A point system can also be introduced, awarding points to users who sort their garbage correctly. Furthermore, it can link with a smartphone app and send messages to increase user motivation based on the emotion estimation data. For example, it can send a push notification saying, "Great! Thank you for helping protect the environment." This can increase user motivation.

[0076] When optimizing trash can placement locations, the location optimization unit analyzes local traffic volume and pedestrian flow to identify locations that are most likely to be used. For example, the location optimization unit collects local traffic volume data, and the generation AI optimizes trash can placement locations. By placing trash cans in locations with heavy traffic, utilization rates can be increased. In addition, cameras and sensors are used to collect data to analyze pedestrian flow, and the generation AI identifies the optimal trash can placement locations. It is also possible to place trash cans at the entrances to commercial facilities or parks. Furthermore, by analyzing local event information, the generation AI suggests the optimal trash can placement locations when an event is held. Temporary trash cans can also be placed around the event venue. This can increase trash can utilization rates.

[0077] The installation location optimization unit will introduce a system that can dynamically change the installation locations of trash cans, allowing the generation AI to suggest the optimal locations in real time. For example, the installation location optimization unit will build a system in which trash cans are placed on movable installation stands and the generation AI will suggest the optimal locations in real time. Trash cans can be moved to locations with high waste generation. A system will also be developed that uses drones to dynamically change the installation locations of trash cans. Drones can also place trash cans in optimal locations when events are held. Furthermore, a system will be built in which the installation locations of trash cans are monitored in real time and the generation AI will suggest the optimal locations. It will also be possible to monitor the usage of trash cans and move them from locations with low usage to locations with high usage. This will allow the installation locations of trash cans to be changed dynamically.

[0078] The installation location optimization unit uses the emotion estimation function to analyze residents' emotions regarding trash can installation locations and can install trash cans in locations that satisfy residents. For example, the installation location optimization unit can analyze residents' emotions regarding trash can installation locations using a camera and measure resident satisfaction using the emotion estimation function. Trash cans can be installed in locations with a lot of smiles. In addition, resident feedback can be collected through a smartphone app and residents' emotions can be analyzed using the emotion estimation function. It is also possible to install trash cans in locations with a lot of positive feedback. Furthermore, residents' emotions regarding trash can installation locations can be measured using voice analysis and resident satisfaction can be evaluated using the emotion estimation function. Trash cans can also be installed in locations with a lot of positive voice tones. This can increase resident satisfaction.

[0079] The automated sorting unit can automatically adjust the capacity of trash bins according to the amount and type of trash. For example, the automated sorting unit will build a system in which sensors measure the amount of trash and the generation AI automatically adjusts the trash bin capacity. This will allow the trash bin capacity to be increased if there is a lot of trash. In addition, a function will be added to identify the type of trash and have the generation AI automatically adjust the trash bin capacity. It will also be possible to increase the capacity of recycling trash bins if there is a lot of recyclable trash. Furthermore, a system will be developed in which the amount of trash generated is monitored in real time and the generation AI dynamically adjusts the trash bin capacity. The trash bin capacity can also be increased if there is a sudden increase in trash generation. This will allow the trash bin capacity to be automatically adjusted.

[0080] The automated sorting unit can monitor the garbage sorting status and issue an alert if an abnormality occurs. For example, the automated sorting unit will build a system that monitors the garbage sorting status with a camera and issues an alert if the generation AI detects an abnormality. An alert can be issued if different types of garbage are mixed together. In addition, a function will be added that monitors the garbage sorting status with a sensor and issues an alert if the generation AI detects an abnormality. It will also be possible to issue an alert if the garbage bin is full. Furthermore, a system will be developed that monitors the garbage sorting status in real time and issues an alert if the generation AI detects an abnormality. An alert can also be issued if there is a sudden increase in garbage output. This will enable abnormalities in garbage sorting to be detected and responded to quickly.

[0081] The automated sorting unit can use the emotion estimation function to introduce a support system to reduce employee stress caused by waste sorting work. For example, the automated sorting unit can analyze the facial expressions of employees while sorting waste with a camera and measure their stress levels using the emotion estimation function. Relaxing music can be played if stress levels are high. The unit can also analyze the tone of employees' voices while sorting waste and measure their stress levels using the emotion estimation function. It can also display a message encouraging employees to take a break if stress levels are high. Furthermore, employee emotional data can be collected through a smartphone app and stress levels can be measured using the emotion estimation function. Advice for relaxation can be provided if stress levels are high. This can reduce employee stress.

[0082] The automated sorting unit can analyze the garbage sorting situation and suggest efficient garbage collection routes. For example, the automated sorting unit will build a system in which the garbage sorting situation is stored in a database and the generation AI suggests efficient garbage collection routes. It can suggest routes that prioritize collection of areas with high garbage output. In addition, a function will be added that monitors the garbage sorting situation in real time and the generation AI suggests efficient garbage collection routes. It will also be possible to suggest routes that prioritize collection of areas where garbage bins are full. Furthermore, a system will be developed in which the generation AI analyzes the garbage sorting situation and suggests garbage collection routes for each season. As beverage container garbage increases in the summer, it can also suggest routes that prioritize collection of beverage container bins. This will enable efficient garbage collection.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The dust identification unit identifies the type of dust. For example, the dust identification unit uses a camera or sensor to collect image data and sensor information about the dust, which the generation AI analyzes to identify the type of dust. Step 2: The location optimization unit optimizes the locations of trash cans based on the type of trash identified by the trash identification unit. For example, the location optimization unit analyzes the population density and trash volume of the area and proposes optimal locations for trash cans. Step 3: The automated sorting unit automatically sorts the trash identified by the trash identification unit. For example, the automated sorting unit sorts the trash into appropriate trash bins depending on the type of trash.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] 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]

[0152] 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. a dust identification unit that identifies the type of dust; an installation location optimization unit that optimizes installation locations of trash cans based on the type of trash identified by the trash identification unit; an automated sorting unit that automatically sorts the garbage identified by the garbage identifying unit; A system characterized by:

2. The dust identifying unit In addition to analyzing the shape and material of the garbage, it also analyzes smells and sounds to identify the type of garbage. The system of claim 1 .

3. The dust identifying unit The results of garbage identification are fed back to the user in real time, enabling the user to learn how to properly separate garbage. The system of claim 1 .

4. The dust identifying unit Analyze the user's emotions when throwing away trash and display messages to elicit positive emotions The system of claim 1 .

5. The dust identifying unit Based on the results of waste identification, we analyze waste trends in each region and generate region-specific waste sorting guidelines. The system of claim 1 .

Images