system

Abstract

The system according to this embodiment aims to efficiently collect and sort waste. [Solution] The system according to the embodiment comprises a deployment unit, a collection unit, a sorting unit, and a transport unit. The deployment unit deploys an AI-equipped garbage collection box network to various locations. The collection unit collects garbage based on the garbage collection box network deployed by the deployment unit. The sorting unit sorts the garbage collected by the collection unit. The transport unit transports the garbage sorted by the sorting unit to a collection point.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, garbage collection and separation have not been carried out efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect and separate garbage.

Means for Solving the Problems

[0006] The system according to the embodiment includes a deployment unit, a collection unit, a separation unit, and a transportation unit. The deployment unit deploys an AI-equipped garbage collection BOX network across various locations. The collection unit collects garbage based on the garbage collection BOX network deployed by the deployment unit. The separation unit separates the garbage collected by the collection unit. The transportation unit transports the garbage separated by the separation unit to a collection point.

Effects of the Invention

[0007] The system according to this embodiment can efficiently collect and sort waste. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The waste collection system according to an embodiment of the present invention is a system that utilizes an AI-equipped waste collection box network and an automated guided vehicle (AGV). This waste collection system consists of the following steps. First, an AI-equipped waste collection box network is deployed in various locations. This waste collection box utilizes MEC and advanced generative AI. The trash cans are opened and closed by user authentication, and only registered users can use them. Furthermore, sorting is performed automatically using image recognition technology, saving users the trouble of sorting. Waste collection volume data is uploaded in real time via SIM. Next, an automated guided vehicle (AGV) travels back and forth between waste collection boxes and waste collection points within the area. The autonomous AGV collects the trash cans along with their cartridges and replaces them with empty cartridges. By utilizing waste volume data and pedestrian flow data, waste overflows are prevented and efficient collection routes are realized. Users can easily use the dedicated trash boxes through a dedicated app. By subscribing, users can use boxes nationwide 24 hours a day, and there is a limit to the amount of waste that can be disposed of. Spot use with single tickets is also possible. Through authentication, the waste disposal status of each user is monitored, and rule violators are identified. In the event of illegal dumping, violators will be identified and dealt with. By collaborating with local governments, developers, and recycling companies, the system will raise funds for installation from those with a need to improve urban cleanliness and expand its target area. It will also contribute to the SDGs by providing sorted, high-quality general waste to recycling companies. This system will reduce urban environmental pollution and stress related to manners caused by street and household waste disposal, and address the shortage of waste disposal sites in cities due to the reduction of trash cans under counter-terrorism measures. It will also enable efficient sorting to revitalize waste resource recycling. General consumers, local governments, real estate developers, and recycling companies will be targeted. As a result, the waste collection system will be able to efficiently collect, sort, and transport waste.

[0029] The waste collection system according to this embodiment comprises a deployment unit, a collection unit, a sorting unit, and a transport unit. The deployment unit deploys an AI-equipped waste collection box network in various locations. The deployment unit utilizes, for example, MEC and advanced generative AI. The deployment unit opens and closes the trash cans through user authentication, and only registered users can use them. The deployment unit automatically sorts waste using image recognition technology, thus saving users the trouble of sorting. The deployment unit uploads waste collection volume data in real time via SIM. The collection unit collects waste based on the waste collection box network deployed by the deployment unit. The collection unit collects waste using, for example, an automated guided vehicle (AGV). The collection unit collects trash cans along with their cartridges and replaces them with empty cartridges. The collection unit prevents waste overflow and realizes efficient collection routes by utilizing waste volume data and human flow data. The sorting unit sorts the waste collected by the collection unit. The sorting unit automatically sorts waste using, for example, image recognition technology. The sorting unit opens and closes the trash cans based on user authentication. The transport unit transports the sorted waste to the collection point. The transport unit uses, for example, an automated guided vehicle (AGV) to transport the waste to the collection point. The transport unit utilizes waste volume data and pedestrian flow data to realize an efficient collection route. As a result, the waste collection system according to this embodiment can efficiently collect, sort, and transport waste.

[0030] The deployment unit will deploy a network of AI-equipped waste collection boxes in various locations. Specifically, the deployment unit will use advanced generative AI to plan the optimal placement of boxes according to the waste collection needs of each region. The generative AI will analyze information such as past waste collection data, local population density, and waste types to propose the most efficient placement of waste collection boxes. As a result, waste collection boxes will be installed in locations easily accessible to users, improving waste collection efficiency. The deployment unit will open and close the waste bins using user authentication, and only registered users will be able to use them. User authentication will use a smartphone app or IC card to enhance user convenience. Furthermore, the deployment unit will use image recognition technology to automatically sort waste. When a user puts waste in, a camera will identify the type of waste and sort it appropriately. This will save users the trouble of sorting. The deployment unit will upload waste collection volume data in real time via SIM. This will allow the usage status of the waste collection boxes to be constantly monitored, enabling appropriate action before the boxes overflow. In addition, the deployment unit will manage maintenance information and anomaly detection data for the waste collection boxes in real time, improving the reliability of the system. This allows the deployment unit to efficiently and effectively deploy the waste collection box network, improving user convenience and system reliability.

[0031] The collection unit collects waste based on the waste collection box network deployed by the deployment unit. Specifically, the collection unit uses automated guided vehicles (AGVs) to collect waste. The AGVs are equipped with autonomous driving technology and patrol the waste collection boxes according to pre-set routes. The collection unit collects the waste bins along with their cartridges and replaces them with empty cartridges. This ensures that waste collection is carried out quickly and efficiently. The collection unit prevents waste overflow and achieves efficient collection routes by utilizing waste volume data and pedestrian flow data. Specifically, sensors installed in the waste collection boxes measure the amount of waste in real time and issue collection orders when a certain amount is reached. In addition, by analyzing pedestrian flow data, the collection unit identifies times and locations with high user traffic and optimizes collection routes. This allows the collection unit to collect waste at the appropriate time before it overflows, achieving efficient operation. Furthermore, the collection unit manages the operating status and maintenance information of the waste collection robots in real time, improving system reliability. As a result, the collection unit can collect waste efficiently and effectively, improving the overall system performance.

[0032] The sorting unit sorts the waste collected by the collection unit. Specifically, the sorting unit automatically sorts waste using image recognition technology. When waste is fed into the sorting line, a camera identifies the type of waste and performs appropriate sorting. For example, it automatically classifies different materials such as plastic, metal, paper, and glass. The sorting unit opens and closes the waste bins based on user authentication. This ensures that sorting is performed accurately and efficiently. Furthermore, the sorting unit can automatically adjust the settings of the sorting line according to the type and amount of waste. This improves the efficiency of sorting and contributes to an increased recycling rate. The sorting unit manages data on sorted waste in real time and provides it to recycling companies and local governments. This improves the efficiency of recycling and contributes to reducing the environmental impact. In addition, the sorting unit uses an anomaly detection algorithm to detect anomalies that occur during sorting early and take appropriate action. This allows the sorting unit to sort waste efficiently and accurately, improving the reliability and performance of the entire system.

[0033] The transportation unit transports the sorted waste from the sorting unit to the collection point. Specifically, the transportation unit uses automated guided vehicles (AGVs) to transport the waste to the collection point. The AGVs autonomously navigate according to pre-set routes, efficiently transporting the sorted waste. The transportation unit utilizes waste volume data and pedestrian flow data to create efficient collection routes. Specifically, it optimizes the transportation route according to the amount and type of waste to improve transportation efficiency. For example, it adjusts the transportation route to match areas and times with high waste volumes to achieve efficient transportation. The transportation unit monitors the condition of the waste in real time during transportation and responds immediately if any abnormalities occur. This prevents problems during transportation and improves the reliability of the system. Furthermore, the transportation unit manages transportation operation data in real time to optimize transportation efficiency and costs. As a result, the transportation unit can transport waste efficiently and effectively to the collection point, improving the overall performance of the system.

[0034] The deployment unit can utilize MEC (Multi-access Edge Computing) and advanced generative AI. For example, the deployment unit uses MEC to deploy edge servers and utilize communication protocols. The deployment unit uses generative AI to streamline the deployment of the waste collection box network. For example, the generative AI uses machine learning algorithms and deep learning technology to select the optimal installation location for waste collection boxes. In this way, the deployment of the waste collection box network becomes more efficient by utilizing MEC and generative AI.

[0035] The collection unit can collect waste using a waste collection robot. For example, the collection unit uses an automated guided vehicle (AGV) to collect waste. The collection unit collects waste bins along with their cartridges and replaces them with empty cartridges. The collection unit utilizes waste volume data and pedestrian flow data to prevent waste overflow and realize efficient collection routes. As a result, waste collection is automated by using a waste collection robot.

[0036] The sorting unit can automatically sort waste using image recognition technology. For example, the sorting unit automatically sorts waste using image recognition technology. The sorting unit opens and closes the trash cans based on user authentication. This automates waste sorting using image recognition technology.

[0037] The transportation unit can use automated guided vehicles (AGVs) to transport waste to collection points. For example, the transportation unit uses automated guided vehicles to transport waste to collection points. The transportation unit utilizes waste volume data and pedestrian flow data to create efficient collection routes. This automates waste transportation through the use of automated guided vehicles.

[0038] The collection unit can upload waste collection volume data in real time via SIM. For example, the collection unit uploads waste collection volume data in real time via SIM. This allows for immediate monitoring of the collection status by uploading waste collection volume data in real time.

[0039] The sorting unit can open and close the trash cans based on user authentication. For example, the sorting unit opens and closes the trash cans based on user authentication. This prevents unauthorized use by requiring user authentication to open and close the trash cans.

[0040] The transportation department can realize efficient collection routes by utilizing data on waste volume and pedestrian flow. For example, the transportation department can realize efficient collection routes by utilizing data on waste volume and pedestrian flow. This allows for the realization of efficient collection routes by utilizing data on waste volume and pedestrian flow.

[0041] The deployment unit can analyze past waste collection data and select the optimal installation location. For example, it can identify areas with high waste generation based on past waste collection data and install waste collection boxes there. The deployment unit analyzes waste collection data and avoids installing boxes in areas with low waste generation. Based on waste collection data, the deployment unit installs boxes in areas where waste generation fluctuates depending on the season or events. In this way, the optimal installation location can be selected by analyzing past data.

[0042] The deployment unit can determine the installation location of garbage collection boxes by taking local event information into consideration. For example, the deployment unit can install garbage collection boxes around event venues based on local event information. The deployment unit can install the necessary number of garbage collection boxes according to the scale of the event. The deployment unit can adjust the installation location of garbage collection boxes according to the event schedule. In this way, garbage collection during events becomes more efficient by taking local event information into consideration.

[0043] The deployment unit can determine the installation location of garbage collection boxes by considering the population density of the area. For example, the deployment unit can prioritize the installation of garbage collection boxes in areas with high population density. In areas with low population density, the deployment unit can install the minimum necessary number of garbage collection boxes. In areas with significant fluctuations in population density, the deployment unit can flexibly adjust the installation location. This improves user convenience by considering the population density of the area.

[0044] The deployment unit can determine the installation location of garbage collection boxes by considering local traffic conditions. For example, the deployment unit can install garbage collection boxes in areas with heavy traffic to improve user convenience. In areas with light traffic, it can install only the minimum necessary number of garbage collection boxes. In areas where traffic conditions fluctuate, the deployment unit can flexibly adjust the installation location. In this way, user convenience is improved by considering local traffic conditions.

[0045] The collection unit can apply different collection methods depending on the type of waste during collection. For example, the collection unit can separate and collect combustible waste from non-combustible waste. The collection unit can separate and collect recyclable waste from non-recyclable waste. The collection unit can separate and collect hazardous waste from general waste. By changing the collection method according to the type of waste, efficient waste collection becomes possible.

[0046] The collection unit can optimize the collection route according to the amount of waste during collection. For example, the collection unit will prioritize collection from areas with a large amount of waste. It will postpone collection from areas with a small amount of waste. The collection unit will set the optimal collection route according to the amount of waste. This allows for efficient waste collection by optimizing the collection route according to the amount of waste.

[0047] The collection unit can adjust its collection route when collecting garbage, taking into account local weather information. For example, during rainy weather, the unit will prioritize routes with roofs. During sunny weather, the unit will use the normal route. On snowy days, the unit will prioritize routes that are less slippery. By considering local weather information, efficient garbage collection becomes possible according to the weather.

[0048] The collection department can adjust its collection routes when collecting garbage, taking into account local event information. For example, when an event is being held, the collection department will prioritize garbage collection around the event venue. The collection department will adjust its collection routes according to the scale of the event. The collection department will adjust its collection routes to match the event schedule. By taking local event information into consideration, efficient garbage collection during events becomes possible.

[0049] The sorting unit can apply different sorting algorithms depending on the type of waste during sorting. For example, it can apply an algorithm to separate combustible waste from non-combustible waste. It can apply an algorithm to separate recyclable waste from non-recyclable waste. It can apply an algorithm to separate hazardous waste from general waste. By changing the sorting algorithm according to the type of waste, efficient waste sorting becomes possible.

[0050] The sorting unit can adjust the precision of sorting according to the amount of waste. For example, if there is a large amount of waste, the sorting unit will increase the precision of sorting. If there is a small amount of waste, the sorting unit will decrease the precision of sorting. The sorting unit adjusts the precision of sorting according to the amount of waste. This allows for efficient waste sorting by adjusting the precision of sorting according to the amount of waste.

[0051] The sorting department can adjust its sorting methods when sorting waste, taking into account information about local recycling facilities. For example, it can adjust sorting methods according to the types of waste that the recycling facility can accept, the processing capacity of the recycling facility, and the operating hours of the recycling facility. This allows for efficient waste sorting by taking into account information about local recycling facilities.

[0052] The sorting department can adjust its sorting method when sorting waste, taking into account information about local waste treatment facilities. For example, it can adjust the sorting method according to the types of waste that the waste treatment facility can accept. It can adjust the sorting method according to the processing capacity of the waste treatment facility. It can adjust the sorting method according to the operating hours of the waste treatment facility. By taking into account information about local waste treatment facilities, efficient waste sorting becomes possible.

[0053] The transportation department can apply different transportation methods depending on the type of waste. For example, it can separate and transport combustible waste from non-combustible waste. It can separate and transport recyclable waste from non-recyclable waste. It can separate and transport hazardous waste from general waste. By changing the transportation method according to the type of waste, efficient waste transportation becomes possible.

[0054] The transport unit can optimize the transport route according to the amount of waste during waste transportation. For example, the transport unit will prioritize transporting waste from locations with a large amount of waste. It will postpone transporting waste from locations with a small amount of waste. The transport unit will set the optimal transport route according to the amount of waste. This optimizes the transport route according to the amount of waste, enabling efficient waste transportation.

[0055] The transportation department can adjust its routes when transporting waste, taking into account local traffic conditions. For example, it avoids transporting waste during peak traffic hours. If traffic congestion occurs, it uses alternative routes. The transportation department sets the optimal transportation route according to traffic conditions. This allows for efficient waste transportation by considering local traffic conditions.

[0056] The transportation department can adjust its routes when transporting waste, taking into account local weather information. For example, in rainy weather, the department prioritizes routes that are less slippery. In sunny weather, the department uses the normal route. On snowy days, the department prioritizes routes that are less prone to snow accumulation. By considering local weather information, efficient waste transportation becomes possible.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The deployment unit can consider the local crime rate when selecting locations for garbage collection boxes. For example, in areas with high crime rates, garbage collection boxes equipped with surveillance cameras can be installed. In areas with low crime rates, standard garbage collection boxes can be installed. In areas where crime rates fluctuate, installation locations can be flexibly adjusted. This improves user confidence by considering the safety of the local area.

[0059] The collection unit can detect the odor of the waste during collection and adjust the collection frequency according to the intensity of the odor. For example, if the odor is strong, the collection frequency will be increased. If the odor is weak, the collection frequency will be decreased. The optimal collection route will be set according to the intensity of the odor. In this way, a comfortable environment can be maintained by taking the odor of the waste into consideration.

[0060] The sorting unit can measure the weight of waste during sorting and adjust the sorting method according to the weight. For example, a specific sorting method is applied to heavier waste, and a different sorting method is applied to lighter waste. The precision of sorting is adjusted according to the weight. This allows for efficient waste sorting by taking the weight of the waste into consideration.

[0061] The transport unit can measure the volume of waste during transport and adjust the transport method accordingly. For example, large waste will use a large transport method, while small waste will use a smaller transport method. The optimal transport route is set according to the volume. This allows for efficient waste transport by taking the volume of the waste into consideration.

[0062] The deployment unit can collect opinions from local residents and determine the installation location when setting up garbage collection boxes. For example, it can conduct a resident survey to identify desired installation locations. The installation locations of the garbage collection boxes can then be adjusted based on residents' opinions. By considering residents' opinions, installation can be carried out in a way that meets the needs of the community.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The deployment unit deploys a network of AI-equipped waste collection boxes in various locations. The deployment unit utilizes MEC (Multi-Access Circuit) and advanced generative AI. Trash cans are opened and closed via user authentication, and only registered users can use them. Image recognition technology automatically sorts the waste, saving users the trouble of sorting. Waste collection volume data is uploaded in real time via SIM. Step 2: The collection unit collects waste based on the waste collection box network deployed by the deployment unit. The collection unit uses automated guided vehicles (AGVs) to collect waste. The waste bins are collected along with their cartridges and replaced with empty cartridges. By utilizing waste volume data and pedestrian flow data, waste overflows are prevented and efficient collection routes are realized. Step 3: The sorting unit sorts the waste collected by the collection unit. The sorting unit automatically sorts the waste using image recognition technology. The trash can is opened and closed based on user authentication. Step 4: The transport unit transports the waste sorted by the sorting unit to the collection point. The transport unit uses automated guided vehicles (AGVs) to transport the waste to the collection point. Efficient collection routes are realized by utilizing waste volume data and pedestrian flow data.

[0065] (Example of form 2) The waste collection system according to an embodiment of the present invention is a system that utilizes an AI-equipped waste collection box network and an automated guided vehicle (AGV). This waste collection system consists of the following steps. First, an AI-equipped waste collection box network is deployed in various locations. This waste collection box utilizes MEC and advanced generative AI. The trash cans are opened and closed by user authentication, and only registered users can use them. Furthermore, sorting is performed automatically using image recognition technology, saving users the trouble of sorting. Waste collection volume data is uploaded in real time via SIM. Next, an automated guided vehicle (AGV) travels back and forth between waste collection boxes and waste collection points within the area. The autonomous AGV collects the trash cans along with their cartridges and replaces them with empty cartridges. By utilizing waste volume data and pedestrian flow data, waste overflows are prevented and efficient collection routes are realized. Users can easily use the dedicated trash boxes through a dedicated app. By subscribing, users can use boxes nationwide 24 hours a day, and there is a limit to the amount of waste that can be disposed of. Spot use with single tickets is also possible. Through authentication, the waste disposal status of each user is monitored, and rule violators are identified. In the event of illegal dumping, violators will be identified and dealt with. By collaborating with local governments, developers, and recycling companies, the system will raise funds for installation from those with a need to improve urban cleanliness and expand its target area. It will also contribute to the SDGs by providing sorted, high-quality general waste to recycling companies. This system will reduce urban environmental pollution and stress related to manners caused by street and household waste disposal, and address the shortage of waste disposal sites in cities due to the reduction of trash cans under counter-terrorism measures. It will also enable efficient sorting to revitalize waste resource recycling. General consumers, local governments, real estate developers, and recycling companies will be targeted. As a result, the waste collection system will be able to efficiently collect, sort, and transport waste.

[0066] The waste collection system according to this embodiment comprises a deployment unit, a collection unit, a sorting unit, and a transport unit. The deployment unit deploys an AI-equipped waste collection box network in various locations. The deployment unit utilizes, for example, MEC and advanced generative AI. The deployment unit opens and closes the trash cans through user authentication, and only registered users can use them. The deployment unit automatically sorts waste using image recognition technology, thus saving users the trouble of sorting. The deployment unit uploads waste collection volume data in real time via SIM. The collection unit collects waste based on the waste collection box network deployed by the deployment unit. The collection unit collects waste using, for example, an automated guided vehicle (AGV). The collection unit collects trash cans along with their cartridges and replaces them with empty cartridges. The collection unit prevents waste overflow and realizes efficient collection routes by utilizing waste volume data and human flow data. The sorting unit sorts the waste collected by the collection unit. The sorting unit automatically sorts waste using, for example, image recognition technology. The sorting unit opens and closes the trash cans based on user authentication. The transport unit transports the sorted waste to the collection point. The transport unit uses, for example, an automated guided vehicle (AGV) to transport the waste to the collection point. The transport unit utilizes waste volume data and pedestrian flow data to realize an efficient collection route. As a result, the waste collection system according to this embodiment can efficiently collect, sort, and transport waste.

[0067] The deployment unit will deploy a network of AI-equipped waste collection boxes in various locations. Specifically, the deployment unit will use advanced generative AI to plan the optimal placement of boxes according to the waste collection needs of each region. The generative AI will analyze information such as past waste collection data, local population density, and waste types to propose the most efficient placement of waste collection boxes. As a result, waste collection boxes will be installed in locations easily accessible to users, improving waste collection efficiency. The deployment unit will open and close the waste bins using user authentication, and only registered users will be able to use them. User authentication will use a smartphone app or IC card to enhance user convenience. Furthermore, the deployment unit will use image recognition technology to automatically sort waste. When a user puts waste in, a camera will identify the type of waste and sort it appropriately. This will save users the trouble of sorting. The deployment unit will upload waste collection volume data in real time via SIM. This will allow the usage status of the waste collection boxes to be constantly monitored, enabling appropriate action before the boxes overflow. In addition, the deployment unit will manage maintenance information and anomaly detection data for the waste collection boxes in real time, improving the reliability of the system. This allows the deployment unit to efficiently and effectively deploy the waste collection box network, improving user convenience and system reliability.

[0068] The collection unit collects waste based on the waste collection box network deployed by the deployment unit. Specifically, the collection unit uses automated guided vehicles (AGVs) to collect waste. The AGVs are equipped with autonomous driving technology and patrol the waste collection boxes according to pre-set routes. The collection unit collects the waste bins along with their cartridges and replaces them with empty cartridges. This ensures that waste collection is carried out quickly and efficiently. The collection unit prevents waste overflow and achieves efficient collection routes by utilizing waste volume data and pedestrian flow data. Specifically, sensors installed in the waste collection boxes measure the amount of waste in real time and issue collection orders when a certain amount is reached. In addition, by analyzing pedestrian flow data, the collection unit identifies times and locations with high user traffic and optimizes collection routes. This allows the collection unit to collect waste at the appropriate time before it overflows, achieving efficient operation. Furthermore, the collection unit manages the operating status and maintenance information of the waste collection robots in real time, improving system reliability. As a result, the collection unit can collect waste efficiently and effectively, improving the overall system performance.

[0069] The sorting unit sorts the waste collected by the collection unit. Specifically, the sorting unit automatically sorts waste using image recognition technology. When waste is fed into the sorting line, a camera identifies the type of waste and performs appropriate sorting. For example, it automatically classifies different materials such as plastic, metal, paper, and glass. The sorting unit opens and closes the waste bins based on user authentication. This ensures that sorting is performed accurately and efficiently. Furthermore, the sorting unit can automatically adjust the settings of the sorting line according to the type and amount of waste. This improves the efficiency of sorting and contributes to an increased recycling rate. The sorting unit manages data on sorted waste in real time and provides it to recycling companies and local governments. This improves the efficiency of recycling and contributes to reducing the environmental impact. In addition, the sorting unit uses an anomaly detection algorithm to detect anomalies that occur during sorting early and take appropriate action. This allows the sorting unit to sort waste efficiently and accurately, improving the reliability and performance of the entire system.

[0070] The transportation unit transports the sorted waste from the sorting unit to the collection point. Specifically, the transportation unit uses automated guided vehicles (AGVs) to transport the waste to the collection point. The AGVs autonomously navigate according to pre-set routes, efficiently transporting the sorted waste. The transportation unit utilizes waste volume data and pedestrian flow data to create efficient collection routes. Specifically, it optimizes the transportation route according to the amount and type of waste to improve transportation efficiency. For example, it adjusts the transportation route to match areas and times with high waste volumes to achieve efficient transportation. The transportation unit monitors the condition of the waste in real time during transportation and responds immediately if any abnormalities occur. This prevents problems during transportation and improves the reliability of the system. Furthermore, the transportation unit manages transportation operation data in real time to optimize transportation efficiency and costs. As a result, the transportation unit can transport waste efficiently and effectively to the collection point, improving the overall performance of the system.

[0071] The deployment unit can utilize MEC (Multi-access Edge Computing) and advanced generative AI. For example, the deployment unit uses MEC to deploy edge servers and utilize communication protocols. The deployment unit uses generative AI to streamline the deployment of the waste collection box network. For example, the generative AI uses machine learning algorithms and deep learning technology to select the optimal installation location for waste collection boxes. In this way, the deployment of the waste collection box network becomes more efficient by utilizing MEC and generative AI.

[0072] The collection unit can collect waste using a waste collection robot. For example, the collection unit uses an automated guided vehicle (AGV) to collect waste. The collection unit collects waste bins along with their cartridges and replaces them with empty cartridges. The collection unit utilizes waste volume data and pedestrian flow data to prevent waste overflow and realize efficient collection routes. As a result, waste collection is automated by using a waste collection robot.

[0073] The sorting unit can automatically sort waste using image recognition technology. For example, the sorting unit automatically sorts waste using image recognition technology. The sorting unit opens and closes the trash cans based on user authentication. This automates waste sorting using image recognition technology.

[0074] The transportation unit can use automated guided vehicles (AGVs) to transport waste to collection points. For example, the transportation unit uses automated guided vehicles to transport waste to collection points. The transportation unit utilizes waste volume data and pedestrian flow data to create efficient collection routes. This automates waste transportation through the use of automated guided vehicles.

[0075] The collection unit can upload waste collection volume data in real time via SIM. For example, the collection unit uploads waste collection volume data in real time via SIM. This allows for immediate monitoring of the collection status by uploading waste collection volume data in real time.

[0076] The sorting unit can open and close the trash cans based on user authentication. For example, the sorting unit opens and closes the trash cans based on user authentication. This prevents unauthorized use by requiring user authentication to open and close the trash cans.

[0077] The transportation department can realize efficient collection routes by utilizing data on waste volume and pedestrian flow. For example, the transportation department can realize efficient collection routes by utilizing data on waste volume and pedestrian flow. This allows for the realization of efficient collection routes by utilizing data on waste volume and pedestrian flow.

[0078] The deployment unit can estimate the user's emotions and adjust the placement of garbage collection boxes based on the estimated emotions. For example, if the user is stressed, the deployment unit will place the garbage collection box in an easily accessible location. If the user is relaxed, the deployment unit will place the garbage collection box in a scenic location. If the user is in a hurry, the deployment unit will place the garbage collection box along a main street. This improves user convenience by adjusting the placement based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The deployment unit can analyze past waste collection data and select the optimal installation location. For example, it can identify areas with high waste generation based on past waste collection data and install waste collection boxes there. The deployment unit analyzes waste collection data and avoids installing boxes in areas with low waste generation. Based on waste collection data, the deployment unit installs boxes in areas where waste generation fluctuates depending on the season or events. In this way, the optimal installation location can be selected by analyzing past data.

[0080] The deployment unit can determine the installation location of garbage collection boxes by taking local event information into consideration. For example, the deployment unit can install garbage collection boxes around event venues based on local event information. The deployment unit can install the necessary number of garbage collection boxes according to the scale of the event. The deployment unit can adjust the installation location of garbage collection boxes according to the event schedule. In this way, garbage collection during events becomes more efficient by taking local event information into consideration.

[0081] The deployment unit can estimate the user's emotions and adjust the timing of the trash collection box installation based on the estimated emotions. For example, if the user is feeling stressed, the deployment unit will quickly install the trash collection box. If the user is relaxed, the deployment unit will flexibly adjust the installation timing. If the user is in a hurry, the deployment unit will immediately install the trash collection box. This improves user convenience by adjusting the installation timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The deployment unit can determine the installation location of garbage collection boxes by considering the population density of the area. For example, the deployment unit can prioritize the installation of garbage collection boxes in areas with high population density. In areas with low population density, the deployment unit can install the minimum necessary number of garbage collection boxes. In areas with significant fluctuations in population density, the deployment unit can flexibly adjust the installation location. This improves user convenience by considering the population density of the area.

[0083] The deployment unit can determine the installation location of garbage collection boxes by considering local traffic conditions. For example, the deployment unit can install garbage collection boxes in areas with heavy traffic to improve user convenience. In areas with light traffic, it can install only the minimum necessary number of garbage collection boxes. In areas where traffic conditions fluctuate, the deployment unit can flexibly adjust the installation location. In this way, user convenience is improved by considering local traffic conditions.

[0084] The collection unit can estimate the user's emotions and adjust the frequency of garbage collection based on the estimated emotions. For example, if the user is stressed, the collection unit will increase the frequency of garbage collection. If the user is relaxed, the collection unit will decrease the frequency of garbage collection. If the user is in a hurry, the collection unit will adjust the frequency of garbage collection. This improves user convenience by adjusting the collection frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The collection unit can apply different collection methods depending on the type of waste during collection. For example, the collection unit can separate and collect combustible waste from non-combustible waste. The collection unit can separate and collect recyclable waste from non-recyclable waste. The collection unit can separate and collect hazardous waste from general waste. By changing the collection method according to the type of waste, efficient waste collection becomes possible.

[0086] The collection unit can optimize the collection route according to the amount of waste during collection. For example, the collection unit will prioritize collection from areas with a large amount of waste. It will postpone collection from areas with a small amount of waste. The collection unit will set the optimal collection route according to the amount of waste. This allows for efficient waste collection by optimizing the collection route according to the amount of waste.

[0087] The collection unit can estimate the user's emotions and determine the priority of garbage collection based on the estimated emotions. For example, if the user is stressed, the collection unit will prioritize garbage collection more highly. If the user is relaxed, the collection unit will prioritize garbage collection less highly. If the user is in a hurry, the collection unit will adjust the priority of garbage collection. This improves user convenience by determining collection priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The collection unit can adjust its collection route when collecting garbage, taking into account local weather information. For example, during rainy weather, the unit will prioritize routes with roofs. During sunny weather, the unit will use the normal route. On snowy days, the unit will prioritize routes that are less slippery. By considering local weather information, efficient garbage collection becomes possible according to the weather.

[0089] The collection department can adjust its collection routes when collecting garbage, taking into account local event information. For example, when an event is being held, the collection department will prioritize garbage collection around the event venue. The collection department will adjust its collection routes according to the scale of the event. The collection department will adjust its collection routes to match the event schedule. By taking local event information into consideration, efficient garbage collection during events becomes possible.

[0090] The sorting unit can estimate the user's emotions and adjust the waste sorting method based on the estimated emotions. For example, if the user is stressed, the sorting unit provides a simple sorting method. If the user is relaxed, the sorting unit provides a detailed sorting method. If the user is in a hurry, the sorting unit provides a method that allows for quick sorting. This improves user convenience by adjusting the sorting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The sorting unit can apply different sorting algorithms depending on the type of waste during sorting. For example, it can apply an algorithm to separate combustible waste from non-combustible waste. It can apply an algorithm to separate recyclable waste from non-recyclable waste. It can apply an algorithm to separate hazardous waste from general waste. By changing the sorting algorithm according to the type of waste, efficient waste sorting becomes possible.

[0092] The sorting unit can adjust the precision of sorting according to the amount of waste. For example, if there is a large amount of waste, the sorting unit will increase the precision of sorting. If there is a small amount of waste, the sorting unit will decrease the precision of sorting. The sorting unit adjusts the precision of sorting according to the amount of waste. This allows for efficient waste sorting by adjusting the precision of sorting according to the amount of waste.

[0093] The sorting unit can estimate the user's emotions and determine the priority of sorting waste based on those emotions. For example, if the user is stressed, the sorting unit will prioritize sorting waste more highly. If the user is relaxed, the sorting unit will prioritize sorting waste less highly. If the user is in a hurry, the sorting unit will adjust the sorting priority. This improves user convenience by determining sorting priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The sorting department can adjust its sorting methods when sorting waste, taking into account information about local recycling facilities. For example, it can adjust sorting methods according to the types of waste that the recycling facility can accept, the processing capacity of the recycling facility, and the operating hours of the recycling facility. This allows for efficient waste sorting by taking into account information about local recycling facilities.

[0095] The sorting department can adjust its sorting method when sorting waste, taking into account information about local waste treatment facilities. For example, it can adjust the sorting method according to the types of waste that the waste treatment facility can accept. It can adjust the sorting method according to the processing capacity of the waste treatment facility. It can adjust the sorting method according to the operating hours of the waste treatment facility. By taking into account information about local waste treatment facilities, efficient waste sorting becomes possible.

[0096] The transport unit can estimate the user's emotions and adjust the method of transporting the trash based on those emotions. For example, if the user is stressed, the transport unit will transport the trash quickly. If the user is relaxed, the transport unit will apply the normal transport method. If the user is in a hurry, the transport unit will transport the trash via the shortest route. This improves user convenience by adjusting the transport method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The transportation department can apply different transportation methods depending on the type of waste. For example, it can separate and transport combustible waste from non-combustible waste. It can separate and transport recyclable waste from non-recyclable waste. It can separate and transport hazardous waste from general waste. By changing the transportation method according to the type of waste, efficient waste transportation becomes possible.

[0098] The transport unit can optimize the transport route according to the amount of waste during waste transportation. For example, the transport unit will prioritize transporting waste from locations with a large amount of waste. It will postpone transporting waste from locations with a small amount of waste. The transport unit will set the optimal transport route according to the amount of waste. This optimizes the transport route according to the amount of waste, enabling efficient waste transportation.

[0099] The waste collection unit can estimate the user's emotions and determine the priority of waste collection based on those emotions. For example, if the user is stressed, the unit will prioritize waste collection more highly. If the user is relaxed, the unit will prioritize waste collection less highly. If the user is in a hurry, the unit will adjust the priority of waste collection. This improves user convenience by determining the priority of waste collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The transportation department can adjust its routes when transporting waste, taking into account local traffic conditions. For example, it avoids transporting waste during peak traffic hours. If traffic congestion occurs, it uses alternative routes. The transportation department sets the optimal transportation route according to traffic conditions. This allows for efficient waste transportation by considering local traffic conditions.

[0101] The transportation department can adjust its routes when transporting waste, taking into account local weather information. For example, in rainy weather, the department prioritizes routes that are less slippery. In sunny weather, the department uses the normal route. On snowy days, the department prioritizes routes that are less prone to snow accumulation. By considering local weather information, efficient waste transportation becomes possible.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The deployment unit can consider the local crime rate when selecting locations for garbage collection boxes. For example, in areas with high crime rates, garbage collection boxes equipped with surveillance cameras can be installed. In areas with low crime rates, standard garbage collection boxes can be installed. In areas where crime rates fluctuate, installation locations can be flexibly adjusted. This improves user confidence by considering the safety of the local area.

[0104] The collection unit can detect the odor of the waste during collection and adjust the collection frequency according to the intensity of the odor. For example, if the odor is strong, the collection frequency will be increased. If the odor is weak, the collection frequency will be decreased. The optimal collection route will be set according to the intensity of the odor. In this way, a comfortable environment can be maintained by taking the odor of the waste into consideration.

[0105] The sorting unit can measure the weight of waste during sorting and adjust the sorting method according to the weight. For example, a specific sorting method is applied to heavier waste, and a different sorting method is applied to lighter waste. The precision of sorting is adjusted according to the weight. This allows for efficient waste sorting by taking the weight of the waste into consideration.

[0106] The transport unit can measure the volume of waste during transport and adjust the transport method accordingly. For example, large waste will use a large transport method, while small waste will use a smaller transport method. The optimal transport route is set according to the volume. This allows for efficient waste transport by taking the volume of the waste into consideration.

[0107] The deployment unit can collect opinions from local residents and determine the installation location when setting up garbage collection boxes. For example, it can conduct a resident survey to identify desired installation locations. The installation locations of the garbage collection boxes can then be adjusted based on residents' opinions. By considering residents' opinions, installation can be carried out in a way that meets the needs of the community.

[0108] The development unit can estimate the user's emotions and adjust the design of the trash collection box based on those emotions. For example, if the user is stressed, a design with calming colors is adopted. If the user is relaxed, a design with bright colors is adopted. If the user is in a hurry, a simple design is adopted. By adjusting the design based on the user's emotions, user satisfaction is improved.

[0109] The collection unit can estimate the user's emotions and adjust the notification method for garbage collection based on those emotions. For example, if the user is stressed, a quiet notification sound will be used. If the user is relaxed, a normal notification sound will be used. If the user is in a hurry, a prominent notification sound will be used. This improves user convenience by adjusting the notification method based on the user's emotions.

[0110] The sorting unit can estimate the user's emotions and adjust the waste sorting interface based on those emotions. For example, if the user is stressed, a simple interface is provided. If the user is relaxed, a detailed interface is provided. If the user is in a hurry, a quick and easy interface is provided. This improves user convenience by adjusting the interface based on the user's emotions.

[0111] The transport unit can estimate the user's emotions and adjust the route display method during waste transport based on those emotions. For example, if the user is stressed, a simple route display is provided. If the user is relaxed, a detailed route display is provided. If the user is in a hurry, the shortest route is highlighted. This improves user convenience by adjusting the route display method based on the user's emotions.

[0112] The deployment unit can estimate the user's emotions and adjust the placement of the trash collection boxes based on those emotions. For example, if the user is stressed, the trash collection box will be placed in an easily accessible location. If the user is relaxed, the trash collection box will be placed in a location with a pleasant view. If the user is in a hurry, the trash collection box will be placed along a main street. By adjusting the placement based on the user's emotions, user convenience is improved.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The deployment unit deploys a network of AI-equipped waste collection boxes in various locations. The deployment unit utilizes MEC (Multi-Access Circuit) and advanced generative AI. Trash cans are opened and closed via user authentication, and only registered users can use them. Image recognition technology automatically sorts the waste, saving users the trouble of sorting. Waste collection volume data is uploaded in real time via SIM. Step 2: The collection unit collects waste based on the waste collection box network deployed by the deployment unit. The collection unit uses automated guided vehicles (AGVs) to collect waste. The waste bins are collected along with their cartridges and replaced with empty cartridges. By utilizing waste volume data and pedestrian flow data, waste overflows are prevented and efficient collection routes are realized. Step 3: The sorting unit sorts the waste collected by the collection unit. The sorting unit automatically sorts the waste using image recognition technology. The trash can is opened and closed based on user authentication. Step 4: The transport unit transports the waste sorted by the sorting unit to the collection point. The transport unit uses automated guided vehicles (AGVs) to transport the waste to the collection point. Efficient collection routes are realized by utilizing waste volume data and pedestrian flow data.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the deployment unit, collection unit, sorting unit, and transport unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the deployment unit is implemented by the control unit 46A of the smart device 14 and deploys an AI-equipped waste collection box network to various locations. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects waste using an automated guided vehicle (AGV). The sorting unit is implemented by, for example, the control unit 46A of the smart device 14 and automatically sorts waste using image recognition technology. The transport unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and transports waste to collection points using an automated guided vehicle. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the deployment unit, collection unit, sorting unit, and transport unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the deployment unit is implemented by the control unit 46A of the smart glasses 214 and deploys an AI-equipped garbage collection box network to various locations. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects garbage using an automated guided vehicle (AGV). The sorting unit is implemented by, for example, the control unit 46A of the smart glasses 214 and automatically sorts garbage using image recognition technology. The transport unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and transports garbage to collection points using an automated guided vehicle. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the deployment unit, collection unit, sorting unit, and transport unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the deployment unit is implemented by the control unit 46A of the headset terminal 314 and deploys an AI-equipped garbage collection box network to various locations. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects garbage using an automated guided vehicle (AGV). The sorting unit is implemented by, for example, the control unit 46A of the headset terminal 314 and automatically sorts garbage using image recognition technology. The transport unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and transports garbage to collection points using an automated guided vehicle. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the deployment unit, collection unit, sorting unit, and transport unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the deployment unit is implemented by the control unit 46A of the robot 414 and deploys an AI-equipped garbage collection box network to various locations. The collection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and collects garbage using an automated guided vehicle (AGV). The sorting unit is implemented by, for example, the control unit 46A of the robot 414 and automatically sorts garbage using image recognition technology. The transport unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and transports garbage to collection points using an automated guided vehicle. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) The deployment department will deploy a network of AI-equipped garbage collection boxes in various locations, A collection unit that collects garbage based on the garbage collection box net deployed by the aforementioned deployment unit, A sorting unit for sorting the waste collected by the aforementioned collection unit, The system includes a transport unit that transports the waste separated by the aforementioned sorting unit to a collection point. A system characterized by the following features. (Note 2) The aforementioned unfolding section is Utilizing MEC and advanced generative AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned recovery unit is Garbage collection robots are used to collect garbage. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned sorting section is Automatically sorting waste using image recognition technology. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transport unit is We use autonomous AGVs to transport waste to collection points. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recovery unit is Upload waste collection data in real time via SIM card. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sorting section is The trash can is opened and closed based on user authentication. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned transport unit is By utilizing data on waste volume and pedestrian flow, we can create efficient collection routes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned unfolding section is The system estimates the user's emotions and adjusts the placement of garbage collection boxes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned unfolding section is We analyze past waste collection data to select the optimal installation location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned unfolding section is When installing garbage collection boxes, we take local event information into consideration when deciding on the installation location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned unfolding section is The system estimates the user's emotions and adjusts the timing of trash collection box placement based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned unfolding section is When installing garbage collection boxes, the location is determined by considering the population density of the area. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned unfolding section is When installing garbage collection boxes, the location is determined considering local traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned recovery unit is The system estimates the user's emotions and adjusts the frequency of garbage collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned recovery unit is When collecting garbage, different collection methods are applied depending on the type of garbage. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned recovery unit is Optimize the collection route according to the amount of waste during waste collection. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned recovery unit is The system estimates user emotions and determines waste collection priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned recovery unit is When collecting garbage, the collection route is adjusted taking into account local weather information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recovery unit is When collecting garbage, we adjust the collection route taking into account local event information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned sorting section is The system estimates the user's emotions and adjusts the waste sorting method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned sorting section is When sorting waste, different sorting algorithms are applied depending on the type of waste. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned sorting section is When sorting waste, adjust the sorting accuracy according to the amount of waste. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned sorting section is It estimates the user's emotions and determines the priority of waste sorting based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned sorting section is When sorting waste, adjust the sorting method while taking into account information about local recycling facilities. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned sorting section is When sorting waste, adjust the sorting method while taking into account information about local waste disposal facilities. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned transport unit is The system estimates the user's emotions and adjusts the waste disposal method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned transport unit is When transporting waste, different transport methods are applied depending on the type of waste. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned transport unit is When transporting waste, optimize the transport route according to the amount of waste. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned transport unit is The system estimates the user's emotions and determines the priority of waste collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned transport unit is When transporting waste, the transport route is adjusted to take into account local traffic conditions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned transport unit is When transporting waste, the transport route is adjusted taking into account local weather information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The deployment unit will deploy a network of AI-equipped garbage collection boxes in various locations, A collection unit that collects garbage based on the garbage collection box net deployed by the aforementioned deployment unit, A sorting unit for sorting the waste collected by the aforementioned collection unit, The system includes a transport unit that transports the waste separated by the sorting unit to a collection point. A system characterized by the following features.

2. The aforementioned unfolding section is Utilizing MEC and advanced generative AI The system according to feature 1.

3. The aforementioned recovery unit is Garbage collection robots are used to collect garbage. The system according to feature 1.

4. The aforementioned sorting section is Automatically sorting waste using image recognition technology. The system according to feature 1.

5. The aforementioned transport unit is We use autonomous AGVs to transport waste to collection points. The system according to feature 1.

6. The aforementioned recovery unit is Upload waste collection data in real time via SIM card. The system according to feature 1.

7. The aforementioned sorting section is The trash can is opened and closed based on user authentication. The system according to feature 1.

8. The aforementioned transport unit is By utilizing data on waste volume and pedestrian flow, we can create efficient collection routes. The system according to feature 1.

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