system

Abstract

The system according to the embodiment aims to efficiently collect garbage from aging households. [Solution] A system according to an embodiment includes an identification unit, a collection unit, a pickup unit, and an optimization unit. The identification unit determines the type of garbage. The collection unit collects the garbage determined by the identification unit. The pickup unit allows a garbage collection truck to pick up the garbage collected by the collection unit based on location information. The optimization unit optimizes the routes of the collection unit and pickup unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology is not efficient in collecting waste from aging households, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently collect garbage from aging households. [Means for solving the problem]

[0006] The system according to the embodiment includes an identification unit, a collection unit, a pickup unit, and an optimization unit. The identification unit determines the type of garbage. The collection unit collects the garbage determined by the identification unit. The pickup unit allows a garbage collection truck to pick up the garbage collected by the collection unit based on location information of the garbage. The optimization unit optimizes the routes of the collection unit and the pickup unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect garbage from aging households. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A garbage collection system according to an embodiment of the present invention efficiently collects garbage from aging households. In this garbage collection system, drones directly collect small garbage items, while garbage trucks pick up large garbage items based on the drone's location information. The entire process is managed by AI, ensuring efficient garbage collection. First, an aging household deposits its garbage at a designated location. Next, a drone flies to the location and collects the small garbage items. After collecting the garbage, the drone transports it to a designated garbage collection site. Meanwhile, for large garbage items, the drone collects their location information and transmits it to the garbage truck. The garbage truck then picks up the large garbage items based on that information. The entire process is managed by AI. AI optimizes the drone's flight route and the garbage truck's route, ensuring efficient garbage collection. Even if a drone falls, since it is only collecting garbage, there is minimal damage, making practical testing possible for drone development. Operating costs are covered by local government expenses and by placing advertisements for industrial waste disposal companies on the drone, allowing users to use the system without significant burden. This enables the garbage collection system to efficiently collect garbage from aging households, reducing operational costs while achieving fast and accurate garbage collection.

[0029] A garbage collection system according to an embodiment includes an identification unit, a collection unit, a pickup unit, and an optimization unit. The identification unit determines the type of garbage. Examples of garbage types include, but are not limited to, combustible garbage, non-combustible garbage, and recyclable garbage. The identification unit determines the type of garbage using, for example, image recognition technology. The identification unit can also determine the material of the garbage using a sensor. For example, the identification unit can determine the material of the garbage using an infrared sensor. The collection unit collects the garbage determined by the identification unit. The collection unit collects small garbage using, for example, a drone. The collection unit can also acquire location information of the garbage when the drone collects the garbage. For example, the collection unit acquires location information of the garbage using GPS. The pickup unit allows a garbage collection truck to pick up the garbage based on the location information collected by the collection unit. For example, the pickup unit uses the location information of the drone when the garbage collection truck picks up large garbage. The pickup unit can also optimize the route for the garbage collection truck to efficiently pick up garbage. The optimization unit optimizes the routes of the collection unit and the pickup unit. The optimization unit, for example, uses AI to optimize the drone's flight route and the garbage collection truck's route. The optimization unit can take into account flight altitude and flight speed when optimizing the drone's flight route. For example, the optimization unit can achieve efficient garbage collection by optimizing the drone's flight altitude. The optimization unit can take into account traffic conditions and road width when optimizing the garbage collection truck's route. For example, the optimization unit can achieve efficient garbage collection by selecting a route that avoids traffic congestion. This allows the garbage collection system according to the embodiment to determine the type of garbage and efficiently collect and pick it up.

[0030] The collection unit can collect small trash using a drone. Examples of small trash include, but are not limited to, plastic bottles, cans, and paper waste. The collection unit collects small trash using, for example, a drone. For example, the collection unit can acquire location information of the trash when the drone collects the trash. The collection unit can also identify the type of trash when the drone collects the trash. For example, the collection unit can identify the type of trash using a camera mounted on the drone. The collection unit can also measure the weight of the trash when the drone collects the trash. For example, the collection unit can measure the weight of the trash using a sensor mounted on the drone. This allows the drone to efficiently collect small trash. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera mounted on the drone into a generation AI and have the generation AI identify the type of trash.

[0031] The pickup unit enables the garbage truck to pick up oversized garbage based on the drone's location information. Examples of oversized garbage include, but are not limited to, furniture, home appliances, and large plastic products. For example, the pickup unit uses the drone's location information when the garbage truck picks up oversized garbage. The pickup unit can also optimize the route for the garbage truck to efficiently pick up garbage. For example, the pickup unit selects the shortest route when the garbage truck picks up oversized garbage. The pickup unit can also consider traffic conditions when the garbage truck picks up oversized garbage. For example, the pickup unit selects a route that avoids traffic congestion. The pickup unit can also identify the type of garbage when the garbage truck picks up oversized garbage. For example, the pickup unit identifies the type of garbage using a camera mounted on the garbage truck. This allows the drone's location information to be used to efficiently pick up oversized garbage. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input the drone's location information into the generation AI and have the generation AI optimize the garbage collection truck's route.

[0032] The optimization unit can optimize the drone's flight route and the garbage truck's route. The optimization unit, for example, uses AI to optimize the drone's flight route and the garbage truck's route. The optimization unit can consider the flight altitude and flight speed when optimizing the drone's flight route. For example, the optimization unit realizes efficient garbage collection by optimizing the drone's flight altitude. The optimization unit can also consider the flight path when optimizing the drone's flight route. For example, the optimization unit realizes efficient garbage collection by optimizing the drone's flight path. The optimization unit can consider traffic conditions and road width when optimizing the garbage truck's route. For example, the optimization unit realizes efficient garbage collection by selecting a route that avoids traffic congestion. The optimization unit can also consider the order of collection points when optimizing the garbage truck's route. For example, the optimization unit realizes efficient garbage collection by optimizing the order of collection points. As a result, efficient garbage collection is possible by optimizing the routes of the drone and the garbage truck. Some or all of the above-described processing in the optimization unit may be performed, for example, using AI or may be performed without using AI. For example, the optimization unit can cause the generation AI to optimize drone flight routes and garbage collection truck routes.

[0033] The collection unit may include an emergency signal unit that transmits an emergency signal when the drone falls. Emergency signals include, but are not limited to, radio signals, internet signals, and satellite signals. For example, the collection unit transmits a radio signal when the drone falls. The collection unit can also transmit an internet signal when the drone falls. For example, the collection unit transmits an emergency signal via the internet when the drone falls. The collection unit can also transmit a satellite signal when the drone falls. For example, the collection unit transmits an emergency signal via a satellite when the drone falls. This allows for a quick response when the drone falls. Some or all of the above-described processing in the emergency signal unit may be performed using, or without, AI. For example, the emergency signal unit may input data from a sensor that detects the drone falling into the generation AI and cause the generation AI to transmit an emergency signal.

[0034] The optimization unit may include a cost management unit that manages operating costs. Operating costs include, but are not limited to, fuel costs, labor costs, and maintenance costs. For example, the optimization unit manages fuel costs to manage operating costs. The optimization unit can also manage labor costs to manage operating costs. For example, the optimization unit optimizes operating costs by managing labor costs. The optimization unit can also manage maintenance costs to manage operating costs. For example, the optimization unit optimizes operating costs by managing maintenance costs. This allows for efficient management of operating costs. Some or all of the above-described processing in the cost management unit may be performed using AI, or may be performed without using AI. For example, the cost management unit may input operating cost data into the generation AI and cause the generation AI to optimize cost management.

[0035] The identification unit can optimize the identification algorithm based on the material and shape of the garbage. For example, the identification unit uses the reflectance characteristics of the material to identify the material of the garbage. The identification unit can also use 3D scanning technology to identify the shape of the garbage. For example, the identification unit 3D scans the shape of the garbage and adjusts the identification algorithm based on the shape data. The identification unit can also use image analysis technology to identify the color and texture of the garbage. For example, the identification unit analyzes the color and texture of the garbage and applies a identification algorithm according to the material. The identification unit improves identification accuracy by optimizing the identification algorithm based on the material and shape of the garbage. For example, the identification unit uses the reflectance characteristics of the material to distinguish between plastic products and metal products. The identification unit can 3D scan the shape of the garbage and adjust the identification algorithm based on the shape data. The identification unit can also analyze the color and texture of the garbage and apply a identification algorithm according to the material. This improves identification accuracy by applying a identification algorithm according to the material and shape of the garbage. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the material and shape of the garbage into the generation AI and have the generation AI optimize the identification algorithm.

[0036] The identification unit can improve the identification accuracy by referring to past identification data. The identification unit, for example, adjusts parameters of the identification algorithm based on the past identification data. The identification unit can also improve the identification accuracy by learning from past identification results. For example, the identification unit stores past identification data in the cloud and improves the identification accuracy in real time. The identification unit improves the identification accuracy by adjusting parameters of the identification algorithm based on the past identification data. The identification unit can also improve the identification accuracy by learning from past identification results. For example, the identification unit stores past identification data in the cloud and improves the identification accuracy in real time. The identification unit can improve the identification accuracy by adjusting parameters of the identification algorithm based on the past identification data. This makes it possible to improve the identification accuracy based on the past identification data. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past identification data to a generation AI and cause the generation AI to optimize the identification algorithm.

[0037] The identification unit can change the identification algorithm based on the time period when the garbage was disposed of. The identification unit changes the identification algorithm based on, for example, the time period when the garbage was disposed of. The identification unit improves identification accuracy by changing the identification algorithm based on the time period when the garbage was disposed of. For example, the identification unit prioritizes an identification algorithm for household garbage in the morning. The identification unit can also apply an identification algorithm for garbage disposed of by commercial facilities at night. For example, the identification unit strengthens an identification algorithm for large garbage on weekends. The identification unit can improve identification accuracy by changing the identification algorithm based on the time period when the garbage was disposed of. In this way, identification accuracy is improved by applying an identification algorithm according to the time period. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the time period when the garbage was disposed of to the generation AI and cause the generation AI to change the identification algorithm.

[0038] The identification unit can improve the identification accuracy based on environmental information of the location where the garbage was disposed. The identification unit adjusts the identification algorithm taking into account, for example, the temperature and humidity of the location where the garbage was disposed. The identification unit can also improve the identification accuracy taking into account the lighting conditions of the location where the garbage was disposed. For example, the identification unit optimizes the identification algorithm taking into account the noise level of the location where the garbage was disposed. The identification unit improves the identification accuracy by adjusting the identification algorithm taking into account the temperature and humidity of the location where the garbage was disposed. The identification unit can also improve the identification accuracy taking into account the lighting conditions of the location where the garbage was disposed. For example, the identification unit optimizes the identification algorithm taking into account the noise level of the location where the garbage was disposed. The identification unit can improve the identification accuracy by improving the identification accuracy based on environmental information of the location where the garbage was disposed. In this way, the identification accuracy is improved by taking environmental information into account. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input environmental information of the location where the garbage was disposed to the generation AI and cause the generation AI to optimize the identification algorithm.

[0039] The collection unit can change the collection method depending on the type of garbage. The collection unit changes the collection method depending on, for example, the type of garbage. The collection unit improves collection efficiency by changing the collection method depending on the type of garbage. For example, the collection unit collects plastic garbage by putting it in a dedicated collection bag. The collection unit can also collect metal garbage using magnetic force. For example, the collection unit collects organic garbage by putting it in a dedicated container. The collection unit can improve collection efficiency by changing the collection method depending on the type of garbage. In this way, collection efficiency is improved by applying a collection method depending on the type of garbage. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the type of garbage into the generation AI and cause the generation AI to change the collection method.

[0040] The collection unit can optimize the collection route taking into account the remaining battery level of the drone. For example, the collection unit optimizes the collection route taking into account the remaining battery level of the drone. By optimizing the collection route taking into account the remaining battery level of the drone, the collection unit achieves efficient garbage collection. For example, when the drone's remaining battery level is low, the collection unit collects garbage via the shortest route. When the drone's remaining battery level is sufficient, the collection unit can also collect garbage via an efficient route. For example, the collection unit monitors the remaining battery level of the drone in real time and dynamically adjusts the collection route. By optimizing the collection route taking into account the remaining battery level of the drone, efficient garbage collection can be achieved. As a result, efficient garbage collection is possible by optimizing the collection route according to the remaining battery level of the drone. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the remaining battery level of the drone to the generation AI and cause the generation AI to optimize the collection route.

[0041] The collection unit can change the collection route taking into account geographic information of the location where the garbage was disposed. The collection unit, for example, changes the collection route taking into account geographic information of the location where the garbage was disposed. The collection unit improves collection efficiency by changing the collection route taking into account geographic information of the location where the garbage was disposed. For example, if the location where the garbage was disposed is remote, the collection unit collects the garbage via the shortest route. If the location where the garbage was disposed is urban, the collection unit can also change the collection route taking into account traffic conditions. For example, if the location where the garbage was disposed is mountainous, the collection unit optimizes the collection route taking into account the terrain. The collection unit can improve collection efficiency by changing the collection route taking into account geographic information of the location where the garbage was disposed. In this way, the collection route can be optimized by taking into account geographic information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographic information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the collection route.

[0042] The collection unit can change the collection method taking into account weather information for the location where the garbage was disposed. For example, the collection unit changes the collection method taking into account weather information for the location where the garbage was disposed. The collection unit improves collection efficiency by changing the collection method taking into account weather information for the location where the garbage was disposed. For example, the collection unit employs a waterproof collection method when it is raining. The collection unit can also employ a normal collection method when it is sunny. For example, the collection unit selects a less slippery route for collection on snowy days. The collection unit can improve collection efficiency by changing the collection method taking into account weather information for the location where the garbage was disposed. In this way, the collection method can be optimized by taking weather information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather information for the location where the garbage was disposed into the generation AI and cause the generation AI to change the collection method.

[0043] The pickup unit can change the pickup method depending on the type of waste. For example, the pickup unit changes the pickup method depending on the type of waste. By changing the pickup method depending on the type of waste, the pickup unit improves pickup efficiency. For example, the pickup unit uses a dedicated crane to pick up large metal waste. The pickup unit can also pick up large plastic waste by placing it in a dedicated container. For example, the pickup unit picks up large organic waste with a dedicated truck. The pickup unit can improve pickup efficiency by changing the pickup method depending on the type of waste. In this way, pickup efficiency is improved by applying a pickup method depending on the type of waste. Some or all of the above-mentioned processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input data on the type of waste into the generation AI and cause the generation AI to change the pickup method.

[0044] The pickup unit can optimize the pickup route by taking into account the remaining fuel level of the garbage truck. The pickup unit, for example, optimizes the pickup route by taking into account the remaining fuel level of the garbage truck. The pickup unit achieves efficient garbage collection by optimizing the pickup route by taking into account the remaining fuel level of the garbage truck. For example, when the garbage truck has low fuel level, the pickup unit picks up the garbage truck via the shortest route. When the garbage truck has sufficient fuel level, the pickup unit can also pick up the garbage truck via an efficient route. For example, the pickup unit monitors the remaining fuel level of the garbage truck in real time and dynamically adjusts the pickup route. The pickup unit can achieve efficient garbage collection by optimizing the pickup route by taking into account the remaining fuel level of the garbage truck. As a result, efficient garbage collection is possible by optimizing the pickup route according to the remaining fuel level of the garbage truck. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit may input data on the remaining fuel level of the garbage truck into the generation AI and cause the generation AI to optimize the pickup route.

[0045] The pickup unit can change the pickup route taking into account geographic information of the location where the garbage was disposed. The pickup unit, for example, changes the pickup route taking into account geographic information of the location where the garbage was disposed. The pickup unit improves pickup efficiency by changing the pickup route taking into account geographic information of the location where the garbage was disposed. For example, if the location where the garbage was disposed is remote, the pickup unit picks up the garbage using the shortest route. If the location where the garbage was disposed is urban, the pickup unit can also change the pickup route taking into account traffic conditions. For example, if the location where the garbage was disposed is mountainous, the pickup unit optimizes the pickup route taking into account the terrain. The pickup unit can improve pickup efficiency by changing the pickup route taking into account geographic information of the location where the garbage was disposed. In this way, the pickup route can be optimized by taking into account geographic information. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input geographic information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the pickup route.

[0046] The pickup unit can change the pickup method taking into account traffic information for the location where the garbage was disposed. For example, the pickup unit changes the pickup method taking into account traffic information for the location where the garbage was disposed. The pickup unit improves pickup efficiency by changing the pickup method taking into account traffic information for the location where the garbage was disposed. For example, the pickup unit selects a detour route when traffic congestion occurs. The pickup unit can also pick up the garbage on a normal route when traffic conditions are good. For example, the pickup unit selects a detour route when a traffic accident occurs. The pickup unit can improve pickup efficiency by changing the pickup method taking into account traffic information for the location where the garbage was disposed. In this way, the pickup method can be optimized by taking traffic information into account. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input traffic information for the location where the garbage was disposed to the generation AI and cause the generation AI to change the pickup method.

[0047] The optimization unit can improve the optimization algorithm by referring to past collected data. The optimization unit, for example, adjusts parameters of the optimization algorithm based on the past collected data. The optimization unit can also improve the optimization algorithm by learning from past collected results. For example, the optimization unit stores past collected data in the cloud and improves the optimization algorithm in real time. The optimization unit improves the optimization algorithm by adjusting parameters of the optimization algorithm based on the past collected data. The optimization unit can also improve the optimization algorithm by learning from past collected results. For example, the optimization unit stores past collected data in the cloud and improves the optimization algorithm in real time. The optimization unit can improve the optimization algorithm by adjusting parameters of the optimization algorithm based on the past collected data. As a result, improving the optimization algorithm based on the past collected data improves collection efficiency. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past collected data to a generation AI and cause the generation AI to improve the optimization algorithm.

[0048] The optimization unit can perform optimization taking into account the cooperation between the drone and the garbage truck. For example, the optimization unit performs optimization taking into account the cooperation between the drone and the garbage truck. By performing optimization taking into account the cooperation between the drone and the garbage truck, the optimization unit maximizes collection efficiency. For example, the optimization unit shares position information between the drone and the garbage truck in real time and calculates an optimal route. The optimization unit can also transmit position information of garbage collected by the drone to the garbage truck to achieve efficient pickup. For example, the optimization unit strengthens the cooperation between the drone and the garbage truck to maximize collection efficiency. The optimization unit can maximize collection efficiency by performing optimization taking into account the cooperation between the drone and the garbage truck. As a result, collection efficiency is maximized by strengthening the cooperation between the drone and the garbage truck. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input cooperation data between the drone and the garbage truck to the generation AI and cause the generation AI to perform optimization.

[0049] The optimization unit can change the optimization algorithm taking into account geographic information of the location where the garbage was disposed. The optimization unit changes the optimization algorithm taking into account, for example, the geographic information of the location where the garbage was disposed. The optimization unit improves optimization efficiency by changing the optimization algorithm taking into account the geographic information of the location where the garbage was disposed. For example, if the location where the garbage was disposed is remote, the optimization unit collects the garbage via the shortest route. If the location where the garbage was disposed is in an urban area, the optimization unit can also change the optimization algorithm taking into account traffic conditions. For example, if the location where the garbage was disposed is in a mountainous area, the optimization unit adjusts the optimization algorithm taking into account the terrain. The optimization unit can improve optimization efficiency by changing the optimization algorithm taking into account the geographic information of the location where the garbage was disposed. In this way, the optimization algorithm can be adjusted by taking into account the geographic information. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input geographic information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the optimization algorithm.

[0050] The optimization unit can change the optimization method taking into account weather information of the location where the garbage was disposed. For example, the optimization unit changes the optimization method taking into account weather information of the location where the garbage was disposed. The optimization unit improves optimization efficiency by changing the optimization method taking into account weather information of the location where the garbage was disposed. For example, the optimization unit adopts an optimization method with waterproofing measures in rainy weather. The optimization unit can also adopt a normal optimization method in sunny weather. For example, the optimization unit selects and optimizes a route that is less slippery on snowy days. The optimization unit can improve optimization efficiency by changing the optimization method taking into account weather information of the location where the garbage was disposed. In this way, the optimization method can be adjusted by taking weather information into account. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input weather information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the optimization method.

[0051] The emergency signal unit can select the optimal signal transmission method taking into account the drone's location information. For example, the emergency signal unit selects the optimal signal transmission method taking into account the drone's location information. The emergency signal unit responds quickly by selecting the optimal signal transmission method taking into account the drone's location information. For example, the emergency signal unit selects the optimal communication means based on the drone's location information and transmits the emergency signal. If the drone is in a remote location, the emergency signal unit can also transmit the emergency signal using satellite communication. For example, if the drone is in an urban area, the emergency signal unit transmits the emergency signal using terrestrial communication infrastructure. The emergency signal unit can respond quickly by selecting the optimal signal transmission method taking into account the drone's location information. This allows for a quick response by selecting the optimal signal transmission method based on the drone's location information. Some or all of the above-described processing in the emergency signal unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency signal unit can input the drone's location information to a generation AI and cause the generation AI to select the optimal signal transmission method.

[0052] The emergency signal unit can change the signal transmission method taking into account the remaining battery level of the drone. For example, the emergency signal unit changes the signal transmission method taking into account the remaining battery level of the drone. The emergency signal unit responds quickly by changing the signal transmission method taking into account the remaining battery level of the drone. For example, when the remaining battery level of the drone is low, the emergency signal unit selects a method to transmit a signal in the shortest time. When the remaining battery level of the drone is sufficient, the emergency signal unit can also adopt a normal signal transmission method. For example, the emergency signal unit monitors the remaining battery level of the drone in real time and dynamically adjusts the signal transmission method. The emergency signal unit can respond quickly by changing the signal transmission method taking into account the remaining battery level of the drone. This allows for a quick response by adjusting the signal transmission method according to the remaining battery level of the drone. Some or all of the above-mentioned processing in the emergency signal unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency signal unit can input data on the remaining battery level of the drone to the generation AI and cause the generation AI to change the signal transmission method.

[0053] The cost management unit can optimize the cost management algorithm by referring to past operation data. The cost management unit, for example, adjusts parameters of the cost management algorithm based on the past operation data. The cost management unit can also improve the cost management algorithm by learning from past operation results. For example, the cost management unit stores past operation data in the cloud and improves the cost management algorithm in real time. The cost management unit optimizes the cost management algorithm by adjusting parameters of the cost management algorithm based on the past operation data. The cost management unit can also improve the cost management algorithm by learning from past operation results. For example, the cost management unit stores past operation data in the cloud and improves the cost management algorithm in real time. The cost management unit can optimize the cost management algorithm by adjusting parameters of the cost management algorithm based on the past operation data. This enables efficient cost management by optimizing the cost management algorithm based on the past operation data. Some or all of the above-described processing in the cost management unit may be performed using, for example, AI, or may be performed without using AI. For example, the cost management unit can input past operation data into a generation AI and cause the generation AI to optimize the cost management algorithm.

[0054] The cost management unit can change the cost management method taking into account the breakdown of operating costs. For example, the cost management unit changes the cost management method taking into account the breakdown of operating costs. By changing the cost management method taking into account the breakdown of operating costs, the cost management unit achieves efficient cost management. For example, the cost management unit analyzes the breakdown of operating costs and selects an optimal cost management method. The cost management unit can also adjust the cost management algorithm based on the breakdown of operating costs. For example, the cost management unit monitors the breakdown of operating costs in real time and dynamically changes the cost management method. By changing the cost management method taking into account the breakdown of operating costs, the cost management unit can achieve efficient cost management. As a result, efficient cost management is possible by adjusting the cost management method based on the breakdown of operating costs. Some or all of the above-mentioned processing in the cost management unit may be performed using, for example, AI, or may be performed without using AI. For example, the cost management unit can input operational cost breakdown data to a generation AI and have the generation AI execute a change to the cost management method.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The identification unit can not only determine the type of garbage, but also determine the condition of the garbage (for example, whether it is damaged or dirty). For example, the identification unit can determine the damage state of the garbage using image recognition technology. The identification unit can also determine the degree of dirtiness of the garbage using a sensor. For example, the identification unit can determine the degree of dirtiness of the garbage using an infrared sensor. This makes it possible to select an appropriate disposal method depending on the condition of the garbage. The identification unit can also distinguish between recyclable garbage and non-recyclable garbage based on the condition of the garbage. For example, the identification unit can prioritize collecting undamaged recyclable garbage. This improves recycling efficiency.

[0057] The collection unit can estimate not only the type of garbage but also the amount of garbage using image data acquired by the camera mounted on the drone. For example, the collection unit can estimate the amount of garbage using image analysis technology and optimize the collection plan. The collection unit can also adjust the collection frequency of the drone based on the amount of garbage. For example, the collection unit can increase the collection frequency if the amount of garbage is high. This can prevent garbage from overflowing. The collection unit can also optimize the collection route based on the amount of garbage. For example, the collection unit can prioritize collection in areas with high amounts of garbage. This can achieve efficient garbage collection.

[0058] The pickup unit can measure the weight of the garbage when picking up large pieces of garbage. For example, the pickup unit measures the weight of the garbage using a weight sensor mounted on the garbage collection truck. The pickup unit can also manage the load capacity of the collection truck based on the weight of the garbage. For example, the pickup unit stops picking up new garbage when the load capacity of the collection truck reaches its limit. This makes it possible to prevent the collection truck from being overloaded. The pickup unit can also optimize the collection route based on the weight of the garbage. For example, the pickup unit prioritizes picking up heavier garbage. This makes it possible to collect garbage efficiently.

[0059] The optimization unit can take weather information into account when optimizing the drone flight route and the garbage collection truck route. For example, the optimization unit changes the drone flight route when it is raining and adjusts the flight altitude when the wind speed is strong. The optimization unit can also take road conditions (e.g., whether the road is icy) into account when optimizing the garbage collection truck route. For example, the optimization unit selects a route that avoids icy roads. This enables safe and efficient garbage collection. The optimization unit can also adjust the collection schedule based on weather information. For example, the optimization unit delays the collection schedule when the weather is bad. This improves the safety of collection work.

[0060] When a drone falls, the collection department not only sends an emergency signal, but also tracks the drone's location information in real time. For example, the collection department uses GPS to obtain the drone's location information and identify the point where it fell. The collection department can also quickly carry out recovery operations based on the drone's location information. For example, the collection department can dispatch a recovery team closest to the point where the drone fell. This allows the drone to be recovered quickly. The collection department can also analyze the cause of the drone's fall and take measures to prevent recurrence. For example, the collection department can analyze the drone's sensor data and identify the cause of the fall. This improves drone safety.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The identification unit determines the type of garbage. Types of garbage include, for example, burnable garbage, non-burnable garbage, and recyclable garbage. The identification unit determines the type and material of the garbage using image recognition technology and sensors. For example, the material of the garbage can be determined using an infrared sensor. Step 2: The collection unit collects the trash identified by the identification unit. The collection unit collects small trash using, for example, a drone and acquires location information of the trash. The collection unit can acquire location information of the trash using GPS. Step 3: The pickup unit uses the location information of the garbage collected by the collection unit to pick up the garbage. The pickup unit can use the drone's location information when the garbage collection truck picks up large pieces of garbage. Step 4: The optimization unit optimizes the routes of the collection and pickup units. The optimization unit uses AI to optimize the drone flight route and the garbage collection truck route. For example, it optimizes the route by taking into account the drone's flight altitude and speed, traffic conditions, and road width.

[0063] (Example 2) A garbage collection system according to an embodiment of the present invention efficiently collects garbage from aging households. In this garbage collection system, drones directly collect small garbage items, while garbage trucks pick up large garbage items based on the drone's location information. The entire process is managed by AI, ensuring efficient garbage collection. First, an aging household deposits its garbage at a designated location. Next, a drone flies to the location and collects the small garbage items. After collecting the garbage, the drone transports it to a designated garbage collection site. Meanwhile, for large garbage items, the drone collects their location information and transmits it to the garbage truck. The garbage truck then picks up the large garbage items based on that information. The entire process is managed by AI. AI optimizes the drone's flight route and the garbage truck's route, ensuring efficient garbage collection. Even if a drone falls, since it is only collecting garbage, there is minimal damage, making practical testing possible for drone development. Operating costs are covered by local government expenses and by placing advertisements for industrial waste disposal companies on the drone, allowing users to use the system without significant burden. This enables the garbage collection system to efficiently collect garbage from aging households, reducing operational costs while achieving fast and accurate garbage collection.

[0064] A garbage collection system according to an embodiment includes an identification unit, a collection unit, a pickup unit, and an optimization unit. The identification unit determines the type of garbage. Examples of garbage types include, but are not limited to, combustible garbage, non-combustible garbage, and recyclable garbage. The identification unit determines the type of garbage using, for example, image recognition technology. The identification unit can also determine the material of the garbage using a sensor. For example, the identification unit can determine the material of the garbage using an infrared sensor. The collection unit collects the garbage determined by the identification unit. The collection unit collects small garbage using, for example, a drone. The collection unit can also acquire location information of the garbage when the drone collects the garbage. For example, the collection unit acquires location information of the garbage using GPS. The pickup unit allows a garbage collection truck to pick up the garbage based on the location information collected by the collection unit. For example, the pickup unit uses the location information of the drone when the garbage collection truck picks up large garbage. The pickup unit can also optimize the route for the garbage collection truck to efficiently pick up garbage. The optimization unit optimizes the routes of the collection unit and the pickup unit. The optimization unit, for example, uses AI to optimize the drone's flight route and the garbage collection truck's route. The optimization unit can take into account flight altitude and flight speed when optimizing the drone's flight route. For example, the optimization unit can achieve efficient garbage collection by optimizing the drone's flight altitude. The optimization unit can take into account traffic conditions and road width when optimizing the garbage collection truck's route. For example, the optimization unit can achieve efficient garbage collection by selecting a route that avoids traffic congestion. This allows the garbage collection system according to the embodiment to determine the type of garbage and efficiently collect and pick it up.

[0065] The collection unit can collect small trash using a drone. Examples of small trash include, but are not limited to, plastic bottles, cans, and paper waste. The collection unit collects small trash using, for example, a drone. For example, the collection unit can acquire location information of the trash when the drone collects the trash. The collection unit can also identify the type of trash when the drone collects the trash. For example, the collection unit can identify the type of trash using a camera mounted on the drone. The collection unit can also measure the weight of the trash when the drone collects the trash. For example, the collection unit can measure the weight of the trash using a sensor mounted on the drone. This allows the drone to efficiently collect small trash. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input image data acquired by a camera mounted on the drone into a generation AI and have the generation AI identify the type of trash.

[0066] The pickup unit enables the garbage truck to pick up oversized garbage based on the drone's location information. Examples of oversized garbage include, but are not limited to, furniture, home appliances, and large plastic products. For example, the pickup unit uses the drone's location information when the garbage truck picks up oversized garbage. The pickup unit can also optimize the route for the garbage truck to efficiently pick up garbage. For example, the pickup unit selects the shortest route when the garbage truck picks up oversized garbage. The pickup unit can also consider traffic conditions when the garbage truck picks up oversized garbage. For example, the pickup unit selects a route that avoids traffic congestion. The pickup unit can also identify the type of garbage when the garbage truck picks up oversized garbage. For example, the pickup unit identifies the type of garbage using a camera mounted on the garbage truck. This allows the drone's location information to be used to efficiently pick up oversized garbage. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input the drone's location information into the generation AI and have the generation AI optimize the garbage collection truck's route.

[0067] The optimization unit can optimize the drone's flight route and the garbage truck's route. The optimization unit, for example, uses AI to optimize the drone's flight route and the garbage truck's route. The optimization unit can consider the flight altitude and flight speed when optimizing the drone's flight route. For example, the optimization unit realizes efficient garbage collection by optimizing the drone's flight altitude. The optimization unit can also consider the flight path when optimizing the drone's flight route. For example, the optimization unit realizes efficient garbage collection by optimizing the drone's flight path. The optimization unit can consider traffic conditions and road width when optimizing the garbage truck's route. For example, the optimization unit realizes efficient garbage collection by selecting a route that avoids traffic congestion. The optimization unit can also consider the order of collection points when optimizing the garbage truck's route. For example, the optimization unit realizes efficient garbage collection by optimizing the order of collection points. As a result, efficient garbage collection is possible by optimizing the routes of the drone and the garbage truck. Some or all of the above-described processing in the optimization unit may be performed, for example, using AI or may be performed without using AI. For example, the optimization unit can cause the generation AI to optimize drone flight routes and garbage collection truck routes.

[0068] The collection unit may include an emergency signal unit that transmits an emergency signal when the drone falls. Emergency signals include, but are not limited to, radio signals, internet signals, and satellite signals. For example, the collection unit transmits a radio signal when the drone falls. The collection unit can also transmit an internet signal when the drone falls. For example, the collection unit transmits an emergency signal via the internet when the drone falls. The collection unit can also transmit a satellite signal when the drone falls. For example, the collection unit transmits an emergency signal via a satellite when the drone falls. This allows for a quick response when the drone falls. Some or all of the above-described processing in the emergency signal unit may be performed using, or without, AI. For example, the emergency signal unit may input data from a sensor that detects the drone falling into the generation AI and cause the generation AI to transmit an emergency signal.

[0069] The optimization unit may include a cost management unit that manages operating costs. Operating costs include, but are not limited to, fuel costs, labor costs, and maintenance costs. For example, the optimization unit manages fuel costs to manage operating costs. The optimization unit can also manage labor costs to manage operating costs. For example, the optimization unit optimizes operating costs by managing labor costs. The optimization unit can also manage maintenance costs to manage operating costs. For example, the optimization unit optimizes operating costs by managing maintenance costs. This allows for efficient management of operating costs. Some or all of the above-described processing in the cost management unit may be performed using AI, or may be performed without using AI. For example, the cost management unit may input operating cost data into the generation AI and cause the generation AI to optimize cost management.

[0070] The identification unit can estimate the user's emotions and adjust the accuracy of dust identification based on the estimated user's emotions. The identification unit can use, for example, facial expression recognition technology to estimate the user's emotions. The identification unit can also use voice analysis technology to estimate the user's emotions. For example, the identification unit can analyze the tone and speed of the user's voice and calculate an emotion score. The identification unit can also use survey results to estimate the user's emotions. For example, the identification unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The identification unit can adjust the accuracy of dust identification based on the estimated user's emotions. For example, if the user is stressed, the identification unit can increase the accuracy of identification to reduce false recognition. If the user is relaxed, the identification unit can maintain normal identification accuracy and prioritize processing speed. For example, if the user is in a hurry, the identification unit can increase the accuracy of identification to quickly identify the type of dust. In this way, false recognition can be reduced by adjusting the accuracy of dust identification according to the user's emotions. Some or all of the above-mentioned processing in the identification unit can be performed using, for example, AI, or without AI. For example, the identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the accuracy of dust identification.

[0071] The identification unit can optimize the identification algorithm based on the material and shape of the garbage. For example, the identification unit uses the reflectance characteristics of the material to identify the material of the garbage. The identification unit can also use 3D scanning technology to identify the shape of the garbage. For example, the identification unit 3D scans the shape of the garbage and adjusts the identification algorithm based on the shape data. The identification unit can also use image analysis technology to identify the color and texture of the garbage. For example, the identification unit analyzes the color and texture of the garbage and applies a identification algorithm according to the material. The identification unit improves identification accuracy by optimizing the identification algorithm based on the material and shape of the garbage. For example, the identification unit uses the reflectance characteristics of the material to distinguish between plastic products and metal products. The identification unit can 3D scan the shape of the garbage and adjust the identification algorithm based on the shape data. The identification unit can also analyze the color and texture of the garbage and apply a identification algorithm according to the material. This improves identification accuracy by applying a identification algorithm according to the material and shape of the garbage. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the material and shape of the garbage into the generation AI and have the generation AI optimize the identification algorithm.

[0072] The identification unit can improve the identification accuracy by referring to past identification data. The identification unit, for example, adjusts parameters of the identification algorithm based on the past identification data. The identification unit can also improve the identification accuracy by learning from past identification results. For example, the identification unit stores past identification data in the cloud and improves the identification accuracy in real time. The identification unit improves the identification accuracy by adjusting parameters of the identification algorithm based on the past identification data. The identification unit can also improve the identification accuracy by learning from past identification results. For example, the identification unit stores past identification data in the cloud and improves the identification accuracy in real time. The identification unit can improve the identification accuracy by adjusting parameters of the identification algorithm based on the past identification data. This makes it possible to improve the identification accuracy based on the past identification data. Some or all of the above-described processing in the identification unit may be performed using AI, for example, or may be performed without using AI. For example, the identification unit can input past identification data to a generation AI and cause the generation AI to optimize the identification algorithm.

[0073] The identification unit can estimate the user's emotion and adjust the display method of the identification result based on the estimated user's emotion. The identification unit, for example, uses facial expression recognition technology to estimate the user's emotion. The identification unit can also use voice analysis technology to estimate the user's emotion. For example, the identification unit can analyze the tone and speed of the user's voice and calculate an emotion score. The identification unit can also use survey results to estimate the user's emotion. For example, the identification unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The identification unit adjusts the display method of the identification result based on the estimated user's emotion. For example, if the user is nervous, the identification unit can provide a simple, highly visible display method. If the user is relaxed, the identification unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the identification unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the identification result according to the user's emotion. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the identification results.

[0074] The identification unit can change the identification algorithm based on the time period when the garbage was disposed of. The identification unit changes the identification algorithm based on, for example, the time period when the garbage was disposed of. The identification unit improves identification accuracy by changing the identification algorithm based on the time period when the garbage was disposed of. For example, the identification unit prioritizes an identification algorithm for household garbage in the morning. The identification unit can also apply an identification algorithm for garbage disposed of by commercial facilities at night. For example, the identification unit strengthens an identification algorithm for large garbage on weekends. The identification unit can improve identification accuracy by changing the identification algorithm based on the time period when the garbage was disposed of. In this way, identification accuracy is improved by applying an identification algorithm according to the time period. Some or all of the above-described processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input data on the time period when the garbage was disposed of to the generation AI and cause the generation AI to change the identification algorithm.

[0075] The identification unit can improve the identification accuracy based on environmental information of the location where the garbage was disposed. The identification unit adjusts the identification algorithm taking into account, for example, the temperature and humidity of the location where the garbage was disposed. The identification unit can also improve the identification accuracy taking into account the lighting conditions of the location where the garbage was disposed. For example, the identification unit optimizes the identification algorithm taking into account the noise level of the location where the garbage was disposed. The identification unit improves the identification accuracy by adjusting the identification algorithm taking into account the temperature and humidity of the location where the garbage was disposed. The identification unit can also improve the identification accuracy taking into account the lighting conditions of the location where the garbage was disposed. For example, the identification unit optimizes the identification algorithm taking into account the noise level of the location where the garbage was disposed. The identification unit can improve the identification accuracy by improving the identification accuracy based on environmental information of the location where the garbage was disposed. In this way, the identification accuracy is improved by taking environmental information into account. Some or all of the above-mentioned processing in the identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the identification unit can input environmental information of the location where the garbage was disposed to the generation AI and cause the generation AI to optimize the identification algorithm.

[0076] The collection unit can estimate the user's emotions and adjust the timing of garbage collection based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. The collection unit can also use voice analysis technology to estimate the user's emotions. For example, the collection unit can analyze the tone and speed of the user's voice to calculate an emotion score. The collection unit can also use survey results to estimate the user's emotions. For example, the collection unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The collection unit can adjust the timing of garbage collection based on the estimated user's emotions. For example, if the user is stressed, the collection unit can speed up the collection timing to collect garbage quickly. The collection unit can also maintain the normal collection timing when the user is relaxed. For example, if the user is in a hurry, the collection unit can adjust the collection timing to collect garbage quickly. In this way, garbage can be collected quickly by adjusting the collection timing according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI adjust the collection timing.

[0077] The collection unit can change the collection method depending on the type of garbage. The collection unit changes the collection method depending on, for example, the type of garbage. The collection unit improves collection efficiency by changing the collection method depending on the type of garbage. For example, the collection unit collects plastic garbage by putting it in a dedicated collection bag. The collection unit can also collect metal garbage using magnetic force. For example, the collection unit collects organic garbage by putting it in a dedicated container. The collection unit can improve collection efficiency by changing the collection method depending on the type of garbage. In this way, collection efficiency is improved by applying a collection method depending on the type of garbage. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the type of garbage into the generation AI and cause the generation AI to change the collection method.

[0078] The collection unit can optimize the collection route taking into account the remaining battery level of the drone. For example, the collection unit optimizes the collection route taking into account the remaining battery level of the drone. By optimizing the collection route taking into account the remaining battery level of the drone, the collection unit achieves efficient garbage collection. For example, when the drone's remaining battery level is low, the collection unit collects garbage via the shortest route. When the drone's remaining battery level is sufficient, the collection unit can also collect garbage via an efficient route. For example, the collection unit monitors the remaining battery level of the drone in real time and dynamically adjusts the collection route. By optimizing the collection route taking into account the remaining battery level of the drone, efficient garbage collection can be achieved. As a result, efficient garbage collection is possible by optimizing the collection route according to the remaining battery level of the drone. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the remaining battery level of the drone to the generation AI and cause the generation AI to optimize the collection route.

[0079] The collection unit can estimate the user's emotions and determine the priority of trash to be collected based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. The collection unit can also use voice analysis technology to estimate the user's emotions. For example, the collection unit can analyze the tone and speed of the user's voice and calculate an emotion score. The collection unit can also use survey results to estimate the user's emotions. For example, the collection unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The collection unit determines the priority of trash to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize trash collection. If the user is relaxed, the collection unit can also prioritize trash collection. For example, if the user is in a hurry, the collection unit can prioritize trash collection. In this way, by determining the priority of trash according to the user's emotions, important trash can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of garbage.

[0080] The collection unit can change the collection route taking into account geographic information of the location where the garbage was disposed. The collection unit, for example, changes the collection route taking into account geographic information of the location where the garbage was disposed. The collection unit improves collection efficiency by changing the collection route taking into account geographic information of the location where the garbage was disposed. For example, if the location where the garbage was disposed is remote, the collection unit collects the garbage via the shortest route. If the location where the garbage was disposed is urban, the collection unit can also change the collection route taking into account traffic conditions. For example, if the location where the garbage was disposed is mountainous, the collection unit optimizes the collection route taking into account the terrain. The collection unit can improve collection efficiency by changing the collection route taking into account geographic information of the location where the garbage was disposed. In this way, the collection route can be optimized by taking into account geographic information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographic information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the collection route.

[0081] The collection unit can change the collection method taking into account weather information for the location where the garbage was disposed. For example, the collection unit changes the collection method taking into account weather information for the location where the garbage was disposed. The collection unit improves collection efficiency by changing the collection method taking into account weather information for the location where the garbage was disposed. For example, the collection unit employs a waterproof collection method when it is raining. The collection unit can also employ a normal collection method when it is sunny. For example, the collection unit selects a less slippery route for collection on snowy days. The collection unit can improve collection efficiency by changing the collection method taking into account weather information for the location where the garbage was disposed. In this way, the collection method can be optimized by taking weather information into account. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input weather information for the location where the garbage was disposed into the generation AI and cause the generation AI to change the collection method.

[0082] The pickup unit can estimate the user's emotions and adjust the timing of pickup based on the estimated user's emotions. The pickup unit, for example, uses facial expression recognition technology to estimate the user's emotions. The pickup unit can also use voice analysis technology to estimate the user's emotions. For example, the pickup unit can analyze the tone and speed of the user's voice and calculate an emotion score. The pickup unit can also use survey results to estimate the user's emotions. For example, the pickup unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The pickup unit adjusts the timing of pickup based on the estimated user's emotions. For example, if the user is feeling stressed, the pickup unit can advance the timing of pickup. If the user is relaxed, the pickup unit can also maintain the normal pickup timing. For example, if the user is in a hurry, the pickup unit adjusts the timing of pickup to quickly collect the trash. As a result, adjusting the timing of pickup according to the user's emotions allows for quick trash collection. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the pickup timing.

[0083] The pickup unit can change the pickup method depending on the type of waste. For example, the pickup unit changes the pickup method depending on the type of waste. By changing the pickup method depending on the type of waste, the pickup unit improves pickup efficiency. For example, the pickup unit uses a dedicated crane to pick up large metal waste. The pickup unit can also pick up large plastic waste by placing it in a dedicated container. For example, the pickup unit picks up large organic waste with a dedicated truck. The pickup unit can improve pickup efficiency by changing the pickup method depending on the type of waste. In this way, pickup efficiency is improved by applying a pickup method depending on the type of waste. Some or all of the above-mentioned processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input data on the type of waste into the generation AI and cause the generation AI to change the pickup method.

[0084] The pickup unit can optimize the pickup route by taking into account the remaining fuel level of the garbage truck. The pickup unit, for example, optimizes the pickup route by taking into account the remaining fuel level of the garbage truck. The pickup unit achieves efficient garbage collection by optimizing the pickup route by taking into account the remaining fuel level of the garbage truck. For example, when the garbage truck has low fuel level, the pickup unit picks up the garbage truck via the shortest route. When the garbage truck has sufficient fuel level, the pickup unit can also pick up the garbage truck via an efficient route. For example, the pickup unit monitors the remaining fuel level of the garbage truck in real time and dynamically adjusts the pickup route. The pickup unit can achieve efficient garbage collection by optimizing the pickup route by taking into account the remaining fuel level of the garbage truck. As a result, efficient garbage collection is possible by optimizing the pickup route according to the remaining fuel level of the garbage truck. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit may input data on the remaining fuel level of the garbage truck into the generation AI and cause the generation AI to optimize the pickup route.

[0085] The pickup unit can estimate the user's emotions and determine the priority of trash to be picked up based on the estimated user's emotions. The pickup unit can use, for example, facial expression recognition technology to estimate the user's emotions. The pickup unit can also use voice analysis technology to estimate the user's emotions. For example, the pickup unit can analyze the tone and speed of the user's voice to calculate an emotion score. The pickup unit can also use survey results to estimate the user's emotions. For example, the pickup unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The pickup unit can determine the priority of trash to be picked up based on the estimated user's emotions. For example, if the user is feeling stressed, the pickup unit can prioritize picking up trash. If the user is relaxed, the pickup unit can also prioritize picking up trash. For example, if the user is in a hurry, the pickup unit can prioritize picking up trash. By determining the priority of trash according to the user's emotions, important trash can be collected preferentially. Some or all of the above-described processing in the pickup unit can be performed using, for example, AI, or without AI. For example, the pickup unit can input the user's emotional data into the generation AI and have the generation AI determine the priority of the garbage.

[0086] The pickup unit can change the pickup route taking into account geographic information of the location where the garbage was disposed. The pickup unit, for example, changes the pickup route taking into account geographic information of the location where the garbage was disposed. The pickup unit improves pickup efficiency by changing the pickup route taking into account geographic information of the location where the garbage was disposed. For example, if the location where the garbage was disposed is remote, the pickup unit picks up the garbage using the shortest route. If the location where the garbage was disposed is urban, the pickup unit can also change the pickup route taking into account traffic conditions. For example, if the location where the garbage was disposed is mountainous, the pickup unit optimizes the pickup route taking into account the terrain. The pickup unit can improve pickup efficiency by changing the pickup route taking into account geographic information of the location where the garbage was disposed. In this way, the pickup route can be optimized by taking into account geographic information. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input geographic information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the pickup route.

[0087] The pickup unit can change the pickup method taking into account traffic information for the location where the garbage was disposed. For example, the pickup unit changes the pickup method taking into account traffic information for the location where the garbage was disposed. The pickup unit improves pickup efficiency by changing the pickup method taking into account traffic information for the location where the garbage was disposed. For example, the pickup unit selects a detour route when traffic congestion occurs. The pickup unit can also pick up the garbage on a normal route when traffic conditions are good. For example, the pickup unit selects a detour route when a traffic accident occurs. The pickup unit can improve pickup efficiency by changing the pickup method taking into account traffic information for the location where the garbage was disposed. In this way, the pickup method can be optimized by taking traffic information into account. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input traffic information for the location where the garbage was disposed to the generation AI and cause the generation AI to change the pickup method.

[0088] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated user's emotions. The optimization unit, for example, uses facial expression recognition technology to estimate the user's emotions. The optimization unit can also use voice analysis technology to estimate the user's emotions. For example, the optimization unit can analyze the tone and speed of the user's voice and calculate an emotion score. The optimization unit can also use survey results to estimate the user's emotions. For example, the optimization unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The optimization unit can adjust the optimization algorithm based on the estimated user's emotions. For example, if the user is stressed, the optimization unit can adjust the optimization algorithm to quickly collect trash. If the user is relaxed, the optimization unit can also apply a normal optimization algorithm. For example, if the user is in a hurry, the optimization unit can adjust the optimization algorithm to quickly collect trash. In this way, by adjusting the optimization algorithm according to the user's emotions, trash can be quickly collected. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input user emotion data into the generation AI and cause the generation AI to adjust the optimization algorithm.

[0089] The optimization unit can improve the optimization algorithm by referring to past collected data. The optimization unit, for example, adjusts parameters of the optimization algorithm based on the past collected data. The optimization unit can also improve the optimization algorithm by learning from past collected results. For example, the optimization unit stores past collected data in the cloud and improves the optimization algorithm in real time. The optimization unit improves the optimization algorithm by adjusting parameters of the optimization algorithm based on the past collected data. The optimization unit can also improve the optimization algorithm by learning from past collected results. For example, the optimization unit stores past collected data in the cloud and improves the optimization algorithm in real time. The optimization unit can improve the optimization algorithm by adjusting parameters of the optimization algorithm based on the past collected data. As a result, improving the optimization algorithm based on the past collected data improves collection efficiency. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past collected data to a generation AI and cause the generation AI to improve the optimization algorithm.

[0090] The optimization unit can perform optimization taking into account the cooperation between the drone and the garbage truck. For example, the optimization unit performs optimization taking into account the cooperation between the drone and the garbage truck. By performing optimization taking into account the cooperation between the drone and the garbage truck, the optimization unit maximizes collection efficiency. For example, the optimization unit shares position information between the drone and the garbage truck in real time and calculates an optimal route. The optimization unit can also transmit position information of garbage collected by the drone to the garbage truck to achieve efficient pickup. For example, the optimization unit strengthens the cooperation between the drone and the garbage truck to maximize collection efficiency. The optimization unit can maximize collection efficiency by performing optimization taking into account the cooperation between the drone and the garbage truck. As a result, collection efficiency is maximized by strengthening the cooperation between the drone and the garbage truck. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input cooperation data between the drone and the garbage truck to the generation AI and cause the generation AI to perform optimization.

[0091] The optimization unit can estimate the user's emotions and adjust the display method of the optimization results based on the estimated user's emotions. The optimization unit can use, for example, facial expression recognition technology to estimate the user's emotions. The optimization unit can also use voice analysis technology to estimate the user's emotions. For example, the optimization unit can analyze the tone and speed of the user's voice and calculate an emotion score. The optimization unit can also use survey results to estimate the user's emotions. For example, the optimization unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The optimization unit can adjust the display method of the optimization results based on the estimated user's emotions. For example, if the user is nervous, the optimization unit can provide a simple, highly visible display method. If the user is relaxed, the optimization unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the optimization unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method of the optimization results according to the user's emotions. Some or all of the above-mentioned processing in the optimization unit can be performed using, for example, AI, or without AI. For example, the optimization unit can input user emotion data into the generation AI and have the generation AI adjust the display method of the optimization results.

[0092] The optimization unit can change the optimization algorithm taking into account geographic information of the location where the garbage was disposed. The optimization unit changes the optimization algorithm taking into account, for example, the geographic information of the location where the garbage was disposed. The optimization unit improves optimization efficiency by changing the optimization algorithm taking into account the geographic information of the location where the garbage was disposed. For example, if the location where the garbage was disposed is remote, the optimization unit collects the garbage via the shortest route. If the location where the garbage was disposed is in an urban area, the optimization unit can also change the optimization algorithm taking into account traffic conditions. For example, if the location where the garbage was disposed is in a mountainous area, the optimization unit adjusts the optimization algorithm taking into account the terrain. The optimization unit can improve optimization efficiency by changing the optimization algorithm taking into account the geographic information of the location where the garbage was disposed. In this way, the optimization algorithm can be adjusted by taking into account the geographic information. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input geographic information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the optimization algorithm.

[0093] The optimization unit can change the optimization method taking into account weather information of the location where the garbage was disposed. For example, the optimization unit changes the optimization method taking into account weather information of the location where the garbage was disposed. The optimization unit improves optimization efficiency by changing the optimization method taking into account weather information of the location where the garbage was disposed. For example, the optimization unit adopts an optimization method with waterproofing measures in rainy weather. The optimization unit can also adopt a normal optimization method in sunny weather. For example, the optimization unit selects and optimizes a route that is less slippery on snowy days. The optimization unit can improve optimization efficiency by changing the optimization method taking into account weather information of the location where the garbage was disposed. In this way, the optimization method can be adjusted by taking weather information into account. Some or all of the above-mentioned processing in the optimization unit may be performed using AI, for example, or may be performed without using AI. For example, the optimization unit can input weather information of the location where the garbage was disposed to the generation AI and cause the generation AI to change the optimization method.

[0094] The emergency signal unit can estimate the user's emotions and adjust the timing of transmitting an emergency signal based on the estimated user's emotions. The emergency signal unit can use, for example, facial expression recognition technology to estimate the user's emotions. The emergency signal unit can also use voice analysis technology to estimate the user's emotions. For example, the emergency signal unit can analyze the tone and speed of the user's voice and calculate an emotion score. The emergency signal unit can also use survey results to estimate the user's emotions. For example, the emergency signal unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The emergency signal unit can adjust the timing of transmitting an emergency signal based on the estimated user's emotions. For example, if the user is stressed, the emergency signal unit can advance the timing of transmitting an emergency signal. If the user is relaxed, the emergency signal unit can maintain the normal timing of transmitting an emergency signal. For example, if the user is in a hurry, the emergency signal unit can adjust the timing of transmitting an emergency signal to respond quickly. This allows for a quick response by adjusting the timing of transmitting an emergency signal according to the user's emotions. Some or all of the above-described processing in the emergency signal unit can be performed, for example, using AI or without AI. For example, the emergency signal unit can input the user's emotional data into the generation AI and have the generation AI adjust the timing of sending the emergency signal.

[0095] The emergency signal unit can select the optimal signal transmission method taking into account the drone's location information. For example, the emergency signal unit selects the optimal signal transmission method taking into account the drone's location information. The emergency signal unit responds quickly by selecting the optimal signal transmission method taking into account the drone's location information. For example, the emergency signal unit selects the optimal communication means based on the drone's location information and transmits the emergency signal. If the drone is in a remote location, the emergency signal unit can also transmit the emergency signal using satellite communication. For example, if the drone is in an urban area, the emergency signal unit transmits the emergency signal using terrestrial communication infrastructure. The emergency signal unit can respond quickly by selecting the optimal signal transmission method taking into account the drone's location information. This allows for a quick response by selecting the optimal signal transmission method based on the drone's location information. Some or all of the above-described processing in the emergency signal unit may be performed using, for example, AI, or may be performed without using AI. For example, the emergency signal unit can input the drone's location information to a generation AI and cause the generation AI to select the optimal signal transmission method.

[0096] The emergency signal unit can estimate the user's emotions and determine the priority of emergency signals based on the estimated user emotions. The emergency signal unit, for example, uses facial expression recognition technology to estimate the user's emotions. The emergency signal unit can also use voice analysis technology to estimate the user's emotions. For example, the emergency signal unit can analyze the tone and speed of the user's voice and calculate an emotion score. The emergency signal unit can also use survey results to estimate the user's emotions. For example, the emergency signal unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The emergency signal unit can determine the priority of emergency signals based on the estimated user emotions. For example, if the user is feeling stressed, the emergency signal unit can increase the priority of emergency signals. If the user is relaxed, the emergency signal unit can also transmit emergency signals with normal priority. For example, if the user is in a hurry, the emergency signal unit can adjust the priority of emergency signals to respond quickly. This allows for a quick response by determining the priority of emergency signals according to the user's emotions. Some or all of the above-described processing in the emergency signal unit can be performed using, for example, AI, or without AI. For example, the emergency signal unit can input user emotion data into the generation AI and have the generation AI determine the priority of emergency signals.

[0097] The emergency signal unit can change the signal transmission method taking into account the remaining battery level of the drone. For example, the emergency signal unit changes the signal transmission method taking into account the remaining battery level of the drone. The emergency signal unit responds quickly by changing the signal transmission method taking into account the remaining battery level of the drone. For example, when the remaining battery level of the drone is low, the emergency signal unit selects a method to transmit a signal in the shortest time. When the remaining battery level of the drone is sufficient, the emergency signal unit can also adopt a normal signal transmission method. For example, the emergency signal unit monitors the remaining battery level of the drone in real time and dynamically adjusts the signal transmission method. The emergency signal unit can respond quickly by changing the signal transmission method taking into account the remaining battery level of the drone. This allows for a quick response by adjusting the signal transmission method according to the remaining battery level of the drone. Some or all of the above-mentioned processing in the emergency signal unit may be performed using AI, for example, or may be performed without using AI. For example, the emergency signal unit can input data on the remaining battery level of the drone to the generation AI and cause the generation AI to change the signal transmission method.

[0098] The expense management unit can estimate the user's emotions and adjust the expense management method based on the estimated user's emotions. The expense management unit, for example, uses facial expression recognition technology to estimate the user's emotions. The expense management unit can also use voice analysis technology to estimate the user's emotions. For example, the expense management unit can analyze the tone and speed of the user's voice to calculate an emotion score. The expense management unit can also use survey results to estimate the user's emotions. For example, the expense management unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The expense management unit can adjust the expense management method based on the estimated user's emotions. For example, if the user is feeling stressed, the expense management unit can simplify the expense management method to reduce the burden. If the user is relaxed, the expense management unit can also provide a detailed expense management method. For example, if the user is in a hurry, the expense management unit can provide a method for quick expense management. This can reduce the burden by adjusting the expense management method according to the user's emotions. Some or all of the above-mentioned processing in the expense management unit can be performed using, for example, AI, or without AI. For example, the expense management unit can input the user's emotional data into the generation AI and have the generation AI adjust the expense management method.

[0099] The cost management unit can optimize the cost management algorithm by referring to past operation data. The cost management unit, for example, adjusts parameters of the cost management algorithm based on the past operation data. The cost management unit can also improve the cost management algorithm by learning from past operation results. For example, the cost management unit stores past operation data in the cloud and improves the cost management algorithm in real time. The cost management unit optimizes the cost management algorithm by adjusting parameters of the cost management algorithm based on the past operation data. The cost management unit can also improve the cost management algorithm by learning from past operation results. For example, the cost management unit stores past operation data in the cloud and improves the cost management algorithm in real time. The cost management unit can optimize the cost management algorithm by adjusting parameters of the cost management algorithm based on the past operation data. This enables efficient cost management by optimizing the cost management algorithm based on the past operation data. Some or all of the above-described processing in the cost management unit may be performed using, for example, AI, or may be performed without using AI. For example, the cost management unit can input past operation data into a generation AI and cause the generation AI to optimize the cost management algorithm.

[0100] The expense management unit can estimate the user's emotions and determine expense management priorities based on the estimated user emotions. The expense management unit, for example, uses facial expression recognition technology to estimate the user's emotions. The expense management unit can also use voice analysis technology to estimate the user's emotions. For example, the expense management unit can analyze the tone and speed of the user's voice to calculate an emotion score. The expense management unit can also use survey results to estimate the user's emotions. For example, the expense management unit can conduct a survey on the user's emotions and estimate the emotion based on the results. The expense management unit determines expense management priorities based on the estimated user emotions. For example, if the user is feeling stressed, the expense management unit can increase the expense management priority. If the user is relaxed, the expense management unit can also perform expense management at a normal priority. For example, if the user is in a hurry, the expense management unit can adjust the expense management priority to respond quickly. This allows for a quick response by determining expense management priorities according to the user's emotions. Some or all of the above-described processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input user emotion data into the generation AI and have the generation AI determine expense management priorities.

[0101] The cost management unit can change the cost management method taking into account the breakdown of operating costs. For example, the cost management unit changes the cost management method taking into account the breakdown of operating costs. By changing the cost management method taking into account the breakdown of operating costs, the cost management unit achieves efficient cost management. For example, the cost management unit analyzes the breakdown of operating costs and selects an optimal cost management method. The cost management unit can also adjust the cost management algorithm based on the breakdown of operating costs. For example, the cost management unit monitors the breakdown of operating costs in real time and dynamically changes the cost management method. By changing the cost management method taking into account the breakdown of operating costs, the cost management unit can achieve efficient cost management. As a result, efficient cost management is possible by adjusting the cost management method based on the breakdown of operating costs. Some or all of the above-mentioned processing in the cost management unit may be performed using, for example, AI, or may be performed without using AI. For example, the cost management unit can input operational cost breakdown data to a generation AI and have the generation AI execute a change to the cost management method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned identification unit, collection unit, pickup unit, and optimization unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the identification unit determines the type of garbage using the camera 42 or sensors of the smart device 14 and processes the information using the control unit 46A. The collection unit controls a drone using the control unit 46A of the smart device 14 to collect small garbage. The pickup unit optimizes the route of the garbage collection truck using the specific processing unit 290 of the data processing device 12 and picks up large garbage based on the drone's position information. The optimization unit optimizes the flight route of the drone and the route of the garbage collection truck using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned identification unit, collection unit, pickup unit, and optimization unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the identification unit determines the type of garbage using the camera 42 or a sensor of the smart glasses 214 and processes the information using the control unit 46A. The collection unit controls, for example, a drone using the control unit 46A of the smart glasses 214 to collect small garbage. The pickup unit optimizes the route of the garbage collection truck using, for example, the specific processing unit 290 of the data processing device 12 and picks up large garbage based on the drone's position information. The optimization unit optimizes the flight route of the drone and the route of the garbage collection truck using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned identification unit, collection unit, pickup unit, and optimization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the identification unit determines the type of garbage using the camera 42 or a sensor of the headset-type terminal 314 and processes the information by the control unit 46A. For example, the collection unit controls a drone by the control unit 46A of the headset-type terminal 314 to collect small garbage. For example, the pickup unit optimizes the route of the garbage collection truck by the specific processing unit 290 of the data processing device 12 and picks up large garbage based on the drone's position information. For example, the optimization unit optimizes the flight route of the drone and the route of the garbage collection truck by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned identification unit, collection unit, pickup unit, and optimization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the identification unit determines the type of garbage using the camera 42 or sensors of the robot 414 and processes the information using the control unit 46A. The collection unit controls, for example, a drone using the control unit 46A of the robot 414 to collect small garbage. The pickup unit optimizes the route of the garbage collection truck using, for example, the specific processing unit 290 of the data processing device 12 and picks up large garbage based on the drone's position information. The optimization unit optimizes the flight route of the drone and the route of the garbage collection truck using, for example, the specific processing unit 290 of the data processing device 12.

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

[0103] The identification unit can not only determine the type of garbage, but also determine the condition of the garbage (for example, whether it is damaged or dirty). For example, the identification unit can determine the damage state of the garbage using image recognition technology. The identification unit can also determine the degree of dirtiness of the garbage using a sensor. For example, the identification unit can determine the degree of dirtiness of the garbage using an infrared sensor. This makes it possible to select an appropriate disposal method depending on the condition of the garbage. The identification unit can also distinguish between recyclable garbage and non-recyclable garbage based on the condition of the garbage. For example, the identification unit can prioritize collecting undamaged recyclable garbage. This improves recycling efficiency.

[0104] The collection unit can estimate not only the type of garbage but also the amount of garbage using image data acquired by the camera mounted on the drone. For example, the collection unit can estimate the amount of garbage using image analysis technology and optimize the collection plan. The collection unit can also adjust the collection frequency of the drone based on the amount of garbage. For example, the collection unit can increase the collection frequency if the amount of garbage is high. This can prevent garbage from overflowing. The collection unit can also optimize the collection route based on the amount of garbage. For example, the collection unit can prioritize collection in areas with high amounts of garbage. This can achieve efficient garbage collection.

[0105] The pickup unit can measure the weight of the garbage when picking up large pieces of garbage. For example, the pickup unit measures the weight of the garbage using a weight sensor mounted on the garbage collection truck. The pickup unit can also manage the load capacity of the collection truck based on the weight of the garbage. For example, the pickup unit stops picking up new garbage when the load capacity of the collection truck reaches its limit. This makes it possible to prevent the collection truck from being overloaded. The pickup unit can also optimize the collection route based on the weight of the garbage. For example, the pickup unit prioritizes picking up heavier garbage. This makes it possible to collect garbage efficiently.

[0106] The optimization unit can take weather information into account when optimizing the drone flight route and the garbage collection truck route. For example, the optimization unit changes the drone flight route when it is raining and adjusts the flight altitude when the wind speed is strong. The optimization unit can also take road conditions (e.g., whether the road is icy) into account when optimizing the garbage collection truck route. For example, the optimization unit selects a route that avoids icy roads. This enables safe and efficient garbage collection. The optimization unit can also adjust the collection schedule based on weather information. For example, the optimization unit delays the collection schedule when the weather is bad. This improves the safety of collection work.

[0107] When a drone falls, the collection department not only sends an emergency signal, but also tracks the drone's location information in real time. For example, the collection department uses GPS to obtain the drone's location information and identify the point where it fell. The collection department can also quickly carry out recovery operations based on the drone's location information. For example, the collection department can dispatch a recovery team closest to the point where the drone fell. This allows the drone to be recovered quickly. The collection department can also analyze the cause of the drone's fall and take measures to prevent recurrence. For example, the collection department can analyze the drone's sensor data and identify the cause of the fall. This improves drone safety.

[0108] The identification unit can estimate the user's emotions and adjust the accuracy of dust identification based on the estimated user emotions. For example, if the user is feeling stressed, the identification unit can increase the identification accuracy to reduce false recognition. If the user is relaxed, the identification unit can also maintain normal identification accuracy and prioritize processing speed. For example, if the user is in a hurry, the identification unit can increase the identification accuracy to quickly determine the type of dust. In this way, false recognition can be reduced by adjusting the dust identification accuracy according to the user's emotions. Some or all of the above-mentioned processing in the identification unit may be performed using, or without, AI. For example, the identification unit can input user emotion data into the generation AI and cause the generation AI to adjust the dust identification accuracy.

[0109] The collection unit can estimate the user's emotions and adjust the timing of garbage collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can advance the collection timing to collect garbage quickly. The collection unit can also maintain the normal collection timing if the user is relaxed. For example, if the user is in a hurry, the collection unit can adjust the collection timing to collect garbage quickly. In this way, garbage can be collected quickly by adjusting the collection timing according to the user's emotions. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the collection timing.

[0110] The pickup unit can estimate the user's emotions and adjust the timing of pickup based on the estimated user's emotions. For example, the pickup unit can advance the timing of pickup if the user is stressed. The pickup unit can also maintain the normal pickup timing if the user is relaxed. For example, the pickup unit can adjust the timing of pickup to quickly collect trash if the user is in a hurry. This allows trash to be collected quickly by adjusting the timing of pickup according to the user's emotions. Some or all of the above-described processing in the pickup unit may be performed using, for example, AI, or may be performed without using AI. For example, the pickup unit can input user emotion data into a generation AI and have the generation AI adjust the pickup timing.

[0111] The optimization unit can estimate the user's emotions and adjust the optimization algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, the optimization unit adjusts the optimization algorithm to quickly collect trash. The optimization unit can also apply a normal optimization algorithm if the user is relaxed. For example, if the user is in a hurry, the optimization unit adjusts the optimization algorithm to quickly collect trash. In this way, by adjusting the optimization algorithm according to the user's emotions, trash can be quickly collected. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input user's emotion data into the generation AI and have the generation AI adjust the optimization algorithm.

[0112] The expense management unit can estimate the user's emotions and adjust the expense management method based on the estimated user emotions. For example, if the expense management unit is feeling stressed, it simplifies the expense management method to reduce the burden. If the user is relaxed, the expense management unit can also provide a detailed expense management method. For example, if the user is in a hurry, the expense management unit provides a method for quickly managing expenses. This allows the burden to be reduced by adjusting the expense management method according to the user's emotions. Some or all of the above-mentioned processing in the expense management unit may be performed using, for example, AI, or may be performed without using AI. For example, the expense management unit can input the user's emotion data into a generation AI and have the generation AI adjust the expense management method.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The identification unit determines the type of garbage. Types of garbage include, for example, burnable garbage, non-burnable garbage, and recyclable garbage. The identification unit determines the type and material of the garbage using image recognition technology and sensors. For example, the material of the garbage can be determined using an infrared sensor. Step 2: The collection unit collects the trash identified by the identification unit. The collection unit collects small trash using, for example, a drone and acquires location information of the trash. The collection unit can acquire location information of the trash using GPS. Step 3: The pickup unit uses the location information of the garbage collected by the collection unit to pick up the garbage. The pickup unit can use the drone's location information when the garbage collection truck picks up large pieces of garbage. Step 4: The optimization unit optimizes the routes of the collection and pickup units. The optimization unit uses AI to optimize the drone flight route and the garbage collection truck route. For example, it optimizes the route by taking into account the drone's flight altitude and speed, traffic conditions, and road width.

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

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

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

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0120] 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

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

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

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

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

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0152] 7, a 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 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

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

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

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

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

Claims

1. an identification unit that determines the type of garbage; a collection unit that collects the dust determined by the identification unit; a pickup unit that allows a garbage collection vehicle to pick up the garbage collected by the collection unit based on location information of the garbage; an optimization unit that optimizes the routes of the collection unit and the pickup unit; A system characterized by:

2. The collecting unit Collecting small pieces of trash with drones The system of claim 1 .

3. The pickup unit includes: Garbage trucks will pick up large pieces of trash based on drone location information. The system of claim 1 .

4. The optimization unit Optimizing drone flight routes and garbage truck routes The system of claim 1 .

5. The collecting unit Equipped with an emergency signal unit that sends an emergency signal if the drone falls. The system of claim 1 .

6. The optimization unit Have a cost management department to manage operational costs The system of claim 1 .

7. The identification unit Estimate the user's emotions and adjust the accuracy of garbage identification based on the estimated user emotions. The system of claim 1 .

8. The identification unit Optimize the classification algorithm based on the material and shape of the trash The system of claim 1 .

Images