system

Abstract

An object of a system according to an embodiment is to efficiently perform a preliminary survey of the amount of collected waste.SOLUTION: A system includes an old PHS base station, a garbage collection sensor, a data collection part, and a data analysis part. The old PHS base station collects data from a garbage collection sensor installed in each home or office. The data collection unit collects data from the dust collection sensor. The data analysis unit analyzes the data collected by the data collection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not efficiently conduct advance surveys of garbage collection volumes, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently carry out a preliminary survey of the amount of garbage to be collected. [Means for solving the problem]

[0006] The system according to the embodiment includes an old PHS base station, a garbage collection sensor, a data collection unit, and a data analysis unit. The old PHS base station collects data from the garbage collection sensors installed in homes and businesses. The data collection unit collects data from the garbage collection sensors. The data analysis unit analyzes the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently carry out a preliminary survey of the amount of garbage to be collected. [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) The waste collection volume advance survey system according to the embodiment of the present invention is a system that uses old PHS base stations to grasp the amount of waste to be collected in real time. As a result, the waste collection volume advance survey system can improve the efficiency of waste collection and reduce costs.

[0029] A waste collection volume assessment system according to an embodiment includes an old PHS base station, a waste collection sensor, a data collection unit, and a data analysis unit. The old PHS base station is a communication infrastructure for the formerly used PHS (Personal Handy-phone System) that is no longer in use, but its communication function is reused. For example, the old PHS base station receives data from waste collection sensors installed in each home or business. The waste collection sensor measures the amount of waste and transmits the data to the old PHS base station. For example, the sensor measures the weight of a garbage bag and detects the fullness of a garbage can. The data collection unit collects data from the waste collection sensors. For example, the data collection unit aggregates the data transmitted via the old PHS base station into a central database. The data analysis unit analyzes the collected data. For example, the data analysis unit proposes an optimal route for a garbage collection truck based on the data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. This allows the waste collection volume assessment system to improve the efficiency of garbage collection and reduce costs. For example, by deploying more collection vehicles in areas with a large amount of garbage and fewer vehicles in areas with a small amount of garbage, garbage collection can be made more efficient. It is also expected that the operating costs of garbage collection vehicles will be reduced.

[0030] The garbage collection sensor can be a sensor that measures the weight of a garbage bag. For example, the garbage collection sensor measures the weight of a garbage bag in real time and transmits the data to the old PHS base station. The sensor that measures the weight of a garbage bag can also issue an alert when the garbage bag is full. The sensor that measures the weight of a garbage bag can also periodically measure the weight of the garbage bag and accumulate the data. In this way, by measuring the weight of the garbage bag, it is possible to accurately grasp the amount of garbage collected.

[0031] The garbage collection sensor can be a sensor that detects the fullness of a garbage can. For example, the garbage collection sensor is a sensor that detects the fullness of a garbage can. For example, a sensor that detects the fullness of a garbage can detects the fullness of the garbage can in real time and transmits the data to the old PHS base station. The sensor that detects the fullness of the garbage can can also issue an alert when the garbage can becomes full. The sensor that detects the fullness of the garbage can can also periodically detect the fullness of the garbage can and accumulate the data. In this way, efficient garbage collection becomes possible by detecting the fullness of the garbage can.

[0032] The data analysis unit can have a function of proposing an optimal route for a garbage collection truck. The data analysis unit has a function of proposing an optimal route for a garbage collection truck, for example. For example, the data analysis unit may propose an optimal route for a garbage collection truck to efficiently travel its route based on data on the amount of garbage collected. The data analysis unit may also optimize the operation schedule and route of the garbage collection truck. The data analysis unit may also propose an optimal route to reduce the operating costs of the garbage collection truck. Thus, by proposing an optimal route for a garbage collection truck, efficient garbage collection becomes possible.

[0033] The data analysis unit can have a function to optimize the allocation of garbage collection trucks. The data analysis unit has a function to optimize the allocation of garbage collection trucks, for example. For example, the data analysis unit can allocate more collection trucks to areas with a large amount of garbage and reduce the number of collection trucks to areas with a small amount of garbage based on data on the amount of garbage collected, thereby enabling efficient garbage collection. The data analysis unit can also propose optimal allocation to reduce the operating costs of garbage collection trucks. The data analysis unit can also optimize the operating schedules and routes of garbage collection trucks. As a result, efficient garbage collection can be achieved by optimizing the allocation of garbage collection trucks.

[0034] The data analysis unit can have a function to reduce the operating costs of garbage collection trucks. The data analysis unit has, for example, a function to reduce the operating costs of garbage collection trucks. For example, the data analysis unit can propose an optimal route to reduce the operating costs of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal placement to reduce the operating costs of garbage collection trucks. This reduces the operating costs of garbage collection trucks, enabling efficient garbage collection.

[0035] The data analysis unit can have a function to reduce the number of times that garbage collection trucks run. The data analysis unit has, for example, a function to reduce the number of times that garbage collection trucks run. For example, the data analysis unit can propose an optimal route to reduce the number of times that garbage collection trucks run, based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal placement to reduce the number of times that garbage collection trucks run. This reduces the number of times that garbage collection trucks run, enabling efficient garbage collection.

[0036] The data analysis unit can have a function to reduce the fuel consumption of garbage collection trucks. The data analysis unit has, for example, a function to reduce the fuel consumption of garbage collection trucks. For example, the data analysis unit proposes an optimal route for reducing the fuel consumption of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing the fuel consumption of garbage collection trucks. This reduces the fuel consumption of garbage collection trucks, thereby enabling efficient garbage collection.

[0037] The data analysis unit can have a function to reduce exhaust gas from garbage collection trucks. The data analysis unit has, for example, a function to reduce exhaust gas from garbage collection trucks. For example, the data analysis unit can propose an optimal route for reducing exhaust gas from garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing exhaust gas from garbage collection trucks. This reduces exhaust gas from garbage collection trucks, thereby mitigating the burden on the environment.

[0038] The data analysis unit can have a function of optimizing the garbage collection truck operation schedule. The data analysis unit has a function of optimizing, for example, the garbage collection truck operation schedule. For example, the data analysis unit optimizes the garbage collection truck operation schedule based on data on the amount of garbage collected. The data analysis unit can also optimize the garbage collection truck operation route. The data analysis unit can also propose an optimal schedule to reduce the garbage collection truck operation cost. In this way, optimizing the garbage collection truck operation schedule enables efficient garbage collection.

[0039] The data analysis unit can have a function of optimizing the operating route of a garbage collection truck. The data analysis unit has a function of optimizing the operating route of a garbage collection truck, for example. For example, the data analysis unit optimizes the operating route of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule of the garbage collection truck. The data analysis unit can also propose an optimal route to reduce the operating cost of the garbage collection truck. As a result, optimizing the operating route of the garbage collection truck enables efficient garbage collection.

[0040] The data analysis unit can have a function to optimize the operating hours of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operating hours of garbage collection trucks. For example, the data analysis unit optimizes the operating hours of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedules and routes of garbage collection trucks. The data analysis unit can also propose optimal times to reduce the operating costs of garbage collection trucks. As a result, optimizing the operating hours of garbage collection trucks enables efficient garbage collection.

[0041] The data analysis unit can have a function of optimizing the travel distance of a garbage collection truck. The data analysis unit has a function of optimizing, for example, the travel distance of a garbage collection truck. For example, the data analysis unit optimizes the travel distance of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal distance to reduce the operating costs of the garbage collection truck. As a result, optimizing the travel distance of the garbage collection truck enables efficient garbage collection.

[0042] The data analysis unit can have a function to optimize the operation frequency of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operation frequency of garbage collection trucks. For example, the data analysis unit optimizes the operation frequency of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal frequency to reduce the operation costs of garbage collection trucks. By optimizing the operation frequency of garbage collection trucks, efficient garbage collection becomes possible.

[0043] The data analysis unit can have a function to optimize the operation efficiency of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation efficiency of a garbage collection truck. For example, the data analysis unit optimizes the operation efficiency of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal efficiency to reduce the operation costs of the garbage collection truck. As a result, optimizing the operation efficiency of the garbage collection truck enables efficient garbage collection.

[0044] The data analysis unit can have a function to optimize the operation pattern of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation pattern of a garbage collection truck. For example, the data analysis unit optimizes the operation pattern of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal pattern to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation pattern of the garbage collection truck enables efficient garbage collection.

[0045] The data analysis unit can have a function to optimize the operation plan of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation plan of a garbage collection truck. For example, the data analysis unit optimizes the operation plan of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal plan to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation plan of the garbage collection truck enables efficient garbage collection.

[0046] The data analysis unit can have a function to optimize the operation performance of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation performance of a garbage collection truck. For example, the data analysis unit optimizes the operation performance of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal performance to reduce the operation costs of the garbage collection truck. In this way, optimizing the operation performance of the garbage collection truck enables efficient garbage collection.

[0047] The data analysis unit can have a function to improve the operation efficiency of a garbage collection truck. The data analysis unit has a function to improve, for example, the operation efficiency of a garbage collection truck. For example, the data analysis unit improves the operation efficiency of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal efficiency to reduce the operation cost of the garbage collection truck. As a result, efficient garbage collection is possible by improving the operation efficiency of the garbage collection truck.

[0048] The data analysis unit can have a function to reduce the operating costs of garbage collection trucks. The data analysis unit has, for example, a function to reduce the operating costs of garbage collection trucks. For example, the data analysis unit can propose an optimal route to reduce the operating costs of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal placement to reduce the operating costs of garbage collection trucks. This reduces the operating costs of garbage collection trucks, enabling efficient garbage collection.

[0049] The data analysis unit can have a function to reduce the number of times that garbage collection trucks run. The data analysis unit has, for example, a function to reduce the number of times that garbage collection trucks run. For example, the data analysis unit can propose an optimal route to reduce the number of times that garbage collection trucks run, based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal placement to reduce the number of times that garbage collection trucks run. This reduces the number of times that garbage collection trucks run, enabling efficient garbage collection.

[0050] The data analysis unit can have a function to reduce the fuel consumption of garbage collection trucks. The data analysis unit has, for example, a function to reduce the fuel consumption of garbage collection trucks. For example, the data analysis unit proposes an optimal route for reducing the fuel consumption of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing the fuel consumption of garbage collection trucks. This reduces the fuel consumption of garbage collection trucks, thereby enabling efficient garbage collection.

[0051] The data analysis unit can have a function to reduce exhaust gas from garbage collection trucks. The data analysis unit has, for example, a function to reduce exhaust gas from garbage collection trucks. For example, the data analysis unit can propose an optimal route for reducing exhaust gas from garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing exhaust gas from garbage collection trucks. This reduces exhaust gas from garbage collection trucks, thereby mitigating the burden on the environment.

[0052] The data analysis unit can have a function of optimizing the garbage collection truck operation schedule. The data analysis unit has a function of optimizing, for example, the garbage collection truck operation schedule. For example, the data analysis unit optimizes the garbage collection truck operation schedule based on data on the amount of garbage collected. The data analysis unit can also optimize the garbage collection truck operation route. The data analysis unit can also propose an optimal schedule to reduce the garbage collection truck operation cost. In this way, optimizing the garbage collection truck operation schedule enables efficient garbage collection.

[0053] The data analysis unit can have a function of optimizing the operating route of a garbage collection truck. The data analysis unit has a function of optimizing the operating route of a garbage collection truck, for example. For example, the data analysis unit optimizes the operating route of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule of the garbage collection truck. The data analysis unit can also propose an optimal route to reduce the operating cost of the garbage collection truck. As a result, optimizing the operating route of the garbage collection truck enables efficient garbage collection.

[0054] The data analysis unit can have a function to optimize the operating hours of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operating hours of garbage collection trucks. For example, the data analysis unit optimizes the operating hours of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedules and routes of garbage collection trucks. The data analysis unit can also propose optimal times to reduce the operating costs of garbage collection trucks. As a result, optimizing the operating hours of garbage collection trucks enables efficient garbage collection.

[0055] The data analysis unit can have a function of optimizing the travel distance of a garbage collection truck. The data analysis unit has a function of optimizing, for example, the travel distance of a garbage collection truck. For example, the data analysis unit optimizes the travel distance of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal distance to reduce the operating costs of the garbage collection truck. As a result, optimizing the travel distance of the garbage collection truck enables efficient garbage collection.

[0056] The data analysis unit can have a function to optimize the operation frequency of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operation frequency of garbage collection trucks. For example, the data analysis unit optimizes the operation frequency of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal frequency to reduce the operation costs of garbage collection trucks. By optimizing the operation frequency of garbage collection trucks, efficient garbage collection becomes possible.

[0057] The data analysis unit can have a function to optimize the operation efficiency of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation efficiency of a garbage collection truck. For example, the data analysis unit optimizes the operation efficiency of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal efficiency to reduce the operation costs of the garbage collection truck. As a result, optimizing the operation efficiency of the garbage collection truck enables efficient garbage collection.

[0058] The data analysis unit can have a function to optimize the operation pattern of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation pattern of a garbage collection truck. For example, the data analysis unit optimizes the operation pattern of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal pattern to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation pattern of the garbage collection truck enables efficient garbage collection.

[0059] The data analysis unit can have a function to optimize the operation plan of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation plan of a garbage collection truck. For example, the data analysis unit optimizes the operation plan of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal plan to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation plan of the garbage collection truck enables efficient garbage collection.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The waste collection volume assessment system can also be equipped with a function to identify the type of waste. For example, a waste collection sensor can scan the contents of a garbage bag and identify whether it is burnable, non-burnable, or recyclable. The data analysis unit can then optimize the routes and schedules of garbage collection trucks based on the identified types of waste. This can improve recycling efficiency and ensure proper waste disposal.

[0062] The garbage collection volume advance survey system can also be equipped with a function to manage garbage collection vehicle maintenance schedules. For example, the data analysis unit can predict when vehicle maintenance is required based on garbage collection vehicle operation data. Furthermore, optimizing the maintenance schedule can prevent vehicle breakdowns and improve operational efficiency. This can increase the utilization rate of garbage collection vehicles and is expected to reduce costs.

[0063] The waste collection volume advance survey system can also be equipped with a function to grasp traffic conditions in real time while the waste collection truck is in operation. For example, traffic sensors and GPS data can be used to collect congestion and accident information and suggest optimal routes. In addition, the data analysis unit can dynamically adjust the operation schedule based on traffic conditions. This is expected to shorten operation times and reduce fuel consumption.

[0064] The garbage collection volume advance survey system can also be equipped with a function to monitor the noise generated while garbage collection trucks are in operation. For example, a noise sensor can be installed to measure the noise level during operation in real time. The data analysis unit can also adjust the operation route and time period based on the noise data. This reduces noise pollution for residents and enables environmentally friendly operation.

[0065] The garbage collection volume advance survey system can also be equipped with a function to monitor the amount of exhaust gas emitted while garbage collection trucks are operating. For example, an exhaust gas sensor can be installed to measure the amount of exhaust gas emitted during operation in real time. The data analysis unit can also adjust the operating route and speed based on the exhaust gas data. This reduces the burden on the environment and enables sustainable operation.

[0066] The garbage collection volume advance survey system can also be equipped with a function to monitor vibrations generated during garbage collection truck operation. For example, a vibration sensor can be installed to measure vibration levels in real time during operation. The data analysis unit can then adjust the operating route and speed based on the vibration data. This can prevent damage to the vehicle and improve operational efficiency.

[0067] The waste collection volume advance survey system can also be equipped with a function to monitor the temperature generated during operation of the garbage collection truck. For example, a temperature sensor can be installed to measure the temperature in real time while the truck is in operation. The data analysis unit can then adjust the route and speed of the truck based on the temperature data. This prevents the truck from overheating and ensures safe operation.

[0068] The garbage collection volume advance survey system can also be equipped with a function to monitor the humidity generated while the garbage collection truck is in operation. For example, a humidity sensor can be installed to measure the humidity in real time while the truck is in operation. The data analysis unit can also adjust the route and speed of the truck based on the humidity data. This can prevent deterioration of the truck and is expected to extend its lifespan.

[0069] The garbage collection volume advance survey system can also be equipped with a function to monitor the air quality generated while garbage collection trucks are operating. For example, an air quality sensor can be installed to measure air quality in real time while the truck is operating. The data analysis unit can then adjust the route and speed of the truck based on the air quality data. This allows the truck to operate with minimal impact on the environment.

[0070] The processing flow of the first embodiment will be briefly explained below.

[0071] Step 1: The old PHS base station reuses the old PHS (Personal Handy-phone System) communications infrastructure to receive data from garbage collection sensors installed in homes and businesses, such as sensors that measure the weight of garbage bags and sensors that detect the fullness of garbage bins. Step 2: The data collector collects data from the garbage collection sensors. For example, the data collector aggregates data transmitted via the old PHS base station into a central database. Step 3: The data analysis unit analyzes the collected data. For example, the data analysis unit may propose the optimal route for the garbage collection truck based on the data on the amount of garbage collected. The data analysis unit may also optimize the operation schedule and route of the garbage collection truck.

[0072] (Example 2) The waste collection volume advance survey system according to the embodiment of the present invention is a system that uses old PHS base stations to grasp the amount of waste to be collected in real time. As a result, the waste collection volume advance survey system can improve the efficiency of waste collection and reduce costs.

[0073] A waste collection volume assessment system according to an embodiment includes an old PHS base station, a waste collection sensor, a data collection unit, and a data analysis unit. The old PHS base station is a communication infrastructure for the formerly used PHS (Personal Handy-phone System) that is no longer in use, but its communication function is reused. For example, the old PHS base station receives data from waste collection sensors installed in each home or business. The waste collection sensor measures the amount of waste and transmits the data to the old PHS base station. For example, the sensor measures the weight of a garbage bag and detects the fullness of a garbage can. The data collection unit collects data from the waste collection sensors. For example, the data collection unit aggregates the data transmitted via the old PHS base station into a central database. The data analysis unit analyzes the collected data. For example, the data analysis unit proposes an optimal route for a garbage collection truck based on the data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. This allows the waste collection volume assessment system to improve the efficiency of garbage collection and reduce costs. For example, by deploying more collection vehicles in areas with a large amount of garbage and fewer vehicles in areas with a small amount of garbage, garbage collection can be made more efficient. It is also expected that the operating costs of garbage collection vehicles will be reduced.

[0074] The garbage collection sensor can be a sensor that measures the weight of a garbage bag. For example, the garbage collection sensor measures the weight of a garbage bag in real time and transmits the data to the old PHS base station. The sensor that measures the weight of a garbage bag can also issue an alert when the garbage bag is full. The sensor that measures the weight of a garbage bag can also periodically measure the weight of the garbage bag and accumulate the data. In this way, by measuring the weight of the garbage bag, it is possible to accurately grasp the amount of garbage collected.

[0075] The garbage collection sensor can be a sensor that detects the fullness of a garbage can. For example, the garbage collection sensor is a sensor that detects the fullness of a garbage can. For example, a sensor that detects the fullness of a garbage can detects the fullness of the garbage can in real time and transmits the data to the old PHS base station. The sensor that detects the fullness of the garbage can can also issue an alert when the garbage can becomes full. The sensor that detects the fullness of the garbage can can also periodically detect the fullness of the garbage can and accumulate the data. In this way, efficient garbage collection becomes possible by detecting the fullness of the garbage can.

[0076] The data analysis unit can have a function of proposing an optimal route for a garbage collection truck. The data analysis unit has a function of proposing an optimal route for a garbage collection truck, for example. For example, the data analysis unit may propose an optimal route for a garbage collection truck to efficiently travel its route based on data on the amount of garbage collected. The data analysis unit may also optimize the operation schedule and route of the garbage collection truck. The data analysis unit may also propose an optimal route to reduce the operating costs of the garbage collection truck. Thus, by proposing an optimal route for a garbage collection truck, efficient garbage collection becomes possible.

[0077] The data analysis unit can have a function to optimize the allocation of garbage collection trucks. The data analysis unit has a function to optimize the allocation of garbage collection trucks, for example. For example, the data analysis unit can allocate more collection trucks to areas with a large amount of garbage and reduce the number of collection trucks to areas with a small amount of garbage based on data on the amount of garbage collected, thereby enabling efficient garbage collection. The data analysis unit can also propose optimal allocation to reduce the operating costs of garbage collection trucks. The data analysis unit can also optimize the operating schedules and routes of garbage collection trucks. As a result, efficient garbage collection can be achieved by optimizing the allocation of garbage collection trucks.

[0078] The data analysis unit can have a function to reduce the operating costs of garbage collection trucks. The data analysis unit has, for example, a function to reduce the operating costs of garbage collection trucks. For example, the data analysis unit can propose an optimal route to reduce the operating costs of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal placement to reduce the operating costs of garbage collection trucks. This reduces the operating costs of garbage collection trucks, enabling efficient garbage collection.

[0079] The data analysis unit can have a function to reduce the number of times that garbage collection trucks run. The data analysis unit has, for example, a function to reduce the number of times that garbage collection trucks run. For example, the data analysis unit can propose an optimal route to reduce the number of times that garbage collection trucks run, based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal placement to reduce the number of times that garbage collection trucks run. This reduces the number of times that garbage collection trucks run, enabling efficient garbage collection.

[0080] The data analysis unit can have a function to reduce the fuel consumption of garbage collection trucks. The data analysis unit has, for example, a function to reduce the fuel consumption of garbage collection trucks. For example, the data analysis unit proposes an optimal route for reducing the fuel consumption of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing the fuel consumption of garbage collection trucks. This reduces the fuel consumption of garbage collection trucks, thereby enabling efficient garbage collection.

[0081] The data analysis unit can have a function to reduce exhaust gas from garbage collection trucks. The data analysis unit has, for example, a function to reduce exhaust gas from garbage collection trucks. For example, the data analysis unit can propose an optimal route for reducing exhaust gas from garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing exhaust gas from garbage collection trucks. This reduces exhaust gas from garbage collection trucks, thereby mitigating the burden on the environment.

[0082] The data analysis unit can have a function of optimizing the garbage collection truck operation schedule. The data analysis unit has a function of optimizing, for example, the garbage collection truck operation schedule. For example, the data analysis unit optimizes the garbage collection truck operation schedule based on data on the amount of garbage collected. The data analysis unit can also optimize the garbage collection truck operation route. The data analysis unit can also propose an optimal schedule to reduce the garbage collection truck operation cost. In this way, optimizing the garbage collection truck operation schedule enables efficient garbage collection.

[0083] The data analysis unit can have a function of optimizing the operating route of a garbage collection truck. The data analysis unit has a function of optimizing the operating route of a garbage collection truck, for example. For example, the data analysis unit optimizes the operating route of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule of the garbage collection truck. The data analysis unit can also propose an optimal route to reduce the operating cost of the garbage collection truck. As a result, optimizing the operating route of the garbage collection truck enables efficient garbage collection.

[0084] The data analysis unit can have a function to optimize the operating hours of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operating hours of garbage collection trucks. For example, the data analysis unit optimizes the operating hours of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedules and routes of garbage collection trucks. The data analysis unit can also propose optimal times to reduce the operating costs of garbage collection trucks. As a result, optimizing the operating hours of garbage collection trucks enables efficient garbage collection.

[0085] The data analysis unit can have a function of optimizing the travel distance of a garbage collection truck. The data analysis unit has a function of optimizing, for example, the travel distance of a garbage collection truck. For example, the data analysis unit optimizes the travel distance of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal distance to reduce the operating costs of the garbage collection truck. As a result, optimizing the travel distance of the garbage collection truck enables efficient garbage collection.

[0086] The data analysis unit can have a function to optimize the operation frequency of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operation frequency of garbage collection trucks. For example, the data analysis unit optimizes the operation frequency of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal frequency to reduce the operation costs of garbage collection trucks. By optimizing the operation frequency of garbage collection trucks, efficient garbage collection becomes possible.

[0087] The data analysis unit can have a function to optimize the operation efficiency of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation efficiency of a garbage collection truck. For example, the data analysis unit optimizes the operation efficiency of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal efficiency to reduce the operation costs of the garbage collection truck. As a result, optimizing the operation efficiency of the garbage collection truck enables efficient garbage collection.

[0088] The data analysis unit can have a function to optimize the operation pattern of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation pattern of a garbage collection truck. For example, the data analysis unit optimizes the operation pattern of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal pattern to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation pattern of the garbage collection truck enables efficient garbage collection.

[0089] The data analysis unit can have a function to optimize the operation plan of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation plan of a garbage collection truck. For example, the data analysis unit optimizes the operation plan of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal plan to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation plan of the garbage collection truck enables efficient garbage collection.

[0090] The data analysis unit can have a function to optimize the operation performance of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation performance of a garbage collection truck. For example, the data analysis unit optimizes the operation performance of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal performance to reduce the operation costs of the garbage collection truck. In this way, optimizing the operation performance of the garbage collection truck enables efficient garbage collection.

[0091] The data analysis unit can have a function to improve the operation efficiency of a garbage collection truck. The data analysis unit has a function to improve, for example, the operation efficiency of a garbage collection truck. For example, the data analysis unit improves the operation efficiency of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal efficiency to reduce the operation cost of the garbage collection truck. As a result, efficient garbage collection is possible by improving the operation efficiency of the garbage collection truck.

[0092] The data analysis unit can have a function to reduce the operating costs of garbage collection trucks. The data analysis unit has, for example, a function to reduce the operating costs of garbage collection trucks. For example, the data analysis unit can propose an optimal route to reduce the operating costs of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal placement to reduce the operating costs of garbage collection trucks. This reduces the operating costs of garbage collection trucks, enabling efficient garbage collection.

[0093] The data analysis unit can have a function to reduce the number of times that garbage collection trucks run. The data analysis unit has, for example, a function to reduce the number of times that garbage collection trucks run. For example, the data analysis unit can propose an optimal route to reduce the number of times that garbage collection trucks run, based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal placement to reduce the number of times that garbage collection trucks run. This reduces the number of times that garbage collection trucks run, enabling efficient garbage collection.

[0094] The data analysis unit can have a function to reduce the fuel consumption of garbage collection trucks. The data analysis unit has, for example, a function to reduce the fuel consumption of garbage collection trucks. For example, the data analysis unit proposes an optimal route for reducing the fuel consumption of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing the fuel consumption of garbage collection trucks. This reduces the fuel consumption of garbage collection trucks, thereby enabling efficient garbage collection.

[0095] The data analysis unit can have a function to reduce exhaust gas from garbage collection trucks. The data analysis unit has, for example, a function to reduce exhaust gas from garbage collection trucks. For example, the data analysis unit can propose an optimal route for reducing exhaust gas from garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal layout for reducing exhaust gas from garbage collection trucks. This reduces exhaust gas from garbage collection trucks, thereby mitigating the burden on the environment.

[0096] The data analysis unit can have a function of optimizing the garbage collection truck operation schedule. The data analysis unit has a function of optimizing, for example, the garbage collection truck operation schedule. For example, the data analysis unit optimizes the garbage collection truck operation schedule based on data on the amount of garbage collected. The data analysis unit can also optimize the garbage collection truck operation route. The data analysis unit can also propose an optimal schedule to reduce the garbage collection truck operation cost. In this way, optimizing the garbage collection truck operation schedule enables efficient garbage collection.

[0097] The data analysis unit can have a function of optimizing the operating route of a garbage collection truck. The data analysis unit has a function of optimizing the operating route of a garbage collection truck, for example. For example, the data analysis unit optimizes the operating route of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedule of the garbage collection truck. The data analysis unit can also propose an optimal route to reduce the operating cost of the garbage collection truck. As a result, optimizing the operating route of the garbage collection truck enables efficient garbage collection.

[0098] The data analysis unit can have a function to optimize the operating hours of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operating hours of garbage collection trucks. For example, the data analysis unit optimizes the operating hours of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operating schedules and routes of garbage collection trucks. The data analysis unit can also propose optimal times to reduce the operating costs of garbage collection trucks. As a result, optimizing the operating hours of garbage collection trucks enables efficient garbage collection.

[0099] The data analysis unit can have a function of optimizing the travel distance of a garbage collection truck. The data analysis unit has a function of optimizing, for example, the travel distance of a garbage collection truck. For example, the data analysis unit optimizes the travel distance of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal distance to reduce the operating costs of the garbage collection truck. As a result, optimizing the travel distance of the garbage collection truck enables efficient garbage collection.

[0100] The data analysis unit can have a function to optimize the operation frequency of garbage collection trucks. The data analysis unit has a function to optimize, for example, the operation frequency of garbage collection trucks. For example, the data analysis unit optimizes the operation frequency of garbage collection trucks based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of garbage collection trucks. The data analysis unit can also propose an optimal frequency to reduce the operation costs of garbage collection trucks. By optimizing the operation frequency of garbage collection trucks, efficient garbage collection becomes possible.

[0101] The data analysis unit can have a function to optimize the operation efficiency of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation efficiency of a garbage collection truck. For example, the data analysis unit optimizes the operation efficiency of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose optimal efficiency to reduce the operation costs of the garbage collection truck. As a result, optimizing the operation efficiency of the garbage collection truck enables efficient garbage collection.

[0102] The data analysis unit can have a function to optimize the operation pattern of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation pattern of a garbage collection truck. For example, the data analysis unit optimizes the operation pattern of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal pattern to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation pattern of the garbage collection truck enables efficient garbage collection.

[0103] The data analysis unit can have a function to optimize the operation plan of a garbage collection truck. The data analysis unit has a function to optimize, for example, the operation plan of a garbage collection truck. For example, the data analysis unit optimizes the operation plan of a garbage collection truck based on data on the amount of garbage collected. The data analysis unit can also optimize the operation schedule and route of the garbage collection truck. The data analysis unit can also propose an optimal plan to reduce the operation cost of the garbage collection truck. In this way, optimizing the operation plan of the garbage collection truck enables efficient garbage collection.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The waste collection volume assessment system can also be equipped with a function to identify the type of waste. For example, a waste collection sensor can scan the contents of a garbage bag and identify whether it is burnable, non-burnable, or recyclable. The data analysis unit can then optimize the routes and schedules of garbage collection trucks based on the identified types of waste. This can improve recycling efficiency and ensure proper waste disposal.

[0106] The garbage collection volume advance survey system can also be equipped with a function to manage garbage collection vehicle maintenance schedules. For example, the data analysis unit can predict when vehicle maintenance is required based on garbage collection vehicle operation data. Furthermore, optimizing the maintenance schedule can prevent vehicle breakdowns and improve operational efficiency. This can increase the utilization rate of garbage collection vehicles and is expected to reduce costs.

[0107] The garbage collection volume advance survey system can also be equipped with a function to monitor the fatigue level of garbage truck drivers. For example, it can monitor the driver's heart rate and driving time, and issue an alert to encourage them to take a break if fatigue accumulates. The data analysis unit can also adjust the driving schedule based on the driver's fatigue level. This ensures driver safety and reduces the risk of accidents.

[0108] The waste collection volume advance survey system can also be equipped with a function to grasp traffic conditions in real time while the waste collection truck is in operation. For example, traffic sensors and GPS data can be used to collect congestion and accident information and suggest optimal routes. In addition, the data analysis unit can dynamically adjust the operation schedule based on traffic conditions. This is expected to shorten operation times and reduce fuel consumption.

[0109] The garbage collection volume advance survey system can also be equipped with a function to monitor the noise generated while garbage collection trucks are in operation. For example, a noise sensor can be installed to measure the noise level during operation in real time. The data analysis unit can also adjust the operation route and time period based on the noise data. This reduces noise pollution for residents and enables environmentally friendly operation.

[0110] The garbage collection volume advance survey system can also be equipped with a function to monitor the amount of exhaust gas emitted while garbage collection trucks are operating. For example, an exhaust gas sensor can be installed to measure the amount of exhaust gas emitted during operation in real time. The data analysis unit can also adjust the operating route and speed based on the exhaust gas data. This reduces the burden on the environment and enables sustainable operation.

[0111] The garbage collection volume advance survey system can also be equipped with a function to monitor vibrations generated during garbage collection truck operation. For example, a vibration sensor can be installed to measure vibration levels in real time during operation. The data analysis unit can then adjust the operating route and speed based on the vibration data. This can prevent damage to the vehicle and improve operational efficiency.

[0112] The waste collection volume advance survey system can also be equipped with a function to monitor the temperature generated during operation of the garbage collection truck. For example, a temperature sensor can be installed to measure the temperature in real time while the truck is in operation. The data analysis unit can then adjust the route and speed of the truck based on the temperature data. This prevents the truck from overheating and ensures safe operation.

[0113] The garbage collection volume advance survey system can also be equipped with a function to monitor the humidity generated while the garbage collection truck is in operation. For example, a humidity sensor can be installed to measure the humidity in real time while the truck is in operation. The data analysis unit can also adjust the route and speed of the truck based on the humidity data. This can prevent deterioration of the truck and is expected to extend its lifespan.

[0114] The garbage collection volume advance survey system can also be equipped with a function to monitor the air quality generated while garbage collection trucks are operating. For example, an air quality sensor can be installed to measure air quality in real time while the truck is operating. The data analysis unit can then adjust the route and speed of the truck based on the air quality data. This allows the truck to operate with minimal impact on the environment.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The old PHS base station reuses the old PHS (Personal Handy-phone System) communications infrastructure to receive data from garbage collection sensors installed in homes and businesses, such as sensors that measure the weight of garbage bags and sensors that detect the fullness of garbage bins. Step 2: The data collector collects data from the garbage collection sensors. For example, the data collector aggregates data transmitted via the old PHS base station into a central database. Step 3: The data analysis unit analyzes the collected data. For example, the data analysis unit may propose the optimal route for the garbage collection truck based on the data on the amount of garbage collected. The data analysis unit may also optimize the operation schedule and route of the garbage collection truck.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

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

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

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

[0151] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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 processing similar to that of the specific processing unit 290 using these models.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0184] 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. Old PHS base station and Garbage collection sensors installed in each home and business, a data collection unit that collects data from the dust collection sensor; a data analysis unit that analyzes the data collected by the data collection unit; A system characterized by:

2. The dust collection sensor This is a sensor that measures the weight of a garbage bag.

2. The system of claim 1.

3. The dust collection sensor This is a sensor that detects how full the trash can is.

2. The system of claim 1.

4. The data analysis unit It has a function to suggest the optimal route for garbage collection trucks.

2. The system of claim 1.

5. The data analysis unit Has the function of optimizing the placement of garbage collection vehicles 2. The system of claim 1.

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