system

Abstract

The system according to the embodiment aims to make it easy to understand the garbage collection schedule and sorting method for each region. [Solution] A system according to an embodiment includes an acquisition unit, an explanation unit, and a display unit. The acquisition unit acquires a local garbage collection schedule. The explanation unit explains how to separate garbage based on the information acquired by the acquisition unit. The display unit displays the garbage collection schedule based on the separation method explained by the explanation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was cumbersome to keep track of local garbage collection schedules and sorting methods, which was particularly time-consuming when moving.

[0005] The system according to the embodiment aims to make it easy to understand the garbage collection schedule and sorting method for each region. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an explanation unit, and a display unit. The acquisition unit acquires a local garbage collection schedule. The explanation unit explains how to separate garbage based on the information acquired by the acquisition unit. The display unit displays the garbage collection schedule based on the separation method explained by the explanation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily grasp the garbage collection schedule and sorting method for each area. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is an application system that allows users to easily understand and efficiently execute local garbage collection schedules and garbage sorting methods. This system supports environmentally friendly garbage disposal, particularly when moving, by eliminating the need to find appropriate garbage collection days and sorting methods for their old and new addresses. Furthermore, the system also assists users with the handling of bulky garbage, providing comprehensive support for sales via flea market apps and the purchase of bulky garbage tickets. For example, when a user enters a new address, the system automatically obtains the local garbage collection schedule and sorting method. For example, information such as Monday for burnable garbage and Tuesday for non-burnable garbage is displayed. Detailed instructions for sorting garbage by category, such as plastic, paper, and metal, are also provided. Next, when moving, the system displays garbage collection schedules for both the old and new addresses, allowing users to smoothly dispose of their garbage. For example, if Monday is the day for burnable garbage at the old address and Tuesday is the day for burnable garbage at the new address, the system notifies the user of this information. Furthermore, the system also supports the handling of bulky garbage. When a user disposes of oversized garbage, the system guides them through the procedure and supports them in purchasing the necessary oversized garbage tickets. It is also possible to link with a flea market app to sell unwanted oversized garbage. For example, if unwanted furniture is put up for sale on a flea market app and a buyer is found, there is no need to dispose of it as garbage. In this way, the system is an application system that makes it easy to understand and efficiently carry out local garbage collection schedules and sorting methods, and it particularly supports garbage disposal when moving. It also provides consistent support for handling oversized garbage, realizing environmentally friendly garbage disposal. This allows the system to efficiently understand and carry out local garbage collection schedules and sorting methods.

[0029] A garbage disposal support system according to an embodiment includes an acquisition unit, an explanation unit, and a display unit. The acquisition unit acquires a local garbage disposal schedule. The acquisition unit acquires information from a local database based on, for example, a new address entered by a user. For example, when a user enters a new address, the acquisition unit can automatically acquire the local garbage disposal schedule corresponding to the address. The explanation unit explains how to separate garbage based on the information acquired by the acquisition unit. The explanation unit provides detailed explanations for each category, such as plastic, paper, and metal. For example, the explanation unit can explain in detail specific steps and precautions for separating plastic garbage. The display unit displays the garbage disposal schedule based on the separation method explained by the explanation unit. The display unit can display the garbage disposal schedules for both the old address and the new address. For example, if Monday is the day for combustible garbage at the old address and Tuesday is the day for combustible garbage at the new address, the display unit can notify the user of this information. This allows the garbage disposal support system according to an embodiment to efficiently understand and implement the garbage disposal schedule and separation method for each region.

[0030] The acquisition unit can acquire information from a local database based on a new address entered by the user. For example, when a user enters a new address, the acquisition unit can automatically acquire the local garbage collection schedule and sorting method corresponding to that address. For example, the acquisition unit can access a local database and acquire information such as combustible garbage on Mondays and non-combustible garbage on Tuesdays. This allows the user to automatically acquire the local garbage collection schedule and sorting method simply by entering a new address. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the new address entered by the user into AI and cause the AI ​​to execute processing to acquire information from the local database.

[0031] The explanation unit can provide detailed explanations for each category of plastic, paper, and metal. The explanation unit can, for example, provide detailed explanations of specific steps and precautions regarding how to separate plastic waste. For example, the explanation unit can explain how to wash plastic waste and remove labels before sorting. The explanation unit can also explain how to separate paper waste by category, such as newspaper, magazines, and cardboard. For example, the explanation unit can explain how to bundle newspapers, tie magazines with string, and flatten cardboard before disposing of them. The explanation unit can also explain how to separate metal waste by category, such as cans, bottles, and metal products. For example, the explanation unit can explain how to wash cans before disposing of them, remove caps from bottles before disposing of them, and disassemble metal products before disposing of them. This allows the user to understand how to separate waste in detail for each category. Some or all of the above-described processing by the explanation unit can be performed using, or without, AI. For example, the explanation unit can input the plastic waste separation method into AI and cause the AI ​​to execute a process of explaining specific steps and precautions.

[0032] The display unit can display garbage collection schedules for the old address and the new address. For example, if Monday is burnable garbage collection day at the old address and Tuesday is burnable garbage collection day at the new address, the display unit can notify the user of this information. For example, the display unit can simultaneously display the garbage collection schedules for the old address and the new address, allowing the user to smoothly dispose of garbage. The display unit can also display the garbage collection schedule in calendar format. For example, the display unit can display information on the calendar such as burnable garbage on Monday, non-burnable garbage on Tuesday, and recyclable garbage on Wednesday. This allows the user to simultaneously check the garbage collection schedules for the old address and the new address when moving. Some or all of the above-mentioned processing on the display unit may be performed using, or without, AI. For example, the display unit can input the garbage collection schedules for the old address and the new address into AI and have the AI ​​execute a process to display them in calendar format.

[0033] The system includes a procedure guidance unit that guides the user through the procedure for disposing of oversized waste. The procedure guidance unit guides the user through the procedure, for example, when disposing of oversized waste. For example, the procedure guidance unit provides information such as how to apply for oversized waste disposal, the necessary documents, and fees. The procedure guidance unit can also provide information on the collection date and location for oversized waste. For example, the procedure guidance unit notifies the user that the collection date is Monday and that the collection location is a designated location. This allows for easy guidance through the procedure for disposing of oversized waste. Some or all of the above-described processing in the procedure guidance unit may be performed using, or without, AI. For example, the procedure guidance unit can input the application method for oversized waste disposal and the necessary documents into AI and have the AI ​​execute the process of guiding the user.

[0034] The system includes a purchase support unit that supports the purchasing procedure for bulky waste tickets. The purchase support unit supports the procedure, for example, when a user purchases a bulky waste ticket. For example, the purchase support unit provides information such as where to purchase the bulky waste ticket, payment methods, and necessary documents. The purchase support unit can also support online purchasing procedures. For example, the purchase support unit guides the user through the necessary procedures so that the user can purchase the bulky waste ticket online. This simplifies the purchasing procedure for the bulky waste ticket. Some or all of the above-described processing in the purchase support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchase support unit can input information about where to purchase the bulky waste ticket and payment methods into AI, and have the AI ​​execute a process to guide the user.

[0035] The system includes a sales support unit that works in conjunction with a flea market app to sell unwanted bulky waste. The sales support unit supports, for example, a user when selling unwanted bulky waste on the flea market app. For example, the sales support unit provides information such as how to use the flea market app, the listing procedure, and interactions with buyers. The sales support unit can also work in conjunction with the flea market app to automatically list unwanted bulky waste. For example, the sales support unit supports a user when listing unwanted furniture on the flea market app and, if a buyer is found, eliminates the need to dispose of the item as trash. This allows unwanted bulky waste to be sold on the flea market app. Some or all of the above-described processing in the sales support unit may be performed, for example, using AI, or may be performed without using AI. For example, the sales support unit may input information about how to use the flea market app and the listing procedure into AI, and have the AI ​​execute a process to guide the user.

[0036] In the system, the acquisition unit analyzes the user's past address change history and selects the optimal information acquisition method. The acquisition unit proposes the optimal information acquisition method, for example, based on addresses that the user has frequently changed in the past. For example, the acquisition unit finds a specific pattern from the user's past address change history and adjusts the information acquisition method based on that pattern. The acquisition unit also analyzes the user's past address change history and proposes the most efficient information acquisition method. For example, the acquisition unit proposes the optimal information acquisition method based on addresses that the user has frequently changed in the past. For example, the acquisition unit finds a specific pattern from the user's past address change history and adjusts the information acquisition method based on that pattern. The acquisition unit also analyzes the user's past address change history and proposes the most efficient information acquisition method. This makes it possible to provide the optimal information acquisition method based on the user's past address change history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past address change history into AI and cause the AI ​​to execute a process of selecting the optimal information acquisition method.

[0037] When the acquisition unit inputs a new address, the system filters the information based on the user's current living situation and areas of interest. For example, if the user is raising children, the acquisition unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the acquisition unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the acquisition unit prioritizes displaying garbage disposal information that is easy to understand. For example, if the user is raising children, the acquisition unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the acquisition unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the acquisition unit prioritizes displaying garbage disposal information that is easy to understand. This makes it possible to provide information tailored to the user's living situation and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's current living situation and areas of interest into AI and have the AI ​​perform the filtering process.

[0038] In the system, when the acquisition unit inputs a new address, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. For example, if the user lives in an urban area, the acquisition unit prioritizes acquiring garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the acquisition unit prioritizes acquiring garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the acquisition unit prioritizes acquiring the garbage disposal schedule for that area. For example, if the user lives in an urban area, the acquisition unit prioritizes acquiring garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the acquisition unit prioritizes acquiring garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the acquisition unit prioritizes acquiring the garbage disposal schedule for that area. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to execute a process of preferentially acquiring highly relevant information.

[0039] In the system, when a new address is input, the acquisition unit analyzes the user's social media activity and acquires related information. The acquisition unit, for example, acquires related trash disposal information based on information shared by the user on social media. For example, the acquisition unit acquires related trash disposal information based on information about accounts the user follows on social media. The acquisition unit also acquires related trash disposal information based on information about groups the user participates in on social media. For example, the acquisition unit acquires related trash disposal information based on information shared by the user on social media. For example, the acquisition unit acquires related trash disposal information based on information about accounts the user follows on social media. The acquisition unit also acquires related trash disposal information based on information about groups the user participates in on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's social media activity into AI and cause the AI ​​to execute a process of acquiring related information.

[0040] In the system, when the explanation unit explains the sorting method, it adjusts the level of detail of the explanation based on the importance of the garbage. For example, the explanation unit provides a detailed explanation for a sorting method for important garbage. For example, the explanation unit provides a concise explanation for a sorting method for less important garbage. Furthermore, the explanation unit adjusts the level of detail of the explanation according to the importance of the garbage to make it easier for the user to understand. For example, the explanation unit provides a detailed explanation for a sorting method for important garbage. For example, the explanation unit provides a concise explanation for a sorting method for less important garbage. Furthermore, the explanation unit adjusts the level of detail of the explanation according to the importance of the garbage to make it easier for the user to understand. In this way, the level of detail of the explanation can be adjusted according to the importance of the garbage. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit may input the importance of the garbage into AI and cause the AI ​​to execute processing to adjust the level of detail of the explanation.

[0041] When the explanation unit explains the sorting method, the system applies different explanation algorithms depending on the category of waste. For example, the explanation unit explains in detail how to recycle plastic waste. For example, the explanation unit explains how to sort and reuse paper waste. Furthermore, the explanation unit explains how to sort and safely dispose of metal waste. For example, the explanation unit explains in detail how to recycle plastic waste. For example, the explanation unit explains how to sort and reuse paper waste. Furthermore, the explanation unit explains how to sort and safely dispose of metal waste. This makes it possible to provide appropriate explanations according to the category of waste. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, AI. For example, the explanation unit may input the category of waste into AI and cause the AI ​​to execute a process of applying different explanation algorithms.

[0042] In the system, when the explanation unit explains the sorting method, it determines the priority of the explanation based on the time of garbage submission. For example, the explanation unit provides explanations preferentially for garbage that is due to be submitted soon. For example, the explanation unit provides explanations later for garbage that is due to be submitted further away. Furthermore, the explanation unit adjusts the priority of the explanations according to the time of garbage submission, allowing the user to dispose of garbage efficiently. For example, the explanation unit provides explanations preferentially for garbage that is due to be submitted soon. For example, the explanation unit provides explanations later for garbage that is due to be submitted further away. Furthermore, the explanation unit adjusts the priority of the explanations according to the time of garbage submission, allowing the user to dispose of garbage efficiently. In this way, the priority of the explanations can be adjusted according to the time of garbage submission. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit may input the time of garbage submission into AI and cause the AI ​​to execute processing to determine the priority of the explanations.

[0043] In the system, when the explanation unit explains the sorting method, it adjusts the order of the explanation based on the relevance of the garbage. For example, the explanation unit provides an explanation preferentially for highly relevant garbage. For example, the explanation unit provides an explanation later for less relevant garbage. Furthermore, the explanation unit adjusts the order of the explanation according to the relevance of the garbage to make it easier for the user to understand. For example, the explanation unit provides an explanation preferentially for highly relevant garbage. For example, the explanation unit provides an explanation later for less relevant garbage. Furthermore, the explanation unit adjusts the order of the explanation according to the relevance of the garbage to make it easier for the user to understand. In this way, the order of the explanation can be adjusted according to the relevance of the garbage. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit inputs the relevance of the garbage into AI and causes the AI ​​to execute processing to adjust the order of the explanation.

[0044] When the display unit displays the garbage disposal schedule, the system selects the optimal display method by referring to the user's past garbage disposal history. The display unit, for example, suggests the optimal display method based on the garbage disposal schedule used by the user in the past. For example, the display unit suggests an efficient display method based on the user's past garbage disposal history. The display unit also analyzes the user's past garbage disposal history and suggests the most visible display method. For example, the display unit suggests the optimal display method based on the garbage disposal schedule used by the user in the past. For example, the display unit suggests an efficient display method based on the user's past garbage disposal history. The display unit also analyzes the user's past garbage disposal history and suggests the most visible display method. This makes it possible to provide the optimal display method based on the user's past garbage disposal history. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's past garbage disposal history into AI and cause the AI ​​to execute a process of selecting the optimal display method.

[0045] When the display unit displays the garbage disposal schedule, the system customizes the display content based on the user's current living situation. For example, if the user is raising children, the display unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the display unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the display unit prioritizes displaying garbage disposal information that is easy to understand. For example, if the user is raising children, the display unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the display unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the display unit prioritizes displaying garbage disposal information that is easy to understand. This makes it possible to provide information customized according to the user's living situation. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's current living situation into AI and cause the AI ​​to execute a process to customize the display content.

[0046] When the display unit displays the garbage disposal schedule, the system selects an optimal display method taking into account the user's geographical location information. For example, if the user lives in an urban area, the display unit prioritizes displaying garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the display unit prioritizes displaying garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the display unit prioritizes displaying the garbage disposal schedule for that area. For example, if the user lives in an urban area, the display unit prioritizes displaying garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the display unit prioritizes displaying garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the display unit prioritizes displaying the garbage disposal schedule for that area. This makes it possible to provide an optimal display method based on the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting an optimal display method.

[0047] When the display unit displays the trash removal schedule, the system analyzes the user's social media activity and adjusts the display content. The display unit, for example, displays related trash removal information based on information shared by the user on social media. For example, the display unit displays related trash removal information based on information about accounts the user follows on social media. The display unit also displays related trash removal information based on information about groups the user participates in on social media. For example, the display unit displays related trash removal information based on information shared by the user on social media. For example, the display unit displays related trash removal information based on information about accounts the user follows on social media. The display unit also displays related trash removal information based on information about groups the user participates in on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the display unit may be performed using, or without, AI. For example, the display unit may input the user's social media activity into AI and cause the AI ​​to perform processing to adjust the display content.

[0048] In the system, when the procedure guidance unit provides guidance on bulky waste disposal procedures, it selects the optimal guidance method by referring to the user's past bulky waste disposal history. The procedure guidance unit, for example, proposes the optimal guidance method based on the procedure methods used by the user in the past. For example, the procedure guidance unit proposes an efficient procedure guidance method based on the user's past bulky waste disposal history. The procedure guidance unit also analyzes the user's past bulky waste disposal history and proposes the easiest-to-understand procedure guidance method. For example, the procedure guidance unit proposes the optimal guidance method based on the procedure methods used by the user in the past. For example, the procedure guidance unit proposes an efficient procedure guidance method based on the user's past bulky waste disposal history. The procedure guidance unit also analyzes the user's past bulky waste disposal history and proposes the easiest-to-understand procedure guidance method. This makes it possible to provide the optimal guidance method based on the user's past bulky waste disposal history. Some or all of the above-described processing in the procedure guidance unit may be performed using, or without, AI. For example, the procedure guidance unit may input the user's past bulky waste disposal history into AI and have the AI ​​execute a process to select the optimal guidance method.

[0049] In the system, when the procedure guidance unit provides guidance for bulky waste collection procedures, the system selects the optimal guidance method taking into account the user's geographical location information. For example, if the user lives in an urban area, the procedure guidance unit prioritizes providing urban-specific procedure guidance. For example, if the user lives in a suburban area, the procedure guidance unit prioritizes providing suburban-specific procedure guidance. Furthermore, if the user lives in a specific region, the procedure guidance unit prioritizes providing procedure guidance for that region. For example, if the user lives in an urban area, the procedure guidance unit prioritizes providing urban-specific procedure guidance. For example, if the user lives in a suburban area, the procedure guidance unit prioritizes providing suburban-specific procedure guidance. Furthermore, if the user lives in a specific region, the procedure guidance unit prioritizes providing procedure guidance for that region. This makes it possible to provide the optimal guidance method based on the user's geographical location information. Some or all of the above-described processing in the procedure guidance unit may be performed using, or without, AI. For example, the procedure guidance unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting the optimal guidance method.

[0050] In the system, when the purchasing support unit purchases a bulky waste ticket, the purchasing support unit selects the optimal procedure by referring to the user's past purchase history. The purchasing support unit, for example, proposes the optimal procedure based on the purchasing methods used by the user in the past. For example, the purchasing support unit proposes an efficient purchasing procedure based on the user's past purchase history. The purchasing support unit also analyzes the user's past purchase history and proposes the easiest-to-understand purchasing procedure. For example, the purchasing support unit proposes the optimal procedure based on the purchasing methods used by the user in the past. For example, the purchasing support unit proposes an efficient purchasing procedure based on the user's past purchase history. The purchasing support unit also analyzes the user's past purchase history and proposes the easiest-to-understand purchasing procedure. This makes it possible to provide the optimal procedure based on the user's past purchase history. Some or all of the above-described processing in the purchasing support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing support unit may input the user's past purchase history into AI and have the AI ​​execute a process to select the optimal procedure.

[0051] In the system, when the purchasing support unit processes the purchase of a bulky waste ticket, the purchasing support unit selects the optimal procedure by taking into consideration the user's geographical location information. For example, if the user lives in an urban area, the purchasing support unit prioritizes providing purchasing procedures specific to urban areas. For example, if the user lives in the suburbs, the purchasing support unit prioritizes providing purchasing procedures specific to suburban areas. Furthermore, if the user lives in a specific region, the purchasing support unit prioritizes providing purchasing procedures specific to that region. For example, if the user lives in an urban area, the purchasing support unit prioritizes providing purchasing procedures specific to urban areas. For example, if the user lives in the suburbs, the purchasing support unit prioritizes providing purchasing procedures specific to suburban areas. Furthermore, if the user lives in a specific region, the purchasing support unit prioritizes providing purchasing procedures specific to that region. This allows the optimal procedure to be provided based on the user's geographical location information. Some or all of the above-described processing in the purchasing support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing support unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting the optimal procedure.

[0052] When selling bulky waste that is not needed by the sales support department, the system selects the optimal sales method by referring to the user's past sales history. The sales support department, for example, proposes the optimal sales method based on sales methods used by the user in the past. For example, the sales support department proposes an efficient sales method based on the user's past sales history. The sales support department also analyzes the user's past sales history and proposes the easiest-to-understand sales method. For example, the sales support department proposes the optimal sales method based on sales methods used by the user in the past. For example, the sales support department proposes an efficient sales method based on the user's past sales history. The sales support department also analyzes the user's past sales history and proposes the easiest-to-understand sales method. This makes it possible to provide the optimal sales method based on the user's past sales history. Some or all of the above-mentioned processing in the sales support department may be performed using, for example, AI, or may be performed without using AI. For example, the sales support department may input the user's past sales history into AI and have the AI ​​execute a process to select the optimal sales method.

[0053] When the sales support unit sells unnecessary bulky waste, the system selects the optimal sales method taking into account the user's geographical location information. For example, if the user lives in an urban area, the sales support unit prioritizes providing urban-specific sales methods. For example, if the user lives in the suburbs, the sales support unit prioritizes suburban-specific sales methods. Furthermore, if the user lives in a specific region, the sales support unit prioritizes providing sales methods for that region. For example, if the user lives in an urban area, the sales support unit prioritizes providing urban-specific sales methods. For example, if the user lives in the suburbs, the sales support unit prioritizes suburban-specific sales methods. Furthermore, if the user lives in a specific region, the sales support unit prioritizes providing sales methods for that region. This makes it possible to provide the optimal sales method based on the user's geographical location information. Some or all of the above-described processing in the sales support unit may be performed using, for example, AI, or may be performed without using AI. For example, the sales support unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting the optimal sales method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can also analyze the user's past garbage disposal history and suggest an optimal garbage disposal schedule. For example, the acquisition unit can analyze which days of the week the user often took out the garbage in the past and suggest a schedule that matches those days. The acquisition unit can also analyze which types of garbage the user often took out in the past and highlight the days to put out that type of garbage. Furthermore, the acquisition unit can analyze days when the user forgot to take out the garbage in the past and set reminders. This makes it possible to provide a more personalized garbage disposal schedule based on the user's past garbage disposal history.

[0056] The acquisition unit can also customize the garbage disposal schedule based on the user's current living situation. For example, if the user is raising children, the acquisition unit can prioritize displaying garbage disposal information related to children. Also, if the user is elderly, the acquisition unit can provide garbage disposal information that is easy to understand. Furthermore, if the user is interested in environmental protection, the acquisition unit can emphasize information about recycling. This makes it possible to provide a garbage disposal schedule that suits the user's living situation.

[0057] When displaying the garbage disposal schedule, the display unit can select the optimal display method by referring to the user's past garbage disposal history. For example, the display unit can suggest the optimal display method based on the garbage disposal schedule used by the user in the past. The display unit can also suggest an efficient display method based on the user's past garbage disposal history. Furthermore, the display unit can analyze the user's past garbage disposal history and suggest the most visible display method. This makes it possible to provide the optimal display method based on the user's past garbage disposal history.

[0058] The purchase support unit can select the optimal purchase procedure method by referring to the user's past purchase history. For example, the purchase support unit can suggest the optimal procedure method based on the purchase methods the user has used in the past. The purchase support unit can also suggest an efficient purchase procedure method based on the user's past purchase history. Furthermore, the purchase support unit can analyze the user's past purchase history and suggest the easiest purchase procedure method to understand. This makes it possible to provide the optimal procedure method based on the user's past purchase history.

[0059] The acquisition unit can analyze the user's social media activity and acquire related trash disposal information. For example, the acquisition unit can acquire related trash disposal information based on information shared by the user on social media. The acquisition unit can also acquire related trash disposal information based on information about accounts the user follows on social media. The acquisition unit can also acquire related trash disposal information based on information about groups the user participates in on social media. This makes it possible to provide related information based on the user's social media activity.

[0060] The explanation unit can adjust the level of detail of the explanation based on the importance of the garbage. For example, the explanation unit can provide a detailed explanation for how to separate important garbage. Also, the explanation unit can provide a concise explanation for how to separate less important garbage. Furthermore, the explanation unit can adjust the level of detail of the explanation according to the importance of the garbage to make it easier for the user to understand. In this way, it is possible to provide a level of detail of the explanation according to the importance of the garbage.

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

[0062] Step 1: The acquisition unit acquires the local garbage collection schedule. For example, it acquires information from a local database based on a new address entered by the user. When the user enters a new address, the garbage collection schedule for the area corresponding to that address can be automatically acquired. Step 2: The explanation unit explains how to separate waste based on the information acquired by the acquisition unit. For example, detailed explanations are provided for each category, such as plastic, paper, and metal. Specific steps and precautions for separating plastic waste can be explained in detail. Step 3: The display unit displays the garbage collection schedule based on the separation method explained by the explanation unit. For example, the display unit can display the garbage collection schedule for both the old address and the new address. If Monday is the garbage collection day at the old address and Tuesday is the garbage collection day at the new address, the display unit can notify the user of this information.

[0063] (Example 2) A system according to an embodiment of the present invention is an application system that allows users to easily understand and efficiently execute local garbage collection schedules and garbage sorting methods. This system supports environmentally friendly garbage disposal, particularly when moving, by eliminating the need to find appropriate garbage collection days and sorting methods for their old and new addresses. Furthermore, the system also assists users with the handling of bulky garbage, providing comprehensive support for sales via flea market apps and the purchase of bulky garbage tickets. For example, when a user enters a new address, the system automatically obtains the local garbage collection schedule and sorting method. For example, information such as Monday for burnable garbage and Tuesday for non-burnable garbage is displayed. Detailed instructions for sorting garbage by category, such as plastic, paper, and metal, are also provided. Next, when moving, the system displays garbage collection schedules for both the old and new addresses, allowing users to smoothly dispose of their garbage. For example, if Monday is the day for burnable garbage at the old address and Tuesday is the day for burnable garbage at the new address, the system notifies the user of this information. Furthermore, the system also supports the handling of bulky garbage. When a user disposes of oversized garbage, the system guides them through the procedure and supports them in purchasing the necessary oversized garbage tickets. It is also possible to link with a flea market app to sell unwanted oversized garbage. For example, if unwanted furniture is put up for sale on a flea market app and a buyer is found, there is no need to dispose of it as garbage. In this way, the system is an application system that makes it easy to understand and efficiently carry out local garbage collection schedules and sorting methods, and it particularly supports garbage disposal when moving. It also provides consistent support for handling oversized garbage, realizing environmentally friendly garbage disposal. This allows the system to efficiently understand and carry out local garbage collection schedules and sorting methods.

[0064] A garbage disposal support system according to an embodiment includes an acquisition unit, an explanation unit, and a display unit. The acquisition unit acquires a local garbage disposal schedule. The acquisition unit acquires information from a local database based on, for example, a new address entered by a user. For example, when a user enters a new address, the acquisition unit can automatically acquire the local garbage disposal schedule corresponding to the address. The explanation unit explains how to separate garbage based on the information acquired by the acquisition unit. The explanation unit provides detailed explanations for each category, such as plastic, paper, and metal. For example, the explanation unit can explain in detail specific steps and precautions for separating plastic garbage. The display unit displays the garbage disposal schedule based on the separation method explained by the explanation unit. The display unit can display the garbage disposal schedules for both the old address and the new address. For example, if Monday is the day for combustible garbage at the old address and Tuesday is the day for combustible garbage at the new address, the display unit can notify the user of this information. This allows the garbage disposal support system according to an embodiment to efficiently understand and implement the garbage disposal schedule and separation method for each region.

[0065] The acquisition unit can acquire information from a local database based on a new address entered by the user. For example, when a user enters a new address, the acquisition unit can automatically acquire the local garbage collection schedule and sorting method corresponding to that address. For example, the acquisition unit can access a local database and acquire information such as combustible garbage on Mondays and non-combustible garbage on Tuesdays. This allows the user to automatically acquire the local garbage collection schedule and sorting method simply by entering a new address. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the new address entered by the user into AI and cause the AI ​​to execute processing to acquire information from the local database.

[0066] The explanation unit can provide detailed explanations for each category of plastic, paper, and metal. The explanation unit can, for example, provide detailed explanations of specific steps and precautions regarding how to separate plastic waste. For example, the explanation unit can explain how to wash plastic waste and remove labels before sorting. The explanation unit can also explain how to separate paper waste by category, such as newspaper, magazines, and cardboard. For example, the explanation unit can explain how to bundle newspapers, tie magazines with string, and flatten cardboard before disposing of them. The explanation unit can also explain how to separate metal waste by category, such as cans, bottles, and metal products. For example, the explanation unit can explain how to wash cans before disposing of them, remove caps from bottles before disposing of them, and disassemble metal products before disposing of them. This allows the user to understand how to separate waste in detail for each category. Some or all of the above-described processing by the explanation unit can be performed using, or without, AI. For example, the explanation unit can input the plastic waste separation method into AI and cause the AI ​​to execute a process of explaining specific steps and precautions.

[0067] The display unit can display garbage collection schedules for the old address and the new address. For example, if Monday is burnable garbage collection day at the old address and Tuesday is burnable garbage collection day at the new address, the display unit can notify the user of this information. For example, the display unit can simultaneously display the garbage collection schedules for the old address and the new address, allowing the user to smoothly dispose of garbage. The display unit can also display the garbage collection schedule in calendar format. For example, the display unit can display information on the calendar such as burnable garbage on Monday, non-burnable garbage on Tuesday, and recyclable garbage on Wednesday. This allows the user to simultaneously check the garbage collection schedules for the old address and the new address when moving. Some or all of the above-mentioned processing on the display unit may be performed using, or without, AI. For example, the display unit can input the garbage collection schedules for the old address and the new address into AI and have the AI ​​execute a process to display them in calendar format.

[0068] The system includes a procedure guidance unit that guides the user through the procedure for disposing of oversized waste. The procedure guidance unit guides the user through the procedure, for example, when disposing of oversized waste. For example, the procedure guidance unit provides information such as how to apply for oversized waste disposal, the necessary documents, and fees. The procedure guidance unit can also provide information on the collection date and location for oversized waste. For example, the procedure guidance unit notifies the user that the collection date is Monday and that the collection location is a designated location. This allows for easy guidance through the procedure for disposing of oversized waste. Some or all of the above-described processing in the procedure guidance unit may be performed using, or without, AI. For example, the procedure guidance unit can input the application method for oversized waste disposal and the necessary documents into AI and have the AI ​​execute the process of guiding the user.

[0069] The system includes a purchase support unit that supports the purchasing procedure for bulky waste tickets. The purchase support unit supports the procedure, for example, when a user purchases a bulky waste ticket. For example, the purchase support unit provides information such as where to purchase the bulky waste ticket, payment methods, and necessary documents. The purchase support unit can also support online purchasing procedures. For example, the purchase support unit guides the user through the necessary procedures so that the user can purchase the bulky waste ticket online. This simplifies the purchasing procedure for the bulky waste ticket. Some or all of the above-described processing in the purchase support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchase support unit can input information about where to purchase the bulky waste ticket and payment methods into AI, and have the AI ​​execute a process to guide the user.

[0070] The system includes a sales support unit that works in conjunction with a flea market app to sell unwanted bulky waste. The sales support unit supports, for example, a user when selling unwanted bulky waste on the flea market app. For example, the sales support unit provides information such as how to use the flea market app, the listing procedure, and interactions with buyers. The sales support unit can also work in conjunction with the flea market app to automatically list unwanted bulky waste. For example, the sales support unit supports a user when listing unwanted furniture on the flea market app and, if a buyer is found, eliminates the need to dispose of the item as trash. This allows unwanted bulky waste to be sold on the flea market app. Some or all of the above-described processing in the sales support unit may be performed, for example, using AI, or may be performed without using AI. For example, the sales support unit may input information about how to use the flea market app and the listing procedure into AI, and have the AI ​​execute a process to guide the user.

[0071] In the system, the acquisition unit estimates the user's emotions and adjusts the timing of inputting a new address based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit simplifies the input procedure, allowing the user to input only the minimum amount of information. For example, if the user is relaxed, the acquisition unit provides detailed input options and suggests a customizable input method. Furthermore, if the user is in a hurry, the acquisition unit prioritizes voice input, allowing the user to quickly input a new address. For example, if the user is feeling stressed, the acquisition unit displays a simple input form, allowing the user to input only the minimum amount of information. Furthermore, if the user is relaxed, the acquisition unit displays a detailed input form and provides options that the user can customize. Furthermore, if the user is in a hurry, the acquisition unit prioritizes voice input, allowing the user to input a new address simply by speaking. This allows the timing of inputting a new address to be adjusted according to the user's emotions. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without AI. For example, the acquisition unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate their emotions. Voice analysis involves inputting the user's voice data captured by a microphone into AI to estimate their emotions. This allows the acquisition unit to adjust the timing of inputting a new address based on the user's emotions.

[0072] In the system, the acquisition unit analyzes the user's past address change history and selects the optimal information acquisition method. The acquisition unit proposes the optimal information acquisition method, for example, based on addresses that the user has frequently changed in the past. For example, the acquisition unit finds a specific pattern from the user's past address change history and adjusts the information acquisition method based on that pattern. The acquisition unit also analyzes the user's past address change history and proposes the most efficient information acquisition method. For example, the acquisition unit proposes the optimal information acquisition method based on addresses that the user has frequently changed in the past. For example, the acquisition unit finds a specific pattern from the user's past address change history and adjusts the information acquisition method based on that pattern. The acquisition unit also analyzes the user's past address change history and proposes the most efficient information acquisition method. This makes it possible to provide the optimal information acquisition method based on the user's past address change history. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's past address change history into AI and cause the AI ​​to execute a process of selecting the optimal information acquisition method.

[0073] When the acquisition unit inputs a new address, the system filters the information based on the user's current living situation and areas of interest. For example, if the user is raising children, the acquisition unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the acquisition unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the acquisition unit prioritizes displaying garbage disposal information that is easy to understand. For example, if the user is raising children, the acquisition unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the acquisition unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the acquisition unit prioritizes displaying garbage disposal information that is easy to understand. This makes it possible to provide information tailored to the user's living situation and areas of interest. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's current living situation and areas of interest into AI and have the AI ​​perform the filtering process.

[0074] In the system, the acquisition unit estimates the user's emotions and determines the priority of information to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit prioritizes displaying important information and postpones other information. For example, when the user is relaxed, the acquisition unit prioritizes displaying detailed information to allow the user to select it. Furthermore, when the user is in a hurry, the acquisition unit quickly displays the most important information. For example, when the user is feeling stressed, the acquisition unit prioritizes displaying important information and postpones other information. Furthermore, when the user is relaxed, the acquisition unit prioritizes displaying detailed information to allow the user to select it. Furthermore, when the user is in a hurry, the acquisition unit quickly displays the most important information. This allows the priority of information to be determined according to the user's emotions. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate the emotion. Voice analysis involves inputting the user's voice data acquired by a microphone into AI to estimate their emotions, allowing the acquisition unit to determine the priority of the information to be acquired based on the user's emotions.

[0075] In the system, when the acquisition unit inputs a new address, the acquisition unit prioritizes acquiring highly relevant information taking into account the user's geographical location information. For example, if the user lives in an urban area, the acquisition unit prioritizes acquiring garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the acquisition unit prioritizes acquiring garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the acquisition unit prioritizes acquiring the garbage disposal schedule for that area. For example, if the user lives in an urban area, the acquisition unit prioritizes acquiring garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the acquisition unit prioritizes acquiring garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the acquisition unit prioritizes acquiring the garbage disposal schedule for that area. This makes it possible to provide highly relevant information based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to execute a process of preferentially acquiring highly relevant information.

[0076] In the system, when a new address is input, the acquisition unit analyzes the user's social media activity and acquires related information. The acquisition unit, for example, acquires related trash disposal information based on information shared by the user on social media. For example, the acquisition unit acquires related trash disposal information based on information about accounts the user follows on social media. The acquisition unit also acquires related trash disposal information based on information about groups the user participates in on social media. For example, the acquisition unit acquires related trash disposal information based on information shared by the user on social media. For example, the acquisition unit acquires related trash disposal information based on information about accounts the user follows on social media. The acquisition unit also acquires related trash disposal information based on information about groups the user participates in on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's social media activity into AI and cause the AI ​​to execute a process of acquiring related information.

[0077] In the system, the explanation unit estimates the user's emotions and adjusts the explanation method for the sorting method based on the estimated user emotions. For example, if the user is feeling stressed, the explanation unit provides a simple and intuitive explanation. For example, if the user is relaxed, the explanation unit provides a detailed explanation to make it easier for the user to understand. Furthermore, if the user is in a hurry, the explanation unit provides a concise explanation that focuses on the main points. For example, if the user is feeling stressed, the explanation unit provides a simple and intuitive explanation. For example, if the user is relaxed, the explanation unit provides a detailed explanation to make it easier for the user to understand. Furthermore, if the user is in a hurry, the explanation unit provides a concise explanation that focuses on the main points. This allows the explanation method for the sorting method to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without AI. For example, the explanation unit can use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition inputs the user's facial expression data captured by a camera into AI to estimate the emotion. Voice analysis inputs the user's voice data captured by a microphone into AI to estimate the emotion. This allows the explanation unit to adjust the explanation method for the classification method based on the user's feelings.

[0078] In the system, when the explanation unit explains the sorting method, it adjusts the level of detail of the explanation based on the importance of the garbage. For example, the explanation unit provides a detailed explanation for a sorting method for important garbage. For example, the explanation unit provides a concise explanation for a sorting method for less important garbage. Furthermore, the explanation unit adjusts the level of detail of the explanation according to the importance of the garbage to make it easier for the user to understand. For example, the explanation unit provides a detailed explanation for a sorting method for important garbage. For example, the explanation unit provides a concise explanation for a sorting method for less important garbage. Furthermore, the explanation unit adjusts the level of detail of the explanation according to the importance of the garbage to make it easier for the user to understand. In this way, the level of detail of the explanation can be adjusted according to the importance of the garbage. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit may input the importance of the garbage into AI and cause the AI ​​to execute processing to adjust the level of detail of the explanation.

[0079] When the explanation unit explains the sorting method, the system applies different explanation algorithms depending on the category of waste. For example, the explanation unit explains in detail how to recycle plastic waste. For example, the explanation unit explains how to sort and reuse paper waste. Furthermore, the explanation unit explains how to sort and safely dispose of metal waste. For example, the explanation unit explains in detail how to recycle plastic waste. For example, the explanation unit explains how to sort and reuse paper waste. Furthermore, the explanation unit explains how to sort and safely dispose of metal waste. This makes it possible to provide appropriate explanations according to the category of waste. Some or all of the above-mentioned processing in the explanation unit may be performed using, or without, AI. For example, the explanation unit may input the category of waste into AI and cause the AI ​​to execute a process of applying different explanation algorithms.

[0080] In the system, the explanation unit estimates the user's emotions and adjusts the length of the explanation based on the estimated user emotions. For example, if the user is stressed, the explanation unit provides a short and to-the-point explanation. For example, if the user is relaxed, the explanation unit provides a detailed explanation to make it easier for the user to understand. Furthermore, if the user is in a hurry, the explanation unit provides a concise and quick explanation. For example, if the user is stressed, the explanation unit provides a short and to-the-point explanation. For example, if the user is relaxed, the explanation unit provides a detailed explanation to make it easier for the user to understand. Furthermore, if the user is in a hurry, the explanation unit provides a concise and quick explanation. This allows the length of the explanation to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the explanation unit may be performed using, for example, AI, or may be performed without AI. For example, the explanation unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition inputs the user's facial expression data captured by a camera into AI to estimate emotions. Voice analysis inputs the user's voice data captured by a microphone into AI to estimate emotions. This allows the explanation unit to adjust the length of the explanation based on the user's feelings.

[0081] In the system, when the explanation unit explains the sorting method, it determines the priority of the explanation based on the time of garbage submission. For example, the explanation unit provides explanations preferentially for garbage that is due to be submitted soon. For example, the explanation unit provides explanations later for garbage that is due to be submitted further away. Furthermore, the explanation unit adjusts the priority of the explanations according to the time of garbage submission, allowing the user to dispose of garbage efficiently. For example, the explanation unit provides explanations preferentially for garbage that is due to be submitted soon. For example, the explanation unit provides explanations later for garbage that is due to be submitted further away. Furthermore, the explanation unit adjusts the priority of the explanations according to the time of garbage submission, allowing the user to dispose of garbage efficiently. In this way, the priority of the explanations can be adjusted according to the time of garbage submission. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit may input the time of garbage submission into AI and cause the AI ​​to execute processing to determine the priority of the explanations.

[0082] In the system, when the explanation unit explains the sorting method, it adjusts the order of the explanation based on the relevance of the garbage. For example, the explanation unit provides an explanation preferentially for highly relevant garbage. For example, the explanation unit provides an explanation later for less relevant garbage. Furthermore, the explanation unit adjusts the order of the explanation according to the relevance of the garbage to make it easier for the user to understand. For example, the explanation unit provides an explanation preferentially for highly relevant garbage. For example, the explanation unit provides an explanation later for less relevant garbage. Furthermore, the explanation unit adjusts the order of the explanation according to the relevance of the garbage to make it easier for the user to understand. In this way, the order of the explanation can be adjusted according to the relevance of the garbage. Some or all of the above-mentioned processing in the explanation unit may be performed using AI, for example, or may be performed without using AI. For example, the explanation unit inputs the relevance of the garbage into AI and causes the AI ​​to execute processing to adjust the order of the explanation.

[0083] In the system, the display unit estimates the user's emotions and adjusts the display method of the garbage disposal schedule based on the estimated user emotions. For example, if the user is feeling stressed, the display unit provides a simple, highly visible display method. For example, if the user is relaxed, the display unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the display unit provides a display method that focuses on the main points. For example, if the user is feeling stressed, the display unit provides a simple, highly visible display method. For example, if the user is relaxed, the display unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the display unit provides a display method that focuses on the main points. This allows the display method of the garbage disposal schedule to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may use facial expression recognition or voice analysis to estimate the user's emotions. For facial expression recognition, data of the user's facial expression captured by a camera is input into AI to estimate the emotion. For voice analysis, data of the user's voice captured by a microphone is input into AI to estimate the emotion. This allows the display unit to adjust the way the trash removal schedule is displayed based on the user's emotions.

[0084] When the display unit displays the garbage disposal schedule, the system selects the optimal display method by referring to the user's past garbage disposal history. The display unit, for example, suggests the optimal display method based on the garbage disposal schedule used by the user in the past. For example, the display unit suggests an efficient display method based on the user's past garbage disposal history. The display unit also analyzes the user's past garbage disposal history and suggests the most visible display method. For example, the display unit suggests the optimal display method based on the garbage disposal schedule used by the user in the past. For example, the display unit suggests an efficient display method based on the user's past garbage disposal history. The display unit also analyzes the user's past garbage disposal history and suggests the most visible display method. This makes it possible to provide the optimal display method based on the user's past garbage disposal history. Some or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's past garbage disposal history into AI and cause the AI ​​to execute a process of selecting the optimal display method.

[0085] When the display unit displays the garbage disposal schedule, the system customizes the display content based on the user's current living situation. For example, if the user is raising children, the display unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the display unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the display unit prioritizes displaying garbage disposal information that is easy to understand. For example, if the user is raising children, the display unit prioritizes displaying garbage disposal information related to children. For example, if the user is interested in environmental protection, the display unit prioritizes displaying information about recycling. Furthermore, if the user is elderly, the display unit prioritizes displaying garbage disposal information that is easy to understand. This makes it possible to provide information customized according to the user's living situation. Some or all of the above-described processing on the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's current living situation into AI and cause the AI ​​to execute a process to customize the display content.

[0086] In the system, the display unit estimates the user's emotions and determines the priority of the trash removal schedule based on the estimated user emotions. For example, when the user is stressed, the display unit prioritizes displaying important information and postpones other information. For example, when the user is relaxed, the display unit prioritizes displaying detailed information to allow the user to select it. Furthermore, when the user is in a hurry, the display unit quickly displays the most important information. For example, when the user is stressed, the display unit prioritizes displaying important information and postpones other information. Furthermore, when the user is relaxed, the display unit prioritizes displaying detailed information to allow the user to select it. Furthermore, when the user is in a hurry, the display unit quickly displays the most important information. This allows the priority of the trash removal schedule to be determined according to the user's emotions. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may use facial expression recognition or voice analysis to estimate the user's emotions. For facial expression recognition, data of the user's facial expression captured by a camera is input into AI to estimate the emotion. Voice analysis uses AI to estimate emotions from user voice data captured by a microphone, allowing the display unit to prioritize the trash removal schedule based on the user's emotions.

[0087] When the display unit displays the garbage disposal schedule, the system selects an optimal display method taking into account the user's geographical location information. For example, if the user lives in an urban area, the display unit prioritizes displaying garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the display unit prioritizes displaying garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the display unit prioritizes displaying the garbage disposal schedule for that area. For example, if the user lives in an urban area, the display unit prioritizes displaying garbage disposal information specific to urban areas. For example, if the user lives in a suburban area, the display unit prioritizes displaying garbage disposal information specific to suburban areas. Furthermore, if the user lives in a specific area, the display unit prioritizes displaying the garbage disposal schedule for that area. This makes it possible to provide an optimal display method based on the user's geographical location information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting an optimal display method.

[0088] When the display unit displays the trash removal schedule, the system analyzes the user's social media activity and adjusts the display content. The display unit, for example, displays related trash removal information based on information shared by the user on social media. For example, the display unit displays related trash removal information based on information about accounts the user follows on social media. The display unit also displays related trash removal information based on information about groups the user participates in on social media. For example, the display unit displays related trash removal information based on information shared by the user on social media. For example, the display unit displays related trash removal information based on information about accounts the user follows on social media. The display unit also displays related trash removal information based on information about groups the user participates in on social media. This makes it possible to provide related information based on the user's social media activity. Some or all of the above-described processing by the display unit may be performed using, or without, AI. For example, the display unit may input the user's social media activity into AI and cause the AI ​​to perform processing to adjust the display content.

[0089] In the system, the procedure guidance unit estimates the user's emotions and adjusts the oversized waste disposal procedure guidance method based on the estimated user emotions. For example, if the user is stressed, the procedure guidance unit provides simple and intuitive procedure guidance. For example, if the user is relaxed, the procedure guidance unit provides detailed procedure guidance to make it easier for the user to understand. Furthermore, if the user is in a hurry, the procedure guidance unit provides concise procedure guidance that focuses on the main points. For example, if the user is stressed, the procedure guidance unit provides simple and intuitive procedure guidance. For example, if the user is relaxed, the procedure guidance unit provides detailed procedure guidance to make it easier for the user to understand. Furthermore, if the user is in a hurry, the procedure guidance unit provides concise procedure guidance that focuses on the main points. This allows the oversized waste disposal procedure guidance method to be adjusted according to the user's emotions. Some or all of the above-described processing in the procedure guidance unit may be performed using, for example, AI, or may be performed without AI. For example, the procedure guidance unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate emotions. Voice analysis involves inputting the user's voice data collected by a microphone into AI to estimate their emotions, allowing the procedure guidance unit to adjust the guidance method for bulky waste disposal procedures based on the user's emotions.

[0090] In the system, when the procedure guidance unit provides guidance on bulky waste disposal procedures, it selects the optimal guidance method by referring to the user's past bulky waste disposal history. The procedure guidance unit, for example, proposes the optimal guidance method based on the procedure methods used by the user in the past. For example, the procedure guidance unit proposes an efficient procedure guidance method based on the user's past bulky waste disposal history. The procedure guidance unit also analyzes the user's past bulky waste disposal history and proposes the easiest-to-understand procedure guidance method. For example, the procedure guidance unit proposes the optimal guidance method based on the procedure methods used by the user in the past. For example, the procedure guidance unit proposes an efficient procedure guidance method based on the user's past bulky waste disposal history. The procedure guidance unit also analyzes the user's past bulky waste disposal history and proposes the easiest-to-understand procedure guidance method. This makes it possible to provide the optimal guidance method based on the user's past bulky waste disposal history. Some or all of the above-described processing in the procedure guidance unit may be performed using, or without, AI. For example, the procedure guidance unit may input the user's past bulky waste disposal history into AI and have the AI ​​execute a process to select the optimal guidance method.

[0091] In the system, the procedure guidance unit estimates the user's emotions and determines the priority of oversized waste procedure guidance based on the estimated user emotions. For example, if the user is stressed, the procedure guidance unit prioritizes important procedures and postpones other procedures. For example, if the user is relaxed, the procedure guidance unit prioritizes detailed procedures, allowing the user to select. Furthermore, if the user is in a hurry, the procedure guidance unit quickly guides the most important procedures. For example, if the user is stressed, the procedure guidance unit prioritizes important procedures and postpones other procedures. For example, if the user is relaxed, the procedure guidance unit prioritizes detailed procedures, allowing the user to select. Furthermore, if the user is in a hurry, the procedure guidance unit quickly guides the most important procedures. This allows the priority of oversized waste procedure guidance to be determined according to the user's emotions. Some or all of the above-described processing in the procedure guidance unit may be performed using AI, or may be performed without AI. For example, the procedure guidance unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate their emotions. Voice analysis involves inputting the user's voice data captured by a microphone into AI to estimate their emotions. This allows the procedure guidance unit to determine the priority of oversized waste procedure guidance based on the user's emotions.

[0092] In the system, when the procedure guidance unit provides guidance for bulky waste collection procedures, the system selects the optimal guidance method taking into account the user's geographical location information. For example, if the user lives in an urban area, the procedure guidance unit prioritizes providing urban-specific procedure guidance. For example, if the user lives in a suburban area, the procedure guidance unit prioritizes providing suburban-specific procedure guidance. Furthermore, if the user lives in a specific region, the procedure guidance unit prioritizes providing procedure guidance for that region. For example, if the user lives in an urban area, the procedure guidance unit prioritizes providing urban-specific procedure guidance. For example, if the user lives in a suburban area, the procedure guidance unit prioritizes providing suburban-specific procedure guidance. Furthermore, if the user lives in a specific region, the procedure guidance unit prioritizes providing procedure guidance for that region. This makes it possible to provide the optimal guidance method based on the user's geographical location information. Some or all of the above-described processing in the procedure guidance unit may be performed using, or without, AI. For example, the procedure guidance unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting the optimal guidance method.

[0093] In the system, the purchasing support unit estimates the user's emotions and adjusts the purchasing procedure for bulky waste tickets based on the estimated user emotions. For example, if the user is stressed, the purchasing support unit provides a simple and intuitive purchasing procedure. For example, if the user is relaxed, the purchasing support unit provides a detailed purchasing procedure that is easy for the user to understand. Furthermore, if the user is in a hurry, the purchasing support unit provides a concise purchasing procedure that focuses on the main points. For example, if the user is stressed, the purchasing support unit provides a simple and intuitive purchasing procedure. For example, if the user is relaxed, the purchasing support unit provides a detailed purchasing procedure that is easy for the user to understand. Furthermore, if the user is in a hurry, the purchasing support unit provides a concise purchasing procedure that focuses on the main points. This allows the purchasing procedure for bulky waste tickets to be adjusted according to the user's emotions. Some or all of the above-described processing in the purchasing support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing support unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate their emotions. Voice analysis involves inputting the user's voice data captured by a microphone into AI to estimate their emotions. This allows the purchasing support department to adjust the bulky waste ticket purchasing procedure based on the user's emotions.

[0094] In the system, when the purchasing support unit purchases a bulky waste ticket, the purchasing support unit selects the optimal procedure by referring to the user's past purchase history. The purchasing support unit, for example, proposes the optimal procedure based on the purchasing methods used by the user in the past. For example, the purchasing support unit proposes an efficient purchasing procedure based on the user's past purchase history. The purchasing support unit also analyzes the user's past purchase history and proposes the easiest-to-understand purchasing procedure. For example, the purchasing support unit proposes the optimal procedure based on the purchasing methods used by the user in the past. For example, the purchasing support unit proposes an efficient purchasing procedure based on the user's past purchase history. The purchasing support unit also analyzes the user's past purchase history and proposes the easiest-to-understand purchasing procedure. This makes it possible to provide the optimal procedure based on the user's past purchase history. Some or all of the above-described processing in the purchasing support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing support unit may input the user's past purchase history into AI and have the AI ​​execute a process to select the optimal procedure.

[0095] In the system, the purchasing support unit estimates the user's emotions and determines the priority of the bulky waste ticket purchasing procedure based on the estimated user emotions. For example, if the user is feeling stressed, the purchasing support unit prioritizes important purchasing procedures and postpones other procedures. For example, if the user is relaxed, the purchasing support unit prioritizes detailed purchasing procedures to allow the user to select. Furthermore, if the user is in a hurry, the purchasing support unit quickly guides the user through the most important purchasing procedures. For example, if the user is feeling stressed, the purchasing support unit prioritizes important purchasing procedures and postpones other procedures. For example, if the user is relaxed, the purchasing support unit prioritizes detailed purchasing procedures to allow the user to select. Furthermore, if the user is in a hurry, the purchasing support unit quickly guides the user through the most important purchasing procedures. This allows the priority of the bulky waste ticket purchasing procedure to be determined according to the user's emotions. Some or all of the above-described processing in the purchasing support unit may be performed using AI, for example, or without AI. For example, the purchasing support unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate their emotions. Voice analysis involves inputting the user's voice data captured by a microphone into AI to estimate their emotions. This allows the purchasing support department to determine the priority of bulky waste ticket purchasing procedures based on the user's emotions.

[0096] In the system, when the purchasing support unit processes the purchase of a bulky waste ticket, the purchasing support unit selects the optimal procedure by taking into consideration the user's geographical location information. For example, if the user lives in an urban area, the purchasing support unit prioritizes providing purchasing procedures specific to urban areas. For example, if the user lives in the suburbs, the purchasing support unit prioritizes providing purchasing procedures specific to suburban areas. Furthermore, if the user lives in a specific region, the purchasing support unit prioritizes providing purchasing procedures specific to that region. For example, if the user lives in an urban area, the purchasing support unit prioritizes providing purchasing procedures specific to urban areas. For example, if the user lives in the suburbs, the purchasing support unit prioritizes providing purchasing procedures specific to suburban areas. Furthermore, if the user lives in a specific region, the purchasing support unit prioritizes providing purchasing procedures specific to that region. This allows the optimal procedure to be provided based on the user's geographical location information. Some or all of the above-described processing in the purchasing support unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing support unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting the optimal procedure.

[0097] In the system, the sales support unit estimates a user's emotions and adjusts the sales method for unwanted bulky waste based on the estimated user emotions. For example, if the user is stressed, the sales support unit provides a simple and intuitive sales method. For example, if the user is relaxed, the sales support unit provides a detailed sales method that is easy for the user to understand. Furthermore, if the user is in a hurry, the sales support unit provides a concise sales method that focuses on the main points. For example, if the user is stressed, the sales support unit provides a simple and intuitive sales method. For example, if the user is relaxed, the sales support unit provides a detailed sales method that is easy for the user to understand. Furthermore, if the user is in a hurry, the sales support unit provides a concise sales method that focuses on the main points. This allows the sales method for unwanted bulky waste to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the sales support unit may be performed using, for example, AI, or may be performed without AI. For example, the sales support unit may use facial expression recognition or voice analysis to estimate the user's emotions. For facial expression recognition, the AI ​​inputs the user's facial expression data captured by a camera and estimates the emotion. Voice analysis uses AI to capture users' voice data via microphone and estimate their emotions, allowing the sales support department to adjust how unwanted bulky waste is sold based on the user's emotions.

[0098] When selling bulky waste that is not needed by the sales support department, the system selects the optimal sales method by referring to the user's past sales history. The sales support department, for example, proposes the optimal sales method based on sales methods used by the user in the past. For example, the sales support department proposes an efficient sales method based on the user's past sales history. The sales support department also analyzes the user's past sales history and proposes the easiest-to-understand sales method. For example, the sales support department proposes the optimal sales method based on sales methods used by the user in the past. For example, the sales support department proposes an efficient sales method based on the user's past sales history. The sales support department also analyzes the user's past sales history and proposes the easiest-to-understand sales method. This makes it possible to provide the optimal sales method based on the user's past sales history. Some or all of the above-mentioned processing in the sales support department may be performed using, for example, AI, or may be performed without using AI. For example, the sales support department may input the user's past sales history into AI and have the AI ​​execute a process to select the optimal sales method.

[0099] In the system, the sales support unit estimates a user's emotions and determines the sales priority of unnecessary bulky waste based on the estimated user emotions. For example, if the user is feeling stressed, the sales support unit prioritizes important sales and postpones other sales. For example, if the user is relaxed, the sales support unit prioritizes detailed sales and allows the user to select. Also, if the user is in a hurry, the sales support unit quickly introduces the most important sales. For example, if the user is feeling stressed, the sales support unit prioritizes important sales and postpones other sales. For example, if the user is relaxed, the sales support unit prioritizes detailed sales and allows the user to select. Also, if the user is in a hurry, the sales support unit quickly introduces the most important sales. This allows the sales priority of unnecessary bulky waste to be determined according to the user's emotions. Some or all of the above-described processing in the sales support unit may be performed using AI, for example, or without AI. For example, the sales support unit may use facial expression recognition or voice analysis to estimate the user's emotions. Facial expression recognition involves inputting the user's facial expression data captured by a camera into AI to estimate their emotions. Voice analysis involves inputting the user's voice data captured by a microphone into AI to estimate their emotions. This allows the sales support department to determine the sales priorities for unwanted bulky waste based on the user's emotions.

[0100] When the sales support unit sells unnecessary bulky waste, the system selects the optimal sales method taking into account the user's geographical location information. For example, if the user lives in an urban area, the sales support unit prioritizes providing urban-specific sales methods. For example, if the user lives in the suburbs, the sales support unit prioritizes suburban-specific sales methods. Furthermore, if the user lives in a specific region, the sales support unit prioritizes providing sales methods for that region. For example, if the user lives in an urban area, the sales support unit prioritizes providing urban-specific sales methods. For example, if the user lives in the suburbs, the sales support unit prioritizes suburban-specific sales methods. Furthermore, if the user lives in a specific region, the sales support unit prioritizes providing sales methods for that region. This makes it possible to provide the optimal sales method based on the user's geographical location information. Some or all of the above-described processing in the sales support unit may be performed using, for example, AI, or may be performed without using AI. For example, the sales support unit may input the user's geographical location information into AI and cause the AI ​​to execute a process of selecting the optimal sales method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, explanation unit, display unit, procedure guidance unit, purchase support unit, sales support unit, and emotion estimation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires information from a local database based on a new address entered by the user. The explanation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and explains how to separate garbage based on the information acquired by the acquisition unit. The display unit is implemented, for example, by the control unit 46A of the smart device 14 and displays a garbage collection schedule based on the separation method explained by the explanation unit. The procedure guidance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and guides the user through the procedure when disposing of bulky garbage. The purchase support unit is implemented, for example, by the control unit 46A of the smart device 14 and supports the procedure for purchasing bulky garbage tickets. The sales support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the procedure of selling unwanted bulky waste through the flea market app. The emotion estimation unit estimates the user's emotion using, for example, the camera 42 or microphone 38B of the smart device 14, and adjusts the operation of the acquisition unit and explanation unit. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, explanation unit, display unit, procedure guidance unit, purchase support unit, sales support unit, and emotion estimation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 and acquires information from a local database based on a new address entered by the user. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains how to separate garbage based on the information acquired by the acquisition unit. The display unit is realized, for example, by the control unit 46A of the smart glasses 214 and displays a garbage collection schedule based on the separation method explained by the explanation unit. The procedure guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and guides the user through the procedure when disposing of bulky garbage. The purchase support unit is realized, for example, by the control unit 46A of the smart glasses 214 and supports the procedure for purchasing bulky garbage tickets. The sales support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the procedure of selling unwanted bulky waste through a flea market app. The emotion estimation unit estimates the user's emotion using, for example, the camera 42 and microphone 238 of the smart glasses 214, and adjusts the operation of the acquisition unit and explanation unit. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, explanation unit, display unit, procedure guidance unit, purchase support unit, sales support unit, and emotion estimation unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires information from a local database based on a new address entered by the user. The explanation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and explains how to separate garbage based on the information acquired by the acquisition unit. The display unit is implemented, for example, by the control unit 46A of the headset terminal 314 and displays a garbage collection schedule based on the separation method explained by the explanation unit. The procedure guidance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and guides the user through the procedure when disposing of oversized garbage. The purchase support unit is implemented, for example, by the control unit 46A of the headset terminal 314 and supports the purchase procedure for oversized garbage tickets. The sales support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the procedure for selling unwanted bulky waste through the flea market app. The emotion estimation unit estimates the user's emotion using, for example, the camera 42 and microphone 238 of the headset terminal 314, and adjusts the operation of the acquisition unit and explanation unit. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, explanation unit, display unit, procedure guidance unit, purchase support unit, sales support unit, and emotion estimation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 and acquires information from a local database based on a new address entered by the user. The explanation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and explains how to separate garbage based on the information acquired by the acquisition unit. The display unit is realized, for example, by the control unit 46A of the robot 414 and displays a garbage collection schedule based on the sorting method explained by the explanation unit. The procedure guidance unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and guides the user through the procedure when disposing of bulky garbage. The purchase support unit is realized, for example, by the control unit 46A of the robot 414 and supports the procedure for purchasing bulky garbage tickets. The sales support unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and supports the procedure for selling unwanted bulky waste on the flea market app. The emotion estimation unit estimates the user's emotion using, for example, the camera 42 and microphone 238 of the robot 414, and adjusts the operation of the acquisition unit and explanation unit.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The acquisition unit can also analyze the user's past garbage disposal history and suggest an optimal garbage disposal schedule. For example, the acquisition unit can analyze which days of the week the user often took out the garbage in the past and suggest a schedule that matches those days. The acquisition unit can also analyze which types of garbage the user often took out in the past and highlight the days to put out that type of garbage. Furthermore, the acquisition unit can analyze days when the user forgot to take out the garbage in the past and set reminders. This makes it possible to provide a more personalized garbage disposal schedule based on the user's past garbage disposal history.

[0103] The acquisition unit can also customize the garbage disposal schedule based on the user's current living situation. For example, if the user is raising children, the acquisition unit can prioritize displaying garbage disposal information related to children. Also, if the user is elderly, the acquisition unit can provide garbage disposal information that is easy to understand. Furthermore, if the user is interested in environmental protection, the acquisition unit can emphasize information about recycling. This makes it possible to provide a garbage disposal schedule that suits the user's living situation.

[0104] The explanation unit can estimate the user's emotions and adjust the explanation of the sorting method based on the estimated emotions. For example, if the user is feeling stressed, the explanation unit provides a simple and intuitive explanation. If the user is relaxed, the explanation unit provides a detailed explanation that is easy for the user to understand. Furthermore, if the user is in a hurry, the explanation unit can provide a concise explanation that focuses on the main points. This makes it possible to provide an explanation of the sorting method according to the user's emotions.

[0105] When displaying the garbage disposal schedule, the display unit can select the optimal display method by referring to the user's past garbage disposal history. For example, the display unit can suggest the optimal display method based on the garbage disposal schedule used by the user in the past. The display unit can also suggest an efficient display method based on the user's past garbage disposal history. Furthermore, the display unit can analyze the user's past garbage disposal history and suggest the most visible display method. This makes it possible to provide the optimal display method based on the user's past garbage disposal history.

[0106] The procedure guidance unit can estimate the user's emotions and adjust the oversized waste disposal procedure guidance method based on the estimated emotions. For example, if the user is feeling stressed, the procedure guidance unit provides simple and intuitive procedure guidance. If the user is feeling relaxed, the procedure guidance unit provides detailed procedure guidance that is easy for the user to understand. Furthermore, if the user is in a hurry, the procedure guidance unit can provide concise procedure guidance that focuses on the main points. This makes it possible to provide a oversized waste disposal procedure guidance method that corresponds to the user's emotions.

[0107] The purchase support unit can select the optimal purchase procedure method by referring to the user's past purchase history. For example, the purchase support unit can suggest the optimal procedure method based on the purchase methods the user has used in the past. The purchase support unit can also suggest an efficient purchase procedure method based on the user's past purchase history. Furthermore, the purchase support unit can analyze the user's past purchase history and suggest the easiest purchase procedure method to understand. This makes it possible to provide the optimal procedure method based on the user's past purchase history.

[0108] The sales support unit can estimate the user's emotions and adjust the method for selling unwanted bulky waste based on the estimated emotions. For example, if the user is feeling stressed, the sales support unit can provide a simple and intuitive sales method. If the user is relaxed, the sales support unit can provide a detailed sales method that is easy for the user to understand. Furthermore, if the user is in a hurry, the sales support unit can provide a concise sales method that focuses on the main points. This makes it possible to provide a method for selling unwanted bulky waste that suits the user's emotions.

[0109] The acquisition unit can analyze the user's social media activity and acquire related trash disposal information. For example, the acquisition unit can acquire related trash disposal information based on information shared by the user on social media. The acquisition unit can also acquire related trash disposal information based on information about accounts the user follows on social media. The acquisition unit can also acquire related trash disposal information based on information about groups the user participates in on social media. This makes it possible to provide related information based on the user's social media activity.

[0110] The explanation unit can adjust the level of detail of the explanation based on the importance of the garbage. For example, the explanation unit can provide a detailed explanation for how to separate important garbage. Also, the explanation unit can provide a concise explanation for how to separate less important garbage. Furthermore, the explanation unit can adjust the level of detail of the explanation according to the importance of the garbage to make it easier for the user to understand. In this way, it is possible to provide a level of detail of the explanation according to the importance of the garbage.

[0111] The display unit can estimate the user's emotions and adjust the display method of the trash removal schedule based on the estimated emotions. For example, if the user is feeling stressed, the display unit provides a simple, highly visible display method. If the user is relaxed, the display unit provides a display method including detailed information. Furthermore, if the user is in a hurry, the display unit can provide a display method that focuses on the main points. This makes it possible to provide a display method of the trash removal schedule that corresponds to the user's emotions.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The acquisition unit acquires the local garbage collection schedule. For example, it acquires information from a local database based on a new address entered by the user. When the user enters a new address, the garbage collection schedule for the area corresponding to that address can be automatically acquired. Step 2: The explanation unit explains how to separate waste based on the information acquired by the acquisition unit. For example, detailed explanations are provided for each category, such as plastic, paper, and metal. Specific steps and precautions for separating plastic waste can be explained in detail. Step 3: The display unit displays the garbage collection schedule based on the separation method explained by the explanation unit. For example, the display unit can display the garbage collection schedule for both the old address and the new address. If Monday is the garbage collection day at the old address and Tuesday is the garbage collection day at the new address, the display unit can notify the user of this information.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

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

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

[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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires local garbage collection schedules; an explanation unit that explains how to separate garbage based on the information acquired by the acquisition unit; a display unit that displays a garbage collection schedule based on the sorting method explained by the explanation unit. A system characterized by:

2. The acquisition unit Retrieve information from a local database based on the new address entered by the user 2. The system of claim 1.

3. The explanation section Provide detailed descriptions for each plastic, paper, and metal category 2. The system of claim 1.

4. The display unit View trash collection schedules for your old and new addresses 2. The system of claim 1.

5. Equipped with a procedure guide that guides you through the procedure for disposing of bulky waste 2. The system of claim 1.

6. A purchasing support department will be set up to assist with the purchasing procedures for bulky waste tickets.

2. The system of claim 1.

7. Equipped with a sales support department that works in conjunction with flea market apps to sell unwanted bulky waste 2. The system of claim 1.

8. The acquisition unit Inferring user emotions and adjusting the timing of new address input based on the estimated user emotions 2. The system of claim 1.

Images