System

Abstract

An object of the system according to the embodiment is to guide a correct disposal method of an electronic device and promote recycling and secondary use.SOLUTION: A system includes a disposal method guide part, an ecological point giving part, a secondary use promotion part, a recycle box guide part, and a waste reduction support part. The discarding method guiding section guides a discarding method of the electronic apparatus by the user. The eco-point giving portion gives an eco-point when the user executes a correct discarding method. The secondary use promotion unit promotes secondary use of an article held by the user. The recycle box guidance unit guides the user to the location of the nearest recycle box based on the position information of the user. The garbage reduction support unit analyzes the consumption behavior of the user and provides advice for not producing garbage.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the disposal methods for electronic devices are complicated, making it difficult to properly dispose of or recycle them.

[0005] The system according to the embodiment aims to guide users in the correct disposal method of electronic devices and promote recycling and secondary use. [Means for solving the problem]

[0006] The system according to the embodiment includes a disposal method guidance unit, an eco-point granting unit, a secondary use promotion unit, a recycle box guidance unit, and a waste reduction support unit. The disposal method guidance unit guides the user on how to dispose of electronic devices. The eco-point granting unit grants eco-points when the user disposes of the devices correctly. The secondary use promotion unit encourages the secondary use of items owned by the user. The recycle box guidance unit guides the user to the location of the nearest recycle box based on the user's location information. The waste reduction support unit analyzes the user's consumption behavior and provides advice on how to reduce waste. [Effects of the Invention]

[0007] The system according to the embodiment can guide users on how to properly dispose of electronic devices and promote recycling and secondary use. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The electronics disposal support system according to an embodiment of the present invention utilizes generative AI to help users easily understand and implement the correct disposal and recycling methods for electronic devices. As a result, the electronics disposal support system can raise users' environmental awareness and contribute to the realization of a sustainable society.

[0029] An electronics disposal support system according to an embodiment includes a disposal method guidance unit, an eco-point granting unit, a secondary use promotion unit, a recycle box guidance unit, and a waste reduction support unit. The disposal method guidance unit guides users on how to dispose of electronic devices. For example, when a user uploads a smartphone photo to the app, the AI ​​analyzes the photo and provides specific guidance on how to dispose of the smartphone. The disposal method guidance unit also guides users to the appropriate disposal method when the user asks for disposal instructions in writing. The eco-point granting unit grants eco-points when the user disposes correctly. For example, the app automatically grants eco-points when the user disposes correctly. The eco-point granting unit can also grant eco-points when the user recycles. The secondary use promotion unit encourages users to reuse their items. For example, users can sell or transfer items to other users through platforms such as Jimoty or flea market apps. The secondary use promotion unit uses the AI ​​to analyze the condition of the user's items and determine whether they can be reused. The recycle box guidance unit guides users to the location of the nearest recycle box based on their location information. For example, the generation AI analyzes the user's location information and displays the location of the nearest recycling box. The recycling box guidance unit can also monitor the usage status of recycling boxes in real time and notify the user of availability. The waste reduction support unit analyzes the user's consumption behavior and provides advice on how to avoid generating waste. For example, the generation AI analyzes the user's consumption behavior and provides advice on how to avoid wasteful purchases. The waste reduction support unit also provides content that raises awareness of the importance of recycling and secondary reuse, thereby raising the user's environmental awareness. As a result, the electronic device disposal support system according to the embodiment can guide users on the correct disposal and recycling methods for electronic devices and raise the user's environmental awareness by awarding eco points.

[0030] The disposal method guidance unit can learn the user's disposal history and suggest individually optimized disposal methods. For example, the generation AI analyzes the user's past disposal history and suggests the optimal disposal method for a specific electronic device. For example, based on data on smartphones and tablets that the user has previously discarded, it can suggest the optimal method for the next disposal. The disposal method guidance unit also learns the user's disposal history and suggests more efficient disposal methods for electronic devices that are frequently discarded. For example, it can guide the user to the nearest recycling facility for cables that the user frequently discards. The disposal method guidance unit also recommends specific disposal methods based on the user's disposal history. For example, based on data on electronic devices that the user has recycled in the past, it can guide the user to separate recyclable parts for the next disposal. This makes it possible to suggest the optimal disposal method based on the user's disposal history.

[0031] The disposal method guidance unit can analyze the materials and components of waste and provide guidance by separating recyclable and non-recyclable parts. For example, the generation AI of the disposal method guidance unit analyzes the materials and components of electronic devices and provides guidance by separating recyclable and non-recyclable parts. For example, it may guide users to the battery and metal parts of a smartphone as recyclable parts. The disposal method guidance unit also analyzes photos of electronic devices uploaded by users to the app and automatically identifies recyclable and non-recyclable parts. For example, it may guide users to the plastic and metal parts of cables separately. The disposal method guidance unit also analyzes the components of electronic devices and identifies recyclable parts. For example, it may guide users to the lithium-ion battery of a mobile battery as a recyclable part. This makes it possible to analyze the materials and components of waste and identify recyclable parts.

[0032] The disposal method guidance unit can suggest not only disposal methods, but also repair and reuse methods. In the disposal method guidance unit, for example, the generation AI suggests ways to repair electronic devices. For example, if a smartphone screen is cracked, it provides information on repair methods and repair companies. In addition, the disposal method guidance unit can suggest ways to reuse electronic devices. For example, it can guide users on how to reuse an old tablet as a digital photo frame. In addition, the disposal method guidance unit presents options for repair and reuse for electronic devices that the user is considering discarding. For example, it can suggest how to replace or reuse a mobile battery. This makes it possible to suggest not only disposal methods, but also repair and reuse methods.

[0033] The disposal method guidance unit can present examples of how other users have disposed of similar waste based on photos of the waste. For example, the generation AI analyzes photos of waste uploaded by the user and presents examples of how other users have disposed of similar waste. For example, it displays examples of how to dispose of a smartphone. The disposal method guidance unit can also analyze photos of electronic devices that the user is considering disposing of and present specific examples of how other users have disposed of them. For example, it displays examples of how to dispose of cables. The disposal method guidance unit can also present successful examples of how other users have disposed of similar waste based on photos of the waste. For example, it displays examples of how to recycle mobile batteries. This makes it possible to present examples of how other users have disposed of similar waste.

[0034] The eco point granting unit can analyze the user's eco point history and individually suggest ways to use and exchange the points. For example, the generation AI in the eco point granting unit analyzes the user's eco point history and individually suggests ways to use and exchange the points. For example, it suggests the next exchange destination based on products and services that the user has exchanged in the past. The eco point granting unit also learns the user's eco point history and recommends specific ways to use the points. For example, it suggests ways to use the points based on the stores and services that the user frequently uses. The eco point granting unit also suggests the optimal exchange destination based on the user's eco point history. For example, it guides the user to the next exchange destination based on data on products and services that the user has exchanged in the past. This makes it possible to suggest the optimal ways to use and exchange the points based on the user's eco point history.

[0035] The eco point granting unit can make it possible for eco points to be applied to other eco activities (for example, tree planting or cleanup activities). The eco point granting unit, for example, makes it possible for eco points to be applied to other eco activities. For example, it makes it possible for eco points to be earned by participating in tree planting activities. The eco point granting unit also grants eco points for cleanup activities in which the user participates. For example, it makes it possible for points to be earned by participating in local cleanup activities. The eco point granting unit also makes it possible for the generation AI to analyze information on other eco activities and apply eco points. For example, it grants points to eco activities other than recycling activities. This makes it possible for eco points to be applied to other eco activities.

[0036] The eco-point granting unit can link eco-points with local shops and services, contributing to the revitalization of the local economy. The eco-point granting unit, for example, links eco-points with local shops and services, contributing to the revitalization of the local economy. For example, it allows eco-points to be used at local shops. The eco-point granting unit also allows users to use eco-points for local services. For example, it allows points to be used at local restaurants and service businesses. The eco-point granting unit also uses a generation AI to analyze information about local shops and services and link eco-points. For example, it applies points to local events and campaigns. This links eco-points with local shops and services, contributing to the revitalization of the local economy.

[0037] The secondary use promotion unit can analyze the usage history and condition of an item and suggest the optimal secondary use method. For example, the generation AI in the secondary use promotion unit analyzes the usage history and condition of an item and suggests the optimal secondary use method. For example, it can guide users to reuse methods based on frequency of use and condition. The secondary use promotion unit also learns the usage history of the user's items and determines whether secondary use is possible. For example, it can suggest reuse methods based on the number of times used and condition. The secondary use promotion unit can also analyze the condition of an item and suggest the optimal secondary use method. For example, it can guide users to reuse methods based on the battery life and screen condition of a smartphone. In this way, the generation AI can analyze the usage history and condition of an item and suggest the optimal secondary use method.

[0038] The secondary use promotion unit can learn successful cases of secondary use and present specific success stories to the user. For example, the generation AI in the secondary use promotion unit learns past successful cases of secondary use and presents specific success stories to the user. For example, it displays methods for reusing smartphones and success stories. The secondary use promotion unit also suggests reuse methods for items the user owns based on past success stories. For example, it displays methods for reusing tablets and success stories. The secondary use promotion unit can also learn successful cases of secondary use and present specific examples to the user. For example, it displays methods for reusing mobile batteries and success stories. In this way, it can learn successful cases of secondary use and present specific success stories to the user.

[0039] The Secondary Use Promotion Department can also suggest ways to repair or modify an item when it is being used for secondary purposes. For example, the Generative AI in the Secondary Use Promotion Department can suggest ways to repair an item. For example, if a smartphone screen is cracked, the Generative AI can provide information on repair methods and repair companies. The Secondary Use Promotion Department can also suggest ways to modify an item. For example, it can provide guidance on how to modify an old tablet into a digital photo frame. The Secondary Use Promotion Department can also present repair and modification options for items that the user is considering using for secondary purposes. For example, it can suggest how to replace or modify a mobile battery. This makes it possible to suggest ways to repair or modify an item when it is being used for secondary purposes.

[0040] The Secondary Use Promotion Department, for example, uses a generation AI to link secondary use platforms together to reach a wider range of users. For example, by linking with Jimoty or a flea market app, the Secondary Use Promotion Department allows users to sell or transfer items they own to other users. The Secondary Use Promotion Department also reaches more users by listing items they own on multiple secondary use platforms simultaneously. For example, by listing items on multiple flea market apps simultaneously. The Secondary Use Promotion Department, for example, uses a generation AI to link secondary use platforms together to reach a wider range of users. For example, by listing items on multiple recycling platforms simultaneously. This allows collaboration between secondary use platforms to reach a wider range of users.

[0041] The recycling box guidance unit can monitor the usage status of recycling boxes in real time and notify the user of availability. In the recycling box guidance unit, for example, the generation AI monitors the usage status of recycling boxes in real time and notifies the user of availability. For example, it checks whether a recycling box is full and notifies the user if it is available. In addition, when a user uses a recycling box, the generation AI checks availability in real time and guides the user to the most suitable recycling box. For example, if a nearby recycling box is full, it guides the user to another recycling box. In addition, the recycling box guidance unit can monitor the usage status of recycling boxes in real time and notify the user of availability. For example, it notifies the user before a recycling box becomes full, allowing the user to recycle smoothly. In this way, the usage status of recycling boxes can be monitored in real time and notifies the user of availability.

[0042] The recycling box information unit can provide not only the locations of recycling boxes, but also information on recycling events and campaigns. For example, the generation AI can provide information on recycling events and campaigns as well as the locations of recycling boxes. For example, the generation AI can provide information on recycling events held in the area. Furthermore, when a user uses a recycling box, the generation AI can provide information on recycling events and campaigns. For example, the generation AI can provide information on how points can be increased during a recycling campaign. Furthermore, the recycling box information unit can provide information on recycling events and campaigns as well as the locations of recycling boxes. For example, the generation AI can provide information on how points can be increased by using a recycling box during a specific event period. This makes it possible to provide not only the locations of recycling boxes, but also information on recycling events and campaigns.

[0043] The recycling box guidance unit can link the locations of recycling boxes with a map app and provide a navigation function. For example, the generation AI of the recycling box guidance unit links the locations of recycling boxes with a map app and provides a navigation function. For example, the recycling box guidance unit guides the user on a route from their current location to the nearest recycling box. Furthermore, when a user uses a recycling box, the generation AI of the recycling box guidance unit links with the map app and provides a navigation function. For example, the recycling box locations are displayed on a map and route guidance is provided. Furthermore, the recycling box guidance unit links with the map app and provides a navigation function. For example, the recycling box locations are guided by placing pins on the map to make it easier for the user to find the recycling box. In this way, the recycling box locations can be linked with the map app and a navigation function can be provided.

[0044] The waste reduction support unit can analyze a user's consumption history and provide specific advice to avoid wasteful purchases. For example, the generation AI analyzes a user's consumption history and provides specific advice to avoid wasteful purchases. For example, it identifies products that were purchased in the past but used infrequently and suggests not purchasing similar products. The waste reduction support unit also learns a user's consumption history and provides advice to avoid wasteful purchases. For example, it lists food and daily necessities that are purchased frequently but cannot be used up and suggests refraining from purchasing them. The waste reduction support unit also provides specific advice to avoid wasteful purchases based on a user's consumption history. For example, it identifies products that were purchased in the past but are no longer needed and suggests not purchasing similar products. In this way, the generation AI can analyze a user's consumption history and provide specific advice to avoid wasteful purchases.

[0045] The waste reduction support unit can analyze the user's lifestyle and make customized suggestions for reducing waste. For example, the generation AI in the waste reduction support unit analyzes the user's lifestyle and makes customized suggestions for reducing waste. For example, specific advice for reducing unnecessary waste is provided based on the user's lifestyle patterns and consumption behavior. The waste reduction support unit also learns the user's lifestyle and makes customized suggestions for reducing waste. For example, it identifies products and services that the user frequently uses and makes suggestions for reducing unnecessary waste. The waste reduction support unit also learns the user's lifestyle and makes customized suggestions for reducing waste based on the user's lifestyle. For example, it analyzes the user's consumption behavior and lifestyle patterns and provides specific advice for reducing unnecessary waste. This makes it possible to analyze the user's lifestyle and make customized suggestions for reducing waste.

[0046] The waste reduction support unit can link efforts to reduce waste with other eco-friendly activities (for example, recycling and secondary use). In the waste reduction support unit, for example, the generation AI links efforts to reduce waste with other eco-friendly activities. For example, it provides information on recycling and secondary use and gives specific advice on reducing waste. In addition, when a user makes efforts to reduce waste, the generation AI links with other eco-friendly activities to make suggestions. For example, it provides guidance on recycling and secondary use and gives specific advice on reducing waste. In addition, the waste reduction support unit links efforts to reduce waste with other eco-friendly activities. For example, it provides information on recycling and secondary use and gives specific advice on reducing waste. In this way, efforts to reduce waste can be linked with other eco-friendly activities.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The disposal method guidance unit can learn the user's disposal history and suggest individually optimized disposal methods. For example, the generation AI analyzes the user's past disposal history and suggests the optimal disposal method for a specific electronic device. Based on data on smartphones and tablets that the user has previously discarded, it can suggest the optimal method for the next disposal. The disposal method guidance unit also learns the user's disposal history and suggests more efficient disposal methods for electronic devices that are frequently discarded. For example, it can guide users to the nearest recycling facility for cables that the user frequently discards. The disposal method guidance unit also recommends specific disposal methods based on the user's disposal history. For example, based on data on electronic devices that the user has recycled in the past, it can guide users to separate recyclable parts for the next disposal. This makes it possible to suggest the optimal disposal method based on the user's disposal history.

[0049] The disposal method guidance unit can analyze the materials and components of waste and provide guidance on separating recyclable and non-recyclable parts. For example, the generation AI analyzes the materials and components of electronic devices and provides guidance on separating recyclable and non-recyclable parts. It will guide users to smartphone batteries and metal parts as recyclable parts. The disposal method guidance unit also analyzes photos of electronic devices uploaded by users to the app and automatically identifies recyclable and non-recyclable parts. It will provide guidance on separating plastic and metal parts of cables. The disposal method guidance unit also analyzes the components of electronic devices and identifies recyclable parts. It will guide users to lithium-ion batteries in mobile batteries as recyclable parts. This makes it possible to analyze the materials and components of waste and identify recyclable parts.

[0050] The disposal method guidance section can suggest not only disposal methods, but also repair and reuse methods. For example, the generation AI can suggest ways to repair electronic devices. If a smartphone screen is cracked, it can provide information on repair methods and repair companies. The disposal method guidance section can also suggest ways to reuse electronic devices. It can guide users on how to reuse an old tablet as a digital photo frame. The disposal method guidance section can also present repair and reuse options for electronic devices that the user is considering discarding. It can suggest ways to replace or reuse a mobile battery. This makes it possible to suggest not only disposal methods, but also repair and reuse methods.

[0051] The disposal method guidance unit can use photos of waste to present examples of how other users have disposed of similar waste. For example, the generation AI analyzes photos of waste uploaded by the user and presents examples of how other users have disposed of similar waste. It displays examples of how to dispose of smartphones. The disposal method guidance unit can also analyze photos of electronic devices the user is considering disposing of and present specific examples of how other users have disposed of them. It displays examples of how to dispose of cables. The disposal method guidance unit can also use photos of waste to present successful examples of how other users have disposed of similar waste. It displays examples of how to recycle mobile batteries. This makes it possible to present examples of how other users have disposed of similar waste.

[0052] The eco point granting unit can analyze the user's eco point history and individually suggest ways to use and exchange the points. For example, the generation AI analyzes the user's eco point history and individually suggests ways to use and exchange the points. It suggests the next exchange destination based on the products and services the user has exchanged in the past. The eco point granting unit also learns the user's eco point history and recommends specific ways to use the points. It suggests ways to use the points based on the stores and services the user frequently uses. The eco point granting unit also suggests the optimal exchange destination based on the user's eco point history. It guides the user to the next exchange destination based on data on the products and services the user has exchanged in the past. This makes it possible to suggest the optimal ways to use and exchange the points based on the user's eco point history.

[0053] The eco-point granting unit can make it possible for eco-points to be applied to other eco-activities (for example, tree planting or cleanup activities). For example, it makes it possible for eco-points to be applied to other eco-activities. It makes it possible for eco-points to be earned by participating in tree-planting activities. The eco-point granting unit also grants eco-points for cleanup activities in which the user participates. It makes it possible for points to be earned by participating in local cleanup activities. The eco-point granting unit also makes it possible for the generation AI to analyze information on other eco-activities and apply eco-points. It grants points to eco-activities other than recycling activities. This makes it possible for eco-points to be applied to other eco-activities.

[0054] The eco-point granting unit can link eco-points with local shops and services, contributing to the revitalization of the local economy. For example, linking eco-points with local shops and services can contribute to the revitalization of the local economy. Eco-points can be used at local shops. The eco-point granting unit also allows users to use eco-points for local services. Points can be used at local restaurants and service businesses. The eco-point granting unit also uses a generation AI to analyze information about local shops and services and link eco-points. Points can be applied to local events and campaigns. This allows eco-points to be linked with local shops and services, contributing to the revitalization of the local economy.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The disposal method guidance unit guides the user on how to dispose of electronic devices. For example, when a user uploads a photo of their smartphone to the app, the generation AI analyzes the photo and provides specific instructions on how to dispose of the smartphone. In addition, if the user asks for disposal instructions in writing, the generation AI will also guide them on the appropriate disposal method. Step 2: The eco-point awarding unit awards eco-points when the user disposes of the product correctly. For example, the app can automatically award eco-points when the user disposes of the product correctly. Eco-points can also be awarded when the user recycles. Step 3: The Secondary Use Promotion Department promotes secondary use of the user's items. For example, they can be sold or transferred to other users through platforms such as Jimoty or flea market apps. The Generative AI also analyzes the condition of the user's items and determines whether they can be reused. Step 4: The recycling box guidance unit guides the user to the location of the nearest recycling box based on their location information. For example, the generation AI analyzes the user's location information and displays the location of the nearest recycling box. It can also monitor the usage status of recycling boxes in real time and notify the user of their availability. Step 5: The waste reduction support unit analyzes the user's consumption behavior and provides advice on how to reduce waste. For example, the generative AI analyzes the user's consumption behavior and provides advice on how to avoid wasteful purchases. It also provides content that raises awareness of the importance of recycling and secondary reuse, raising the user's environmental awareness.

[0057] (Example 2) The electronics disposal support system according to an embodiment of the present invention utilizes generative AI to help users easily understand and implement the correct disposal and recycling methods for electronic devices. As a result, the electronics disposal support system can raise users' environmental awareness and contribute to the realization of a sustainable society.

[0058] An electronics disposal support system according to an embodiment includes a disposal method guidance unit, an eco-point granting unit, a secondary use promotion unit, a recycle box guidance unit, and a waste reduction support unit. The disposal method guidance unit guides users on how to dispose of electronic devices. For example, when a user uploads a smartphone photo to the app, the AI ​​analyzes the photo and provides specific guidance on how to dispose of the smartphone. The disposal method guidance unit also guides users to the appropriate disposal method when the user asks for disposal instructions in writing. The eco-point granting unit grants eco-points when the user disposes correctly. For example, the app automatically grants eco-points when the user disposes correctly. The eco-point granting unit can also grant eco-points when the user recycles. The secondary use promotion unit encourages users to reuse their items. For example, users can sell or transfer items to other users through platforms such as Jimoty or flea market apps. The secondary use promotion unit uses the AI ​​to analyze the condition of the user's items and determine whether they can be reused. The recycle box guidance unit guides users to the location of the nearest recycle box based on their location information. For example, the generation AI analyzes the user's location information and displays the location of the nearest recycling box. The recycling box guidance unit can also monitor the usage status of recycling boxes in real time and notify the user of availability. The waste reduction support unit analyzes the user's consumption behavior and provides advice on how to avoid generating waste. For example, the generation AI analyzes the user's consumption behavior and provides advice on how to avoid wasteful purchases. The waste reduction support unit also provides content that raises awareness of the importance of recycling and secondary reuse, thereby raising the user's environmental awareness. As a result, the electronic device disposal support system according to the embodiment can guide users on the correct disposal and recycling methods for electronic devices and raise the user's environmental awareness by awarding eco points.

[0059] The disposal method guidance unit can learn the user's disposal history and suggest individually optimized disposal methods. For example, the generation AI analyzes the user's past disposal history and suggests the optimal disposal method for a specific electronic device. For example, based on data on smartphones and tablets that the user has previously discarded, it can suggest the optimal method for the next disposal. The disposal method guidance unit also learns the user's disposal history and suggests more efficient disposal methods for electronic devices that are frequently discarded. For example, it can guide the user to the nearest recycling facility for cables that the user frequently discards. The disposal method guidance unit also recommends specific disposal methods based on the user's disposal history. For example, based on data on electronic devices that the user has recycled in the past, it can guide the user to separate recyclable parts for the next disposal. This makes it possible to suggest the optimal disposal method based on the user's disposal history.

[0060] The disposal method guidance unit can analyze the materials and components of waste and provide guidance by separating recyclable and non-recyclable parts. For example, the generation AI of the disposal method guidance unit analyzes the materials and components of electronic devices and provides guidance by separating recyclable and non-recyclable parts. For example, it may guide users to the battery and metal parts of a smartphone as recyclable parts. The disposal method guidance unit also analyzes photos of electronic devices uploaded by users to the app and automatically identifies recyclable and non-recyclable parts. For example, it may guide users to the plastic and metal parts of cables separately. The disposal method guidance unit also analyzes the components of electronic devices and identifies recyclable parts. For example, it may guide users to the lithium-ion battery of a mobile battery as a recyclable part. This makes it possible to analyze the materials and components of waste and identify recyclable parts.

[0061] The disposal method guidance unit can use the emotion estimation function to provide advice to reduce the user's resistance to disposal. The disposal method guidance unit, for example, uses the emotion estimation function to provide advice to reduce the user's resistance to disposal. For example, it displays a message emphasizing the benefits of disposal and its contribution to the environment. The disposal method guidance unit also analyzes the user's emotional state in real time and provides specific advice to reduce the user's resistance to disposal. For example, it displays a step-by-step guide showing that disposal is easy. The disposal method guidance unit also uses the emotion estimation function to provide advice to alleviate the user's anxiety or doubts about disposal. For example, it provides information showing that disposal is safe. This makes it possible to provide advice to reduce the user's resistance to disposal.

[0062] The disposal method guidance unit can suggest not only disposal methods, but also repair and reuse methods. In the disposal method guidance unit, for example, the generation AI suggests ways to repair electronic devices. For example, if a smartphone screen is cracked, it provides information on repair methods and repair companies. In addition, the disposal method guidance unit can suggest ways to reuse electronic devices. For example, it can guide users on how to reuse an old tablet as a digital photo frame. In addition, the disposal method guidance unit presents options for repair and reuse for electronic devices that the user is considering discarding. For example, it can suggest how to replace or reuse a mobile battery. This makes it possible to suggest not only disposal methods, but also repair and reuse methods.

[0063] The disposal method guidance unit can present examples of how other users have disposed of similar waste based on photos of the waste. For example, the generation AI analyzes photos of waste uploaded by the user and presents examples of how other users have disposed of similar waste. For example, it displays examples of how to dispose of a smartphone. The disposal method guidance unit can also analyze photos of electronic devices that the user is considering disposing of and present specific examples of how other users have disposed of them. For example, it displays examples of how to dispose of cables. The disposal method guidance unit can also present successful examples of how other users have disposed of similar waste based on photos of the waste. For example, it displays examples of how to recycle mobile batteries. This makes it possible to present examples of how other users have disposed of similar waste.

[0064] The disposal method guidance unit can use the emotion estimation function to monitor the user's emotions when disposing of items in real time and provide positive feedback. The disposal method guidance unit, for example, uses the emotion estimation function to monitor the user's emotions when disposing of items in real time and provide positive feedback. For example, it displays a message emphasizing the positive impact that disposal has on the environment. The disposal method guidance unit also analyzes the user's emotional state in real time and provides positive feedback about disposal. For example, it displays a step-by-step guide showing that disposal is easy. The disposal method guidance unit also uses the emotion estimation function to monitor the user's emotions when disposing of items and provide positive feedback. For example, it provides information emphasizing the contribution that disposal makes to the environment. In this way, the user's emotions when disposing of items can be monitored in real time and provide positive feedback.

[0065] The eco point granting unit can analyze the user's eco point history and individually suggest ways to use and exchange the points. For example, the generation AI in the eco point granting unit analyzes the user's eco point history and individually suggests ways to use and exchange the points. For example, it suggests the next exchange destination based on products and services that the user has exchanged in the past. The eco point granting unit also learns the user's eco point history and recommends specific ways to use the points. For example, it suggests ways to use the points based on the stores and services that the user frequently uses. The eco point granting unit also suggests the optimal exchange destination based on the user's eco point history. For example, it guides the user to the next exchange destination based on data on products and services that the user has exchanged in the past. This makes it possible to suggest the optimal ways to use and exchange the points based on the user's eco point history.

[0066] The eco point granting unit uses the emotion estimation function to measure the user's satisfaction when receiving eco points and encourages the user to take action next time. The eco point granting unit, for example, uses the emotion estimation function to measure the user's satisfaction when receiving eco points and encourages the user to take action next time. For example, if the user's satisfaction is high, a message recommending the user to take eco-friendly action next time is displayed. The eco point granting unit also analyzes the user's emotional state in real time to measure the user's satisfaction when receiving eco points. For example, if the user's satisfaction is high, the message recommends the user to take eco-friendly action next time. The eco point granting unit also uses the emotion estimation function to monitor the user's emotions when receiving eco points and encourages the user to take action next time. For example, if the user's satisfaction is high, a message recommending the user to take eco-friendly action next time is displayed. This makes it possible to measure the user's satisfaction when receiving eco points and encourage the user to take action next time.

[0067] The eco point granting unit can make it possible for eco points to be applied to other eco activities (for example, tree planting or cleanup activities). The eco point granting unit, for example, makes it possible for eco points to be applied to other eco activities. For example, it makes it possible for eco points to be earned by participating in tree planting activities. The eco point granting unit also grants eco points for cleanup activities in which the user participates. For example, it makes it possible for points to be earned by participating in local cleanup activities. The eco point granting unit also makes it possible for the generation AI to analyze information on other eco activities and apply eco points. For example, it grants points to eco activities other than recycling activities. This makes it possible for eco points to be applied to other eco activities.

[0068] The eco-point granting unit can link eco-points with local shops and services, contributing to the revitalization of the local economy. The eco-point granting unit, for example, links eco-points with local shops and services, contributing to the revitalization of the local economy. For example, it allows eco-points to be used at local shops. The eco-point granting unit also allows users to use eco-points for local services. For example, it allows points to be used at local restaurants and service businesses. The eco-point granting unit also uses a generation AI to analyze information about local shops and services and link eco-points. For example, it applies points to local events and campaigns. This links eco-points with local shops and services, contributing to the revitalization of the local economy.

[0069] The eco point granting unit can use the emotion estimation function to analyze the emotion a user feels when using eco points and suggest the optimal way to use them. The eco point granting unit, for example, uses the emotion estimation function to analyze the emotion a user feels when using eco points and suggest the optimal way to use them. For example, it prioritizes suggestions for ways to use them that bring joy to the user. The eco point granting unit also analyzes the user's emotional state in real time and suggests the optimal way to use the eco points. For example, it suggests ways to use them that will give the user high satisfaction. The eco point granting unit also uses the emotion estimation function to monitor the emotion a user feels when using eco points and suggest the optimal way to use them. For example, it suggests ways to use them that will make the user feel positive. In this way, it is possible to analyze the emotion a user feels when using eco points and suggest the optimal way to use them.

[0070] The secondary use promotion unit can analyze the usage history and condition of an item and suggest the optimal secondary use method. For example, the generation AI in the secondary use promotion unit analyzes the usage history and condition of an item and suggests the optimal secondary use method. For example, it can guide users to reuse methods based on frequency of use and condition. The secondary use promotion unit also learns the usage history of the user's items and determines whether secondary use is possible. For example, it can suggest reuse methods based on the number of times used and condition. The secondary use promotion unit can also analyze the condition of an item and suggest the optimal secondary use method. For example, it can guide users to reuse methods based on the battery life and screen condition of a smartphone. In this way, the generation AI can analyze the usage history and condition of an item and suggest the optimal secondary use method.

[0071] The secondary use promotion unit can learn successful cases of secondary use and present specific success stories to the user. For example, the generation AI in the secondary use promotion unit learns past successful cases of secondary use and presents specific success stories to the user. For example, it displays methods for reusing smartphones and success stories. The secondary use promotion unit also suggests reuse methods for items the user owns based on past success stories. For example, it displays methods for reusing tablets and success stories. The secondary use promotion unit can also learn successful cases of secondary use and present specific examples to the user. For example, it displays methods for reusing mobile batteries and success stories. In this way, it can learn successful cases of secondary use and present specific success stories to the user.

[0072] The secondary use promotion unit can use the emotion estimation function to provide advice to alleviate the anxiety and doubts the user has about secondary use. For example, the secondary use promotion unit uses the emotion estimation function to provide advice to alleviate the anxiety and doubts the user has about secondary use. For example, the secondary use promotion unit displays a message emphasizing the benefits and safety of reuse. The secondary use promotion unit also analyzes the user's emotional state in real time and provides specific advice to alleviate the anxiety and doubts the user has about secondary use. For example, the secondary use promotion unit displays a step-by-step guide showing how easy reuse is. The secondary use promotion unit also uses the emotion estimation function to provide advice to alleviate the anxiety and doubts the user has about secondary use. For example, the secondary use promotion unit provides information emphasizing the positive impact reuse has on the environment. This makes it possible to provide advice to alleviate the anxiety and doubts the user has about secondary use.

[0073] The Secondary Use Promotion Department can also suggest ways to repair or modify an item when it is being used for secondary purposes. For example, the Generative AI in the Secondary Use Promotion Department can suggest ways to repair an item. For example, if a smartphone screen is cracked, the Generative AI can provide information on repair methods and repair companies. The Secondary Use Promotion Department can also suggest ways to modify an item. For example, it can provide guidance on how to modify an old tablet into a digital photo frame. The Secondary Use Promotion Department can also present repair and modification options for items that the user is considering using for secondary purposes. For example, it can suggest how to replace or modify a mobile battery. This makes it possible to suggest ways to repair or modify an item when it is being used for secondary purposes.

[0074] The Secondary Use Promotion Department, for example, uses a generation AI to link secondary use platforms together to reach a wider range of users. For example, by linking with Jimoty or a flea market app, the Secondary Use Promotion Department allows users to sell or transfer items they own to other users. The Secondary Use Promotion Department also reaches more users by listing items they own on multiple secondary use platforms simultaneously. For example, by listing items on multiple flea market apps simultaneously. The Secondary Use Promotion Department, for example, uses a generation AI to link secondary use platforms together to reach a wider range of users. For example, by listing items on multiple recycling platforms simultaneously. This allows collaboration between secondary use platforms to reach a wider range of users.

[0075] The secondary use promotion unit can use the emotion estimation function to monitor the user's emotions when performing secondary use in real time and provide positive feedback. The secondary use promotion unit, for example, uses the emotion estimation function to monitor the user's emotions when performing secondary use in real time and provide positive feedback. For example, it displays a message emphasizing the positive impact that secondary use has on the environment. The secondary use promotion unit also analyzes the user's emotional state in real time and provides positive feedback on secondary use. For example, it displays a step-by-step guide showing that secondary use is easy. The secondary use promotion unit also uses the emotion estimation function to monitor the user's emotions when performing secondary use and provide positive feedback. For example, it provides information emphasizing the contribution that secondary use makes to the environment. In this way, the secondary use promotion unit can monitor the user's emotions when performing secondary use in real time and provide positive feedback.

[0076] The recycling box guidance unit can monitor the usage status of recycling boxes in real time and notify the user of availability. In the recycling box guidance unit, for example, the generation AI monitors the usage status of recycling boxes in real time and notifies the user of availability. For example, it checks whether a recycling box is full and notifies the user if it is available. In addition, when a user uses a recycling box, the generation AI checks availability in real time and guides the user to the most suitable recycling box. For example, if a nearby recycling box is full, it guides the user to another recycling box. In addition, the recycling box guidance unit can monitor the usage status of recycling boxes in real time and notify the user of availability. For example, it notifies the user before a recycling box becomes full, allowing the user to recycle smoothly. In this way, the usage status of recycling boxes can be monitored in real time and notifies the user of availability.

[0077] The recycling box guidance unit can use the emotion estimation function to measure the user's level of satisfaction when using the recycling box and encourage the user to use it next time. The recycling box guidance unit can, for example, use the emotion estimation function to measure the user's level of satisfaction when using the recycling box and encourage the user to use it next time. For example, if satisfaction is high, it displays a message recommending recycling next time. The recycling box guidance unit can also analyze the user's emotional state in real time to measure the user's satisfaction with using the recycling box. For example, if satisfaction is high, it recommends recycling next time. The recycling box guidance unit can also use the emotion estimation function to monitor the user's emotions when using the recycling box and encourage the user to use it next time. For example, if satisfaction is high, it displays a message recommending recycling next time. This makes it possible to measure the user's level of satisfaction when using the recycling box and encourage the user to use it next time.

[0078] The recycling box information unit can provide not only the locations of recycling boxes, but also information on recycling events and campaigns. For example, the generation AI can provide information on recycling events and campaigns as well as the locations of recycling boxes. For example, the generation AI can provide information on recycling events held in the area. Furthermore, when a user uses a recycling box, the generation AI can provide information on recycling events and campaigns. For example, the generation AI can provide information on how points can be increased during a recycling campaign. Furthermore, the recycling box information unit can provide information on recycling events and campaigns as well as the locations of recycling boxes. For example, the generation AI can provide information on how points can be increased by using a recycling box during a specific event period. This makes it possible to provide not only the locations of recycling boxes, but also information on recycling events and campaigns.

[0079] The recycling box guidance unit can link the locations of recycling boxes with a map app and provide a navigation function. For example, the generation AI of the recycling box guidance unit links the locations of recycling boxes with a map app and provides a navigation function. For example, the recycling box guidance unit guides the user on a route from their current location to the nearest recycling box. Furthermore, when a user uses a recycling box, the generation AI of the recycling box guidance unit links with the map app and provides a navigation function. For example, the recycling box locations are displayed on a map and route guidance is provided. Furthermore, the recycling box guidance unit links with the map app and provides a navigation function. For example, the recycling box locations are guided by placing pins on the map to make it easier for the user to find the recycling box. In this way, the recycling box locations can be linked with the map app and a navigation function can be provided.

[0080] The recycling box guide unit can use the emotion estimation function to monitor the user's emotions when using the recycling box in real time and provide positive feedback. For example, the recycling box guide unit can use the emotion estimation function to monitor the user's emotions when using the recycling box in real time and provide positive feedback. For example, the recycling box guide unit can display a message emphasizing the positive impact that recycling has on the environment. The recycling box guide unit can also analyze the user's emotional state in real time and provide positive feedback on using the recycling box. For example, the recycling box guide unit can display a step-by-step guide showing that recycling is easy. The recycling box guide unit can also use the emotion estimation function to monitor the user's emotions when using the recycling box and provide positive feedback. For example, the recycling box guide unit can provide information emphasizing the contribution that recycling makes to the environment. This allows the user's emotions when using the recycling box to be monitored in real time and provide positive feedback.

[0081] The waste reduction support unit can analyze a user's consumption history and provide specific advice to avoid wasteful purchases. For example, the generation AI analyzes a user's consumption history and provides specific advice to avoid wasteful purchases. For example, it identifies products that were purchased in the past but used infrequently and suggests not purchasing similar products. The waste reduction support unit also learns a user's consumption history and provides advice to avoid wasteful purchases. For example, it lists food and daily necessities that are purchased frequently but cannot be used up and suggests refraining from purchasing them. The waste reduction support unit also provides specific advice to avoid wasteful purchases based on a user's consumption history. For example, it identifies products that were purchased in the past but are no longer needed and suggests not purchasing similar products. In this way, the generation AI can analyze a user's consumption history and provide specific advice to avoid wasteful purchases.

[0082] The waste reduction support unit can analyze the user's lifestyle and make customized suggestions for reducing waste. For example, the generation AI in the waste reduction support unit analyzes the user's lifestyle and makes customized suggestions for reducing waste. For example, specific advice for reducing unnecessary waste is provided based on the user's lifestyle patterns and consumption behavior. The waste reduction support unit also learns the user's lifestyle and makes customized suggestions for reducing waste. For example, it identifies products and services that the user frequently uses and makes suggestions for reducing unnecessary waste. The waste reduction support unit also learns the user's lifestyle and makes customized suggestions for reducing waste based on the user's lifestyle. For example, it analyzes the user's consumption behavior and lifestyle patterns and provides specific advice for reducing unnecessary waste. This makes it possible to analyze the user's lifestyle and make customized suggestions for reducing waste.

[0083] The waste reduction support unit uses the emotion estimation function to measure the user's level of satisfaction with their efforts to reduce waste and encourages them to take action next time. The waste reduction support unit, for example, uses the emotion estimation function to measure the user's level of satisfaction with their efforts to reduce waste and encourages them to take action next time. For example, if satisfaction is high, a message recommending the next eco-friendly activity is displayed. The waste reduction support unit also analyzes the user's emotional state in real time to measure the user's satisfaction with their efforts to reduce waste. For example, if satisfaction is high, the message recommends the next eco-friendly activity. The waste reduction support unit also uses the emotion estimation function to monitor the user's level of satisfaction with their efforts to reduce waste and encourages them to take action next time. For example, if satisfaction is high, a message recommending the next eco-friendly activity is displayed. In this way, the user's level of satisfaction with their efforts to reduce waste can be measured and encourages them to take action next time.

[0084] The waste reduction support unit can link efforts to reduce waste with other eco-friendly activities (for example, recycling and secondary use). In the waste reduction support unit, for example, the generation AI links efforts to reduce waste with other eco-friendly activities. For example, it provides information on recycling and secondary use and gives specific advice on reducing waste. In addition, when a user makes efforts to reduce waste, the generation AI links with other eco-friendly activities to make suggestions. For example, it provides guidance on recycling and secondary use and gives specific advice on reducing waste. In addition, the waste reduction support unit links efforts to reduce waste with other eco-friendly activities. For example, it provides information on recycling and secondary use and gives specific advice on reducing waste. In this way, efforts to reduce waste can be linked with other eco-friendly activities.

[0085] The waste reduction support unit can use the emotion estimation function to monitor the user's emotions in real time when they are taking steps to reduce waste and provide positive feedback. For example, the waste reduction support unit can use the emotion estimation function to monitor the user's emotions in real time when they are taking steps to reduce waste and provide positive feedback. For example, the waste reduction support unit can display a message emphasizing the positive impact that taking steps to reduce waste has on the environment. The waste reduction support unit can also analyze the user's emotional state in real time and provide positive feedback about the efforts to reduce waste. For example, the waste reduction support unit can display a step-by-step guide showing how easy taking steps to reduce waste are. The waste reduction support unit can also use the emotion estimation function to monitor the user's emotions in real time when they are taking steps to reduce waste and provide positive feedback. For example, the waste reduction support unit can provide information emphasizing the contribution that taking steps to reduce waste has on the environment. In this way, the waste reduction support unit can monitor the user's emotions in real time when they are taking steps to reduce waste and provide positive feedback.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The disposal method guidance unit can learn the user's disposal history and suggest individually optimized disposal methods. For example, the generation AI analyzes the user's past disposal history and suggests the optimal disposal method for a specific electronic device. Based on data on smartphones and tablets that the user has previously discarded, it can suggest the optimal method for the next disposal. The disposal method guidance unit also learns the user's disposal history and suggests more efficient disposal methods for electronic devices that are frequently discarded. For example, it can guide users to the nearest recycling facility for cables that the user frequently discards. The disposal method guidance unit also recommends specific disposal methods based on the user's disposal history. For example, based on data on electronic devices that the user has recycled in the past, it can guide users to separate recyclable parts for the next disposal. This makes it possible to suggest the optimal disposal method based on the user's disposal history.

[0088] The disposal method guidance unit can analyze the materials and components of waste and provide guidance on separating recyclable and non-recyclable parts. For example, the generation AI analyzes the materials and components of electronic devices and provides guidance on separating recyclable and non-recyclable parts. It will guide users to smartphone batteries and metal parts as recyclable parts. The disposal method guidance unit also analyzes photos of electronic devices uploaded by users to the app and automatically identifies recyclable and non-recyclable parts. It will provide guidance on separating plastic and metal parts of cables. The disposal method guidance unit also analyzes the components of electronic devices and identifies recyclable parts. It will guide users to lithium-ion batteries in mobile batteries as recyclable parts. This makes it possible to analyze the materials and components of waste and identify recyclable parts.

[0089] The disposal method guidance unit can use the emotion estimation function to provide advice to reduce the user's resistance to disposal. For example, the emotion estimation function is used to provide advice to reduce the user's resistance to disposal. A message emphasizing the benefits of disposal and its contribution to the environment is displayed. The disposal method guidance unit also analyzes the user's emotional state in real time and provides specific advice to reduce the user's resistance to disposal. A step-by-step guide showing that disposal is easy is displayed. The disposal method guidance unit also uses the emotion estimation function to provide advice to alleviate the user's anxiety and doubts about disposal. Information showing that disposal is safe is provided. This makes it possible to provide advice to reduce the user's resistance to disposal.

[0090] The disposal method guidance section can suggest not only disposal methods, but also repair and reuse methods. For example, the generation AI can suggest ways to repair electronic devices. If a smartphone screen is cracked, it can provide information on repair methods and repair companies. The disposal method guidance section can also suggest ways to reuse electronic devices. It can guide users on how to reuse an old tablet as a digital photo frame. The disposal method guidance section can also present repair and reuse options for electronic devices that the user is considering discarding. It can suggest ways to replace or reuse a mobile battery. This makes it possible to suggest not only disposal methods, but also repair and reuse methods.

[0091] The disposal method guidance unit can use photos of waste to present examples of how other users have disposed of similar waste. For example, the generation AI analyzes photos of waste uploaded by the user and presents examples of how other users have disposed of similar waste. It displays examples of how to dispose of smartphones. The disposal method guidance unit can also analyze photos of electronic devices the user is considering disposing of and present specific examples of how other users have disposed of them. It displays examples of how to dispose of cables. The disposal method guidance unit can also use photos of waste to present successful examples of how other users have disposed of similar waste. It displays examples of how to recycle mobile batteries. This makes it possible to present examples of how other users have disposed of similar waste.

[0092] The disposal method guidance unit can use the emotion estimation function to monitor the user's emotions when disposing of items in real time and provide positive feedback. For example, the emotion estimation function can be used to monitor the user's emotions when disposing of items in real time and provide positive feedback. A message emphasizing the positive impact that disposal has on the environment can be displayed. The disposal method guidance unit can also analyze the user's emotional state in real time and provide positive feedback on disposal. A step-by-step guide showing that disposal is easy can be displayed. The disposal method guidance unit can also use the emotion estimation function to monitor the user's emotions when disposing of items and provide positive feedback. Information emphasizing the contribution that disposal makes to the environment can be provided. In this way, the user's emotions when disposing of items can be monitored in real time and provide positive feedback.

[0093] The eco point granting unit can analyze the user's eco point history and individually suggest ways to use and exchange the points. For example, the generation AI analyzes the user's eco point history and individually suggests ways to use and exchange the points. It suggests the next exchange destination based on the products and services the user has exchanged in the past. The eco point granting unit also learns the user's eco point history and recommends specific ways to use the points. It suggests ways to use the points based on the stores and services the user frequently uses. The eco point granting unit also suggests the optimal exchange destination based on the user's eco point history. It guides the user to the next exchange destination based on data on the products and services the user has exchanged in the past. This makes it possible to suggest the optimal ways to use and exchange the points based on the user's eco point history.

[0094] The eco-point granting unit can use the emotion estimation function to measure the user's level of satisfaction when receiving eco-points and encourage the user to take action next time. For example, the emotion estimation function can be used to measure the user's level of satisfaction when receiving eco-points and encourage the user to take action next time. If the user's satisfaction is high, a message recommending the user to take eco-friendly action next time is displayed. The eco-point granting unit can also analyze the user's emotional state in real time and measure the user's level of satisfaction when receiving eco-points. If the user's satisfaction is high, the message recommending the user to take eco-friendly action next time is displayed. The eco-point granting unit can also use the emotion estimation function to monitor the user's emotions when receiving eco-points and encourage the user to take action next time. If the user's satisfaction is high, a message recommending the user to take eco-friendly action next time is displayed. This makes it possible to measure the user's level of satisfaction when receiving eco-points and encourage the user to take action next time.

[0095] The eco-point granting unit can make it possible for eco-points to be applied to other eco-activities (for example, tree planting or cleanup activities). For example, it makes it possible for eco-points to be applied to other eco-activities. It makes it possible for eco-points to be earned by participating in tree-planting activities. The eco-point granting unit also grants eco-points for cleanup activities in which the user participates. It makes it possible for points to be earned by participating in local cleanup activities. The eco-point granting unit also makes it possible for the generation AI to analyze information on other eco-activities and apply eco-points. It grants points to eco-activities other than recycling activities. This makes it possible for eco-points to be applied to other eco-activities.

[0096] The eco-point granting unit can link eco-points with local shops and services, contributing to the revitalization of the local economy. For example, linking eco-points with local shops and services can contribute to the revitalization of the local economy. Eco-points can be used at local shops. The eco-point granting unit also allows users to use eco-points for local services. Points can be used at local restaurants and service businesses. The eco-point granting unit also uses a generation AI to analyze information about local shops and services and link eco-points. Points can be applied to local events and campaigns. This allows eco-points to be linked with local shops and services, contributing to the revitalization of the local economy.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The disposal method guidance unit guides the user on how to dispose of electronic devices. For example, when a user uploads a photo of their smartphone to the app, the generation AI analyzes the photo and provides specific instructions on how to dispose of the smartphone. In addition, if the user asks for disposal instructions in writing, the generation AI will also guide them on the appropriate disposal method. Step 2: The eco-point awarding unit awards eco-points when the user disposes of the product correctly. For example, the app can automatically award eco-points when the user disposes of the product correctly. Eco-points can also be awarded when the user recycles. Step 3: The Secondary Use Promotion Department promotes secondary use of the user's items. For example, they can be sold or transferred to other users through platforms such as Jimoty or flea market apps. The Generative AI also analyzes the condition of the user's items and determines whether they can be reused. Step 4: The recycling box guidance unit guides the user to the location of the nearest recycling box based on their location information. For example, the generation AI analyzes the user's location information and displays the location of the nearest recycling box. It can also monitor the usage status of recycling boxes in real time and notify the user of their availability. Step 5: The waste reduction support unit analyzes the user's consumption behavior and provides advice on how to reduce waste. For example, the generative AI analyzes the user's consumption behavior and provides advice on how to avoid wasteful purchases. It also provides content that raises awareness of the importance of recycling and secondary reuse, raising the user's environmental awareness.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

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

[0116] 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 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. Equipped with applications powered by generative AI, The application a disposal method guidance unit that guides a user on how to dispose of an electronic device; an eco-point awarding unit that awards eco-points when the user performs a correct disposal method; a secondary use promotion unit that promotes secondary use of items owned by the user; a recycling box guide unit that guides the user to the location of the nearest recycling box based on the user's location information; a waste reduction support unit that analyzes the consumption behavior of the user and provides advice on how to reduce waste. A system characterized by:

2. The disposal method guidance unit Learning the user's disposal history and proposing an individually optimized disposal method 2. The system of claim 1.

3. The disposal method guidance unit Analyzing the materials and components of waste and separating and providing guidance on recyclable and non-recyclable parts 2. The system of claim 1.

4. The disposal method guidance unit To provide the advice for reducing the resistance that the user feels toward disposal 2. The system of claim 1.

5. The disposal method guidance unit In addition to the above disposal methods, we will also propose repair and reuse methods.

2. The system of claim 1.

6. The disposal method guidance unit Using photos of waste, provide examples of how other users have disposed of similar waste.

2. The system of claim 1.

Images