System

Abstract

An object of a system according to an embodiment is to enable a user to easily and accurately sort waste.SOLUTION: A system includes an image acquisition part, an analysis part, an advice part, an education part, a point return part, and a community participation part. The image acquisition unit acquires a photograph of dust taken by a user. The analysis part analyzes the photograph of the dust acquired by the image acquisition part and discriminates the kind of the dust. The advice section advises an appropriate sorting method based on the type of waste determined by the analysis section. The education unit provides environmental education to the user. A point return part returns points when the user performs correct classification. The community participation section promotes the user to participate in the ecological action community.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 techniques have had the problem that separating garbage is complicated and it is difficult for users to separate garbage accurately.

[0005] The system according to the embodiment aims to enable users to easily and accurately separate garbage. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an analysis unit, an advice unit, an education unit, a point redemption unit, and a community participation unit. The image acquisition unit acquires photos of garbage taken by a user. The analysis unit analyzes the photos of garbage acquired by the image acquisition unit and determines the type of garbage. The advice unit advises on appropriate garbage sorting methods based on the type of garbage determined by the analysis unit. The education unit provides environmental education to the user. The point redemption unit redeems points when the user correctly sorts garbage. The community participation unit encourages users to participate in eco-activity communities. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to easily and accurately separate garbage. [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 garbage sorting system according to an embodiment of the present invention automatically analyzes photos of garbage taken by users, and the AI ​​generator provides advice on how to sort garbage, provides environmental education, offers a points reward system, and promotes participation in an eco-friendly community. This allows users to sort garbage easily and effectively, encourages eco-friendly activities, and promotes community participation.

[0029] A garbage sorting system according to an embodiment includes an image acquisition unit, an analysis unit, an advice unit, an education unit, a point redemption unit, and a community participation unit. The image acquisition unit acquires a photo of garbage taken by a user. For example, a photo of garbage is taken using a smartphone camera, and the image acquisition unit acquires the photo. The image acquisition unit can also acquire photos from other devices, such as a digital camera or tablet. The analysis unit analyzes the photo of garbage acquired by the image acquisition unit and determines the type of garbage. For example, the generation AI determines the type of garbage using an image recognition algorithm. The generation AI can also determine the type of garbage using a machine learning model. The generation AI can also determine the type of garbage based on the shape and color of the garbage. The advice unit advises the user on the appropriate garbage sorting method based on the type of garbage determined by the analysis unit. For example, the generation AI provides specific instructions such as, "Please remove the cap from this plastic bottle and recycle it." The generation AI can also suggest to the user how to sort garbage according to the type of garbage. The education unit provides environmental education to the user. For example, the generation AI provides information on the correct method of garbage sorting and the benefits of recycling. The generation AI can also provide content that raises awareness of the importance of environmental protection. The point redemption unit redeems points when a user correctly separates their trash. For example, the generation AI can award points when a user correctly separates their trash, allowing the user to exchange those points for eco-friendly products. The generation AI can also encourage users to engage in eco-friendly activities through a point redemption system. The community participation unit encourages users to participate in eco-friendly activity communities. For example, the generation AI can provide information on local eco-events and cleanup activities, allowing users to easily participate. The generation AI can also provide a platform where users can interact with each other, promoting information exchange and cooperation in eco-friendly activities. As a result, the waste sorting system according to the embodiment allows users to easily and effectively separate their trash, encourages eco-friendly activities, and promotes community participation.

[0030] When analyzing a photo of garbage, the analysis unit can remove the background and emphasize the shape and color of the garbage. For example, when taking a photo of garbage, the generation AI automatically removes the background and emphasizes the shape and color of the garbage. For example, removing background noise and clarifying the outline of the garbage improves the accuracy of garbage identification. Furthermore, when taking a photo of garbage, the generation AI uses an algorithm to remove the background and emphasize the color and shape of the garbage. For example, edge detection technology is used to emphasize the outline of the garbage. Furthermore, when taking a photo of garbage, the analysis unit uses a filter to remove the background and emphasize the shape and color of the garbage. For example, a color filter is used to make the color of the garbage clearer. This improves the accuracy of garbage identification.

[0031] The analysis unit can improve classification accuracy by taking photos of the garbage from multiple angles and generating a 3D model. For example, the analysis unit takes photos of the garbage from multiple angles, and the generation AI generates the 3D model. For example, to grasp the overall appearance of the garbage, the analysis unit combines photos from different angles. The analysis unit also takes photos of the garbage from multiple angles, and the generation AI uses an algorithm to generate the 3D model. For example, stereo vision technology is used to reproduce the three-dimensional shape of the garbage. The analysis unit also automates the process of taking photos of the garbage from multiple angles and the generation AI generating the 3D model. For example, a system is built that automatically combines multiple photos to generate a 3D model. This further improves the accuracy of garbage classification.

[0032] The advice unit can refer to the user's past sorting history and provide individually customized advice. For example, the generation AI in the advice unit refers to the user's past sorting history and provides individually customized advice. For example, it may point out incorrect sorting methods that were used in the past. The advice unit also builds a system in which the generation AI provides individually customized advice based on the user's past sorting history. For example, it stores the user's sorting history in a database and reflects this in the advice. The advice unit also builds a system in which the generation AI analyzes the user's past sorting history and provides individually customized advice. For example, it may provide advice based on the types of garbage that the user often separates. This allows the user to receive more appropriate advice.

[0033] The advice unit can take into account regional sorting rules and provide advice specific to the region. For example, the generation AI of the advice unit takes into account regional sorting rules and provides advice specific to the region. For example, it proposes a sorting method based on regional recycling rules. The advice unit also registers regional sorting rules in a database, and builds a system in which the generation AI provides region-specific advice based on that information. For example, it applies regional sorting rules based on the user's location information. The advice unit also analyzes regional sorting rules and provides region-specific advice. For example, it provides advice that takes into account sorting rules that only apply in a specific region. This makes it possible to provide appropriate advice based on regional sorting rules.

[0034] The education department can provide environmental education content customized based on the user's interests and concerns. For example, the generation AI analyzes the user's interests and concerns and provides environmental education content customized based on the analysis. For example, information related to topics that interest the user is prioritized. The education department also builds a system in which the generation AI provides customized environmental education content based on the user's past behavioral data. For example, information related to content that the user frequently views is provided. The education department also builds a system in which the generation AI analyzes the user's interests and concerns in real time and provides environmental education content customized based on the analysis. For example, the latest information related to topics that the user has shown interest in is displayed. This makes it possible to provide environmental education tailored to the user's interests and concerns.

[0035] The point redemption unit can track the progress of a user's eco-activities and provide rewards according to the level of achievement. For example, the point redemption unit uses a generation AI to track the progress of a user's eco-activities and provide rewards according to the level of achievement. For example, points are awarded to users who achieve a certain level of eco-activities. The point redemption unit also builds a system in which the progress of a user's eco-activities is tracked in real time and the generation AI provides rewards according to the level of achievement. For example, the record of eco-activities is stored in a database and rewards are calculated. The point redemption unit also uses a generation AI to analyze the progress of a user's eco-activities and provide rewards according to the level of achievement. For example, bonus points are awarded when a user completes a specific eco-activity. This encourages users to engage in eco-activities and provides rewards.

[0036] The community participation unit can recommend appropriate eco-friendly events and activities based on the user's interests and concerns. In the community participation unit, for example, the generation AI analyzes the user's interests and concerns and recommends appropriate eco-friendly events and activities based on that. For example, it displays events related to topics that interest the user. The community participation unit also builds a system in which the generation AI recommends appropriate eco-friendly events and activities based on the user's past behavioral data. For example, it provides information related to events that the user frequently participates in. The community participation unit also analyzes the user's interests and concerns in real time and recommends appropriate eco-friendly events and activities based on that. For example, it displays the latest events related to topics that the user has shown interest in. This makes it possible to recommend eco-friendly events and activities that match the user's interests and concerns.

[0037] The community participation unit can track a user's activity history within the eco-activity community and award badges and titles according to the level of contribution. In the community participation unit, for example, a generation AI tracks a user's activity history within the eco-activity community and awards badges and titles according to the level of contribution. For example, a badge is awarded to a user who has achieved a certain level of activity. The community participation unit also builds a system that tracks a user's activity history in real time and awards badges and titles according to the level of contribution by the generation AI. For example, the generation AI stores records of eco-activities in a database and calculates badges. In the community participation unit, a generation AI analyzes a user's activity history within the eco-activity community and awards badges and titles according to the level of contribution. For example, a title is awarded when a user completes a specific eco-activity. This allows the user's contribution to eco-activities to be evaluated and their motivation to be increased.

[0038] The community participation unit can provide a virtual eco-activity experience using virtual reality (VR) technology. For example, when the generation AI promotes participation in an eco-activity community, the community participation unit provides a virtual eco-activity experience using virtual reality (VR) technology. For example, users can experience cleaning activities in a virtual space. The community participation unit also uses VR technology to build a system in which the generation AI provides a virtual eco-activity experience. For example, a user experiences eco-activities in a virtual space using VR goggles. The community participation unit also provides a virtual eco-activity experience using VR technology in order for the generation AI to promote participation in the eco-activity community. For example, users can experience a tour of a recycling factory in a virtual space. This allows users to virtually experience eco-activities.

[0039] The community participation unit can match users with common interests. For example, the generation AI provides a function to match users with common interests. For example, it can connect users who are interested in the same eco-activities. The community participation unit also analyzes users' interests and concerns, and builds a system in which the generation AI matches users with common interests. For example, it can match users who share the same hobby of eco-activities. The community participation unit also provides a function in which the generation AI matches users with common interests. For example, it can connect users who are engaged in eco-activities in the same area. This can promote interaction between users.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The garbage sorting system may further include a voice recognition unit. The voice recognition unit allows a user to ask about the type of garbage and how to sort it by voice. For example, if the user asks, "How should I sort this garbage?", the voice recognition unit analyzes the question and provides advice on the appropriate sorting method. The voice recognition unit also allows the user to input the type of garbage by voice. For example, if the user says, "PET bottle," the voice recognition unit sends that information to the analysis unit, which then provides the appropriate sorting method. The voice recognition unit also allows the user to request environmental education content by voice. For example, if the user says, "Please tell me about the importance of recycling," the voice recognition unit sends the request to the education unit, which then provides the appropriate content. This allows the user to easily receive garbage sorting and environmental education using voice.

[0042] The garbage sorting system can further include a temperature sensor unit. The temperature sensor unit measures the temperature of the garbage and advises on the appropriate disposal method. For example, the temperature sensor unit measures the temperature of the garbage and, if the garbage is hot, issues a warning to avoid the risk of fire. The temperature sensor unit can also advise on the appropriate disposal method for low-temperature garbage, such as food garbage just taken out of the refrigerator. For example, it can urge the user to dispose of low-temperature garbage immediately. The temperature sensor unit can also send garbage temperature data to the analysis unit, which can then provide an appropriate sorting method based on that data. This makes it possible to provide the user with an appropriate disposal method according to the garbage temperature.

[0043] The garbage sorting system can further include an odor sensor unit. The odor sensor unit detects the odor of garbage and advises the user on the appropriate disposal method. For example, if the odor sensor unit detects the odor of food waste, it will urge the user to dispose of the garbage immediately. Furthermore, if the odor sensor unit detects the odor of harmful chemicals, it can advise the user on a special disposal method. For example, it can instruct the user to take garbage containing harmful substances to a dedicated disposal facility. Furthermore, the odor sensor unit can send garbage odor data to an analysis unit, which can then provide the user with an appropriate sorting method based on that data. This makes it possible to provide the user with an appropriate disposal method according to the odor of the garbage.

[0044] The garbage sorting system can further include a weight sensor unit. The weight sensor unit measures the weight of the garbage and advises on the appropriate disposal method. For example, the weight sensor unit measures the weight of the garbage, and if the garbage is heavy, it issues a warning about how to sort and transport it. The weight sensor unit can also advise on the appropriate disposal method for light garbage. For example, it can urge the user to put light garbage securely in a bag so that it will not be blown away by the wind. The weight sensor unit can also transmit garbage weight data to an analysis unit, which can then provide an appropriate sorting method based on that data. This makes it possible to provide the user with an appropriate disposal method according to the weight of the garbage.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The image acquisition unit acquires a photo of the garbage taken by the user. For example, a photo of the garbage is taken using a smartphone camera, and the image acquisition unit acquires the photo. The image acquisition unit can also acquire photos from other devices such as a digital camera or tablet. Step 2: The analysis unit analyzes the photo of the garbage acquired by the image acquisition unit and determines the type of garbage. For example, the generation AI uses an image recognition algorithm to determine the type of garbage. The generation AI can also determine the type of garbage using a machine learning model. Furthermore, the generation AI can also determine the type of garbage based on the shape and color of the garbage. Step 3: The advice unit advises the user on the appropriate method of sorting based on the type of garbage identified by the analysis unit. For example, the generation AI can provide specific instructions such as, "Please remove the cap from this plastic bottle and recycle it." The generation AI can also suggest to the user how to sort garbage according to its type. Step 4: The Ministry of Education will provide environmental education to users. For example, the AI ​​generator can provide information on the correct way to separate garbage and the benefits of recycling. The AI ​​generator can also provide content that raises awareness of the importance of environmental protection. Step 5: The point redemption unit redeems points when the user separates their trash correctly. For example, the generation AI can award points when the user separates their trash correctly, and allow the user to exchange those points for eco-friendly products. The generation AI can also encourage users to engage in eco-friendly activities through the point redemption system. Step 6: The community participation section encourages users to participate in eco-activity communities. For example, the generation AI provides information about local eco-events and cleanup activities, allowing users to easily participate. The generation AI can also provide a platform where users can interact with each other, promoting the exchange of information and cooperation on eco-activities.

[0047] (Example 2) The garbage sorting system according to an embodiment of the present invention automatically analyzes photos of garbage taken by users, and the AI ​​generator provides advice on how to sort garbage, provides environmental education, offers a points reward system, and promotes participation in an eco-friendly community. This allows users to sort garbage easily and effectively, encourages eco-friendly activities, and promotes community participation.

[0048] A garbage sorting system according to an embodiment includes an image acquisition unit, an analysis unit, an advice unit, an education unit, a point redemption unit, and a community participation unit. The image acquisition unit acquires a photo of garbage taken by a user. For example, a photo of garbage is taken using a smartphone camera, and the image acquisition unit acquires the photo. The image acquisition unit can also acquire photos from other devices, such as a digital camera or tablet. The analysis unit analyzes the photo of garbage acquired by the image acquisition unit and determines the type of garbage. For example, the generation AI determines the type of garbage using an image recognition algorithm. The generation AI can also determine the type of garbage using a machine learning model. The generation AI can also determine the type of garbage based on the shape and color of the garbage. The advice unit advises the user on the appropriate garbage sorting method based on the type of garbage determined by the analysis unit. For example, the generation AI provides specific instructions such as, "Please remove the cap from this plastic bottle and recycle it." The generation AI can also suggest to the user how to sort garbage according to the type of garbage. The education unit provides environmental education to the user. For example, the generation AI provides information on the correct method of garbage sorting and the benefits of recycling. The generation AI can also provide content that raises awareness of the importance of environmental protection. The point redemption unit redeems points when a user correctly separates their trash. For example, the generation AI can award points when a user correctly separates their trash, allowing the user to exchange those points for eco-friendly products. The generation AI can also encourage users to engage in eco-friendly activities through a point redemption system. The community participation unit encourages users to participate in eco-friendly activity communities. For example, the generation AI can provide information on local eco-events and cleanup activities, allowing users to easily participate. The generation AI can also provide a platform where users can interact with each other, promoting information exchange and cooperation in eco-friendly activities. As a result, the waste sorting system according to the embodiment allows users to easily and effectively separate their trash, encourages eco-friendly activities, and promotes community participation.

[0049] When analyzing a photo of garbage, the analysis unit can remove the background and emphasize the shape and color of the garbage. For example, when taking a photo of garbage, the generation AI automatically removes the background and emphasizes the shape and color of the garbage. For example, removing background noise and clarifying the outline of the garbage improves the accuracy of garbage identification. Furthermore, when taking a photo of garbage, the generation AI uses an algorithm to remove the background and emphasize the color and shape of the garbage. For example, edge detection technology is used to emphasize the outline of the garbage. Furthermore, when taking a photo of garbage, the analysis unit uses a filter to remove the background and emphasize the shape and color of the garbage. For example, a color filter is used to make the color of the garbage clearer. This improves the accuracy of garbage identification.

[0050] The analysis unit can improve classification accuracy by taking photos of the garbage from multiple angles and generating a 3D model. For example, the analysis unit takes photos of the garbage from multiple angles, and the generation AI generates the 3D model. For example, to grasp the overall appearance of the garbage, the analysis unit combines photos from different angles. The analysis unit also takes photos of the garbage from multiple angles, and the generation AI uses an algorithm to generate the 3D model. For example, stereo vision technology is used to reproduce the three-dimensional shape of the garbage. The analysis unit also automates the process of taking photos of the garbage from multiple angles and the generation AI generating the 3D model. For example, a system is built that automatically combines multiple photos to generate a 3D model. This further improves the accuracy of garbage classification.

[0051] The advice unit can refer to the user's past sorting history and provide individually customized advice. For example, the generation AI in the advice unit refers to the user's past sorting history and provides individually customized advice. For example, it may point out incorrect sorting methods that were used in the past. The advice unit also builds a system in which the generation AI provides individually customized advice based on the user's past sorting history. For example, it stores the user's sorting history in a database and reflects this in the advice. The advice unit also builds a system in which the generation AI analyzes the user's past sorting history and provides individually customized advice. For example, it may provide advice based on the types of garbage that the user often separates. This allows the user to receive more appropriate advice.

[0052] The advice unit can take into account regional sorting rules and provide advice specific to the region. For example, the generation AI of the advice unit takes into account regional sorting rules and provides advice specific to the region. For example, it proposes a sorting method based on regional recycling rules. The advice unit also registers regional sorting rules in a database, and builds a system in which the generation AI provides region-specific advice based on that information. For example, it applies regional sorting rules based on the user's location information. The advice unit also analyzes regional sorting rules and provides region-specific advice. For example, it provides advice that takes into account sorting rules that only apply in a specific region. This makes it possible to provide appropriate advice based on regional sorting rules.

[0053] The education department can provide environmental education content customized based on the user's interests and concerns. For example, the generation AI analyzes the user's interests and concerns and provides environmental education content customized based on the analysis. For example, information related to topics that interest the user is prioritized. The education department also builds a system in which the generation AI provides customized environmental education content based on the user's past behavioral data. For example, information related to content that the user frequently views is provided. The education department also builds a system in which the generation AI analyzes the user's interests and concerns in real time and provides environmental education content customized based on the analysis. For example, the latest information related to topics that the user has shown interest in is displayed. This makes it possible to provide environmental education tailored to the user's interests and concerns.

[0054] The point redemption unit can track the progress of a user's eco-activities and provide rewards according to the level of achievement. For example, the point redemption unit uses a generation AI to track the progress of a user's eco-activities and provide rewards according to the level of achievement. For example, points are awarded to users who achieve a certain level of eco-activities. The point redemption unit also builds a system in which the progress of a user's eco-activities is tracked in real time and the generation AI provides rewards according to the level of achievement. For example, the record of eco-activities is stored in a database and rewards are calculated. The point redemption unit also uses a generation AI to analyze the progress of a user's eco-activities and provide rewards according to the level of achievement. For example, bonus points are awarded when a user completes a specific eco-activity. This encourages users to engage in eco-activities and provides rewards.

[0055] The community participation unit can recommend appropriate eco-friendly events and activities based on the user's interests and concerns. In the community participation unit, for example, the generation AI analyzes the user's interests and concerns and recommends appropriate eco-friendly events and activities based on that. For example, it displays events related to topics that interest the user. The community participation unit also builds a system in which the generation AI recommends appropriate eco-friendly events and activities based on the user's past behavioral data. For example, it provides information related to events that the user frequently participates in. The community participation unit also analyzes the user's interests and concerns in real time and recommends appropriate eco-friendly events and activities based on that. For example, it displays the latest events related to topics that the user has shown interest in. This makes it possible to recommend eco-friendly events and activities that match the user's interests and concerns.

[0056] The community participation unit can track a user's activity history within the eco-activity community and award badges and titles according to the level of contribution. In the community participation unit, for example, a generation AI tracks a user's activity history within the eco-activity community and awards badges and titles according to the level of contribution. For example, a badge is awarded to a user who has achieved a certain level of activity. The community participation unit also builds a system that tracks a user's activity history in real time and awards badges and titles according to the level of contribution by the generation AI. For example, the generation AI stores records of eco-activities in a database and calculates badges. In the community participation unit, a generation AI analyzes a user's activity history within the eco-activity community and awards badges and titles according to the level of contribution. For example, a title is awarded when a user completes a specific eco-activity. This allows the user's contribution to eco-activities to be evaluated and their motivation to be increased.

[0057] The community participation unit can provide a virtual eco-activity experience using virtual reality (VR) technology. For example, when the generation AI promotes participation in an eco-activity community, the community participation unit provides a virtual eco-activity experience using virtual reality (VR) technology. For example, users can experience cleaning activities in a virtual space. The community participation unit also uses VR technology to build a system in which the generation AI provides a virtual eco-activity experience. For example, a user experiences eco-activities in a virtual space using VR goggles. The community participation unit also provides a virtual eco-activity experience using VR technology in order for the generation AI to promote participation in the eco-activity community. For example, users can experience a tour of a recycling factory in a virtual space. This allows users to virtually experience eco-activities.

[0058] The community participation unit can match users with common interests. For example, the generation AI provides a function to match users with common interests. For example, it can connect users who are interested in the same eco-activities. The community participation unit also analyzes users' interests and concerns, and builds a system in which the generation AI matches users with common interests. For example, it can match users who share the same hobby of eco-activities. The community participation unit also provides a function in which the generation AI matches users with common interests. For example, it can connect users who are engaged in eco-activities in the same area. This can promote interaction between users.

[0059] The community participation unit can use the emotion estimation function to analyze the emotions of a user when participating in an eco-activity community and provide mental support to reduce negative emotions. The community participation unit, for example, uses the emotion estimation function to analyze the emotions of a user when participating in an eco-activity community. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The community participation unit also builds a system that analyzes the user's emotions in real time and provides mental support to reduce negative emotions. For example, if the emotion score is low, it plays relaxation music. The community participation unit also uses the emotion estimation function to provide mental support based on the user's emotions. For example, if the user is feeling stressed, it displays a relaxing video. This reduces the user's negative emotions and promotes participation in eco-activities.

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

[0061] The garbage sorting system may further include a voice recognition unit. The voice recognition unit allows a user to ask about the type of garbage and how to sort it by voice. For example, if the user asks, "How should I sort this garbage?", the voice recognition unit analyzes the question and provides advice on the appropriate sorting method. The voice recognition unit also allows the user to input the type of garbage by voice. For example, if the user says, "PET bottle," the voice recognition unit sends that information to the analysis unit, which then provides the appropriate sorting method. The voice recognition unit also allows the user to request environmental education content by voice. For example, if the user says, "Please tell me about the importance of recycling," the voice recognition unit sends the request to the education unit, which then provides the appropriate content. This allows the user to easily receive garbage sorting and environmental education using voice.

[0062] The garbage sorting system can further include a temperature sensor unit. The temperature sensor unit measures the temperature of the garbage and advises on the appropriate disposal method. For example, the temperature sensor unit measures the temperature of the garbage and, if the garbage is hot, issues a warning to avoid the risk of fire. The temperature sensor unit can also advise on the appropriate disposal method for low-temperature garbage, such as food garbage just taken out of the refrigerator. For example, it can urge the user to dispose of low-temperature garbage immediately. The temperature sensor unit can also send garbage temperature data to the analysis unit, which can then provide an appropriate sorting method based on that data. This makes it possible to provide the user with an appropriate disposal method according to the garbage temperature.

[0063] The garbage sorting system can further include an odor sensor unit. The odor sensor unit detects the odor of garbage and advises the user on the appropriate disposal method. For example, if the odor sensor unit detects the odor of food waste, it will urge the user to dispose of the garbage immediately. Furthermore, if the odor sensor unit detects the odor of harmful chemicals, it can advise the user on a special disposal method. For example, it can instruct the user to take garbage containing harmful substances to a dedicated disposal facility. Furthermore, the odor sensor unit can send garbage odor data to an analysis unit, which can then provide the user with an appropriate sorting method based on that data. This makes it possible to provide the user with an appropriate disposal method according to the odor of the garbage.

[0064] The garbage sorting system can further include a weight sensor unit. The weight sensor unit measures the weight of the garbage and advises on the appropriate disposal method. For example, the weight sensor unit measures the weight of the garbage, and if the garbage is heavy, it issues a warning about how to sort and transport it. The weight sensor unit can also advise on the appropriate disposal method for light garbage. For example, it can urge the user to put light garbage securely in a bag so that it will not be blown away by the wind. The weight sensor unit can also transmit garbage weight data to an analysis unit, which can then provide an appropriate sorting method based on that data. This makes it possible to provide the user with an appropriate disposal method according to the weight of the garbage.

[0065] The garbage sorting system can also estimate the user's emotions and provide garbage sorting advice based on the estimated emotions. For example, if the user feels stressed about garbage sorting, it can suggest a simple method of sorting. If the user is interested in garbage sorting, it can provide detailed information on how to sort garbage and the effects of recycling. Furthermore, if the user has positive emotions about garbage sorting, it can provide advice encouraging further participation in eco-friendly activities. This makes it possible to provide appropriate advice according to the user's emotions.

[0066] The garbage sorting system can also estimate the user's emotions and provide environmental education content based on the estimated emotions. For example, if the user feels anxious about environmental issues, it can provide information that gives a sense of security. If the user is interested in environmental issues, it can provide detailed information and the latest research results. Furthermore, if the user has positive emotions about environmental issues, it can provide content that encourages further participation in eco-activities. This makes it possible to provide appropriate environmental education that matches the user's emotions.

[0067] The garbage sorting system can further estimate the user's emotions and provide a point reward system based on the estimated emotions. For example, if the user feels motivated to engage in eco-friendly activities, the system can strengthen the point reward system. Alternatively, if the user has lost interest in eco-friendly activities, the system can provide special bonus points. Furthermore, if the user has positive emotions toward eco-friendly activities, the system can provide advice to encourage users to exchange their points for eco-friendly products. This makes it possible to provide an appropriate point reward system according to the user's emotions.

[0068] The garbage sorting system can also estimate the user's emotions and promote community participation based on the estimated emotions. For example, if the user feels uneasy about eco-friendly activities, it can suggest events for beginners. If the user is interested in eco-friendly activities, it can provide detailed event information and reviews from participants. Furthermore, if the user has positive feelings about eco-friendly activities, it can suggest roles in which the user can demonstrate leadership. This makes it possible to promote appropriate community participation according to the user's emotions.

[0069] The waste sorting system can also estimate the user's emotions and provide feedback on eco-friendly activities based on the estimated emotions. For example, if the user feels satisfied with the eco-friendly activities, feedback emphasizing the results of the activities can be provided. If the user feels dissatisfied with the eco-friendly activities, improvements and next steps can be suggested. Furthermore, if the user feels positive about the eco-friendly activities, feedback encouraging further efforts can be provided. This makes it possible to provide appropriate feedback according to the user's emotions.

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

[0071] Step 1: The image acquisition unit acquires a photo of the garbage taken by the user. For example, a photo of the garbage is taken using a smartphone camera, and the image acquisition unit acquires the photo. The image acquisition unit can also acquire photos from other devices such as a digital camera or tablet. Step 2: The analysis unit analyzes the photo of the garbage acquired by the image acquisition unit and determines the type of garbage. For example, the generation AI uses an image recognition algorithm to determine the type of garbage. The generation AI can also determine the type of garbage using a machine learning model. Furthermore, the generation AI can also determine the type of garbage based on the shape and color of the garbage. Step 3: The advice unit advises the user on the appropriate method of sorting based on the type of garbage identified by the analysis unit. For example, the generation AI can provide specific instructions such as, "Please remove the cap from this plastic bottle and recycle it." The generation AI can also suggest to the user how to sort garbage according to its type. Step 4: The Ministry of Education will provide environmental education to users. For example, the AI ​​generator can provide information on the correct way to separate garbage and the benefits of recycling. The AI ​​generator can also provide content that raises awareness of the importance of environmental protection. Step 5: The point redemption unit redeems points when the user separates their trash correctly. For example, the generation AI can award points when the user separates their trash correctly, and allow the user to exchange those points for eco-friendly products. The generation AI can also encourage users to engage in eco-friendly activities through the point redemption system. Step 6: The community participation section encourages users to participate in eco-activity communities. For example, the generation AI provides information about local eco-events and cleanup activities, allowing users to easily participate. The generation AI can also provide a platform where users can interact with each other, promoting the exchange of information and cooperation on eco-activities.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires a photo of garbage taken by a user; an analysis unit that analyzes the photograph of the dust acquired by the image acquisition unit and determines the type of dust; an advice unit that advises on an appropriate sorting method based on the type of waste determined by the analysis unit; An education department that provides environmental education to users; a point redemption unit that redeems points when the user correctly separates the trash; A community participation unit that encourages users to participate in an eco-activity community. A system characterized by:

2. The analysis unit When analyzing the photo of the dust, the background is removed and the shape and color of the dust are emphasized.

2. The system of claim 1.

3. The analysis unit Photograph the garbage from multiple angles and generate a 3D model to improve classification accuracy.

2. The system of claim 1.

4. The advice unit Refer to the user's past segregation history and provide the above advice that is individually customized 2. The system of claim 1.

5. The advice unit Considering the rules for sorting in each region, we provide advice specific to the region.

2. The system of claim 1.

Images