System

Abstract

An object of a system according to an embodiment is to accurately determine a type of waste and propose an appropriate sorting method in accordance with sorting rules for each region.SOLUTION: A system according to an embodiment includes a photographing unit, a determination unit, and a proposal unit. The photograph taking section takes a photograph of the waste. The determination part analyzes the photograph of the dust photographed by the photographing part to determine the kind of the dust. The proposing section proposes an appropriate sorting method in accordance with sorting rules for each region on the basis of the type of the waste determined by the determining section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately identify the type of garbage and propose appropriate sorting methods that comply with local sorting rules.

[0005] The system according to the embodiment aims to accurately identify the type of garbage and propose an appropriate sorting method in accordance with the sorting rules for each region. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, a discrimination unit, and a suggestion unit. The photographing unit takes a photograph of garbage. The discrimination unit analyzes the photograph of garbage taken by the photographing unit to discriminate the type of garbage. The suggestion unit suggests an appropriate sorting method in accordance with regional sorting rules based on the type of garbage discriminated by the discrimination unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately identify the type of garbage and propose an appropriate sorting method in accordance with the sorting rules for each region. [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 support system according to an embodiment of the present invention is a system in which a user uploads a photo of garbage taken with a smartphone camera to an app, and a generation AI identifies the type of garbage and suggests an appropriate sorting method based on the sorting rules for each region. This allows the garbage sorting support system to correctly sort garbage even if the user does not have specialized knowledge about sorting garbage.

[0029] A garbage sorting support system according to an embodiment includes a photography unit, a discrimination unit, and a suggestion unit. The photography unit takes photos of garbage. For example, the photography unit takes photos of garbage using a smartphone camera. The photography unit can take photos of garbage at high resolution. For example, the photography unit can maximize the resolution of the smartphone camera to capture clear images of even the smallest details of the garbage. The photography unit can automatically upload photos of the garbage. For example, the photography unit can be configured to automatically upload photos of the garbage to an app after they are taken. The discrimination unit analyzes the photos of the garbage taken by the photography unit to discriminate the type of garbage. For example, the generation AI uses an image recognition algorithm to discriminate the type of garbage. The generation AI can discriminate the type of garbage with high accuracy using a machine learning model. The generation AI can learn criteria for classifying garbage types and improve discrimination accuracy. For example, the generation AI can discriminate between types of garbage such as plastic, paper, and metal with high accuracy. The suggestion unit proposes an appropriate sorting method in accordance with regional sorting rules based on the type of garbage discriminated by the discrimination unit. For example, the generation AI retrieves regional sorting rules from a database and provides specific instructions to the user. Furthermore, the generation AI can propose appropriate disposal methods for each type of garbage based on regional sorting rules. The generation AI can also explain the sorting methods to the user in an easy-to-understand manner. For example, the generation AI provides specific disposal methods for each type of garbage, supporting the user in sorting the garbage correctly. This allows the garbage sorting support system according to the embodiment to properly sort garbage even without specialized knowledge of garbage sorting. For example, a user can simply take a photo of the garbage using their smartphone camera and upload it to the app, and the generation AI can identify the type of garbage and propose an appropriate sorting method. Furthermore, the user can properly sort the garbage according to regional sorting rules. This contributes to environmental consideration.

[0030] The photographing unit can automatically measure the weight and material of the garbage using the smartphone's sensors and transmit the data as additional information when uploading. For example, the photographing unit can measure the weight of the garbage using the smartphone's built-in sensors and automatically record it when photographing. For example, when photographing garbage, the smartphone's acceleration sensor can be used to estimate the weight and upload that data along with the photo. The photographing unit can also work in conjunction with the smartphone's camera to create a system that automatically analyzes the material of the garbage. For example, the camera's image analysis technology can be used to identify materials such as plastic, metal, and paper, and this information can be added when uploading. The photographing unit can also use the smartphone's sensors to measure the surface temperature of the garbage when photographing and transmit that data when uploading. For example, an infrared sensor can be used to measure the temperature of the garbage and use this data to determine whether it is recyclable. This enables more accurate garbage sorting based on the weight and material of the garbage.

[0031] The photographing unit can automatically analyze the shape and color of the garbage using the smartphone camera and instruct the user on the optimal shooting angle and light intensity. The photographing unit, for example, constructs a system in which the smartphone camera analyzes the shape of the garbage in real time and instructs the user on the optimal shooting angle. For example, the camera's image analysis technology can be used to recognize the shape of the garbage and display the appropriate shooting position to the user. The photographing unit also adds a function in which the smartphone camera analyzes the color of the garbage and automatically adjusts the optimal light intensity. For example, the camera's color recognition technology can be used to automatically set the light intensity according to the color of the garbage to take a clear photo. The photographing unit also analyzes the shape and color of the garbage using the smartphone camera when taking a photo and displays the optimal shooting conditions to the user in real time. For example, the camera's image analysis technology can be used to instruct the user on the appropriate shooting angle and light intensity based on the shape and color of the garbage. This provides optimal shooting conditions, thereby improving the accuracy of garbage identification.

[0032] The photography unit can provide audio guidance when taking a photograph of garbage, allowing the user to easily understand the appropriate photography method. The photography unit, for example, builds a system that provides audio guidance when taking a photograph of garbage. For example, a smartphone app issues audio instructions such as "Place the garbage in the center" or "Adjust the light intensity." The photography unit also adds a function that uses audio guidance to explain the appropriate photography method to the user when taking a photograph. For example, specific audio instructions such as "Take the photograph so that the entire garbage can be seen." The photography unit also develops a system that uses audio guidance to allow the user to easily understand the appropriate photography method. For example, audio advice such as "Take the photograph so that the shape of the garbage can be clearly seen" is provided. This allows the user to easily understand the appropriate photography method through the audio guidance.

[0033] The photography unit can add a function to share photos of trash that have been taken with other users and receive community-based trash sorting advice. For example, the photography unit adds a function to the app that allows users to share photos of trash that have been taken with other users. For example, users can post photos of trash within a community and receive trash sorting advice from other users. The photography unit also builds a system for sharing photos of trash and receiving community-based trash sorting advice. For example, it provides a function that allows other users to post comments and advice. The photography unit also develops a function for sharing photos of trash that have been taken and receiving trash sorting advice within the community. For example, other users can provide advice such as "This trash is recyclable." This allows community-based trash sorting advice to improve users' trash sorting behavior.

[0034] The discrimination unit will be able to perform a detailed analysis of the material and components of the garbage and display recyclable and non-recyclable parts separately. For example, the discrimination unit will build a system in which the generation AI will perform a detailed analysis of the material of the garbage and display recyclable and non-recyclable parts separately. For example, the cap of a plastic container will be displayed as recyclable, but the body of the container will be displayed as non-recyclable. The discrimination unit will also add a function to analyze the components of the garbage and display recyclable and non-recyclable parts separately. For example, it will detect metal clips contained in paper waste and display them as non-recyclable parts. The discrimination unit will also develop a system in which the generation AI will perform a detailed analysis of the material and components of the garbage and display recyclable and non-recyclable parts separately. For example, it will display aluminum can labels as non-recyclable. This will make it easier for users to dispose of the garbage appropriately by displaying recyclable and non-recyclable parts separately.

[0035] The discrimination unit can compare the garbage discrimination results with past data and build a feedback loop to continuously improve discrimination accuracy. The discrimination unit, for example, develops a system to compare the garbage discrimination results with past data and build a feedback loop to improve discrimination accuracy. For example, it compares past discrimination results with current results and corrects misclassification. The discrimination unit also develops a feedback loop to compare the garbage discrimination results with past data and continuously improve accuracy. For example, it adjusts the discrimination algorithm based on past data. The discrimination unit also develops a system to compare the garbage discrimination results with past data and build a feedback loop to improve discrimination accuracy. For example, it uses past discrimination results as learning data to improve the accuracy of the generation AI. This continuously improves discrimination accuracy through the feedback loop.

[0036] The discrimination unit can detect the smell of garbage using a sensor and use the smell information for discrimination. For example, the discrimination unit will build a system in which the generation AI detects the smell of garbage using a sensor and uses the smell information for discrimination. For example, an odor sensor will be used to distinguish between organic and inorganic garbage. The discrimination unit will also add a function to detect the smell of garbage using a sensor and the generation AI will use that information to discriminate the type of garbage. For example, it will detect a putrid smell and classify it as food waste. The discrimination unit will also develop a system in which the generation AI detects the smell of garbage using a sensor and use the smell information for discrimination. For example, it will detect the smell of a specific chemical substance and classify it as hazardous garbage. In this way, the accuracy of garbage discrimination will be improved by utilizing smell information.

[0037] The discrimination unit can add a function to share the garbage discrimination results with other users and receive community-based feedback. The discrimination unit, for example, adds a function to the app to share the garbage discrimination results with other users. For example, the discrimination result is posted within the community and feedback is received from other users. The discrimination unit also builds a system to share the garbage discrimination results and receive community-based feedback. For example, a function is provided that allows other users to post comments and advice. The discrimination unit also develops a function to share the garbage discrimination results and receive feedback within the community. For example, other users provide advice such as "This garbage is recyclable." This improves discrimination accuracy through community-based feedback.

[0038] When proposing sorting rules for each region, the proposal unit allows the generation AI to analyze past sorting data and propose the most effective sorting method. For example, the proposal unit builds a system in which the generation AI analyzes past sorting data and proposes the most effective sorting method. For example, it proposes the most successful sorting method in a specific region based on past data. The proposal unit also adds a function to analyze past sorting data and have the generation AI propose the optimal sorting method for each region. For example, it makes a proposal based on data from regions where a specific waste sorting method was effective. The proposal unit also develops a system in which the generation AI analyzes past sorting data and proposes the most effective sorting method. For example, it proposes the optimal sorting method for a specific day of the week or time of day based on past data. This improves the effectiveness of sorting by proposing the optimal sorting method based on past data.

[0039] The proposal unit can reflect the latest information from local environmental protection organizations and local governments in real time when proposing sorting rules. For example, the proposal unit may build a system that acquires the latest information from local environmental protection organizations and local governments in real time and reflects it in proposed sorting rules. For example, the latest recycling regulations and sorting rules may be automatically updated. The proposal unit may also add a function that reflects the latest information from local environmental protection organizations and local governments in real time when proposing sorting rules. For example, new sorting rules may be immediately reflected in the app when they are announced. The proposal unit may also develop a system that acquires the latest information from local environmental protection organizations and local governments in real time and reflects it in proposed sorting rules. For example, information on local environmental events and campaigns may be incorporated into the sorting rules. This allows the latest information to be reflected in real time, making it possible to always propose the optimal sorting method.

[0040] When proposing sorting rules for each region, the proposal unit allows the generation AI to suggest the optimal sorting method based on the region's climate and season. The proposal unit, for example, builds a system in which the generation AI suggests the optimal sorting method based on the region's climate and season. For example, it may recommend strengthening food waste disposal methods in the summer and separating fuel waste in the winter. The proposal unit also adds a function to suggest sorting methods based on the region's climate and season. For example, it may recommend using waterproof garbage bags in the rainy season. The proposal unit also develops a system in which the generation AI suggests the optimal sorting method based on the region's climate and season. For example, it may suggest a sorting method that reduces the risk of fire for combustible waste in the dry season. This improves the effectiveness of sorting by suggesting sorting methods based on the climate and season.

[0041] The proposal unit can reflect feedback from local residents in its proposed sorting rules, thereby optimizing them on a community basis. The proposal unit, for example, builds a system that collects feedback from local residents and reflects it in proposed sorting rules. For example, it reflects the sorting methods proposed by residents in an app. The proposal unit also adds a function that reflects feedback from local residents in proposed sorting rules. For example, it provides a function that allows residents to post comments and advice. The proposal unit also collects feedback from local residents and develops a system that aims to optimize them on a community basis. For example, it adjusts sorting rules based on residents' opinions and proposes optimal methods. In this way, the sorting rules can be optimized by reflecting feedback from residents.

[0042] When providing specific disposal methods for waste, the proposal unit allows the generation AI to propose the optimal disposal method based on the waste's material and composition. For example, the proposal unit builds a system in which the generation AI analyzes the waste's material and composition and proposes the optimal disposal method. For example, plastic waste may be sent to a recycling facility, and paper waste may be treated as combustible waste. The proposal unit also adds a function in which the generation AI proposes the optimal disposal method based on the waste's material and composition. For example, metal waste may be separated into recyclable and non-recyclable parts for disposal. The proposal unit also develops a system in which the generation AI analyzes the waste's material and composition and proposes the optimal disposal method. For example, waste containing hazardous substances may be sent to a specialized disposal facility. This improves the effectiveness of disposal by proposing the optimal disposal method based on the waste's material and composition.

[0043] The proposal unit can analyze past processing data and reflect the most effective processing method when proposing a processing method. The proposal unit, for example, analyzes past processing data and builds a system in which the generation AI proposes the most effective processing method. For example, it proposes the optimal processing method for specific waste based on past data. The proposal unit also adds a function to analyze past processing data and reflect the most effective method when proposing a processing method. For example, it proposes the optimal processing method for waste of specific materials based on past data. The proposal unit also develops a system in which the generation AI analyzes past processing data and proposes the most effective processing method. For example, it proposes the optimal processing method for specific waste based on past data. This improves the effectiveness of processing by proposing the optimal processing method based on past data.

[0044] The proposal unit allows the generation AI to reflect the latest information on local recycling facilities and treatment facilities when providing specific waste disposal methods. For example, the proposal unit builds a system in which the generation AI obtains the latest information on local recycling facilities and treatment facilities and reflects this in the specific waste disposal method. For example, it proposes a treatment method based on the latest recycling facility information. The proposal unit also adds a function to reflect the latest information on local recycling facilities and treatment facilities in the specific waste disposal method. For example, it reflects information when a new recycling facility opens. The proposal unit also develops a system in which the generation AI obtains the latest information on local recycling facilities and treatment facilities and reflects this in the specific waste disposal method. For example, it proposes a treatment method based on the latest treatment facility information. In this way, by reflecting the latest information, the optimal treatment method can always be proposed.

[0045] The suggestion unit can reflect feedback from other users in the proposed disposal methods, thereby achieving community-based optimization. The suggestion unit, for example, builds a system that collects feedback from other users and reflects it in proposals of specific waste disposal methods. For example, the suggestion unit reflects the disposal methods proposed by users in the app. The suggestion unit also adds a function that reflects feedback from other users in the proposed disposal methods. For example, it provides a function that allows users to post comments and advice. The suggestion unit also collects feedback from other users and develops a system that achieves community-based optimization. For example, it adjusts the disposal method based on user opinions and proposes the optimal method. In this way, the disposal method can be optimized by reflecting feedback from other users.

[0046] The suggestion unit allows the generation AI to refer to the user's past question history when providing an explanation in AI chat format, and provide a more personalized answer. The suggestion unit, for example, builds a system in which the generation AI refers to the user's past question history and provides a more personalized answer. For example, it provides related information based on the content of questions asked in the past. The suggestion unit also adds a function to refer to the user's past question history when providing an explanation in AI chat format. For example, it provides answers that match the user's interests based on the content of past questions. The suggestion unit also develops a system in which the generation AI refers to the user's past question history and provides a more personalized answer. For example, it provides answers that match the user's preferences based on the content of past questions. In this way, more personalized answers are provided by referring to the past question history.

[0047] The suggestion unit allows the generation AI to insert relevant videos and images into chat-style explanations, thereby providing visually easy-to-understand explanations. The suggestion unit, for example, builds a system in which the generation AI inserts relevant videos and images to provide visually easy-to-understand explanations. For example, when explaining how to sort garbage, it displays relevant videos. The suggestion unit also adds a function in which the generation AI inserts relevant videos and images into chat-style explanations. For example, when explaining how to dispose of garbage, it displays relevant images. The suggestion unit also develops a system in which the generation AI inserts relevant videos and images to provide visually easy-to-understand explanations. For example, when explaining how to recycle garbage, it displays relevant videos. In this way, by inserting videos and images, explanations that are visually easy to understand are provided.

[0048] The suggestion unit allows the generation AI to reflect feedback from other users in explanations given in AI chat format, thereby enabling the generation AI to provide more effective answers. The suggestion unit, for example, builds a system in which the generation AI reflects feedback from other users and provides more effective answers. For example, the answer is improved based on information provided by other users. The suggestion unit also adds a function to reflect feedback from other users in explanations given in chat format. For example, the answer is improved based on advice provided by other users. The suggestion unit also develops a system in which the generation AI reflects feedback from other users and provides more effective answers. For example, the answer is improved based on information provided by other users. In this way, a more effective answer is provided by reflecting feedback from other users.

[0049] The suggestion unit enables the generation AI to use optimal expressions according to the user's language and culture when providing explanations in chat format. The suggestion unit, for example, builds a system in which the generation AI uses optimal expressions according to the user's language and culture. For example, it provides explanations in the user's native language and uses expressions that take cultural background into consideration. The suggestion unit also adds a function in which the generation AI uses optimal expressions according to the user's language and culture when providing explanations in chat format. For example, it provides explanations using examples that are tailored to the user's culture. The suggestion unit also develops a system in which the generation AI uses optimal expressions according to the user's language and culture. For example, it provides appropriate expressions and examples based on the user's language and culture. This promotes understanding by using optimal expressions according to the user's language and culture.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] When taking a photo of the garbage, the photography unit can use the smartphone camera to analyze the shape and color of the garbage in real time and instruct the user on the optimal shooting angle and light intensity. For example, the camera's image analysis technology can be used to recognize the shape of the garbage and display the appropriate shooting position to the user. It is also possible to add a function in which the smartphone camera analyzes the color of the garbage and automatically adjusts the optimal light intensity. For example, the camera's color recognition technology can be used to automatically set the light intensity according to the color of the garbage to take a clear photo. The smartphone camera can also analyze the shape and color of the garbage when taking a photo and display the optimal shooting conditions to the user in real time. For example, the camera's image analysis technology can be used to instruct the user on the appropriate shooting angle and light intensity based on the shape and color of the garbage. This provides optimal shooting conditions and improves the accuracy of garbage identification.

[0052] The photography unit can automatically measure the weight and material of the garbage using the smartphone's sensors and transmit this information as additional information when uploading. For example, the smartphone's built-in sensor can be used to measure the weight of the garbage and automatically record it when photographing. For example, when photographing garbage, the smartphone's acceleration sensor can be used to estimate the weight and upload that data along with the photo. It is also possible to build a system that automatically analyzes the material of the garbage in conjunction with the smartphone camera. For example, the camera's image analysis technology can be used to identify materials such as plastic, metal, and paper, and this information can be added when uploading. The smartphone's sensors can also be used to measure the surface temperature of the garbage when photographing and transmit that data when uploading. For example, an infrared sensor can be used to measure the temperature of the garbage and use this information to determine whether it is recyclable. This enables more accurate garbage sorting based on its weight and material.

[0053] The photography unit can provide audio guidance when taking a photograph of garbage, allowing the user to easily understand the appropriate shooting method. For example, a system can be built that provides audio guidance when taking a photograph of garbage. For example, a smartphone app can issue voice instructions such as "Place the garbage in the center" or "Adjust the light intensity." It is also possible to add a function that uses audio guidance to explain the appropriate shooting method to the user when taking a photograph. For example, specific voice instructions such as "Take the photograph so that the entire garbage can be seen" can be provided. It is also possible to develop a system that uses audio guidance to easily understand the appropriate shooting method for the user. For example, audio advice such as "Take the photograph so that the shape of the garbage can be clearly seen" can be provided. This allows the user to easily understand the appropriate shooting method using the audio guidance.

[0054] The photography unit can add a function that allows users to share photos of trash they have taken with other users and receive trash sorting advice on a community basis. For example, a function that allows users to share photos of trash they have taken with other users can be added to the app. For example, users can post photos of trash within a community and receive trash sorting advice from other users. A system can also be built that allows users to share photos of trash and receive trash sorting advice on a community basis. For example, a function can be provided that allows other users to post comments and advice. A function can also be developed that allows users to share photos of trash they have taken and receive trash sorting advice within the community. For example, other users can provide advice such as "This trash is recyclable." This community-based trash sorting advice can improve users' trash sorting behavior.

[0055] The discrimination unit can perform a detailed analysis of the material and components of the garbage and display recyclable and non-recyclable parts separately. For example, a system can be built in which the generation AI performs a detailed analysis of the material of the garbage and displays recyclable and non-recyclable parts separately. For example, the cap of a plastic container can be displayed as recyclable, but the body of the container cannot be displayed as non-recyclable. It is also possible to add a function that analyzes the components of the garbage and displays recyclable and non-recyclable parts separately. For example, a metal clip contained in paper waste can be detected and displayed as non-recyclable. It is also possible to develop a system in which the generation AI performs a detailed analysis of the material and components of the garbage and displays recyclable and non-recyclable parts separately. For example, aluminum can labels can be displayed as non-recyclable. This makes it easier for users to dispose of the garbage appropriately by displaying recyclable and non-recyclable parts separately.

[0056] The discrimination unit can compare the garbage discrimination results with past data and build a feedback loop to continuously improve discrimination accuracy. For example, we will develop a system that compares the garbage discrimination results with past data and builds a feedback loop to improve discrimination accuracy. For example, past discrimination results can be compared with current results and misdiscrimination can be corrected. We can also build a feedback loop in which the generation AI compares the garbage discrimination results with past data and continuously improves accuracy. For example, we can adjust the discrimination algorithm based on past data. We can also develop a system that compares the garbage discrimination results with past data and builds a feedback loop to improve discrimination accuracy. For example, we can use past discrimination results as learning data to improve the accuracy of the generation AI. This feedback loop continuously improves discrimination accuracy.

[0057] The discrimination unit can detect the smell of garbage using a sensor and use the smell information for discrimination. For example, a system can be constructed in which the generation AI detects the smell of garbage using a sensor and uses the smell information for discrimination. For example, an odor sensor can be used to distinguish between organic and inorganic garbage. It is also possible to add a function that detects the smell of garbage using a sensor and allows the generation AI to discriminate the type of garbage based on that information. For example, it can detect the smell of putrefaction and classify it as food waste. It is also possible to develop a system in which the generation AI detects the smell of garbage using a sensor and uses the smell information for discrimination. For example, it can detect the smell of a specific chemical substance and classify it as hazardous garbage. In this way, the accuracy of garbage discrimination can be improved by utilizing smell information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The photography unit takes a photo of the garbage. For example, the photo of the garbage is taken using a smartphone camera. The photography unit can also take high-resolution photos of the garbage. For example, the resolution of the smartphone camera can be maximized to capture clear images of even the finest details of the garbage. The photography unit can also automatically upload photos of the garbage. For example, the photography unit can be set to automatically upload photos to the app after they are taken. Step 2: The discrimination unit analyzes the photograph of the garbage taken by the photography unit to discriminate the type of garbage. For example, the generation AI uses an image recognition algorithm to discriminate the type of garbage. The generation AI can also discriminate the type of garbage with high accuracy using a machine learning model. The generation AI can also learn criteria for classifying types of garbage and improve its discrimination accuracy. For example, the generation AI can discriminate types of garbage such as plastic, paper, and metal with high accuracy. Step 3: The suggestion unit suggests an appropriate sorting method in accordance with the sorting rules for each region, based on the type of garbage identified by the discrimination unit. For example, the generation AI retrieves the sorting rules for each region from a database and provides specific instructions to the user. The generation AI can also suggest an appropriate disposal method for each type of garbage based on the sorting rules for each region. The generation AI can also explain the sorting method to the user in an easy-to-understand manner. For example, the generation AI can provide specific disposal methods for each type of garbage, supporting the user in sorting correctly.

[0060] (Example 2) The garbage sorting support system according to an embodiment of the present invention is a system in which a user uploads a photo of garbage taken with a smartphone camera to an app, and a generation AI identifies the type of garbage and suggests an appropriate sorting method based on the sorting rules for each region. This allows the garbage sorting support system to correctly sort garbage even if the user does not have specialized knowledge about sorting garbage.

[0061] A garbage sorting support system according to an embodiment includes a photography unit, a discrimination unit, and a suggestion unit. The photography unit takes photos of garbage. For example, the photography unit takes photos of garbage using a smartphone camera. The photography unit can take photos of garbage at high resolution. For example, the photography unit can maximize the resolution of the smartphone camera to capture clear images of even the smallest details of the garbage. The photography unit can automatically upload photos of the garbage. For example, the photography unit can be configured to automatically upload photos of the garbage to an app after they are taken. The discrimination unit analyzes the photos of the garbage taken by the photography unit to discriminate the type of garbage. For example, the generation AI uses an image recognition algorithm to discriminate the type of garbage. The generation AI can discriminate the type of garbage with high accuracy using a machine learning model. The generation AI can learn criteria for classifying garbage types and improve discrimination accuracy. For example, the generation AI can discriminate between types of garbage such as plastic, paper, and metal with high accuracy. The suggestion unit proposes an appropriate sorting method in accordance with regional sorting rules based on the type of garbage discriminated by the discrimination unit. For example, the generation AI retrieves regional sorting rules from a database and provides specific instructions to the user. Furthermore, the generation AI can propose appropriate disposal methods for each type of garbage based on regional sorting rules. The generation AI can also explain the sorting methods to the user in an easy-to-understand manner. For example, the generation AI provides specific disposal methods for each type of garbage, supporting the user in sorting the garbage correctly. This allows the garbage sorting support system according to the embodiment to properly sort garbage even without specialized knowledge of garbage sorting. For example, a user can simply take a photo of the garbage using their smartphone camera and upload it to the app, and the generation AI can identify the type of garbage and propose an appropriate sorting method. Furthermore, the user can properly sort the garbage according to regional sorting rules. This contributes to environmental consideration.

[0062] The photographing unit can automatically measure the weight and material of the garbage using the smartphone's sensors and transmit the data as additional information when uploading. For example, the photographing unit can measure the weight of the garbage using the smartphone's built-in sensors and automatically record it when photographing. For example, when photographing garbage, the smartphone's acceleration sensor can be used to estimate the weight and upload that data along with the photo. The photographing unit can also work in conjunction with the smartphone's camera to create a system that automatically analyzes the material of the garbage. For example, the camera's image analysis technology can be used to identify materials such as plastic, metal, and paper, and this information can be added when uploading. The photographing unit can also use the smartphone's sensors to measure the surface temperature of the garbage when photographing and transmit that data when uploading. For example, an infrared sensor can be used to measure the temperature of the garbage and use this data to determine whether it is recyclable. This enables more accurate garbage sorting based on the weight and material of the garbage.

[0063] The photographing unit can automatically analyze the shape and color of the garbage using the smartphone camera and instruct the user on the optimal shooting angle and light intensity. The photographing unit, for example, constructs a system in which the smartphone camera analyzes the shape of the garbage in real time and instructs the user on the optimal shooting angle. For example, the camera's image analysis technology can be used to recognize the shape of the garbage and display the appropriate shooting position to the user. The photographing unit also adds a function in which the smartphone camera analyzes the color of the garbage and automatically adjusts the optimal light intensity. For example, the camera's color recognition technology can be used to automatically set the light intensity according to the color of the garbage to take a clear photo. The photographing unit also analyzes the shape and color of the garbage using the smartphone camera when taking a photo and displays the optimal shooting conditions to the user in real time. For example, the camera's image analysis technology can be used to instruct the user on the appropriate shooting angle and light intensity based on the shape and color of the garbage. This provides optimal shooting conditions, thereby improving the accuracy of garbage identification.

[0064] The photographing unit uses an emotion estimation function to analyze the emotions of a user when photographing trash and provide positive feedback, thereby promoting trash sorting behavior. The photographing unit, for example, uses a smartphone camera to analyze the user's facial expression and builds a system that estimates the emotion of the user when photographing trash. For example, the photographing unit uses facial recognition technology to analyze the user's facial expression and displays an encouraging message if a positive emotion is detected. The photographing unit also adds a function to analyze the user's voice when photographing trash and estimate the emotion. For example, the photographing unit uses voice recognition technology to analyze the tone of the user's voice and displays a compliment if a positive emotion is detected. The photographing unit also uses the emotion estimation function to display the user's emotion when photographing trash in real time and provide advice to elicit positive emotions. For example, the photographing unit displays the user's emotion score and makes suggestions to reinforce positive emotions. This positive feedback promotes the user's trash sorting behavior.

[0065] The photography unit can provide audio guidance when taking a photograph of garbage, allowing the user to easily understand the appropriate photography method. The photography unit, for example, builds a system that provides audio guidance when taking a photograph of garbage. For example, a smartphone app issues audio instructions such as "Place the garbage in the center" or "Adjust the light intensity." The photography unit also adds a function that uses audio guidance to explain the appropriate photography method to the user when taking a photograph. For example, specific audio instructions such as "Take the photograph so that the entire garbage can be seen." The photography unit also develops a system that uses audio guidance to allow the user to easily understand the appropriate photography method. For example, audio advice such as "Take the photograph so that the shape of the garbage can be clearly seen" is provided. This allows the user to easily understand the appropriate photography method through the audio guidance.

[0066] The photography unit can add a function to share photos of trash that have been taken with other users and receive community-based trash sorting advice. For example, the photography unit adds a function to the app that allows users to share photos of trash that have been taken with other users. For example, users can post photos of trash within a community and receive trash sorting advice from other users. The photography unit also builds a system for sharing photos of trash and receiving community-based trash sorting advice. For example, it provides a function that allows other users to post comments and advice. The photography unit also develops a function for sharing photos of trash that have been taken and receiving trash sorting advice within the community. For example, other users can provide advice such as "This trash is recyclable." This allows community-based trash sorting advice to improve users' trash sorting behavior.

[0067] The discrimination unit will be able to perform a detailed analysis of the material and components of the garbage and display recyclable and non-recyclable parts separately. For example, the discrimination unit will build a system in which the generation AI will perform a detailed analysis of the material of the garbage and display recyclable and non-recyclable parts separately. For example, the cap of a plastic container will be displayed as recyclable, but the body of the container will be displayed as non-recyclable. The discrimination unit will also add a function to analyze the components of the garbage and display recyclable and non-recyclable parts separately. For example, it will detect metal clips contained in paper waste and display them as non-recyclable parts. The discrimination unit will also develop a system in which the generation AI will perform a detailed analysis of the material and components of the garbage and display recyclable and non-recyclable parts separately. For example, it will display aluminum can labels as non-recyclable. This will make it easier for users to dispose of the garbage appropriately by displaying recyclable and non-recyclable parts separately.

[0068] The discrimination unit can compare the garbage discrimination results with past data and build a feedback loop to continuously improve discrimination accuracy. The discrimination unit, for example, develops a system to compare the garbage discrimination results with past data and build a feedback loop to improve discrimination accuracy. For example, it compares past discrimination results with current results and corrects misclassification. The discrimination unit also develops a feedback loop to compare the garbage discrimination results with past data and continuously improve accuracy. For example, it adjusts the discrimination algorithm based on past data. The discrimination unit also develops a system to compare the garbage discrimination results with past data and build a feedback loop to improve discrimination accuracy. For example, it uses past discrimination results as learning data to improve the accuracy of the generation AI. This continuously improves discrimination accuracy through the feedback loop.

[0069] The discrimination unit can detect the smell of garbage using a sensor and use the smell information for discrimination. For example, the discrimination unit will build a system in which the generation AI detects the smell of garbage using a sensor and uses the smell information for discrimination. For example, an odor sensor will be used to distinguish between organic and inorganic garbage. The discrimination unit will also add a function to detect the smell of garbage using a sensor and the generation AI will use that information to discriminate the type of garbage. For example, it will detect a putrid smell and classify it as food waste. The discrimination unit will also develop a system in which the generation AI detects the smell of garbage using a sensor and use the smell information for discrimination. For example, it will detect the smell of a specific chemical substance and classify it as hazardous garbage. In this way, the accuracy of garbage discrimination will be improved by utilizing smell information.

[0070] The discrimination unit can add a function to share the garbage discrimination results with other users and receive community-based feedback. The discrimination unit, for example, adds a function to the app to share the garbage discrimination results with other users. For example, the discrimination result is posted within the community and feedback is received from other users. The discrimination unit also builds a system to share the garbage discrimination results and receive community-based feedback. For example, a function is provided that allows other users to post comments and advice. The discrimination unit also develops a function to share the garbage discrimination results and receive feedback within the community. For example, other users provide advice such as "This garbage is recyclable." This improves discrimination accuracy through community-based feedback.

[0071] When proposing sorting rules for each region, the proposal unit allows the generation AI to analyze past sorting data and propose the most effective sorting method. For example, the proposal unit builds a system in which the generation AI analyzes past sorting data and proposes the most effective sorting method. For example, it proposes the most successful sorting method in a specific region based on past data. The proposal unit also adds a function to analyze past sorting data and have the generation AI propose the optimal sorting method for each region. For example, it makes a proposal based on data from regions where a specific waste sorting method was effective. The proposal unit also develops a system in which the generation AI analyzes past sorting data and proposes the most effective sorting method. For example, it proposes the optimal sorting method for a specific day of the week or time of day based on past data. This improves the effectiveness of sorting by proposing the optimal sorting method based on past data.

[0072] The proposal unit can reflect the latest information from local environmental protection organizations and local governments in real time when proposing sorting rules. For example, the proposal unit may build a system that acquires the latest information from local environmental protection organizations and local governments in real time and reflects it in proposed sorting rules. For example, the latest recycling regulations and sorting rules may be automatically updated. The proposal unit may also add a function that reflects the latest information from local environmental protection organizations and local governments in real time when proposing sorting rules. For example, new sorting rules may be immediately reflected in the app when they are announced. The proposal unit may also develop a system that acquires the latest information from local environmental protection organizations and local governments in real time and reflects it in proposed sorting rules. For example, information on local environmental events and campaigns may be incorporated into the sorting rules. This allows the latest information to be reflected in real time, making it possible to always propose the optimal sorting method.

[0073] When proposing sorting rules for each region, the proposal unit allows the generation AI to suggest the optimal sorting method based on the region's climate and season. The proposal unit, for example, builds a system in which the generation AI suggests the optimal sorting method based on the region's climate and season. For example, it may recommend strengthening food waste disposal methods in the summer and separating fuel waste in the winter. The proposal unit also adds a function to suggest sorting methods based on the region's climate and season. For example, it may recommend using waterproof garbage bags in the rainy season. The proposal unit also develops a system in which the generation AI suggests the optimal sorting method based on the region's climate and season. For example, it may suggest a sorting method that reduces the risk of fire for combustible waste in the dry season. This improves the effectiveness of sorting by suggesting sorting methods based on the climate and season.

[0074] The proposal unit can reflect feedback from local residents in its proposed sorting rules, thereby optimizing them on a community basis. The proposal unit, for example, builds a system that collects feedback from local residents and reflects it in proposed sorting rules. For example, it reflects the sorting methods proposed by residents in an app. The proposal unit also adds a function that reflects feedback from local residents in proposed sorting rules. For example, it provides a function that allows residents to post comments and advice. The proposal unit also collects feedback from local residents and develops a system that aims to optimize them on a community basis. For example, it adjusts sorting rules based on residents' opinions and proposes optimal methods. In this way, the sorting rules can be optimized by reflecting feedback from residents.

[0075] The suggestion unit can use the emotion estimation function to display in real time how the user feels about the sorting rules and provide advice to elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to build a system that displays in real time how the user feels about the sorting rules. For example, the suggestion unit analyzes the user's facial expressions using a smartphone camera and displays an emotion score. The suggestion unit also adds a function that displays the user's emotion about the sorting rules in real time and provides advice to elicit positive emotions. For example, the suggestion unit displays a message such as "Great! Keep it up!" The suggestion unit also uses the emotion estimation function to develop a system that displays in real time how the user feels about the sorting rules and makes suggestions to reinforce positive emotions. For example, the suggestion unit displays an encouraging message based on the user's emotion score. This elicits positive emotions and promotes the user's sorting behavior.

[0076] When providing specific disposal methods for waste, the proposal unit allows the generation AI to propose the optimal disposal method based on the waste's material and composition. For example, the proposal unit builds a system in which the generation AI analyzes the waste's material and composition and proposes the optimal disposal method. For example, plastic waste may be sent to a recycling facility, and paper waste may be treated as combustible waste. The proposal unit also adds a function in which the generation AI proposes the optimal disposal method based on the waste's material and composition. For example, metal waste may be separated into recyclable and non-recyclable parts for disposal. The proposal unit also develops a system in which the generation AI analyzes the waste's material and composition and proposes the optimal disposal method. For example, waste containing hazardous substances may be sent to a specialized disposal facility. This improves the effectiveness of disposal by proposing the optimal disposal method based on the waste's material and composition.

[0077] The proposal unit can analyze past processing data and reflect the most effective processing method when proposing a processing method. The proposal unit, for example, analyzes past processing data and builds a system in which the generation AI proposes the most effective processing method. For example, it proposes the optimal processing method for specific waste based on past data. The proposal unit also adds a function to analyze past processing data and reflect the most effective method when proposing a processing method. For example, it proposes the optimal processing method for waste of specific materials based on past data. The proposal unit also develops a system in which the generation AI analyzes past processing data and proposes the most effective processing method. For example, it proposes the optimal processing method for specific waste based on past data. This improves the effectiveness of processing by proposing the optimal processing method based on past data.

[0078] The proposal unit allows the generation AI to reflect the latest information on local recycling facilities and treatment facilities when providing specific waste disposal methods. For example, the proposal unit builds a system in which the generation AI obtains the latest information on local recycling facilities and treatment facilities and reflects this in the specific waste disposal method. For example, it proposes a treatment method based on the latest recycling facility information. The proposal unit also adds a function to reflect the latest information on local recycling facilities and treatment facilities in the specific waste disposal method. For example, it reflects information when a new recycling facility opens. The proposal unit also develops a system in which the generation AI obtains the latest information on local recycling facilities and treatment facilities and reflects this in the specific waste disposal method. For example, it proposes a treatment method based on the latest treatment facility information. In this way, by reflecting the latest information, the optimal treatment method can always be proposed.

[0079] The suggestion unit can reflect feedback from other users in the proposed disposal methods, thereby achieving community-based optimization. The suggestion unit, for example, builds a system that collects feedback from other users and reflects it in proposals of specific waste disposal methods. For example, the suggestion unit reflects the disposal methods proposed by users in the app. The suggestion unit also adds a function that reflects feedback from other users in the proposed disposal methods. For example, it provides a function that allows users to post comments and advice. The suggestion unit also collects feedback from other users and develops a system that achieves community-based optimization. For example, it adjusts the disposal method based on user opinions and proposes the optimal method. In this way, the disposal method can be optimized by reflecting feedback from other users.

[0080] The suggestion unit can use the emotion estimation function to display in real time how the user feels about a processing method and provide advice to elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to build a system that displays in real time how the user feels about a processing method. For example, the suggestion unit analyzes the user's facial expression using a smartphone camera and displays an emotion score. The suggestion unit also adds a function that displays the user's emotion about a processing method in real time and provides advice to elicit positive emotions. For example, the suggestion unit displays a message such as "Great! Keep it up!". The suggestion unit also uses the emotion estimation function to develop a system that displays in real time how the user feels about a processing method and makes suggestions to reinforce positive emotions. For example, the suggestion unit displays an encouraging message according to the user's emotion score. This elicits positive emotions and promotes the user's processing behavior.

[0081] The suggestion unit allows the generation AI to refer to the user's past question history when providing an explanation in AI chat format, and provide a more personalized answer. The suggestion unit, for example, builds a system in which the generation AI refers to the user's past question history and provides a more personalized answer. For example, it provides related information based on the content of questions asked in the past. The suggestion unit also adds a function to refer to the user's past question history when providing an explanation in AI chat format. For example, it provides answers that match the user's interests based on the content of past questions. The suggestion unit also develops a system in which the generation AI refers to the user's past question history and provides a more personalized answer. For example, it provides answers that match the user's preferences based on the content of past questions. In this way, more personalized answers are provided by referring to the past question history.

[0082] The suggestion unit allows the generation AI to insert relevant videos and images into chat-style explanations, thereby providing visually easy-to-understand explanations. The suggestion unit, for example, builds a system in which the generation AI inserts relevant videos and images to provide visually easy-to-understand explanations. For example, when explaining how to sort garbage, it displays relevant videos. The suggestion unit also adds a function in which the generation AI inserts relevant videos and images into chat-style explanations. For example, when explaining how to dispose of garbage, it displays relevant images. The suggestion unit also develops a system in which the generation AI inserts relevant videos and images to provide visually easy-to-understand explanations. For example, when explaining how to recycle garbage, it displays relevant videos. In this way, by inserting videos and images, explanations that are visually easy to understand are provided.

[0083] The suggestion unit can use the emotion estimation function to analyze how a user feels about an explanation in chat format and make improvements to elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to build a system that analyzes how a user feels about an explanation in chat format. For example, the suggestion unit analyzes the user's facial expressions and voice and calculates an emotion score. The suggestion unit also adds a function that analyzes the user's emotions about an explanation in chat format and makes improvements to elicit positive emotions. For example, the suggestion unit displays a message to reinforce positive emotions. The suggestion unit also uses the emotion estimation function to develop a system that displays in real time how a user feels about an explanation in chat format and provides advice to elicit positive emotions. For example, the suggestion unit displays an encouraging message according to the emotion score. This elicits positive emotions and promotes user understanding.

[0084] The suggestion unit allows the generation AI to reflect feedback from other users in explanations given in AI chat format, thereby enabling the generation AI to provide more effective answers. The suggestion unit, for example, builds a system in which the generation AI reflects feedback from other users and provides more effective answers. For example, the answer is improved based on information provided by other users. The suggestion unit also adds a function to reflect feedback from other users in explanations given in chat format. For example, the answer is improved based on advice provided by other users. The suggestion unit also develops a system in which the generation AI reflects feedback from other users and provides more effective answers. For example, the answer is improved based on information provided by other users. In this way, a more effective answer is provided by reflecting feedback from other users.

[0085] The suggestion unit enables the generation AI to use optimal expressions according to the user's language and culture when providing explanations in chat format. The suggestion unit, for example, builds a system in which the generation AI uses optimal expressions according to the user's language and culture. For example, it provides explanations in the user's native language and uses expressions that take cultural background into consideration. The suggestion unit also adds a function in which the generation AI uses optimal expressions according to the user's language and culture when providing explanations in chat format. For example, it provides explanations using examples that are tailored to the user's culture. The suggestion unit also develops a system in which the generation AI uses optimal expressions according to the user's language and culture. For example, it provides appropriate expressions and examples based on the user's language and culture. This promotes understanding by using optimal expressions according to the user's language and culture.

[0086] The suggestion unit can use the emotion estimation function to display in real time how a user feels about an explanation in chat format and provide advice to elicit positive emotions. For example, the suggestion unit uses the emotion estimation function to build a system that displays in real time how a user feels about an explanation in chat format. For example, the suggestion unit analyzes the user's facial expression using a smartphone camera and displays an emotion score. The suggestion unit also adds a function that displays in real time how a user feels about an explanation in chat format and provides advice to elicit positive emotions. For example, the suggestion unit displays a message such as "Great! Keep it up." The suggestion unit also uses the emotion estimation function to develop a system that displays in real time how a user feels about an explanation in chat format and makes suggestions to strengthen positive emotions. For example, the suggestion unit displays an encouraging message according to the user's emotion score. This elicits positive emotions and promotes user understanding.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] When taking a photo of the garbage, the photography unit can use the smartphone camera to analyze the shape and color of the garbage in real time and instruct the user on the optimal shooting angle and light intensity. For example, the camera's image analysis technology can be used to recognize the shape of the garbage and display the appropriate shooting position to the user. It is also possible to add a function in which the smartphone camera analyzes the color of the garbage and automatically adjusts the optimal light intensity. For example, the camera's color recognition technology can be used to automatically set the light intensity according to the color of the garbage to take a clear photo. The smartphone camera can also analyze the shape and color of the garbage when taking a photo and display the optimal shooting conditions to the user in real time. For example, the camera's image analysis technology can be used to instruct the user on the appropriate shooting angle and light intensity based on the shape and color of the garbage. This provides optimal shooting conditions and improves the accuracy of garbage identification.

[0089] The photography unit can automatically measure the weight and material of the garbage using the smartphone's sensors and transmit this information as additional information when uploading. For example, the smartphone's built-in sensor can be used to measure the weight of the garbage and automatically record it when photographing. For example, when photographing garbage, the smartphone's acceleration sensor can be used to estimate the weight and upload that data along with the photo. It is also possible to build a system that automatically analyzes the material of the garbage in conjunction with the smartphone camera. For example, the camera's image analysis technology can be used to identify materials such as plastic, metal, and paper, and this information can be added when uploading. The smartphone's sensors can also be used to measure the surface temperature of the garbage when photographing and transmit that data when uploading. For example, an infrared sensor can be used to measure the temperature of the garbage and use this information to determine whether it is recyclable. This enables more accurate garbage sorting based on its weight and material.

[0090] The photography unit can use the emotion estimation function to analyze the emotions of users when taking photos of trash and provide positive feedback to encourage trash sorting. For example, a system can be constructed using a smartphone camera to analyze the user's facial expressions and estimate the emotions of users when taking photos of trash. For example, facial recognition technology can be used to analyze the user's facial expressions and display an encouraging message if a positive emotion is detected. A function can also be added to analyze the user's voice when taking photos and estimate emotions. For example, voice recognition technology can be used to analyze the user's tone of voice and display a compliment if a positive emotion is detected. The emotion estimation function can also be used to display the user's emotions when taking photos of trash in real time and provide advice to elicit positive emotions. For example, the user's emotion score can be displayed and suggestions made to reinforce positive emotions. This positive feedback can encourage users to sort their trash.

[0091] The photography unit can provide audio guidance when taking a photograph of garbage, allowing the user to easily understand the appropriate shooting method. For example, a system can be built that provides audio guidance when taking a photograph of garbage. For example, a smartphone app can issue voice instructions such as "Place the garbage in the center" or "Adjust the light intensity." It is also possible to add a function that uses audio guidance to explain the appropriate shooting method to the user when taking a photograph. For example, specific voice instructions such as "Take the photograph so that the entire garbage can be seen" can be provided. It is also possible to develop a system that uses audio guidance to easily understand the appropriate shooting method for the user. For example, audio advice such as "Take the photograph so that the shape of the garbage can be clearly seen" can be provided. This allows the user to easily understand the appropriate shooting method using the audio guidance.

[0092] The photography unit can add a function that allows users to share photos of trash they have taken with other users and receive trash sorting advice on a community basis. For example, a function that allows users to share photos of trash they have taken with other users can be added to the app. For example, users can post photos of trash within a community and receive trash sorting advice from other users. A system can also be built that allows users to share photos of trash and receive trash sorting advice on a community basis. For example, a function can be provided that allows other users to post comments and advice. A function can also be developed that allows users to share photos of trash they have taken and receive trash sorting advice within the community. For example, other users can provide advice such as "This trash is recyclable." This community-based trash sorting advice can improve users' trash sorting behavior.

[0093] The discrimination unit can perform a detailed analysis of the material and components of the garbage and display recyclable and non-recyclable parts separately. For example, a system can be built in which the generation AI performs a detailed analysis of the material of the garbage and displays recyclable and non-recyclable parts separately. For example, the cap of a plastic container can be displayed as recyclable, but the body of the container cannot be displayed as non-recyclable. It is also possible to add a function that analyzes the components of the garbage and displays recyclable and non-recyclable parts separately. For example, a metal clip contained in paper waste can be detected and displayed as non-recyclable. It is also possible to develop a system in which the generation AI performs a detailed analysis of the material and components of the garbage and displays recyclable and non-recyclable parts separately. For example, aluminum can labels can be displayed as non-recyclable. This makes it easier for users to dispose of the garbage appropriately by displaying recyclable and non-recyclable parts separately.

[0094] The discrimination unit can compare the garbage discrimination results with past data and build a feedback loop to continuously improve discrimination accuracy. For example, we will develop a system that compares the garbage discrimination results with past data and builds a feedback loop to improve discrimination accuracy. For example, past discrimination results can be compared with current results and misdiscrimination can be corrected. We can also build a feedback loop in which the generation AI compares the garbage discrimination results with past data and continuously improves accuracy. For example, we can adjust the discrimination algorithm based on past data. We can also develop a system that compares the garbage discrimination results with past data and builds a feedback loop to improve discrimination accuracy. For example, we can use past discrimination results as learning data to improve the accuracy of the generation AI. This feedback loop continuously improves discrimination accuracy.

[0095] The discrimination unit can detect the smell of garbage using a sensor and use the smell information for discrimination. For example, a system can be constructed in which the generation AI detects the smell of garbage using a sensor and uses the smell information for discrimination. For example, an odor sensor can be used to distinguish between organic and inorganic garbage. It is also possible to add a function that detects the smell of garbage using a sensor and allows the generation AI to discriminate the type of garbage based on that information. For example, it can detect the smell of putrefaction and classify it as food waste. It is also possible to develop a system in which the generation AI detects the smell of garbage using a sensor and uses the smell information for discrimination. For example, it can detect the smell of a specific chemical substance and classify it as hazardous garbage. In this way, the accuracy of garbage discrimination can be improved by utilizing smell information.

[0096] The suggestion unit can use the emotion estimation function to display in real time how the user feels about the sorting rules and provide advice to elicit positive emotions. For example, a system can be built using the emotion estimation function to display in real time how the user feels about the sorting rules. For example, a smartphone camera can be used to analyze the user's facial expression and display an emotion score. A function can also be added to display the user's emotion about the sorting rules in real time and provide advice to elicit positive emotions. For example, a message such as "Great! Keep it up!" can be displayed. A system can also be developed using the emotion estimation function to display in real time how the user feels about the sorting rules and provide suggestions to reinforce positive emotions. For example, an encouraging message can be displayed based on the user's emotion score. This elicits positive emotions and promotes the user's sorting behavior.

[0097] The suggestion unit can use the emotion estimation function to display in real time how the user feels about a processing method and provide advice to elicit positive emotions. For example, a system can be built that uses the emotion estimation function to display in real time how the user feels about a processing method. For example, a smartphone camera can be used to analyze the user's facial expression and display an emotion score. It is also possible to add a function that displays the user's emotion about a processing method in real time and provides advice to elicit positive emotions. For example, a message such as "Great! Keep it up!" can be displayed. It is also possible to develop a system that uses the emotion estimation function to display in real time how the user feels about a processing method and provide suggestions to reinforce positive emotions. For example, an encouraging message can be displayed according to the user's emotion score. This elicits positive emotions and promotes the user's processing behavior.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The photography unit takes a photo of the garbage. For example, the photo of the garbage is taken using a smartphone camera. The photography unit can also take high-resolution photos of the garbage. For example, the resolution of the smartphone camera can be maximized to capture clear images of even the finest details of the garbage. The photography unit can also automatically upload photos of the garbage. For example, the photography unit can be set to automatically upload photos to the app after they are taken. Step 2: The discrimination unit analyzes the photograph of the garbage taken by the photography unit to discriminate the type of garbage. For example, the generation AI uses an image recognition algorithm to discriminate the type of garbage. The generation AI can also discriminate the type of garbage with high accuracy using a machine learning model. The generation AI can also learn criteria for classifying types of garbage and improve its discrimination accuracy. For example, the generation AI can discriminate types of garbage such as plastic, paper, and metal with high accuracy. Step 3: The suggestion unit suggests an appropriate sorting method in accordance with the sorting rules for each region, based on the type of garbage identified by the discrimination unit. For example, the generation AI retrieves the sorting rules for each region from a database and provides specific instructions to the user. The generation AI can also suggest an appropriate disposal method for each type of garbage based on the sorting rules for each region. The generation AI can also explain the sorting method to the user in an easy-to-understand manner. For example, the generation AI can provide specific disposal methods for each type of garbage, supporting the user in sorting correctly.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 AI 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

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

[0121] The 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.

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

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0125] Fig. 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. A photography group that takes photos of trash, a discrimination unit that analyzes the photograph of the dust taken by the photographing unit and discriminates the type of the dust; a suggestion unit that suggests an appropriate sorting method in accordance with regional sorting rules based on the type of waste identified by the identification unit. A system characterized by:

2. The photography unit is The weight and material of the trash are automatically measured using the smartphone's sensors and sent as additional information when the trash is uploaded.

2. The system of claim 1.

3. The photography unit is The smartphone camera automatically analyzes the shape and color of the dust and instructs the user on the optimal shooting angle and light intensity.

2. The system of claim 1.

4. The photography unit is The system analyzes the emotions users have when taking photos of the trash and provides positive feedback to encourage sorting behavior.

2. The system of claim 1.

5. The photography unit is When taking a photo of the trash, audio guidance is provided to allow the user to easily understand the appropriate way to take the photo.

2. The system of claim 1.

6. The photography unit is Add a feature to share photos of the trash taken with other users and receive community-based advice on sorting.

2. The system of claim 1.

Images