System

Abstract

An object of a system according to an embodiment is to determine a type of waste and propose a sorting method according to a rule of each region.SOLUTION: A system includes an imaging unit, a determination unit, and a proposal unit. The imaging unit captures an image of the dust. The determination unit analyzes the image of the dust captured by the imaging unit and determines a type of the dust. The proposal unit proposes a waste sorting method according to a rule of each region based on the type of waste determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to properly separate waste as the number of types of waste increases, and there is a risk that the recycling rate will decline.

[0005] The system according to the embodiment aims to identify the type of garbage and propose a method for sorting the garbage in accordance with local rules. [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 captures an image of garbage. The discrimination unit analyzes the image of the garbage captured by the photographing unit and discriminates the type of garbage. The suggestion unit proposes a garbage sorting method that complies with local rules based on the type of garbage discriminated by the discrimination unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify the type of garbage and suggest a separation method that complies with local rules. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A waste sorting support system according to an embodiment of the present invention captures images of waste, classifies the type of waste using image recognition technology based on deep learning, and proposes a waste sorting method in accordance with local rules. The waste sorting support system captures images of waste using a smartphone camera or a camera at a waste incineration plant, and then uses image recognition technology based on deep learning to distinguish the type of waste. Based on the distinguished waste type, the system proposes a waste sorting method in accordance with local rules. For example, the waste sorting support system may photograph household waste using a smartphone camera or waste being processed at a waste incineration plant using a camera. In this case, the captured image is analyzed using image recognition technology based on deep learning. Next, the deep learning image recognition technology distinguishes the type of waste captured. For example, types of waste can be distinguished, such as plastic, paper, metal, and glass. This allows recyclable waste to be identified from mixed waste. Based on the distinguished waste type, the system proposes a waste sorting method in accordance with local rules. For example, in certain areas, plastic waste may be collected on certain days of the week, while paper waste may be collected on another day. Based on these local rules, appropriate sorting methods can be proposed. This improves waste recycling rates and promotes the realization of a sustainable society. This allows the waste sorting support system to respond to the increasing variety of waste and improve recycling rates. For example, it makes it easier for households to sort waste, and by properly sorting recyclable waste, recycling rates improve. It also improves waste processing efficiency at incineration plants, and by properly disposing of recyclable waste, it promotes the realization of a sustainable society. Furthermore, this system is effective even in the current situation where household waste is increasing compared to before the COVID-19 pandemic. For example, even in a situation where household waste has increased by 131.5%, it is possible to improve recycling rates by proposing appropriate sorting methods. This reduces the amount of waste and contributes to the realization of a sustainable society.

[0029] The waste sorting support system according to the embodiment includes a photographing unit, a classification unit, and a suggestion unit. The photographing unit photographs images of waste. Examples of waste images include, but are not limited to, plastic waste, paper waste, and metal waste. The photographing unit, for example, uses a smartphone camera to photograph household waste. The photographing unit can also photograph waste being processed using a camera at an incineration plant. For example, the smartphone camera photographs waste at high resolution, while the incineration plant camera photographs waste using a fixed or mobile camera. The classification unit uses a deep learning algorithm to analyze the images of waste photographed by the photographing unit and classify the type of waste. For example, the classification unit uses a convolutional neural network (CNN) as the deep learning algorithm. The classification unit can also use a recurrent neural network (RNN) to classify the type of waste. Furthermore, the classification unit can combine deep learning algorithms to classify the type of waste. For example, the classification unit extracts features from the waste image using a CNN and classifies the type of waste using an RNN. The suggestion unit suggests a garbage sorting method in accordance with local rules based on the type of garbage identified by the identification unit. The suggestion unit, for example, suggests a household garbage sorting method via a smartphone app. The suggestion unit can also introduce an automatic garbage sorting system at an incineration plant. Furthermore, the suggestion unit can also suggest a garbage sorting method based on the rules of a specific area. For example, plastic garbage may be collected on a specific day of the week, and paper garbage may be collected on a different day of the week. The suggestion unit can suggest an appropriate sorting method based on such local rules. As a result, the garbage sorting support system according to the embodiment can improve the garbage recycling rate and promote the realization of a sustainable society.

[0030] The photographing unit may include a smartphone camera and a waste disposal plant camera. Examples of the smartphone camera include, but are not limited to, a high-resolution camera, a wide-angle camera, and a nighttime photography mode. For example, the photographing unit may use the smartphone camera to photograph household waste. The photographing unit may also use a waste disposal plant camera to photograph waste being disposed of. Examples of the waste disposal plant camera include, but are not limited to, a fixed camera, a mobile camera, and an infrared camera. For example, the smartphone camera may photograph waste in high resolution, and the waste disposal plant camera may photograph waste using a fixed or mobile camera. This allows waste images to be photographed using a variety of cameras. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit may input images of waste photographed with the smartphone camera into a generation AI, causing the generation AI to analyze the images.

[0031] The discrimination unit can discriminate the type of garbage using a deep learning algorithm. Examples of deep learning algorithms include, but are not limited to, CNN (convolutional neural network), RNN (recurrent neural network), and LSTM (long short-term memory). The discrimination unit uses, for example, CNN as a deep learning algorithm. The discrimination unit can also discriminate the type of garbage using an RNN. Furthermore, the discrimination unit can discriminate the type of garbage by combining deep learning algorithms. For example, the discrimination unit uses CNN to extract features from an image of garbage and then uses an RNN to discriminate the type of garbage. In this way, the use of deep learning enables the type of garbage to be discriminated with high accuracy. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can discriminate the type of garbage using an AI model that executes a deep learning algorithm.

[0032] The suggestion unit can suggest a household garbage sorting method through a smartphone app. The smartphone app may include, but is not limited to, an interface, a notification function, and a history management function. The suggestion unit can suggest a household garbage sorting method through the smartphone app. For example, the smartphone app may analyze images of garbage taken by a user and suggest an appropriate sorting method. The smartphone app may also notify the user of collection dates and sorting methods based on local sorting rules. The smartphone app may also manage the user's sorting history and improve the accuracy of the suggestions based on past sorting results. For example, the smartphone app may analyze images of garbage taken by a user and suggest an appropriate sorting method. The smartphone app may also notify the user of collection dates and sorting methods based on local sorting rules. This facilitates household garbage sorting. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input image data of garbage acquired through the smartphone app into a generation AI and cause the generation AI to suggest a sorting method.

[0033] The proposal unit may implement an automatic sorting system at an incineration plant. The automatic sorting system may include, but is not limited to, sensors to be used, a sorting procedure, and a robotic arm. For example, the proposal unit may implement an automatic sorting system at an incineration plant. The automatic sorting system may, for example, use sensors to detect the type of waste and a robotic arm to sort the waste. The automatic sorting system may also use AI to identify the type of waste and implement an appropriate sorting method. Furthermore, the automatic sorting system may automate the sorting procedure and efficiently process waste. For example, the automatic sorting system may use sensors to detect the type of waste and a robotic arm to sort the waste. The automatic sorting system may also use AI to identify the type of waste and implement an appropriate sorting method. This automates waste sorting at an incineration plant and improves efficiency. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without AI. For example, the proposal unit may input waste data acquired at an incineration plant into a generation AI and cause the generation AI to control the automatic sorting system.

[0034] The suggestion unit can suggest a garbage sorting method based on the rules of a specific area. The rules of a specific area include, for example, a garbage sorting method, collection days, and garbage sorting procedures for each area, but are not limited to these examples. The suggestion unit, for example, suggests a garbage sorting method based on the rules of a specific area. The suggestion unit stores the garbage sorting rules for each area in a database and suggests an appropriate garbage sorting method based on the user's location. The suggestion unit can also notify the user of collection days for each area and encourage the user to put out garbage on the appropriate day. The suggestion unit can also explain the garbage sorting procedures for each area in detail to help the user sort garbage accurately. For example, the suggestion unit stores the garbage sorting rules for each area in a database and suggests an appropriate garbage sorting method based on the user's location. The suggestion unit can also notify the user of collection days for each area and encourage the user to put out garbage on the appropriate day. This enables appropriate garbage sorting in accordance with the rules of each area. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI or without AI. For example, the proposal unit can input regional sorting rules into the generation AI and have the generation AI execute a proposed sorting method.

[0035] When taking a photo, the imaging unit can analyze the user's past garbage sorting history and select the optimal imaging method. For example, the imaging unit can analyze the types of garbage photographed by the user in the past and suggest the optimal imaging method if similar garbage is found. The imaging unit can also customize the imaging method for specific garbage based on the user's past sorting history to enable efficient imaging. Furthermore, the imaging unit can improve the imaging method for garbage that the user has failed to separate in the past to prevent the same mistake from occurring again. For example, the imaging unit can store the user's past sorting history in a database and suggest the optimal imaging method if similar garbage is found. The imaging unit can also analyze the user's past sorting history and customize the imaging method for specific garbage. This allows the optimal imaging method to be provided based on the past sorting history. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without AI. For example, the imaging unit can input the user's past sorting history data into a generation AI and have the generation AI select the optimal imaging method.

[0036] The photographing unit can filter images based on the user's current lifestyle and areas of interest when photographing. For example, if the user is busy, the photographing unit can suggest an easy way to photograph, saving time. Furthermore, if the user is interested in environmental protection, the photographing unit can prioritize photographing recyclable waste. Furthermore, if the user is interested in sorting specific types of waste, the photographing unit can provide detailed guidance on how to photograph that type of waste. For example, the photographing unit can store the user's lifestyle patterns in a database and suggest an easy way to photograph when the user is busy. Furthermore, the photographing unit can analyze the user's areas of interest and prioritize photographing recyclable waste. This makes it possible to provide a photographing method that suits the user's lifestyle and areas of interest. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit can input data on the user's lifestyle and areas of interest into the generation AI and have the generation AI perform filtering.

[0037] The photographing unit can select the optimal photographing means according to the user's input method when photographing. For example, when the user uses voice input, the photographing unit can start photographing with a voice command, saving the user time and effort. Furthermore, when the user uses text input, the photographing unit can take photographs by issuing text instructions. Furthermore, when the user uses image input, the photographing unit can analyze the image and suggest the optimal photographing method. For example, the photographing unit can start photographing with a voice command for a user using voice input. Furthermore, the photographing unit can take photographs by issuing text instructions for a user using text input. This makes it possible to provide the optimal photographing means according to the user's input method. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's input method data into a generation AI and cause the generation AI to select the optimal photographing means.

[0038] When taking a photo, the photographing unit can prioritize photographing highly relevant trash by taking into account the user's geographical location information. For example, if the user is in a specific area, the photographing unit prioritizes photographing trash that is commonly produced in that area. Furthermore, if the user is traveling, the photographing unit can select trash to photograph based on the trash sorting rules of the travel destination. Furthermore, if the user does not know the trash sorting rules of the destination, the photographing unit can suggest trash to photograph based on the rules of the destination. For example, the photographing unit stores the user's geographical location information in a database and prioritizes photographing trash that is commonly produced in the area. Furthermore, if the user is traveling, the photographing unit can select trash to photograph based on the trash sorting rules of the travel destination. This allows highly relevant trash to be prioritized based on the geographical location information. Some or all of the above-described processing in the photographing unit may be performed, for example, using AI or without AI. For example, the photographing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant trash.

[0039] The photographing unit can analyze the user's social media activity when photographing and photograph related trash. For example, the photographing unit photographs similar trash based on images of trash shared by the user on social media. The photographing unit can also analyze the user's social media posts and photograph related trash. Furthermore, the photographing unit can photograph related trash by referring to the activities of the user's friends on social media. For example, the photographing unit stores the user's social media activity in a database and suggests the optimal photographing method when similar trash appears. The photographing unit can also analyze the user's social media posts and photograph related trash. In this way, related trash can be photographed based on social media activity. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit can input the user's social media activity data into a generation AI and have the generation AI photograph related trash.

[0040] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. The photographing unit, for example, improves the photographing method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular photographing method, the photographing unit can preferentially suggest that method. Furthermore, if the user has previously failed at photographing, the photographing unit can suggest a method to avoid that failure. For example, the photographing unit stores the user's past feedback in a database and suggests the optimal photographing method when similar garbage is produced. The photographing unit can also analyze the user's past feedback and customize a specific photographing method. This allows the photographing method to be customized based on the past feedback. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's past feedback data into a generation AI and have the generation AI customize the photographing method.

[0041] The discrimination unit can adjust the level of detail of discrimination based on the importance of the garbage when discriminating. For example, the discrimination unit performs detailed discrimination in the case of recyclable garbage to encourage accurate sorting. The discrimination unit can also perform simple discrimination in the case of burnable garbage to quickly process it. Furthermore, the discrimination unit can also perform detailed discrimination with particular attention to hazardous materials. For example, the discrimination unit performs detailed discrimination in the case of recyclable garbage to encourage accurate sorting. The discrimination unit can also perform simple discrimination in the case of burnable garbage to quickly process it. This allows the level of detail of discrimination to be adjusted depending on the importance of the garbage. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input garbage importance data to the generation AI and cause the generation AI to adjust the level of detail of discrimination.

[0042] The discrimination unit can apply different discrimination algorithms depending on the category of waste when discriminating. For example, in the case of plastic waste, the discrimination unit applies a discrimination algorithm dedicated to plastic. Furthermore, in the case of paper waste, the discrimination unit can also apply a discrimination algorithm dedicated to paper. Furthermore, in the case of metal waste, the discrimination unit can also apply a discrimination algorithm dedicated to metal. For example, in the case of plastic waste, the discrimination unit applies a discrimination algorithm dedicated to plastic. Furthermore, in the case of paper waste, the discrimination unit can also apply a discrimination algorithm dedicated to paper. This makes it possible to provide an optimal discrimination algorithm depending on the category of waste. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input waste category data into the generation AI and cause the generation AI to apply different discrimination algorithms.

[0043] The discrimination unit can improve the accuracy of discrimination by referring to the user's past discrimination results when discriminating. For example, the discrimination unit improves the discrimination algorithm based on data on garbage that the user has previously discriminated. The discrimination unit can also suggest a method to prevent misdiscrimination based on data on garbage that the user has previously misdiscriminated. The discrimination unit can also analyze the user's past discrimination results to improve the accuracy of discrimination. For example, the discrimination unit improves the discrimination algorithm based on data on garbage that the user has previously discriminated. The discrimination unit can also suggest a method to prevent misdiscrimination based on data on garbage that the user has previously misdiscriminated. This makes it possible to improve the accuracy of discrimination based on past discrimination results. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the user's past discrimination result data into the generation AI and cause the generation AI to improve the accuracy of discrimination.

[0044] The discrimination unit can determine the discrimination priority based on the time of submission of the garbage when discriminating. For example, the discrimination unit prioritizes discrimination of garbage whose submission deadline is approaching. The discrimination unit can also prioritize discrimination of garbage that is submitted regularly. The discrimination unit can also prioritize discrimination of garbage that is submitted seasonally. For example, the discrimination unit prioritizes discrimination of garbage whose submission deadline is approaching. The discrimination unit can also prioritize discrimination of garbage that is submitted regularly. This makes it possible to determine the discrimination priority based on the time of submission of the garbage. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input garbage submission time data to the generation AI and have the generation AI determine the discrimination priority.

[0045] The discrimination unit can adjust the discrimination order based on the relevance of the garbage during discrimination. For example, the discrimination unit discriminates garbage of the same category together. The discrimination unit can also discriminate recyclable garbage with priority. The discrimination unit can also discriminate hazardous materials with priority. For example, the discrimination unit discriminates garbage of the same category together. The discrimination unit can also discriminate recyclable garbage with priority. This makes it possible to adjust the discrimination order based on the relevance of the garbage. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input garbage relevance data to the generation AI and cause the generation AI to adjust the discrimination order.

[0046] The discrimination unit can adjust the use of technical terminology for discrimination according to the user's level of expertise during discrimination. For example, if the user has technical knowledge, the discrimination unit can use detailed technical terminology to make the discrimination. Also, if the user does not have technical knowledge, the discrimination unit can make the discrimination using simple language. Furthermore, the discrimination unit can adjust the explanation of the discrimination according to the user's level of expertise. For example, if the user has technical knowledge, the discrimination unit can use detailed technical terminology to make the discrimination. Also, if the user does not have technical knowledge, the discrimination unit can make the discrimination using simple language. This makes it possible to provide a discrimination method according to the user's level of expertise. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the waste. For example, the suggestion unit can propose a detailed sorting method for recyclable waste. The suggestion unit can also propose a simple sorting method for burnable waste. Furthermore, the suggestion unit can propose a detailed sorting method with particular attention paid to hazardous materials. For example, the suggestion unit can propose a detailed sorting method for recyclable waste. The suggestion unit can also propose a simple sorting method for burnable waste. This allows the level of detail of the proposal to be adjusted depending on the importance of the waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input waste importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of waste. For example, in the case of plastic waste, the proposal unit can propose a sorting method specifically for plastic. Furthermore, in the case of paper waste, the proposal unit can also propose a sorting method specifically for paper. Furthermore, in the case of metal waste, the proposal unit can also propose a sorting method specifically for metal. For example, in the case of plastic waste, the proposal unit can propose a sorting method specifically for plastic. Furthermore, in the case of paper waste, the proposal unit can also propose a sorting method specifically for paper. This makes it possible to provide an optimal proposal algorithm depending on the category of waste. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input waste category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the proposal algorithm based on proposals received by the user in the past. The suggestion unit can also suggest a method to prevent mis-sorting based on data on garbage that the user has mis-sorted in the past. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. For example, the suggestion unit improves the proposal algorithm based on proposals received by the user in the past. The suggestion unit can also suggest a method to prevent mis-sorting based on data on garbage that the user has mis-sorted in the past. This allows the accuracy of the proposal to be improved based on the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0050] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the waste. For example, the suggestion unit prioritizes the proposal of a waste sorting method for which the submission deadline is approaching. The suggestion unit can also prioritize the proposal of a waste sorting method for which regular submission is made. The suggestion unit can also prioritize the proposal of a waste sorting method for which seasonal submission is made. For example, the suggestion unit prioritizes the proposal of a waste sorting method for which the submission deadline is approaching. The suggestion unit can also prioritize the proposal of a waste sorting method for which regular submission is made. This makes it possible to determine the priority of the proposals based on the time of submission of the waste. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input waste submission time data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0051] When making a proposal, the suggestion unit can adjust the order of the proposals based on the relevance of the waste. For example, the suggestion unit collectively proposes sorting methods for waste in the same category. The suggestion unit can also prioritize suggesting sorting methods for recyclable waste. The suggestion unit can also prioritize suggesting sorting methods for hazardous materials. For example, the suggestion unit collectively proposes sorting methods for waste in the same category. The suggestion unit can also prioritize suggesting sorting methods for recyclable waste. This makes it possible to adjust the order of the proposals based on the relevance of the waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input waste relevance data into the generation AI and cause the generation AI to adjust the order of the proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make the proposal using detailed technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make the proposal in simple language. Furthermore, the suggestion unit can adjust the explanation of the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make the proposal using detailed technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make the proposal in simple language. This makes it possible to provide a suggestion method that is appropriate for the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The photographing unit can learn the user's lifestyle patterns and suggest the optimal timing for photographing. For example, if the user is busy in the morning, the photographing unit can suggest photographing trash at night. Also, if the user has time on the weekend, the photographing unit can suggest photographing trash on the weekend. Furthermore, the photographing unit can suggest photographing trash on a specific day of the week or time of day based on the user's lifestyle patterns. This makes it possible to provide the optimal photographing timing according to the user's lifestyle patterns. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's lifestyle pattern data into the generation AI and have the generation AI suggest the optimal photographing timing.

[0055] The suggestion unit can analyze the user's past sorting history and suggest an optimal sorting method. For example, the suggestion unit can suggest an optimal sorting method based on data on waste sorted by the user in the past. The suggestion unit can also suggest a method to prevent incorrect sorting based on data on waste sorted incorrectly by the user in the past. Furthermore, the suggestion unit can analyze the user's past sorting history and improve the accuracy of the sorting method. This makes it possible to provide an optimal sorting method based on the past sorting history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past sorting history data into the generation AI and have the generation AI suggest an optimal sorting method.

[0056] The suggestion unit can customize the proposed sorting method according to the user's level of expertise. For example, if the user has specialized knowledge, the suggestion unit can suggest a sorting method using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the suggestion unit can suggest a sorting method in simple terms. Furthermore, the explanation of the sorting method can be adjusted according to the user's level of expertise. This makes it possible to provide an optimal sorting method according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and have the generation AI execute the proposed sorting method.

[0057] The photographing unit can prioritize photographing highly relevant trash by taking into account the user's geographical location information. For example, if the user is in a specific area, it prioritizes photographing trash that is commonly produced in that area. Also, if the user is traveling, it can select trash to photograph based on the trash sorting rules of the travel destination. Furthermore, if the user does not know the trash sorting rules of the area where they are moving, it can also suggest trash to photograph based on the rules of that area. This allows highly relevant trash to be photographed preferentially based on the geographical location information. Some or all of the above-mentioned processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant trash.

[0058] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the waste. For example, the suggestion unit can prioritize the proposal of a waste sorting method for which the submission deadline is approaching. The suggestion unit can also prioritize the proposal of a waste sorting method for which waste is submitted periodically. Furthermore, the suggestion unit can also prioritize the proposal of a waste sorting method for which waste is submitted seasonally. This makes it possible to determine the priority of the proposal based on the time of submission of the waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input waste submission time data into the generation AI and have the generation AI determine the priority of the proposals.

[0059] When making a proposal, the proposal unit can adjust the order of the proposals based on the relevance of the waste. For example, the proposal unit can collectively propose sorting methods for waste in the same category. The proposal unit can also prioritize suggesting sorting methods for recyclable waste. Furthermore, the proposal unit can prioritize suggesting sorting methods for hazardous materials. This allows the order of proposals to be adjusted based on the relevance of the waste. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input waste relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The camera unit takes images of the waste. Images of waste include plastic waste, paper waste, metal waste, etc. The camera unit can use a smartphone camera to take images of waste generated at home, and can also use cameras at waste incineration plants to take images of waste being processed. The smartphone camera takes high-resolution images of the waste, while the waste incineration plants use fixed or mobile cameras to take images of the waste. Step 2: The classification unit uses a deep learning algorithm to analyze the image of the garbage captured by the photographing unit and classify the type of garbage. The classification unit can use a CNN (convolutional neural network) or an RNN (recurrent neural network) to classify the type of garbage. Furthermore, the classification unit can also combine deep learning algorithms to classify the type of garbage. For example, a CNN can be used to extract features from the image of the garbage, and an RNN can be used to classify the type of garbage. Step 3: The proposal unit proposes a waste sorting method that conforms to local rules based on the type of waste identified by the classification unit. The proposal unit can propose a household waste sorting method via a smartphone app, and can also introduce an automatic sorting system at waste disposal plants. Furthermore, the proposal unit can propose a waste sorting method based on the rules of a specific area. For example, plastic waste may be collected on a specific day of the week, and paper waste on another day. The proposal unit proposes an appropriate sorting method based on these local rules.

[0062] (Example 2) A waste sorting support system according to an embodiment of the present invention captures images of waste, classifies the type of waste using image recognition technology based on deep learning, and proposes a waste sorting method in accordance with local rules. The waste sorting support system captures images of waste using a smartphone camera or a camera at a waste incineration plant, and then uses image recognition technology based on deep learning to distinguish the type of waste. Based on the distinguished waste type, the system proposes a waste sorting method in accordance with local rules. For example, the waste sorting support system may photograph household waste using a smartphone camera or waste being processed at a waste incineration plant using a camera. In this case, the captured image is analyzed using image recognition technology based on deep learning. Next, the deep learning image recognition technology distinguishes the type of waste captured. For example, types of waste can be distinguished, such as plastic, paper, metal, and glass. This allows recyclable waste to be identified from mixed waste. Based on the distinguished waste type, the system proposes a waste sorting method in accordance with local rules. For example, in certain areas, plastic waste may be collected on certain days of the week, while paper waste may be collected on another day. Based on these local rules, appropriate sorting methods can be proposed. This improves waste recycling rates and promotes the realization of a sustainable society. This allows the waste sorting support system to respond to the increasing variety of waste and improve recycling rates. For example, it makes it easier for households to sort waste, and by properly sorting recyclable waste, recycling rates improve. It also improves waste processing efficiency at incineration plants, and by properly disposing of recyclable waste, it promotes the realization of a sustainable society. Furthermore, this system is effective even in the current situation where household waste is increasing compared to before the COVID-19 pandemic. For example, even in a situation where household waste has increased by 131.5%, it is possible to improve recycling rates by proposing appropriate sorting methods. This reduces the amount of waste and contributes to the realization of a sustainable society.

[0063] The waste sorting support system according to the embodiment includes a photographing unit, a classification unit, and a suggestion unit. The photographing unit photographs images of waste. Examples of waste images include, but are not limited to, plastic waste, paper waste, and metal waste. The photographing unit, for example, uses a smartphone camera to photograph household waste. The photographing unit can also photograph waste being processed using a camera at an incineration plant. For example, the smartphone camera photographs waste at high resolution, while the incineration plant camera photographs waste using a fixed or mobile camera. The classification unit uses a deep learning algorithm to analyze the images of waste photographed by the photographing unit and classify the type of waste. For example, the classification unit uses a convolutional neural network (CNN) as the deep learning algorithm. The classification unit can also use a recurrent neural network (RNN) to classify the type of waste. Furthermore, the classification unit can combine deep learning algorithms to classify the type of waste. For example, the classification unit extracts features from the waste image using a CNN and classifies the type of waste using an RNN. The suggestion unit suggests a garbage sorting method in accordance with local rules based on the type of garbage identified by the identification unit. The suggestion unit, for example, suggests a household garbage sorting method via a smartphone app. The suggestion unit can also introduce an automatic garbage sorting system at an incineration plant. Furthermore, the suggestion unit can also suggest a garbage sorting method based on the rules of a specific area. For example, plastic garbage may be collected on a specific day of the week, and paper garbage may be collected on a different day of the week. The suggestion unit can suggest an appropriate sorting method based on such local rules. As a result, the garbage sorting support system according to the embodiment can improve the garbage recycling rate and promote the realization of a sustainable society.

[0064] The photographing unit may include a smartphone camera and a waste disposal plant camera. Examples of the smartphone camera include, but are not limited to, a high-resolution camera, a wide-angle camera, and a nighttime photography mode. For example, the photographing unit may use the smartphone camera to photograph household waste. The photographing unit may also use a waste disposal plant camera to photograph waste being disposed of. Examples of the waste disposal plant camera include, but are not limited to, a fixed camera, a mobile camera, and an infrared camera. For example, the smartphone camera may photograph waste in high resolution, and the waste disposal plant camera may photograph waste using a fixed or mobile camera. This allows waste images to be photographed using a variety of cameras. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit may input images of waste photographed with the smartphone camera into a generation AI, causing the generation AI to analyze the images.

[0065] The discrimination unit can discriminate the type of garbage using a deep learning algorithm. Examples of deep learning algorithms include, but are not limited to, CNN (convolutional neural network), RNN (recurrent neural network), and LSTM (long short-term memory). The discrimination unit uses, for example, CNN as a deep learning algorithm. The discrimination unit can also discriminate the type of garbage using an RNN. Furthermore, the discrimination unit can discriminate the type of garbage by combining deep learning algorithms. For example, the discrimination unit uses CNN to extract features from an image of garbage and then uses an RNN to discriminate the type of garbage. In this way, the use of deep learning enables the type of garbage to be discriminated with high accuracy. Some or all of the above-described processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can discriminate the type of garbage using an AI model that executes a deep learning algorithm.

[0066] The suggestion unit can suggest a household garbage sorting method through a smartphone app. The smartphone app may include, but is not limited to, an interface, a notification function, and a history management function. The suggestion unit can suggest a household garbage sorting method through the smartphone app. For example, the smartphone app may analyze images of garbage taken by a user and suggest an appropriate sorting method. The smartphone app may also notify the user of collection dates and sorting methods based on local sorting rules. The smartphone app may also manage the user's sorting history and improve the accuracy of the suggestions based on past sorting results. For example, the smartphone app may analyze images of garbage taken by a user and suggest an appropriate sorting method. The smartphone app may also notify the user of collection dates and sorting methods based on local sorting rules. This facilitates household garbage sorting. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input image data of garbage acquired through the smartphone app into a generation AI and cause the generation AI to suggest a sorting method.

[0067] The proposal unit may implement an automatic sorting system at an incineration plant. The automatic sorting system may include, but is not limited to, sensors to be used, a sorting procedure, and a robotic arm. For example, the proposal unit may implement an automatic sorting system at an incineration plant. The automatic sorting system may, for example, use sensors to detect the type of waste and a robotic arm to sort the waste. The automatic sorting system may also use AI to identify the type of waste and implement an appropriate sorting method. Furthermore, the automatic sorting system may automate the sorting procedure and efficiently process waste. For example, the automatic sorting system may use sensors to detect the type of waste and a robotic arm to sort the waste. The automatic sorting system may also use AI to identify the type of waste and implement an appropriate sorting method. This automates waste sorting at an incineration plant and improves efficiency. Some or all of the above-described processing in the proposal unit may be performed using AI, or may be performed without AI. For example, the proposal unit may input waste data acquired at an incineration plant into a generation AI and cause the generation AI to control the automatic sorting system.

[0068] The suggestion unit can suggest a garbage sorting method based on the rules of a specific area. The rules of a specific area include, for example, a garbage sorting method, collection days, and garbage sorting procedures for each area, but are not limited to these examples. The suggestion unit, for example, suggests a garbage sorting method based on the rules of a specific area. The suggestion unit stores the garbage sorting rules for each area in a database and suggests an appropriate garbage sorting method based on the user's location. The suggestion unit can also notify the user of collection days for each area and encourage the user to put out garbage on the appropriate day. The suggestion unit can also explain the garbage sorting procedures for each area in detail to help the user sort garbage accurately. For example, the suggestion unit stores the garbage sorting rules for each area in a database and suggests an appropriate garbage sorting method based on the user's location. The suggestion unit can also notify the user of collection days for each area and encourage the user to put out garbage on the appropriate day. This enables appropriate garbage sorting in accordance with the rules of each area. Some or all of the above-described processing by the suggestion unit may be performed, for example, using AI or without AI. For example, the proposal unit can input regional sorting rules into the generation AI and have the generation AI execute a proposed sorting method.

[0069] The image capture unit can estimate the user's emotions and adjust the timing of capturing images of the trash based on the estimated user emotions. For example, if the user is feeling stressed, the image capture unit can automatically delay the timing of capturing images so that the user can capture images in a relaxed state. Furthermore, if the user is relaxed, the image capture unit can immediately start capturing images to efficiently capture images of the trash. Furthermore, if the user is in a hurry, the image capture unit can quickly adjust the timing of capturing images to quickly capture images of the trash. For example, the image capture unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on changes in facial expression. The image capture unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the tone and speed of the voice can be analyzed to calculate the emotion score. The image capture unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the emotion score can be calculated based on heart rate fluctuations. This allows the optimal capture timing to be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit may be performed using AI, or may be performed without using AI. For example, the image capture unit may input user emotion data into the generation AI and cause the generation AI to adjust the timing of the image capture.

[0070] When taking a photo, the imaging unit can analyze the user's past garbage sorting history and select the optimal imaging method. For example, the imaging unit can analyze the types of garbage photographed by the user in the past and suggest the optimal imaging method if similar garbage is found. The imaging unit can also customize the imaging method for specific garbage based on the user's past sorting history to enable efficient imaging. Furthermore, the imaging unit can improve the imaging method for garbage that the user has failed to separate in the past to prevent the same mistake from occurring again. For example, the imaging unit can store the user's past sorting history in a database and suggest the optimal imaging method if similar garbage is found. The imaging unit can also analyze the user's past sorting history and customize the imaging method for specific garbage. This allows the optimal imaging method to be provided based on the past sorting history. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without AI. For example, the imaging unit can input the user's past sorting history data into a generation AI and have the generation AI select the optimal imaging method.

[0071] The photographing unit can filter images based on the user's current lifestyle and areas of interest when photographing. For example, if the user is busy, the photographing unit can suggest an easy way to photograph, saving time. Furthermore, if the user is interested in environmental protection, the photographing unit can prioritize photographing recyclable waste. Furthermore, if the user is interested in sorting specific types of waste, the photographing unit can provide detailed guidance on how to photograph that type of waste. For example, the photographing unit can store the user's lifestyle patterns in a database and suggest an easy way to photograph when the user is busy. Furthermore, the photographing unit can analyze the user's areas of interest and prioritize photographing recyclable waste. This makes it possible to provide a photographing method that suits the user's lifestyle and areas of interest. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit can input data on the user's lifestyle and areas of interest into the generation AI and have the generation AI perform filtering.

[0072] The photographing unit can select the optimal photographing means according to the user's input method when photographing. For example, when the user uses voice input, the photographing unit can start photographing with a voice command, saving the user time and effort. Furthermore, when the user uses text input, the photographing unit can take photographs by issuing text instructions. Furthermore, when the user uses image input, the photographing unit can analyze the image and suggest the optimal photographing method. For example, the photographing unit can start photographing with a voice command for a user using voice input. Furthermore, the photographing unit can take photographs by issuing text instructions for a user using text input. This makes it possible to provide the optimal photographing means according to the user's input method. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's input method data into a generation AI and cause the generation AI to select the optimal photographing means.

[0073] The camera unit can estimate the user's emotions and prioritize the trash to be photographed based on the estimated user's emotions. For example, if the user is stressed, the camera unit can prioritize photographing trash that is easy to separate. Furthermore, if the user is relaxed, the camera unit can prioritize photographing trash that requires more complex separation. Furthermore, if the user is in a hurry, the camera unit can prioritize photographing trash that does not take much time. For example, the camera unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the camera unit can calculate an emotion score based on changes in facial expression. The camera unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the camera unit can analyze the tone and speed of the voice and calculate an emotion score. The camera unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. For example, the camera unit can calculate an emotion score based on heart rate fluctuations. This allows the camera unit to prioritize the trash to be photographed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit may be performed using AI, or may be performed without using AI. For example, the image capture unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of the garbage to be photographed.

[0074] When taking a photo, the photographing unit can prioritize photographing highly relevant trash by taking into account the user's geographical location information. For example, if the user is in a specific area, the photographing unit prioritizes photographing trash that is commonly produced in that area. Furthermore, if the user is traveling, the photographing unit can select trash to photograph based on the trash sorting rules of the travel destination. Furthermore, if the user does not know the trash sorting rules of the destination, the photographing unit can suggest trash to photograph based on the rules of the destination. For example, the photographing unit stores the user's geographical location information in a database and prioritizes photographing trash that is commonly produced in the area. Furthermore, if the user is traveling, the photographing unit can select trash to photograph based on the trash sorting rules of the travel destination. This allows highly relevant trash to be prioritized based on the geographical location information. Some or all of the above-described processing in the photographing unit may be performed, for example, using AI or without AI. For example, the photographing unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant trash.

[0075] The photographing unit can analyze the user's social media activity when photographing and photograph related trash. For example, the photographing unit photographs similar trash based on images of trash shared by the user on social media. The photographing unit can also analyze the user's social media posts and photograph related trash. Furthermore, the photographing unit can photograph related trash by referring to the activities of the user's friends on social media. For example, the photographing unit stores the user's social media activity in a database and suggests the optimal photographing method when similar trash appears. The photographing unit can also analyze the user's social media posts and photograph related trash. In this way, related trash can be photographed based on social media activity. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit can input the user's social media activity data into a generation AI and have the generation AI photograph related trash.

[0076] The photographing unit can customize the photographing method by reflecting the user's past feedback when photographing. The photographing unit, for example, improves the photographing method based on feedback provided by the user in the past. Furthermore, if the user prefers a particular photographing method, the photographing unit can preferentially suggest that method. Furthermore, if the user has previously failed at photographing, the photographing unit can suggest a method to avoid that failure. For example, the photographing unit stores the user's past feedback in a database and suggests the optimal photographing method when similar garbage is produced. The photographing unit can also analyze the user's past feedback and customize a specific photographing method. This allows the photographing method to be customized based on the past feedback. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's past feedback data into a generation AI and have the generation AI customize the photographing method.

[0077] The discrimination unit can estimate the user's emotions and adjust the garbage classification method based on the estimated user emotions. For example, if the user is stressed, the discrimination unit can provide a simple classification method to reduce the user's burden. Furthermore, if the user is relaxed, the discrimination unit can provide a detailed classification method to encourage accurate garbage sorting. Furthermore, if the user is in a hurry, the discrimination unit can provide a quick classification method. For example, the discrimination unit can capture the user's facial expression with a camera and estimate the user's emotions using an emotion estimation algorithm. For example, the discrimination unit can calculate an emotion score based on changes in facial expression. The discrimination unit can also record the user's voice and estimate the user's emotions using voice analysis technology. For example, the discrimination unit can analyze the tone and speed of the voice and calculate an emotion score. The discrimination unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the user's emotions using an emotion estimation algorithm. For example, the discrimination unit can calculate an emotion score based on heart rate fluctuations. This allows the system to provide an optimal classification method depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit may input user emotion data into the generation AI and cause the generation AI to adjust the determination method.

[0078] The discrimination unit can adjust the level of detail of discrimination based on the importance of the garbage when discriminating. For example, the discrimination unit performs detailed discrimination in the case of recyclable garbage to encourage accurate sorting. The discrimination unit can also perform simple discrimination in the case of burnable garbage to quickly process it. Furthermore, the discrimination unit can also perform detailed discrimination with particular attention to hazardous materials. For example, the discrimination unit performs detailed discrimination in the case of recyclable garbage to encourage accurate sorting. The discrimination unit can also perform simple discrimination in the case of burnable garbage to quickly process it. This allows the level of detail of discrimination to be adjusted depending on the importance of the garbage. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input garbage importance data to the generation AI and cause the generation AI to adjust the level of detail of discrimination.

[0079] The discrimination unit can apply different discrimination algorithms depending on the category of waste when discriminating. For example, in the case of plastic waste, the discrimination unit applies a discrimination algorithm dedicated to plastic. Furthermore, in the case of paper waste, the discrimination unit can also apply a discrimination algorithm dedicated to paper. Furthermore, in the case of metal waste, the discrimination unit can also apply a discrimination algorithm dedicated to metal. For example, in the case of plastic waste, the discrimination unit applies a discrimination algorithm dedicated to plastic. Furthermore, in the case of paper waste, the discrimination unit can also apply a discrimination algorithm dedicated to paper. This makes it possible to provide an optimal discrimination algorithm depending on the category of waste. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input waste category data into the generation AI and cause the generation AI to apply different discrimination algorithms.

[0080] The discrimination unit can improve the accuracy of discrimination by referring to the user's past discrimination results when discriminating. For example, the discrimination unit improves the discrimination algorithm based on data on garbage that the user has previously discriminated. The discrimination unit can also suggest a method to prevent misdiscrimination based on data on garbage that the user has previously misdiscriminated. The discrimination unit can also analyze the user's past discrimination results to improve the accuracy of discrimination. For example, the discrimination unit improves the discrimination algorithm based on data on garbage that the user has previously discriminated. The discrimination unit can also suggest a method to prevent misdiscrimination based on data on garbage that the user has previously misdiscriminated. This makes it possible to improve the accuracy of discrimination based on past discrimination results. Some or all of the above-mentioned processing in the discrimination unit may be performed using, for example, AI, or may be performed without using AI. For example, the discrimination unit can input the user's past discrimination result data into the generation AI and cause the generation AI to improve the accuracy of discrimination.

[0081] The discrimination unit can estimate the user's emotion and adjust the length of discrimination based on the estimated user emotion. For example, the discrimination unit provides a method for quickly discriminating if the user is stressed. Furthermore, the discrimination unit can also perform detailed discrimination and promote accurate judgment if the user is relaxed. Furthermore, the discrimination unit can also provide a method for quickly discriminating if the user is in a hurry. For example, the discrimination unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the discrimination unit calculates an emotion score based on changes in facial expression. The discrimination unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the discrimination unit can analyze the tone and speed of the voice and calculate an emotion score. The discrimination unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the discrimination unit calculates an emotion score based on fluctuations in heart rate. This allows the length of discrimination to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the discrimination unit may be performed using AI, or may be performed without using AI. For example, the discrimination unit may input user emotion data to the generation AI and cause the generation AI to adjust the discrimination length.

[0082] The discrimination unit can determine the discrimination priority based on the time of submission of the garbage when discriminating. For example, the discrimination unit prioritizes discrimination of garbage whose submission deadline is approaching. The discrimination unit can also prioritize discrimination of garbage that is submitted regularly. The discrimination unit can also prioritize discrimination of garbage that is submitted seasonally. For example, the discrimination unit prioritizes discrimination of garbage whose submission deadline is approaching. The discrimination unit can also prioritize discrimination of garbage that is submitted regularly. This makes it possible to determine the discrimination priority based on the time of submission of the garbage. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input garbage submission time data to the generation AI and have the generation AI determine the discrimination priority.

[0083] The discrimination unit can adjust the discrimination order based on the relevance of the garbage during discrimination. For example, the discrimination unit discriminates garbage of the same category together. The discrimination unit can also discriminate recyclable garbage with priority. The discrimination unit can also discriminate hazardous materials with priority. For example, the discrimination unit discriminates garbage of the same category together. The discrimination unit can also discriminate recyclable garbage with priority. This makes it possible to adjust the discrimination order based on the relevance of the garbage. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input garbage relevance data to the generation AI and cause the generation AI to adjust the discrimination order.

[0084] The discrimination unit can adjust the use of technical terminology for discrimination according to the user's level of expertise during discrimination. For example, if the user has technical knowledge, the discrimination unit can use detailed technical terminology to make the discrimination. Also, if the user does not have technical knowledge, the discrimination unit can make the discrimination using simple language. Furthermore, the discrimination unit can adjust the explanation of the discrimination according to the user's level of expertise. For example, if the user has technical knowledge, the discrimination unit can use detailed technical terminology to make the discrimination. Also, if the user does not have technical knowledge, the discrimination unit can make the discrimination using simple language. This makes it possible to provide a discrimination method according to the user's level of expertise. Some or all of the above-mentioned processing in the discrimination unit may be performed using AI, for example, or may be performed without using AI. For example, the discrimination unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0085] The suggestion unit can estimate the user's emotions and adjust the proposed sorting method based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a simple sorting method. Furthermore, if the user is relaxed, the suggestion unit can also suggest a more detailed sorting method. Furthermore, if the user is in a hurry, the suggestion unit can suggest a quick sorting method. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on heart rate fluctuations. This allows the system to suggest the optimal sorting method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into the generation AI and have the generation AI execute a proposed classification method.

[0086] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the waste. For example, the suggestion unit can propose a detailed sorting method for recyclable waste. The suggestion unit can also propose a simple sorting method for burnable waste. Furthermore, the suggestion unit can propose a detailed sorting method with particular attention paid to hazardous materials. For example, the suggestion unit can propose a detailed sorting method for recyclable waste. The suggestion unit can also propose a simple sorting method for burnable waste. This allows the level of detail of the proposal to be adjusted depending on the importance of the waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input waste importance data into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0087] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of waste. For example, in the case of plastic waste, the proposal unit can propose a sorting method specifically for plastic. Furthermore, in the case of paper waste, the proposal unit can also propose a sorting method specifically for paper. Furthermore, in the case of metal waste, the proposal unit can also propose a sorting method specifically for metal. For example, in the case of plastic waste, the proposal unit can propose a sorting method specifically for plastic. Furthermore, in the case of paper waste, the proposal unit can also propose a sorting method specifically for paper. This makes it possible to provide an optimal proposal algorithm depending on the category of waste. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input waste category data into the generation AI and cause the generation AI to apply different proposal algorithms.

[0088] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, improves the proposal algorithm based on proposals received by the user in the past. The suggestion unit can also suggest a method to prevent mis-sorting based on data on garbage that the user has mis-sorted in the past. The suggestion unit can also analyze the user's past proposal results and improve the accuracy of the proposal. For example, the suggestion unit improves the proposal algorithm based on proposals received by the user in the past. The suggestion unit can also suggest a method to prevent mis-sorting based on data on garbage that the user has mis-sorted in the past. This allows the accuracy of the proposal to be improved based on the past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide short, to-the-point suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide quick, concise suggestions. For example, the suggestion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on changes in facial expression. The suggestion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the suggestion unit can analyze the tone and speed of the voice and calculate an emotion score. The suggestion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the suggestion unit can calculate an emotion score based on heart rate fluctuations. This allows the length of suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to adjust the length of the suggestion.

[0090] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the waste. For example, the suggestion unit prioritizes the proposal of a waste sorting method for which the submission deadline is approaching. The suggestion unit can also prioritize the proposal of a waste sorting method for which regular submission is made. The suggestion unit can also prioritize the proposal of a waste sorting method for which seasonal submission is made. For example, the suggestion unit prioritizes the proposal of a waste sorting method for which the submission deadline is approaching. The suggestion unit can also prioritize the proposal of a waste sorting method for which regular submission is made. This makes it possible to determine the priority of the proposals based on the time of submission of the waste. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input waste submission time data into the generation AI and cause the generation AI to determine the priority of the proposals.

[0091] When making a proposal, the suggestion unit can adjust the order of the proposals based on the relevance of the waste. For example, the suggestion unit collectively proposes sorting methods for waste in the same category. The suggestion unit can also prioritize suggesting sorting methods for recyclable waste. The suggestion unit can also prioritize suggesting sorting methods for hazardous materials. For example, the suggestion unit collectively proposes sorting methods for waste in the same category. The suggestion unit can also prioritize suggesting sorting methods for recyclable waste. This makes it possible to adjust the order of the proposals based on the relevance of the waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input waste relevance data into the generation AI and cause the generation AI to adjust the order of the proposals.

[0092] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make the proposal using detailed technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make the proposal in simple language. Furthermore, the suggestion unit can adjust the explanation of the proposal depending on the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make the proposal using detailed technical terminology. Also, if the user does not have technical expertise, the suggestion unit can make the proposal in simple language. This makes it possible to provide a suggestion method that is appropriate for the user's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements including the above-described photographing unit, discrimination unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit can photograph an image of garbage using the camera of the smart device 14. For example, the discrimination unit can execute an algorithm using deep learning by the specific processing unit 290 of the data processing device 12 to discriminate the type of garbage. For example, the suggestion unit can suggest a garbage sorting method in accordance with local rules by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can also be realized by the control unit 46A of the smart device 14 and can suggest a household garbage sorting method via a smartphone app. === Hard Collateral 1-2 === Each of the multiple elements including the above-described photographing unit, discrimination unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit can capture an image of garbage using the camera of the smart glasses 214. For example, the discrimination unit can execute an algorithm using deep learning by the specific processing unit 290 of the data processing device 12 to discriminate the type of garbage. For example, the suggestion unit can suggest a garbage sorting method in accordance with local rules by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can also be realized by the control unit 46A of the smart glasses 214 and can suggest a household garbage sorting method via a smartphone app. === Hard Collateral 1-3 === Each of the multiple elements including the above-described photographing unit, discrimination unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit can photograph an image of garbage using the camera of the headset-type terminal 314. For example, the discrimination unit can execute an algorithm using deep learning by the specific processing unit 290 of the data processing device 12 to discriminate the type of garbage. For example, the suggestion unit can suggest a garbage sorting method in accordance with local rules by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can also be realized by the control unit 46A of the headset-type terminal 314 and can suggest a household garbage sorting method via a smartphone app. === Hard Collateral 1-4 === Each of the multiple elements including the above-described photographing unit, discrimination unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit can photograph images of garbage using a camera of the robot 414. For example, the discrimination unit can execute an algorithm using deep learning by the specific processing unit 290 of the data processing device 12 to discriminate the type of garbage. For example, the suggestion unit can suggest a garbage sorting method in accordance with local rules by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit can also be realized by the control unit 46A of the robot 414 and can suggest a household garbage sorting method via a smartphone app.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The photographing unit can learn the user's lifestyle patterns and suggest the optimal timing for photographing. For example, if the user is busy in the morning, the photographing unit can suggest photographing trash at night. Also, if the user has time on the weekend, the photographing unit can suggest photographing trash on the weekend. Furthermore, the photographing unit can suggest photographing trash on a specific day of the week or time of day based on the user's lifestyle patterns. This makes it possible to provide the optimal photographing timing according to the user's lifestyle patterns. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input the user's lifestyle pattern data into the generation AI and have the generation AI suggest the optimal photographing timing.

[0095] The discrimination unit can estimate the user's emotions and adjust the accuracy of garbage classification based on the estimated user emotions. For example, if the user is stressed, the discrimination unit performs simple classification to reduce the user's burden. Furthermore, if the user is relaxed, the discrimination unit can perform detailed classification to encourage accurate garbage sorting. Furthermore, if the user is in a hurry, the discrimination unit can perform classification quickly. This allows for optimal classification accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the discrimination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the discrimination unit can input the user's emotion data into the generation AI and have the generation AI adjust the classification accuracy.

[0096] The suggestion unit can analyze the user's past sorting history and suggest an optimal sorting method. For example, the suggestion unit can suggest an optimal sorting method based on data on waste sorted by the user in the past. The suggestion unit can also suggest a method to prevent incorrect sorting based on data on waste sorted incorrectly by the user in the past. Furthermore, the suggestion unit can analyze the user's past sorting history and improve the accuracy of the sorting method. This makes it possible to provide an optimal sorting method based on the past sorting history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past sorting history data into the generation AI and have the generation AI suggest an optimal sorting method.

[0097] The photographing unit can estimate the user's emotions and prioritize the trash to be photographed based on the estimated user's emotions. For example, if the user is feeling stressed, it can prioritize photographing trash that can be easily sorted. Furthermore, if the user is relaxed, it can prioritize photographing trash that requires complex sorting. Furthermore, if the user is in a hurry, it can prioritize photographing trash that does not take much time. This allows the priority of photographing trash to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the photographing unit can be performed using, for example, AI, or without AI. For example, the photographing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the trash to be photographed.

[0098] The suggestion unit can customize the proposed sorting method according to the user's level of expertise. For example, if the user has specialized knowledge, the suggestion unit can suggest a sorting method using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the suggestion unit can suggest a sorting method in simple terms. Furthermore, the explanation of the sorting method can be adjusted according to the user's level of expertise. This makes it possible to provide an optimal sorting method according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and have the generation AI execute the proposed sorting method.

[0099] The photographing unit can prioritize photographing highly relevant trash by taking into account the user's geographical location information. For example, if the user is in a specific area, it prioritizes photographing trash that is commonly produced in that area. Also, if the user is traveling, it can select trash to photograph based on the trash sorting rules of the travel destination. Furthermore, if the user does not know the trash sorting rules of the area where they are moving, it can also suggest trash to photograph based on the rules of that area. This allows highly relevant trash to be photographed preferentially based on the geographical location information. Some or all of the above-mentioned processing in the photographing unit may be performed using AI, for example, or may be performed without using AI. For example, the photographing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant trash.

[0100] The discrimination unit can estimate the user's emotion and adjust the length of discrimination based on the estimated user emotion. For example, if the user is feeling stressed, a method that can discriminate in a short time can be provided. Also, if the user is relaxed, detailed discrimination can be performed to promote accurate discrimination. Furthermore, if the user is in a hurry, a method that can discriminate quickly can be provided. This allows the length of discrimination to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the discrimination unit can be performed using, for example, AI, or without AI. For example, the discrimination unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of discrimination.

[0101] The suggestion unit can estimate the user's emotions and adjust the proposed sorting method based on the estimated user's emotions. For example, if the user is stressed, a simple sorting method can be suggested. Alternatively, if the user is relaxed, a more detailed sorting method can be suggested. Furthermore, if the user is in a hurry, a quick sorting method can be suggested. This allows the optimal sorting method to be suggested according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI execute the proposed sorting method.

[0102] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time of submission of the waste. For example, the suggestion unit can prioritize the proposal of a waste sorting method for which the submission deadline is approaching. The suggestion unit can also prioritize the proposal of a waste sorting method for which waste is submitted periodically. Furthermore, the suggestion unit can also prioritize the proposal of a waste sorting method for which waste is submitted seasonally. This makes it possible to determine the priority of the proposal based on the time of submission of the waste. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input waste submission time data into the generation AI and have the generation AI determine the priority of the proposals.

[0103] When making a proposal, the proposal unit can adjust the order of the proposals based on the relevance of the waste. For example, the proposal unit can collectively propose sorting methods for waste in the same category. The proposal unit can also prioritize suggesting sorting methods for recyclable waste. Furthermore, the proposal unit can prioritize suggesting sorting methods for hazardous materials. This allows the order of proposals to be adjusted based on the relevance of the waste. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input waste relevance data into the generation AI and cause the generation AI to adjust the order of proposals.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The camera unit takes images of the waste. Images of waste include plastic waste, paper waste, metal waste, etc. The camera unit can use a smartphone camera to take images of waste generated at home, and can also use cameras at waste incineration plants to take images of waste being processed. The smartphone camera takes high-resolution images of the waste, while the waste incineration plants use fixed or mobile cameras to take images of the waste. Step 2: The classification unit uses a deep learning algorithm to analyze the image of the garbage captured by the photographing unit and classify the type of garbage. The classification unit can use a CNN (convolutional neural network) or an RNN (recurrent neural network) to classify the type of garbage. Furthermore, the classification unit can also combine deep learning algorithms to classify the type of garbage. For example, a CNN can be used to extract features from the image of the garbage, and an RNN can be used to classify the type of garbage. Step 3: The proposal unit proposes a waste sorting method that conforms to local rules based on the type of waste identified by the classification unit. The proposal unit can propose a household waste sorting method via a smartphone app, and can also introduce an automatic sorting system at waste disposal plants. Furthermore, the proposal unit can propose a waste sorting method based on the rules of a specific area. For example, plastic waste may be collected on a specific day of the week, and paper waste on another day. The proposal unit proposes an appropriate sorting method based on these local rules.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 photographing unit that photographs an image of the garbage; a discrimination unit that analyzes the image of the waste captured by the photographing unit and discriminates the type of waste; and a suggestion unit that suggests a garbage sorting method in accordance with local rules based on the type of garbage identified by the identification unit. A system characterized by:

2. The imaging unit is Including smartphone cameras and cameras at waste disposal plants 2. The system of claim 1.

3. The determination unit Using deep learning algorithms to identify types of waste 2. The system of claim 1.

4. The proposal unit Proposing ways to separate garbage at home through a smartphone app 2. The system of claim 1.

5. The proposal unit Introducing an automatic sorting system at waste disposal plants 2. The system of claim 1.

6. The proposal unit Suggesting how to separate garbage based on specific local rules 2. The system of claim 1.

7. The imaging unit is Estimates the user's emotions and adjusts the timing of photographing garbage based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is When taking a photo, the system analyzes the user's past garbage sorting history and selects the optimal shooting method.

2. The system of claim 1.

Images