system

Abstract

The system according to the embodiment aims to automate the waste sorting process and improve efficiency. [Solution] A system according to an embodiment includes an image acquisition unit, a feature extraction unit, a determination unit, and a sorting unit. The image acquisition unit acquires image data. The feature extraction unit analyzes the image data acquired by the image acquisition unit and extracts feature quantities of the garbage. The determination unit determines the type of garbage based on the feature quantities extracted by the feature extraction unit. The sorting unit automatically performs sorting based on the type of garbage determined by the determination unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, waste sorting was often done manually, which was inefficient.

[0005] The system according to the embodiment aims to automate the waste sorting process and improve efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a feature extraction unit, a determination unit, and a sorting unit. The image acquisition unit acquires image data. The feature extraction unit analyzes the image data acquired by the image acquisition unit and extracts feature quantities of the garbage. The determination unit determines the type of garbage based on the feature quantities extracted by the feature extraction unit. The sorting unit automatically performs sorting based on the type of garbage determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the waste sorting process and improve efficiency. [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 system according to an embodiment of the present invention analyzes image data of waste collected at a waste disposal plant and automatically separates the waste. Taking into account that waste changes shape during disposal, this system uses a generative AI to extract features from the waste data and improve sorting accuracy. Specifically, the system comprises the following steps: First, image data of the waste collected at the waste disposal plant is acquired. Next, a generative AI analyzes the image data and extracts features of the waste. Based on the extracted features, the type of waste is determined and automatically sorted. This system is expected to improve the efficiency of waste sorting and the recycling rate. For example, the part that acquires image data of the waste is defined as an "image acquisition unit," which acquires image data in real time using a camera or sensor. Next, the part where the generative AI extracts features is defined as a "feature extraction unit," which uses image recognition technology to analyze the shape, color, texture, etc. of the waste. The part that determines the type of waste is defined as a "determination unit," which classifies the waste into types such as plastic, metal, and paper. The part that transfers the sorted waste to the appropriate container is defined as a "sorting unit." To clarify that these components are related to each other, for example, they can be expressed as follows: "The feature extraction unit extracts features based on the image data acquired by the image acquisition unit, the judgment unit judges the type of garbage, and the sorting unit automatically sorts the garbage." Furthermore, to clarify how each component works together, for example, they can be expressed as follows: "The feature extraction unit analyzes the image data acquired by the image acquisition unit, and the judgment unit judges the type of garbage based on the features extracted by the feature extraction unit." Finally, to clarify the processing performed by the generation AI, they can be expressed as follows: "The feature extraction unit analyzes the shape, color, texture, etc. of the garbage using the generation AI." This enables the garbage sorting system to streamline garbage sorting work and improve recycling rates.

[0029] A waste sorting system according to an embodiment includes an image acquisition unit, a feature extraction unit, a determination unit, and a sorting unit. The image acquisition unit acquires image data of waste collected at a waste disposal facility. The waste image data may include, but is not limited to, JPEG, PNG, and RAW data. The image acquisition unit acquires image data in real time using, for example, a camera or a sensor. For example, the image acquisition unit may capture images of the waste using a camera and detect the shape, color, texture, and other characteristics of the waste using a sensor. The image acquisition unit may also track the movement of the waste and dynamically acquire images. For example, the image acquisition unit may continuously acquire images of the waste as it moves along a conveyor belt. The feature extraction unit uses a generative AI to analyze the image data acquired by the image acquisition unit and extract features of the waste. Examples of features include, but are not limited to, the shape, color, and texture of the waste. For example, the feature extraction unit may use image recognition technology to analyze the shape of the waste and extract the features. The feature extraction unit may also analyze the color of the waste and extract the features. Furthermore, the feature extraction unit can analyze the texture of the garbage and extract it as a feature. The determination unit determines the type of garbage based on the feature extracted by the feature extraction unit. Examples of garbage types include, but are not limited to, plastic, metal, and paper. For example, the determination unit can classify garbage into plastic, metal, and paper based on the feature extracted by the feature extraction unit. The sorting unit automatically separates garbage based on the type of garbage determined by the determination unit. For example, the sorting unit can move the separated garbage to the respective containers. For example, the sorting unit can move plastic garbage to a plastic container, metal garbage to a metal container, and paper garbage to a paper container. This allows the garbage sorting system according to the embodiment to improve the efficiency of garbage sorting operations and achieve an improved recycling rate.

[0030] The image acquisition unit can acquire image data in real time using a camera or a sensor. For example, the image acquisition unit can take an image of the garbage using a camera and acquire the image data in real time. For example, the image acquisition unit can acquire detailed images of the garbage using a high-resolution camera. The image acquisition unit can also use a sensor to detect the shape, color, texture, etc. of the garbage and acquire image data in real time. For example, the image acquisition unit can analyze the internal structure of the garbage using an infrared sensor and acquire image data in real time. Furthermore, the image acquisition unit can track the movement of the garbage and acquire images dynamically. For example, the image acquisition unit can continuously acquire images as the garbage moves on the conveyor belt. This allows for real-time image data acquisition, enabling the latest garbage data to be analyzed.

[0031] The feature extraction unit can analyze the shape, color, and texture of the garbage using image recognition technology. The feature extraction unit can, for example, analyze the shape of the garbage using image recognition technology and extract the feature. For example, the feature extraction unit can analyze the shape of the garbage with high accuracy using deep learning technology. The feature extraction unit can also analyze the color of the garbage and extract the feature. For example, the feature extraction unit can analyze the color of the garbage using computer vision technology and extract the feature. The feature extraction unit can also analyze the texture of the garbage and extract the feature. For example, the feature extraction unit can analyze the texture of the garbage using image processing technology and extract the feature. As a result, the feature of the garbage can be extracted with high accuracy using image recognition technology.

[0032] The determination unit can classify the waste into types: plastic, metal, and paper. The determination unit classifies the waste into types: plastic, metal, and paper, for example, based on the feature quantities extracted by the feature extraction unit. For example, the determination unit can analyze feature quantities such as the shape, color, and texture of the waste and classify it into plastic waste, metal waste, and paper waste. The determination unit can also classify the waste based on criteria such as the material of the waste and its recyclability. For example, the determination unit can analyze the material of the waste and classify it into recyclable plastic waste and non-recyclable plastic waste. This allows for accurate determination of the type of waste, enabling appropriate sorting.

[0033] The sorting unit can move the separated waste to the respective containers. For example, the sorting unit can move the separated waste to the respective containers. For example, the sorting unit can move plastic waste to a plastic container, metal waste to a metal container, and paper waste to a paper container. The sorting unit can also automatically select and execute different sorting methods depending on the type of waste. For example, the sorting unit can separate recyclable plastic waste from non-recyclable plastic waste. Furthermore, the sorting unit can adjust the sorting speed depending on the amount of waste. For example, if the amount of waste is large, the sorting unit can increase the sorting speed to quickly process the waste. This allows the separated waste to be automatically moved to the appropriate container, making the sorting process more efficient.

[0034] The image acquisition unit can automatically select a different camera or sensor to acquire images depending on the type or amount of garbage. The image acquisition unit can automatically select a different camera or sensor to acquire images depending on, for example, the type and amount of garbage. For example, in the case of plastic garbage, the image acquisition unit can use a high-resolution camera to acquire detailed images. In addition, in the case of metal garbage, the image acquisition unit can use an infrared sensor to analyze the internal structure. Furthermore, in the case of paper garbage, the image acquisition unit can use a standard camera to grasp the overall shape. In this way, detailed data can be collected by selecting the optimal camera or sensor depending on the type and amount of garbage.

[0035] The image acquisition unit can collect specific data by tracking the movement of the garbage and dynamically acquiring images. The image acquisition unit can collect more detailed data by, for example, tracking the movement of the garbage and dynamically acquiring images. For example, the image acquisition unit can continuously acquire images as the garbage moves on a conveyor belt. The image acquisition unit can also capture the moment the garbage falls and analyze dynamic changes in shape. Furthermore, the image acquisition unit can track the process of the garbage being compressed and compare the shapes before and after compression. In this way, dynamic changes in shape can be analyzed by tracking the movement of the garbage.

[0036] The image acquisition unit can simultaneously acquire the temperature and humidity of the garbage and integrate it with the image data for analysis. The image acquisition unit can simultaneously acquire environmental data, such as the temperature and humidity of the garbage, and integrate it with the image data for analysis. For example, the image acquisition unit can measure the temperature of the garbage and identify garbage that is easily burned. The image acquisition unit can also measure the humidity of the garbage and identify garbage that is easily putrefied. Furthermore, the image acquisition unit can integrate the environmental data with the image data and perform a comprehensive analysis. This makes it possible to perform a comprehensive analysis by integrating the environmental data.

[0037] The image acquisition unit can acquire acoustic data of the garbage and analyze it in combination with image data. The image acquisition unit can, for example, acquire audio data of the garbage and analyze it in combination with image data. For example, the image acquisition unit can analyze the sound of garbage being crushed and evaluate the degree of crushing. The image acquisition unit can also analyze the sound of garbage being compressed and evaluate the effectiveness of compression. Furthermore, the image acquisition unit can identify different audio patterns for each type of garbage and use them for classification. This makes it possible to evaluate the degree of crushing and compression by combining audio data.

[0038] The feature extraction unit can analyze garbage odor data in addition to the shape, color, and texture of the garbage and extract the data as features. The feature extraction unit, for example, analyzes the shape of the garbage and extracts the data as features. For example, the feature extraction unit can analyze the shape of the garbage with high accuracy using deep learning technology. The feature extraction unit can also analyze the color of the garbage and extract the data as features. For example, the feature extraction unit can analyze the color of the garbage using computer vision technology and extract the data as features. The feature extraction unit can also analyze the texture of the garbage and extract the data as features. For example, the feature extraction unit can analyze the texture of the garbage using image processing technology and extract the data as features. The feature extraction unit can also analyze garbage odor data and extract the data as features. For example, the feature extraction unit can acquire and analyze garbage odor data using a gas sensor. This allows more features to be extracted by analyzing the odor data.

[0039] The feature extraction unit can analyze fine patterns on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine scratches on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine scratches on the surface of the garbage with high accuracy using image processing technology. The feature extraction unit can also analyze fine irregularities on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine irregularities on the surface of the garbage using computer vision technology and extract them as features. Furthermore, the feature extraction unit can analyze fine color changes on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine color changes on the surface of the garbage using deep learning technology and extract them as features. This makes it possible to extract more detailed features by analyzing fine patterns.

[0040] The feature extraction unit can analyze the chemical components of the garbage and extract them as features. The feature extraction unit can, for example, analyze the chemical components of the garbage and extract them as features. For example, the feature extraction unit can analyze the chemical components of the garbage using elemental analysis technology and extract them as features. The feature extraction unit can also analyze the chemical components of the garbage using compound identification technology and extract them as features. Furthermore, the feature extraction unit can analyze the chemical components of the garbage and identify the type of plastic. For example, the feature extraction unit can analyze the chemical components of the garbage and identify the type of metal. This allows the type of garbage to be identified more accurately by analyzing the chemical components.

[0041] The feature extraction unit can analyze radiation information of the garbage and extract it as a feature. The feature extraction unit can, for example, analyze radiation data of the garbage and extract it as a feature. For example, the feature extraction unit can acquire and analyze radiation data of the garbage using a Geiger counter. The feature extraction unit can also analyze radiation data of the garbage using a radiation spectrum and extract it as a feature. Furthermore, the feature extraction unit can analyze the radiation data of the garbage and identify radioactive substances. For example, the feature extraction unit can analyze the radiation data of the garbage and evaluate the radiation intensity. This makes it possible to identify radioactive substances and evaluate their safety by analyzing the radiation data.

[0042] The determination unit can determine whether the waste is recyclable or non-recyclable. The determination unit can determine whether the waste is recyclable or non-recyclable, for example, based on the feature quantities of the waste. For example, the determination unit can determine whether the plastic waste is recyclable or non-recyclable, based on criteria such as the material of the waste or the level of contamination. The determination unit can also evaluate the recyclability of the waste and determine whether the metal waste is recyclable or non-recyclable. Furthermore, the determination unit can evaluate the recyclability of the waste and determine whether the paper waste is recyclable or non-recyclable. This can be expected to improve the recycling rate by accurately determining whether the waste is recyclable or non-recyclable.

[0043] The determination unit can determine whether the waste can be reused. The determination unit determines the reusability of the waste, for example, based on the feature quantities of the waste. For example, the determination unit can determine whether the plastic waste is reusable or not based on criteria such as the material, condition, and use of the waste. The determination unit can also evaluate the reusability of the waste and determine whether the metal waste is reusable or not. Furthermore, the determination unit can evaluate the reusability of the waste and determine whether the paper waste is reusable or not. This enables the effective use of resources by accurately determining whether the waste can be reused.

[0044] The determination unit can determine the risk level of the garbage based on the feature amount of the garbage. The determination unit determines the risk of the garbage based on, for example, the feature amount of the garbage. For example, the determination unit can evaluate the risk of chemical substances in the garbage and identify toxic garbage. The determination unit can also evaluate the risk of radioactive materials in the garbage and identify radioactive garbage. Furthermore, the determination unit can evaluate the risk of explosives in the garbage and identify explosive garbage. This allows for accurate determination of the risk of garbage, thereby improving safety.

[0045] The determination unit can determine the decomposition speed of the garbage based on the feature amount of the garbage. The determination unit determines the decomposition speed of the garbage, for example, based on the feature amount of the garbage. For example, the determination unit can determine the decomposition speed of plastic based on criteria such as the material of the garbage and environmental conditions. The determination unit can also evaluate the decomposition speed of the garbage and determine the decomposition speed of metal. Furthermore, the determination unit can evaluate the decomposition speed of the garbage and determine the decomposition speed of paper. This allows the decomposition speed of the garbage to be accurately determined, thereby enabling the selection of an appropriate disposal method.

[0046] The sorting unit can automatically select and execute an appropriate sorting method depending on the type of waste. The sorting unit can automatically select and execute different sorting methods depending on, for example, the type of waste. For example, the sorting unit can separate recyclable plastic waste from non-recyclable plastic waste. In addition, the sorting unit can separate iron and aluminum in the case of metal waste. Furthermore, in the case of paper waste, the sorting unit can separate blank paper from printed paper. As a result, by selecting the optimal sorting method depending on the type of waste, the accuracy of sorting is improved.

[0047] The sorting unit can adjust the sorting speed according to the amount of garbage. The sorting unit adjusts the sorting speed according to, for example, the amount of garbage. For example, when the amount of garbage is large, the sorting unit can increase the sorting speed to process the garbage quickly. Furthermore, when the amount of garbage is small, the sorting unit can decrease the sorting speed to improve accuracy. Furthermore, the sorting unit can automatically adjust the sorting speed according to the amount of garbage. This allows for efficient sorting by adjusting the sorting speed according to the amount of garbage.

[0048] After sorting the garbage, the sorting unit can monitor the fullness of each container in real time and notify at the appropriate time. For example, after sorting the garbage, the sorting unit can monitor the fullness of each container in real time and notify at the appropriate time. For example, the sorting unit can monitor the fullness of each container with a sensor and notify when it is full. The sorting unit can also display the fullness of each container in real time and notify the manager. Furthermore, the sorting unit can monitor the fullness of each container and suggest garbage collection at the appropriate time. In this way, by monitoring the fullness of each container, garbage collection can be performed at the appropriate time.

[0049] After sorting the garbage, the sorting unit can re-inspect the contents of each container to prevent mis-sorting. For example, after sorting the garbage, the sorting unit can re-inspect the contents of each container to prevent mis-sorting. For example, the sorting unit can perform image analysis of the contents of each container again to detect mis-sorting. The sorting unit can also inspect the contents of each container again with a sensor to prevent mis-sorting. Furthermore, the sorting unit can analyze the contents of each container again with AI to prevent mis-sorting. In this way, by re-inspecting the contents of each container, mis-sorting can be prevented.

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

[0051] The waste sorting system can further include a voice recognition unit. The voice recognition unit can recognize voice instructions from workers at the waste disposal plant and adjust the operation of the system. For example, if a worker instructs, "Give priority to plastic waste," the voice recognition unit recognizes this instruction and transmits it to the determination unit. The determination unit prioritizes determining plastic waste based on the voice instruction, and the sorting unit can prioritize sorting of plastic waste. The voice recognition unit can also adjust the operating speed of the system based on the worker's instructions. For example, if a worker instructs, "Increase the processing speed," the voice recognition unit recognizes this instruction and transmits it to the sorting unit. The sorting unit can increase the sorting speed based on the instruction. This allows the operation of the system to be flexibly adjusted based on the worker's voice instructions.

[0052] The waste sorting system can further include a temperature sensor unit. The temperature sensor unit measures the temperature of the waste and provides the information to the determination unit. For example, if the temperature of the waste is high, the temperature sensor unit can provide information to the determination unit that the waste is combustible. Based on this information, the determination unit can prioritize sorting of combustible waste. Furthermore, if the temperature of the waste is low, the temperature sensor unit can also provide information to the determination unit that the waste is specific to certain types of waste, such as packaging materials for frozen foods. This allows for more accurate sorting by utilizing the temperature information of the waste.

[0053] The waste sorting system can further include a humidity sensor unit. The humidity sensor unit measures the humidity of the waste and provides the information to the determination unit. For example, if the humidity of the waste is high, the humidity sensor unit can provide information to the determination unit that the waste is perishable. Based on this information, the determination unit can prioritize sorting of perishable waste. Furthermore, if the humidity of the waste is low, the humidity sensor unit can also provide information to the determination unit that the waste is specific waste, such as dry paper or cloth. This makes it possible to utilize the humidity information of the waste to perform more accurate sorting.

[0054] The garbage sorting system can further include a weight sensor unit. The weight sensor unit measures the weight of the garbage and provides the information to the determination unit. For example, if the weight of the garbage is heavy, the weight sensor unit can provide information to the determination unit that the garbage is a specific type of garbage, such as metal or glass. Based on this information, the determination unit can prioritize sorting of heavy garbage. Furthermore, if the weight of the garbage is light, the weight sensor unit can also provide information to the determination unit that the garbage is a specific type of garbage, such as plastic or paper. This enables more accurate sorting by utilizing the garbage weight information.

[0055] The garbage sorting system can further include a location tracking unit. The location tracking unit tracks the location of garbage in real time and provides the information to the determination unit. For example, the location tracking unit can track the location of garbage as it moves on a conveyor belt and provide the information to the determination unit. The determination unit can sort the garbage according to its location based on this information. The location tracking unit can also notify the sorting unit when garbage reaches a specific area and start sorting at the appropriate time. This makes it possible to use garbage location information to sort the garbage efficiently.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The image acquisition unit acquires image data of garbage collected at the garbage disposal facility. Image data includes JPEG, PNG, RAW data, etc. The image acquisition unit acquires image data in real time using cameras and sensors to detect the shape, color, texture, etc. of the garbage. It can also track the movement of the garbage and acquire images dynamically. For example, images can be continuously acquired as the garbage moves on a conveyor belt. Step 2: The feature extraction unit uses the generative AI to analyze the image data acquired by the image acquisition unit and extract features of the garbage. Features include the shape, color, and texture of the garbage. For example, image recognition technology can be used to analyze the shape of the garbage and extract it as a feature. It is also possible to analyze the color and texture of the garbage and extract them as features. Step 3: The determination unit determines the type of garbage based on the feature values ​​extracted by the feature extraction unit. Types of garbage include plastic, metal, paper, etc. For example, the garbage is classified into types such as plastic, metal, and paper based on the feature values. Step 4: The sorting unit automatically separates the waste based on the type determined by the determination unit. For example, it moves the separated waste to the appropriate container. For example, it moves plastic waste to a plastic container, metal waste to a metal container, and paper waste to a paper container.

[0058] (Example 2) A waste sorting system according to an embodiment of the present invention analyzes image data of waste collected at a waste disposal plant and automatically separates the waste. Taking into account that waste changes shape during disposal, this system uses a generative AI to extract features from the waste data and improve sorting accuracy. Specifically, the system comprises the following steps: First, image data of the waste collected at the waste disposal plant is acquired. Next, a generative AI analyzes the image data and extracts features of the waste. Based on the extracted features, the type of waste is determined and automatically sorted. This system is expected to improve the efficiency of waste sorting and the recycling rate. For example, the part that acquires image data of the waste is defined as an "image acquisition unit," which acquires image data in real time using a camera or sensor. Next, the part where the generative AI extracts features is defined as a "feature extraction unit," which uses image recognition technology to analyze the shape, color, texture, etc. of the waste. The part that determines the type of waste is defined as a "determination unit," which classifies the waste into types such as plastic, metal, and paper. The part that transfers the sorted waste to the appropriate container is defined as a "sorting unit." To clarify that these components are related to each other, for example, they can be expressed as follows: "The feature extraction unit extracts features based on the image data acquired by the image acquisition unit, the judgment unit judges the type of garbage, and the sorting unit automatically sorts the garbage." Furthermore, to clarify how each component works together, for example, they can be expressed as follows: "The feature extraction unit analyzes the image data acquired by the image acquisition unit, and the judgment unit judges the type of garbage based on the features extracted by the feature extraction unit." Finally, to clarify the processing performed by the generation AI, they can be expressed as follows: "The feature extraction unit analyzes the shape, color, texture, etc. of the garbage using the generation AI." This enables the garbage sorting system to streamline garbage sorting work and improve recycling rates.

[0059] A waste sorting system according to an embodiment includes an image acquisition unit, a feature extraction unit, a determination unit, and a sorting unit. The image acquisition unit acquires image data of waste collected at a waste disposal facility. The waste image data may include, but is not limited to, JPEG, PNG, and RAW data. The image acquisition unit acquires image data in real time using, for example, a camera or a sensor. For example, the image acquisition unit may capture images of the waste using a camera and detect the shape, color, texture, and other characteristics of the waste using a sensor. The image acquisition unit may also track the movement of the waste and dynamically acquire images. For example, the image acquisition unit may continuously acquire images of the waste as it moves along a conveyor belt. The feature extraction unit uses a generative AI to analyze the image data acquired by the image acquisition unit and extract features of the waste. Examples of features include, but are not limited to, the shape, color, and texture of the waste. For example, the feature extraction unit may use image recognition technology to analyze the shape of the waste and extract the features. The feature extraction unit may also analyze the color of the waste and extract the features. Furthermore, the feature extraction unit can analyze the texture of the garbage and extract it as a feature. The determination unit determines the type of garbage based on the feature extracted by the feature extraction unit. Examples of garbage types include, but are not limited to, plastic, metal, and paper. For example, the determination unit can classify garbage into plastic, metal, and paper based on the feature extracted by the feature extraction unit. The sorting unit automatically separates garbage based on the type of garbage determined by the determination unit. For example, the sorting unit can move the separated garbage to the respective containers. For example, the sorting unit can move plastic garbage to a plastic container, metal garbage to a metal container, and paper garbage to a paper container. This allows the garbage sorting system according to the embodiment to improve the efficiency of garbage sorting operations and achieve an improved recycling rate.

[0060] The image acquisition unit can acquire image data in real time using a camera or a sensor. For example, the image acquisition unit can take an image of the garbage using a camera and acquire the image data in real time. For example, the image acquisition unit can acquire detailed images of the garbage using a high-resolution camera. The image acquisition unit can also use a sensor to detect the shape, color, texture, etc. of the garbage and acquire image data in real time. For example, the image acquisition unit can analyze the internal structure of the garbage using an infrared sensor and acquire image data in real time. Furthermore, the image acquisition unit can track the movement of the garbage and acquire images dynamically. For example, the image acquisition unit can continuously acquire images as the garbage moves on the conveyor belt. This allows for real-time image data acquisition, enabling the latest garbage data to be analyzed.

[0061] The feature extraction unit can analyze the shape, color, and texture of the garbage using image recognition technology. The feature extraction unit can, for example, analyze the shape of the garbage using image recognition technology and extract the feature. For example, the feature extraction unit can analyze the shape of the garbage with high accuracy using deep learning technology. The feature extraction unit can also analyze the color of the garbage and extract the feature. For example, the feature extraction unit can analyze the color of the garbage using computer vision technology and extract the feature. The feature extraction unit can also analyze the texture of the garbage and extract the feature. For example, the feature extraction unit can analyze the texture of the garbage using image processing technology and extract the feature. As a result, the feature of the garbage can be extracted with high accuracy using image recognition technology.

[0062] The determination unit can classify the waste into types: plastic, metal, and paper. The determination unit classifies the waste into types: plastic, metal, and paper, for example, based on the feature quantities extracted by the feature extraction unit. For example, the determination unit can analyze feature quantities such as the shape, color, and texture of the waste and classify it into plastic waste, metal waste, and paper waste. The determination unit can also classify the waste based on criteria such as the material of the waste and its recyclability. For example, the determination unit can analyze the material of the waste and classify it into recyclable plastic waste and non-recyclable plastic waste. This allows for accurate determination of the type of waste, enabling appropriate sorting.

[0063] The sorting unit can move the separated waste to the respective containers. For example, the sorting unit can move the separated waste to the respective containers. For example, the sorting unit can move plastic waste to a plastic container, metal waste to a metal container, and paper waste to a paper container. The sorting unit can also automatically select and execute different sorting methods depending on the type of waste. For example, the sorting unit can separate recyclable plastic waste from non-recyclable plastic waste. Furthermore, the sorting unit can adjust the sorting speed depending on the amount of waste. For example, if the amount of waste is large, the sorting unit can increase the sorting speed to quickly process the waste. This allows the separated waste to be automatically moved to the appropriate container, making the sorting process more efficient.

[0064] The image acquisition unit can estimate the user's emotion and adjust the timing of image acquisition based on the estimated user's emotion. For example, the image acquisition unit can estimate the user's emotion and adjust the timing of image acquisition based on the estimated user's emotion. For example, if the user is feeling stressed, the image acquisition unit can reduce the frequency of image acquisition to reduce the load on the system. Furthermore, if the user is relaxed, the image acquisition unit can increase the frequency of image acquisition to collect detailed data. Furthermore, if the user is in a hurry, the image acquisition unit can optimize the timing of image acquisition to quickly collect data. This allows the load on the system to be optimized by adjusting the timing of image acquisition according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0065] The image acquisition unit can automatically select a different camera or sensor to acquire images depending on the type or amount of garbage. The image acquisition unit can automatically select a different camera or sensor to acquire images depending on, for example, the type and amount of garbage. For example, in the case of plastic garbage, the image acquisition unit can use a high-resolution camera to acquire detailed images. In addition, in the case of metal garbage, the image acquisition unit can use an infrared sensor to analyze the internal structure. Furthermore, in the case of paper garbage, the image acquisition unit can use a standard camera to grasp the overall shape. In this way, detailed data can be collected by selecting the optimal camera or sensor depending on the type and amount of garbage.

[0066] The image acquisition unit can collect specific data by tracking the movement of the garbage and dynamically acquiring images. The image acquisition unit can collect more detailed data by, for example, tracking the movement of the garbage and dynamically acquiring images. For example, the image acquisition unit can continuously acquire images as the garbage moves on a conveyor belt. The image acquisition unit can also capture the moment the garbage falls and analyze dynamic changes in shape. Furthermore, the image acquisition unit can track the process of the garbage being compressed and compare the shapes before and after compression. In this way, dynamic changes in shape can be analyzed by tracking the movement of the garbage.

[0067] The image acquisition unit can estimate the user's emotions and determine the priority of images to be acquired based on the estimated user emotions. The image acquisition unit can, for example, estimate the user's emotions and determine the priority of images to be acquired based on the estimated user emotions. For example, when the user is stressed, the image acquisition unit can prioritize acquiring images of important dust particles. Furthermore, when the user is relaxed, the image acquisition unit can equally acquire images of all dust particles. Furthermore, when the user is in a hurry, the image acquisition unit can prioritize acquiring images of dust particles that need to be processed quickly. This allows important data to be collected preferentially by determining the priority of images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0068] The image acquisition unit can simultaneously acquire the temperature and humidity of the garbage and integrate it with the image data for analysis. The image acquisition unit can simultaneously acquire environmental data, such as the temperature and humidity of the garbage, and integrate it with the image data for analysis. For example, the image acquisition unit can measure the temperature of the garbage and identify garbage that is easily burned. The image acquisition unit can also measure the humidity of the garbage and identify garbage that is easily putrefied. Furthermore, the image acquisition unit can integrate the environmental data with the image data and perform a comprehensive analysis. This makes it possible to perform a comprehensive analysis by integrating the environmental data.

[0069] The image acquisition unit can acquire acoustic data of the garbage and analyze it in combination with image data. The image acquisition unit can, for example, acquire audio data of the garbage and analyze it in combination with image data. For example, the image acquisition unit can analyze the sound of garbage being crushed and evaluate the degree of crushing. The image acquisition unit can also analyze the sound of garbage being compressed and evaluate the effectiveness of compression. Furthermore, the image acquisition unit can identify different audio patterns for each type of garbage and use them for classification. This makes it possible to evaluate the degree of crushing and compression by combining audio data.

[0070] The feature extraction unit can estimate the user's emotion and adjust the feature extraction algorithm based on the estimated user emotion. For example, the feature extraction unit can estimate the user's emotion and adjust the feature extraction algorithm based on the estimated user emotion. For example, if the user is stressed, the feature extraction unit can quickly extract features using a simple algorithm. Also, if the user is relaxed, the feature extraction unit can extract highly accurate features using a detailed algorithm. Furthermore, if the user is in a hurry, the feature extraction unit can optimize the feature extraction for quick feature extraction. This enables quick and accurate feature extraction by adjusting the algorithm according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0071] The feature extraction unit can analyze garbage odor data in addition to the shape, color, and texture of the garbage and extract the data as features. The feature extraction unit, for example, analyzes the shape of the garbage and extracts the data as features. For example, the feature extraction unit can analyze the shape of the garbage with high accuracy using deep learning technology. The feature extraction unit can also analyze the color of the garbage and extract the data as features. For example, the feature extraction unit can analyze the color of the garbage using computer vision technology and extract the data as features. The feature extraction unit can also analyze the texture of the garbage and extract the data as features. For example, the feature extraction unit can analyze the texture of the garbage using image processing technology and extract the data as features. The feature extraction unit can also analyze garbage odor data and extract the data as features. For example, the feature extraction unit can acquire and analyze garbage odor data using a gas sensor. This allows more features to be extracted by analyzing the odor data.

[0072] The feature extraction unit can analyze fine patterns on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine scratches on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine scratches on the surface of the garbage with high accuracy using image processing technology. The feature extraction unit can also analyze fine irregularities on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine irregularities on the surface of the garbage using computer vision technology and extract them as features. Furthermore, the feature extraction unit can analyze fine color changes on the surface of the garbage and extract them as features. For example, the feature extraction unit can analyze fine color changes on the surface of the garbage using deep learning technology and extract them as features. This makes it possible to extract more detailed features by analyzing fine patterns.

[0073] The feature extraction unit can estimate the user's emotion and determine the priority of feature quantities to be extracted based on the estimated user's emotion. The feature extraction unit, for example, estimates the user's emotion and determines the priority of feature quantities to be extracted based on the estimated user's emotion. For example, the feature extraction unit can prioritize extraction of important feature quantities when the user is stressed. Furthermore, the feature extraction unit can equally extract all feature quantities when the user is relaxed. Furthermore, the feature extraction unit can prioritize extraction of feature quantities that need to be extracted quickly when the user is in a hurry. Thus, by prioritizing feature quantities according to the user's emotion, important feature quantities can be preferentially extracted. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] The feature extraction unit can analyze the chemical components of the garbage and extract them as features. The feature extraction unit can, for example, analyze the chemical components of the garbage and extract them as features. For example, the feature extraction unit can analyze the chemical components of the garbage using elemental analysis technology and extract them as features. The feature extraction unit can also analyze the chemical components of the garbage using compound identification technology and extract them as features. Furthermore, the feature extraction unit can analyze the chemical components of the garbage and identify the type of plastic. For example, the feature extraction unit can analyze the chemical components of the garbage and identify the type of metal. This allows the type of garbage to be identified more accurately by analyzing the chemical components.

[0075] The feature extraction unit can analyze radiation information of the garbage and extract it as a feature. The feature extraction unit can, for example, analyze radiation data of the garbage and extract it as a feature. For example, the feature extraction unit can acquire and analyze radiation data of the garbage using a Geiger counter. The feature extraction unit can also analyze radiation data of the garbage using a radiation spectrum and extract it as a feature. Furthermore, the feature extraction unit can analyze the radiation data of the garbage and identify radioactive substances. For example, the feature extraction unit can analyze the radiation data of the garbage and evaluate the radiation intensity. This makes it possible to identify radioactive substances and evaluate their safety by analyzing the radiation data.

[0076] The determination unit can estimate the user's emotion and adjust the determination algorithm based on the estimated user's emotion. The determination unit, for example, estimates the user's emotion and adjusts the determination algorithm based on the estimated user's emotion. For example, if the user is feeling stressed, the determination unit can quickly determine the user's emotion using a simple algorithm. If the user is relaxed, the determination unit can make a highly accurate determination using a detailed algorithm. Furthermore, if the user is in a hurry, the determination unit can optimize the algorithm for quick determination. This allows for quick and accurate determination by adjusting the algorithm according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The determination unit can determine whether the waste is recyclable or non-recyclable. The determination unit can determine whether the waste is recyclable or non-recyclable, for example, based on the feature quantities of the waste. For example, the determination unit can determine whether the plastic waste is recyclable or non-recyclable, based on criteria such as the material of the waste or the level of contamination. The determination unit can also evaluate the recyclability of the waste and determine whether the metal waste is recyclable or non-recyclable. Furthermore, the determination unit can evaluate the recyclability of the waste and determine whether the paper waste is recyclable or non-recyclable. This can be expected to improve the recycling rate by accurately determining whether the waste is recyclable or non-recyclable.

[0078] The determination unit can determine whether the waste can be reused. The determination unit determines the reusability of the waste, for example, based on the feature quantities of the waste. For example, the determination unit can determine whether the plastic waste is reusable or not based on criteria such as the material, condition, and use of the waste. The determination unit can also evaluate the reusability of the waste and determine whether the metal waste is reusable or not. Furthermore, the determination unit can evaluate the reusability of the waste and determine whether the paper waste is reusable or not. This enables the effective use of resources by accurately determining whether the waste can be reused.

[0079] The determination unit can estimate the user's emotion and adjust the display method of the determination result based on the estimated user's emotion. The determination unit, for example, estimates the user's emotion and adjusts the display method of the determination result based on the estimated user's emotion. For example, if the user is feeling stressed, the determination unit can provide a simple, highly visible display method. If the user is relaxed, the determination unit can provide a display method including detailed information. If the user is in a hurry, the determination unit can provide a display method that focuses on the main points. This improves visibility by adjusting the display method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The determination unit can determine the risk level of the garbage based on the feature amount of the garbage. The determination unit determines the risk of the garbage based on, for example, the feature amount of the garbage. For example, the determination unit can evaluate the risk of chemical substances in the garbage and identify toxic garbage. The determination unit can also evaluate the risk of radioactive materials in the garbage and identify radioactive garbage. Furthermore, the determination unit can evaluate the risk of explosives in the garbage and identify explosive garbage. This allows for accurate determination of the risk of garbage, thereby improving safety.

[0081] The determination unit can determine the decomposition speed of the garbage based on the feature amount of the garbage. The determination unit determines the decomposition speed of the garbage, for example, based on the feature amount of the garbage. For example, the determination unit can determine the decomposition speed of plastic based on criteria such as the material of the garbage and environmental conditions. The determination unit can also evaluate the decomposition speed of the garbage and determine the decomposition speed of metal. Furthermore, the determination unit can evaluate the decomposition speed of the garbage and determine the decomposition speed of paper. This allows the decomposition speed of the garbage to be accurately determined, thereby enabling the selection of an appropriate disposal method.

[0082] The classification unit can estimate the user's emotions and adjust the classification method based on the estimated user's emotions. For example, the classification unit can estimate the user's emotions and adjust the classification method based on the estimated user's emotions. For example, if the user is stressed, the classification unit can quickly classify using a simple classification method. If the user is relaxed, the classification unit can perform highly accurate classification using a detailed classification method. Furthermore, if the user is in a hurry, the classification unit can optimize for quick classification. This allows for quick and accurate classification by adjusting the classification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The sorting unit can automatically select and execute an appropriate sorting method depending on the type of waste. The sorting unit can automatically select and execute different sorting methods depending on, for example, the type of waste. For example, the sorting unit can separate recyclable plastic waste from non-recyclable plastic waste. In addition, the sorting unit can separate iron and aluminum in the case of metal waste. Furthermore, in the case of paper waste, the sorting unit can separate blank paper from printed paper. As a result, by selecting the optimal sorting method depending on the type of waste, the accuracy of sorting is improved.

[0084] The sorting unit can adjust the sorting speed according to the amount of garbage. The sorting unit adjusts the sorting speed according to, for example, the amount of garbage. For example, when the amount of garbage is large, the sorting unit can increase the sorting speed to process the garbage quickly. Furthermore, when the amount of garbage is small, the sorting unit can decrease the sorting speed to improve accuracy. Furthermore, the sorting unit can automatically adjust the sorting speed according to the amount of garbage. This allows for efficient sorting by adjusting the sorting speed according to the amount of garbage.

[0085] The sorting unit can estimate the user's emotions and determine sorting priorities based on the estimated user emotions. The sorting unit, for example, estimates the user's emotions and determines sorting priorities based on the estimated user emotions. For example, when the user is stressed, the sorting unit can prioritize sorting important garbage. Furthermore, when the user is relaxed, the sorting unit can sort all garbage evenly. Furthermore, when the user is in a hurry, the sorting unit can prioritize sorting garbage that needs to be processed quickly. In this way, by determining sorting priorities according to the user's emotions, important garbage can be processed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] After sorting the garbage, the sorting unit can monitor the fullness of each container in real time and notify at the appropriate time. For example, after sorting the garbage, the sorting unit can monitor the fullness of each container in real time and notify at the appropriate time. For example, the sorting unit can monitor the fullness of each container with a sensor and notify when it is full. The sorting unit can also display the fullness of each container in real time and notify the manager. Furthermore, the sorting unit can monitor the fullness of each container and suggest garbage collection at the appropriate time. In this way, by monitoring the fullness of each container, garbage collection can be performed at the appropriate time.

[0087] After sorting the garbage, the sorting unit can re-inspect the contents of each container to prevent mis-sorting. For example, after sorting the garbage, the sorting unit can re-inspect the contents of each container to prevent mis-sorting. For example, the sorting unit can perform image analysis of the contents of each container again to detect mis-sorting. The sorting unit can also inspect the contents of each container again with a sensor to prevent mis-sorting. Furthermore, the sorting unit can analyze the contents of each container again with AI to prevent mis-sorting. In this way, by re-inspecting the contents of each container, mis-sorting can be prevented. === Hard Collateral 1-1 === Each of the multiple elements, including the image acquisition unit, feature extraction unit, determination unit, and sorting unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image acquisition unit acquires image data of the garbage using the camera 42 or a sensor of the smart device 14. The feature extraction unit extracts garbage features using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines the type of garbage based on the features extracted by the identification processing unit 290 of the data processing device 12. The sorting unit moves the garbage determined by the control unit 46A of the smart device 14 to the respective containers. === Hard Collateral 1-2 === Each of the multiple elements, including the image acquisition unit, feature extraction unit, determination unit, and sorting unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image acquisition unit acquires image data of the dust using the camera 42 or a sensor of the smart glasses 214. The feature extraction unit extracts dust feature amounts using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines the type of dust based on the feature amounts extracted by the identification processing unit 290 of the data processing device 12. The sorting unit moves the dust determined by the control unit 46A of the smart glasses 214 to the respective containers. === Hard Collateral 1-3 === Each of the multiple elements including the image acquisition unit, feature extraction unit, determination unit, and sorting unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the image acquisition unit acquires image data of the garbage using the camera 42 or a sensor of the headset type terminal 314. The feature extraction unit extracts garbage features using AI generated by the identification processing unit 290 of the data processing device 12. The determination unit determines the type of garbage based on the features extracted by the identification processing unit 290 of the data processing device 12. The sorting unit moves the garbage determined by the control unit 46A of the headset type terminal 314 to the respective containers. === Hard Collateral 1-4 === Each of the multiple elements including the image acquisition unit, feature extraction unit, determination unit, and sorting unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image acquisition unit acquires image data of the garbage using the camera 42 or a sensor of the robot 414. The feature extraction unit extracts garbage features using a generation AI by the identification processing unit 290 of the data processing device 12. The determination unit determines the type of garbage based on the features extracted by the identification processing unit 290 of the data processing device 12. The sorting unit moves the garbage determined by the control unit 46A of the robot 414 to the respective containers.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The waste sorting system can further include a voice recognition unit. The voice recognition unit can recognize voice instructions from workers at the waste disposal plant and adjust the operation of the system. For example, if a worker instructs, "Give priority to plastic waste," the voice recognition unit recognizes this instruction and transmits it to the determination unit. The determination unit prioritizes determining plastic waste based on the voice instruction, and the sorting unit can prioritize sorting of plastic waste. The voice recognition unit can also adjust the operating speed of the system based on the worker's instructions. For example, if a worker instructs, "Increase the processing speed," the voice recognition unit recognizes this instruction and transmits it to the sorting unit. The sorting unit can increase the sorting speed based on the instruction. This allows the operation of the system to be flexibly adjusted based on the worker's voice instructions.

[0090] The waste sorting system can further include a temperature sensor unit. The temperature sensor unit measures the temperature of the waste and provides the information to the determination unit. For example, if the temperature of the waste is high, the temperature sensor unit can provide information to the determination unit that the waste is combustible. Based on this information, the determination unit can prioritize sorting of combustible waste. Furthermore, if the temperature of the waste is low, the temperature sensor unit can also provide information to the determination unit that the waste is specific to certain types of waste, such as packaging materials for frozen foods. This allows for more accurate sorting by utilizing the temperature information of the waste.

[0091] The waste sorting system can further include a humidity sensor unit. The humidity sensor unit measures the humidity of the waste and provides the information to the determination unit. For example, if the humidity of the waste is high, the humidity sensor unit can provide information to the determination unit that the waste is perishable. Based on this information, the determination unit can prioritize sorting of perishable waste. Furthermore, if the humidity of the waste is low, the humidity sensor unit can also provide information to the determination unit that the waste is specific waste, such as dry paper or cloth. This makes it possible to utilize the humidity information of the waste to perform more accurate sorting.

[0092] The garbage sorting system can further include a weight sensor unit. The weight sensor unit measures the weight of the garbage and provides the information to the determination unit. For example, if the weight of the garbage is heavy, the weight sensor unit can provide information to the determination unit that the garbage is a specific type of garbage, such as metal or glass. Based on this information, the determination unit can prioritize sorting of heavy garbage. Furthermore, if the weight of the garbage is light, the weight sensor unit can also provide information to the determination unit that the garbage is a specific type of garbage, such as plastic or paper. This enables more accurate sorting by utilizing the garbage weight information.

[0093] The garbage sorting system can further include a location tracking unit. The location tracking unit tracks the location of garbage in real time and provides the information to the determination unit. For example, the location tracking unit can track the location of garbage as it moves on a conveyor belt and provide the information to the determination unit. The determination unit can sort the garbage according to its location based on this information. The location tracking unit can also notify the sorting unit when garbage reaches a specific area and start sorting at the appropriate time. This makes it possible to use garbage location information to sort the garbage efficiently.

[0094] The garbage sorting system can further estimate the user's emotions and adjust the garbage sorting method based on the estimated user's emotions. For example, if the user is stressed, the system can simplify the sorting task and process it quickly. Alternatively, if the user is relaxed, the system can use a detailed sorting method to perform highly accurate sorting. Furthermore, if the user is in a hurry, the system can optimize the sorting speed and process it quickly. This allows for efficient and flexible garbage sorting by adjusting the sorting method according to the user's emotions.

[0095] The garbage sorting system can further estimate the user's emotions and determine the garbage sorting priority based on the estimated user's emotions. For example, if the user is feeling stressed, the system can prioritize sorting important garbage. Also, if the user is relaxed, the system can sort all garbage evenly. Furthermore, if the user is in a hurry, the system can prioritize sorting garbage that needs to be processed quickly. In this way, by determining the sorting priority according to the user's emotions, important garbage can be processed first.

[0096] The garbage sorting system can further estimate the user's emotions and adjust the way in which the garbage sorting results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the system can provide a simple, highly visible display method. If the user is relaxed, the system can provide a display method that includes detailed information. If the user is in a hurry, the system can provide a display method that focuses on the main points. This improves visibility by adjusting the display method according to the user's emotions.

[0097] The garbage sorting system can further estimate the user's emotions and adjust the garbage sorting algorithm based on the estimated user emotions. For example, if the user is stressed, the system can use a simple algorithm to quickly sort garbage. If the user is relaxed, the system can use a detailed algorithm to quickly sort garbage. Furthermore, if the user is in a hurry, the system can optimize for quick sorting. This allows for quick and accurate sorting by adjusting the algorithm according to the user's emotions.

[0098] The garbage sorting system can further estimate the user's emotions and provide feedback on the garbage sorting task based on the estimated user emotions. For example, if the user is feeling stressed, the system can provide positive feedback to increase the user's motivation. If the user is relaxed, the system can provide detailed feedback to suggest areas for improvement in the garbage sorting task. Furthermore, if the user is in a hurry, the system can provide quick feedback to support efficient work. This improves the efficiency and accuracy of the garbage sorting task by providing appropriate feedback according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The image acquisition unit acquires image data of garbage collected at the garbage disposal facility. Image data includes JPEG, PNG, RAW data, etc. The image acquisition unit acquires image data in real time using cameras and sensors to detect the shape, color, texture, etc. of the garbage. It can also track the movement of the garbage and acquire images dynamically. For example, images can be continuously acquired as the garbage moves on a conveyor belt. Step 2: The feature extraction unit uses the generative AI to analyze the image data acquired by the image acquisition unit and extract features of the garbage. Features include the shape, color, and texture of the garbage. For example, image recognition technology can be used to analyze the shape of the garbage and extract it as a feature. It is also possible to analyze the color and texture of the garbage and extract them as features. Step 3: The determination unit determines the type of garbage based on the feature values ​​extracted by the feature extraction unit. Types of garbage include plastic, metal, paper, etc. For example, the garbage is classified into types such as plastic, metal, and paper based on the feature values. Step 4: The sorting unit automatically separates the waste based on the type determined by the determination unit. For example, it moves the separated waste to the appropriate container. For example, it moves plastic waste to a plastic container, metal waste to a metal container, and paper waste to a paper container.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The 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.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires image data; a feature extraction unit that analyzes the image data acquired by the image acquisition unit and extracts dust feature amounts; a determination unit for determining the type of dust based on the feature amount extracted by the feature extraction unit; a sorting unit that automatically sorts the waste based on the type of waste determined by the determination unit. A system characterized by:

2. The image acquisition unit Using a camera or sensor to capture image data in real time The system of claim 1 .

3. The feature extraction unit Image recognition technology is used to analyze the shape, color, and texture of the trash. The system of claim 1 .

4. The determination unit Categorize into plastic, metal, and paper types The system of claim 1 .

5. The sorting unit includes: Move the separated waste to the appropriate containers The system of claim 1 .

6. The image acquisition unit The system estimates the user's emotions and adjusts the timing of image acquisition based on the estimated user emotions. The system of claim 1 .

7. The image acquisition unit Automatically selects different cameras or sensors to capture images depending on the type or amount of trash The system of claim 1 .

8. The image acquisition unit Collect specific data by tracking the movement of garbage and capturing dynamic images The system of claim 1 .

Images