system

Abstract

The system according to this embodiment aims to analyze the type and quantity of waste and propose the optimal treatment method. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects waste data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes the optimal processing method based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the types and quantities of waste have not been accurately grasped, and the optimal treatment method has not been sufficiently proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the types and quantities of waste and propose an optimal treatment method.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects waste data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal treatment method based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze the type and quantity of waste and propose the optimal treatment method. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The waste management system according to an embodiment of the present invention is a system that uses AI to analyze the type and quantity of waste and proposes the optimal processing method. This waste management system collects waste data, and the AI ​​analyzes that data. Next, based on the analysis results, it identifies recyclable resources and proposes the optimal processing method. This system enables sustainable waste management. For example, the waste management system collects waste data. At this time, it collects detailed data such as the type and quantity of waste and the place of origin. For example, at a waste treatment facility, the weight and volume of each type of waste are measured and the data is collected. This makes it possible to grasp detailed information about the waste. Next, the waste management system's AI analyzes the collected data. Based on the collected data, the AI ​​analyzes the type and quantity of waste and identifies recyclable resources. For example, by utilizing image recognition technology using deep learning, it is possible to analyze images of waste and identify recyclable resources such as plastics, metals, and paper. This improves the accuracy of waste classification and is expected to improve the recycling rate. Furthermore, the waste management system proposes the optimal processing method based on the analysis results. The AI ​​proposes the optimal processing method considering the type and quantity of waste, the proportion of recyclable resources, etc. For example, if there are many recyclable resources, the system suggests transporting them to a recycling facility, and suggests processing non-recyclable waste at an appropriate treatment facility. This improves the efficiency of waste treatment and is expected to reduce waste treatment costs. This system enables sustainable waste management. By collecting detailed waste data and having AI analyze it, the system can identify recyclable resources and suggest the optimal treatment method. This improves the efficiency of waste treatment, is expected to reduce waste treatment costs, and improve the recycling rate. It can also contribute to reducing the burden on the environment. For example, at a waste treatment facility, by having AI analyze waste data and identify recyclable resources, the recycling rate can be improved and resource reuse can be expected to increase. In addition, economic benefits can be enjoyed by reducing waste treatment costs. Furthermore, by reducing the burden on the environment, it can contribute to the realization of a sustainable society.This enables sustainable waste management by collecting and analyzing waste data and proposing optimal disposal methods.

[0029] The waste management system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects waste data. The collection unit collects detailed data such as the type and quantity of waste and the location where it is generated. For example, the collection unit can measure the weight and volume of each type of waste at a waste treatment facility and collect the data. The collection unit can also collect waste data using sensors or cameras. For example, the collection unit can collect waste data using infrared sensors or CCD cameras. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes images of waste using image recognition technology with deep learning to identify recyclable resources. For example, the analysis unit can analyze images of waste using CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network). For example, the analysis unit can perform component analysis of waste to determine the proportion of recyclable resources in detail. For example, the analysis unit can analyze the components of waste using chemical analysis or physical analysis. The proposal unit proposes the optimal processing method based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal processing method considering, for example, the type and quantity of waste and the proportion of recyclable resources. For example, if there are many recyclable resources, the proposal unit proposes transportation to a recycling facility, and if the waste is not recyclable, it proposes processing at an appropriate processing facility. The proposal unit can also propose the optimal processing method considering, for example, the results of the waste component analysis. The proposal unit can also propose different processing methods for each waste source. As a result, the waste management system according to this embodiment can achieve sustainable waste management by collecting and analyzing waste data and proposing the optimal processing method.

[0030] The collection unit collects data on waste. For example, it collects detailed data such as the type, quantity, and location of waste generation. Specifically, at waste treatment facilities, it can measure the weight and volume of each type of waste and collect that data. For example, when waste is brought in, it can accurately measure it using weighing scales and volumetric meters and record the data in digital format. The collection unit can also collect waste data using sensors and cameras. For example, it can collect waste data using infrared sensors and CCD cameras. Infrared sensors detect the temperature and shape of the waste, and CCD cameras capture images of the waste and save them as digital data. This allows the collection unit to understand the type and condition of the waste in detail. Furthermore, the collection unit can use IoT technology to track the location of waste generation and transportation routes in real time. For example, it can attach tracking devices equipped with GPS sensors to waste containers to monitor the movement route of the waste. This allows for understanding the entire process from the waste source to the final treatment facility, enabling efficient waste management. The collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses deep learning-based image recognition technology to analyze images of waste and identify recyclable resources. Specifically, it can analyze images of waste using CNNs (Convolutional Neural Networks) or RNNs (Recurrent Neural Networks). CNNs extract features from images of waste and identify recyclable resources with high accuracy. RNNs are suitable for analyzing time-series data and are used to analyze waste generation patterns and fluctuations. The analysis unit can also perform component analysis of waste to determine the proportion of recyclable resources in detail. Component analysis can utilize chemical or physical analysis. Chemical analysis examines the chemical composition of waste in detail to identify recyclable components. Physical analysis examines the physical properties of waste to identify recyclable resources. For example, spectroscopic analysis or mass spectrometry can be used to analyze the components of waste in detail. Furthermore, the analysis unit can integrate the collected data using AI to predict waste generation trends and the distribution of recyclable resources. This allows the analysis unit to quickly and accurately analyze collected data, contributing to the optimization of waste management. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical waste data, it can predict fluctuations in waste generation in specific areas or time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The proposal department proposes the optimal processing method based on the analysis results obtained by the analysis department. For example, the proposal department proposes the optimal processing method considering the type and quantity of waste, the proportion of recyclable resources, etc. Specifically, if there are many recyclable resources, it proposes transportation to a recycling facility, and if the waste is not recyclable, it proposes processing at an appropriate processing facility. The proposal department can also propose the optimal processing method considering the results of the waste's component analysis. Based on the component analysis results, it proposes a processing method that makes the most use of the recyclable components of the waste. For example, it proposes separating recyclable resources such as metals and plastics and reusing them at a recycling facility. Furthermore, the proposal department can propose different processing methods depending on the source of the waste. For example, since the processing methods for industrial waste generated from factories and general waste generated from households are different, it proposes processing methods appropriate for each. In addition, the proposal department can propose an optimal processing schedule according to the amount and type of waste generated. For example, if the amount of waste generated is large, it adjusts the processing schedule to maximize the utilization rate of the processing facility. In this way, the proposal department can achieve efficient and effective waste processing and support sustainable waste management. Furthermore, the proposal department can propose optimal waste treatment methods by considering the latest technologies and regulations related to waste treatment. For example, it can optimize waste treatment by taking into account new recycling technologies and environmental protection regulations. This allows the proposal department to always provide highly accurate proposals based on the latest information, thereby achieving sustainable waste management.

[0033] The collection unit can collect data on waste using sensors and cameras. For example, the collection unit can measure the temperature of waste using an infrared sensor and collect data. For example, the collection unit can take images of waste using a CCD camera and collect data. For example, the collection unit can measure the weight of waste using a weight sensor and collect data. This allows for accurate collection of waste data using sensors and cameras. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by sensors and cameras into a generating AI and have the generating AI perform data analysis.

[0034] The analysis unit can analyze images of waste using deep learning-based image recognition technology and identify recyclable resources. For example, the analysis unit can use a CNN (Convolutional Neural Network) to analyze images of waste and identify recyclable resources such as plastics, metals, and paper. For example, the analysis unit can use an RNN (Recurrent Neural Network) to analyze images of waste and identify recyclable resources. For example, the analysis unit can use deep learning-based image recognition technology to improve the accuracy of waste classification. As a result, by using deep learning, the accuracy of waste classification can be improved, and an improvement in the recycling rate can be expected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input waste image data into a generating AI and have the generating AI perform the identification of recyclable resources.

[0035] The proposal unit can propose the optimal processing method by considering the type and quantity of waste, the proportion of recyclable resources, etc. For example, the proposal unit can propose the optimal processing method based on the type and quantity of waste. For example, the proposal unit can propose the optimal processing method based on the proportion of recyclable resources. For example, the proposal unit can propose the optimal processing method by considering the results of the waste component analysis. In this way, the optimal processing method can be proposed by considering the type and quantity of waste and the proportion of recyclable resources. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input waste data into a generating AI and have the generating AI execute a proposal for the optimal processing method.

[0036] The proposal unit can suggest transporting recyclable resources to a recycling facility if there are many recyclable resources, and suggest processing non-recyclable waste at an appropriate treatment facility. For example, if there are many recyclable resources, the proposal unit can suggest transporting them to a plastic recycling facility or a metal recycling facility. For example, if non-recyclable waste is to be processed at an incineration facility or a landfill facility, the proposal unit can suggest processing it at an incineration facility or a landfill facility. For example, the proposal unit can appropriately classify recyclable resources and non-recyclable waste and propose the most suitable processing method for each. This improves the efficiency of waste processing by appropriately processing recyclable resources and non-recyclable waste. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input waste data into a generating AI and have the generating AI classify recyclable resources and non-recyclable waste.

[0037] The analysis unit can optimize the processing plan in real time. For example, the analysis unit can dynamically adjust the processing plan according to the waste generation situation. For example, the analysis unit can optimize the processing plan based on the type and quantity of waste. For example, the analysis unit can apply different processing plans to each waste source. This improves the efficiency of waste processing by optimizing the processing plan in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input waste data into a generating AI and have the generating AI perform real-time optimization of the processing plan.

[0038] The collection unit can collect more detailed data using different sensors for each waste source. For example, the collection unit can use chemical sensors to analyze the components of waste from factories in detail. For example, the collection unit can use cameras to identify the type and quantity of waste from households. For example, the collection unit can use weight sensors to accurately measure the weight of waste from commercial facilities. This allows for the collection of detailed data by using different sensors for each source. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by different sensors for each waste source into a generating AI and have the generating AI perform data analysis.

[0039] The collection unit can apply different collection methods depending on the type of waste during waste collection. For example, the collection unit can separate and collect plastic waste using special collection bags. For example, the collection unit can automatically separate metal waste using magnetic sensors. For example, the collection unit can collect organic waste using special compost containers. This enables efficient collection by applying different collection methods to each type of waste. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input different collection methods for each type of waste into a generating AI and have the generating AI execute the application of the collection methods.

[0040] The collection unit can simultaneously collect environmental data of the waste generation site when collecting waste. For example, the collection unit can measure the temperature and humidity of the waste generation site with sensors and collect data. For example, the collection unit can measure the noise level of the waste generation site and collect data. For example, the collection unit can measure the air quality of the waste generation site with sensors and collect data. By simultaneously collecting environmental data of the waste generation site, more detailed data can be obtained. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input environmental data of the waste generation site into a generating AI and have the generating AI perform data analysis.

[0041] The collection unit can adjust the collection frequency when collecting waste, taking into account the time of day the waste is generated. For example, the collection unit can increase the collection frequency during times when there is a lot of waste generated. For example, the collection unit can decrease the collection frequency during times when there is little waste generated. For example, the collection unit can analyze the waste generation pattern and set the optimal collection frequency. This makes efficient collection possible by adjusting the collection frequency while taking into account the time of day the waste is generated. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input data on the time of day the waste is generated into a generating AI and have the generating AI adjust the collection frequency.

[0042] The analysis unit can perform a component analysis of the waste during the analysis process and identify in detail the proportion of recyclable resources. For example, the analysis unit can chemically analyze the components of the waste to identify the proportion of recyclable resources. For example, the analysis unit can physically analyze the components of the waste to identify the proportion of recyclable resources. For example, the analysis unit can biologically analyze the components of the waste to identify the proportion of recyclable resources. In this way, by performing a component analysis of the waste, the proportion of recyclable resources can be identified in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input waste component data into a generating AI and have the generating AI perform the identification of the proportion of recyclable resources.

[0043] The analysis unit can apply different analysis algorithms to each waste source during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes chemical composition to waste from factories. For example, the analysis unit can apply an analysis algorithm that emphasizes type and quantity to waste from households. For example, the analysis unit can apply an analysis algorithm that emphasizes weight and volume to waste from commercial facilities. By applying different analysis algorithms to each source, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different data for each waste source into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can improve the accuracy of the analysis by considering environmental data of the waste generation site during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the temperature and humidity of the waste generation site. For example, the analysis unit can improve the accuracy of the analysis by considering the noise level of the waste generation site. For example, the analysis unit can improve the accuracy of the analysis by considering the air quality of the waste generation site. In this way, the accuracy of the analysis is improved by considering environmental data of the generation site. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input environmental data of the waste generation site into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0045] The analysis unit can adjust the analysis algorithm during analysis, taking into account the time of waste generation. For example, the analysis unit can speed up the analysis algorithm during times when waste generation is high. For example, the analysis unit can refine the analysis algorithm during times when waste generation is low. For example, the analysis unit can analyze waste generation patterns and apply the optimal analysis algorithm. By adjusting the analysis algorithm to take into account the time of generation, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the time of waste generation into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0046] The proposal unit can propose the optimal treatment method when making a proposal, taking into account the results of the waste's component analysis. For example, if the waste's component analysis results show a large amount of recyclable resources, the proposal unit can propose transport to a recycling facility. For example, if the waste's component analysis results show a large amount of non-recyclable waste, the proposal unit can propose treatment at an appropriate treatment facility. For example, if the waste's component analysis results show a large amount of a particular component, the proposal unit can propose a treatment method specifically for that component. In this way, the optimal treatment method can be proposed by taking into account the waste's component analysis results. Some or all of the above-mentioned processes in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input waste component data into a generating AI and have the generating AI propose the optimal treatment method.

[0047] The proposal unit can propose different treatment methods depending on the source of the waste. For example, the proposal unit can propose chemical treatment for waste from factories. For example, the proposal unit can propose recycling for waste from households. For example, the proposal unit can propose incineration for waste from commercial facilities. By proposing different treatment methods for each source, efficient waste treatment becomes possible. Some or all of the above-mentioned treatments in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data for each waste source into a generating AI and have the generating AI execute the proposal of treatment methods.

[0048] The proposal unit can propose the optimal treatment method by considering environmental data of the waste generation site when making a proposal. For example, the proposal unit can propose the optimal treatment method by considering the temperature and humidity of the waste generation site. For example, the proposal unit can propose the optimal treatment method by considering the noise level of the waste generation site. For example, the proposal unit can propose the optimal treatment method by considering the air quality of the waste generation site. In this way, the optimal treatment method can be proposed by considering environmental data of the generation site. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input environmental data of the waste generation site into a generating AI and have the generating AI execute a proposal for the optimal treatment method.

[0049] The proposal unit can adjust the processing method when making a proposal, taking into account the time of waste generation. For example, the proposal unit can speed up the processing method during times when waste generation is high. For example, the proposal unit can refine the processing method during times when waste generation is low. For example, the proposal unit can analyze waste generation patterns and apply the optimal processing method. This makes efficient waste disposal possible by adjusting the processing method considering the time of generation. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the time of waste generation into a generating AI and have the generating AI perform the adjustment of the processing method.

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

[0051] The collection unit can apply different data collection methods depending on the source of the waste when collecting waste data. For example, chemical sensors can be used to analyze the components of waste from factories. Cameras can be used to identify the type and quantity of waste from households. Weight sensors can be used to measure the exact weight of waste from commercial facilities. This allows for the collection of detailed data by using different sensors for each source. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input data acquired by different sensors for each waste source into a generating AI and have the generating AI perform data analysis.

[0052] The analysis unit can apply different analysis algorithms to each waste source when performing component analysis of waste. For example, an analysis algorithm that emphasizes chemical composition can be applied to waste from factories. An analysis algorithm that emphasizes type and quantity can be applied to waste from households. An analysis algorithm that emphasizes weight and volume can be applied to waste from commercial facilities. By applying different analysis algorithms to each source, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data for each waste source into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0053] The proposal unit can propose the optimal treatment method by considering the results of the waste's component analysis. For example, if the waste's component analysis results show a large amount of recyclable resources, it can propose transport to a recycling facility. For non-recyclable waste, it can propose treatment at an appropriate treatment facility. If a particular component is present in large quantities, it can propose a treatment method specifically tailored to that component. In this way, the optimal treatment method can be proposed by considering the waste's component analysis results. Some or all of the above-mentioned processes in the proposal unit may be performed using AI, or they may not be performed using AI. For example, the proposal unit can input waste component data into a generating AI and have the generating AI propose the optimal treatment method.

[0054] The collection unit can simultaneously collect environmental data from the waste generation site when collecting waste. For example, it can measure and collect data on the temperature and humidity of the waste generation site using sensors. It can also measure and collect data on the noise level of the waste generation site. It can also measure and collect data on the air quality of the waste generation site using sensors. By simultaneously collecting environmental data from the waste generation site, more detailed data can be obtained. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input environmental data from the waste generation site into a generating AI and have the generating AI perform data analysis.

[0055] The analysis unit can improve the accuracy of the analysis by considering environmental data of the waste generation site. For example, it can improve the accuracy of the analysis by considering the temperature and humidity of the waste generation site. It can also improve the accuracy of the analysis by considering the noise level of the waste generation site. It can also improve the accuracy of the analysis by considering the air quality of the waste generation site. In this way, the accuracy of the analysis is improved by considering the environmental data of the generation site. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input environmental data of the waste generation site into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The collection unit collects waste data. The collection unit collects detailed data such as the type and quantity of waste and the location where it was generated. For example, the collection unit can measure the weight and volume of each type of waste at a waste treatment facility and collect that data. The collection unit can also collect waste data using sensors or cameras. For example, the collection unit can collect waste data using infrared sensors or CCD cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes images of waste using, for example, deep learning-based image recognition technology to identify recyclable resources. The analysis unit can analyze images of waste using, for example, CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network). The analysis unit can also perform, for example, a component analysis of the waste to determine in detail the proportion of recyclable resources. The analysis unit can analyze the components of waste using, for example, chemical analysis or physical analysis. Step 3: The proposal unit proposes the optimal treatment method based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal treatment method considering, for example, the type and quantity of waste and the proportion of recyclable resources. For example, if there are many recyclable resources, the proposal unit proposes transport to a recycling facility, and if the waste is not recyclable, it proposes treatment at an appropriate treatment facility. The proposal unit can also propose the optimal treatment method considering, for example, the results of the waste component analysis. The proposal unit can also propose different treatment methods for each waste source.

[0058] (Example of form 2) The waste management system according to an embodiment of the present invention is a system that uses AI to analyze the type and quantity of waste and proposes the optimal processing method. This waste management system collects waste data, and the AI ​​analyzes that data. Next, based on the analysis results, it identifies recyclable resources and proposes the optimal processing method. This system enables sustainable waste management. For example, the waste management system collects waste data. At this time, it collects detailed data such as the type and quantity of waste and the place of origin. For example, at a waste treatment facility, the weight and volume of each type of waste are measured and the data is collected. This makes it possible to grasp detailed information about the waste. Next, the waste management system's AI analyzes the collected data. Based on the collected data, the AI ​​analyzes the type and quantity of waste and identifies recyclable resources. For example, by utilizing image recognition technology using deep learning, it is possible to analyze images of waste and identify recyclable resources such as plastics, metals, and paper. This improves the accuracy of waste classification and is expected to improve the recycling rate. Furthermore, the waste management system proposes the optimal processing method based on the analysis results. The AI ​​proposes the optimal processing method considering the type and quantity of waste, the proportion of recyclable resources, etc. For example, if there are many recyclable resources, the system suggests transporting them to a recycling facility, and suggests processing non-recyclable waste at an appropriate treatment facility. This improves the efficiency of waste treatment and is expected to reduce waste treatment costs. This system enables sustainable waste management. By collecting detailed waste data and having AI analyze it, the system can identify recyclable resources and suggest the optimal treatment method. This improves the efficiency of waste treatment, is expected to reduce waste treatment costs, and improve the recycling rate. It can also contribute to reducing the burden on the environment. For example, at a waste treatment facility, by having AI analyze waste data and identify recyclable resources, the recycling rate can be improved and resource reuse can be expected to increase. In addition, economic benefits can be enjoyed by reducing waste treatment costs. Furthermore, by reducing the burden on the environment, it can contribute to the realization of a sustainable society.This enables sustainable waste management by collecting and analyzing waste data and proposing optimal disposal methods.

[0059] The waste management system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects waste data. The collection unit collects detailed data such as the type and quantity of waste and the location where it is generated. For example, the collection unit can measure the weight and volume of each type of waste at a waste treatment facility and collect the data. The collection unit can also collect waste data using sensors or cameras. For example, the collection unit can collect waste data using infrared sensors or CCD cameras. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes images of waste using image recognition technology with deep learning to identify recyclable resources. For example, the analysis unit can analyze images of waste using CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network). For example, the analysis unit can perform component analysis of waste to determine the proportion of recyclable resources in detail. For example, the analysis unit can analyze the components of waste using chemical analysis or physical analysis. The proposal unit proposes the optimal processing method based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal processing method considering, for example, the type and quantity of waste and the proportion of recyclable resources. For example, if there are many recyclable resources, the proposal unit proposes transportation to a recycling facility, and if the waste is not recyclable, it proposes processing at an appropriate processing facility. The proposal unit can also propose the optimal processing method considering, for example, the results of the waste component analysis. The proposal unit can also propose different processing methods for each waste source. As a result, the waste management system according to this embodiment can achieve sustainable waste management by collecting and analyzing waste data and proposing the optimal processing method.

[0060] The collection unit collects data on waste. For example, it collects detailed data such as the type, quantity, and location of waste generation. Specifically, at waste treatment facilities, it can measure the weight and volume of each type of waste and collect that data. For example, when waste is brought in, it can accurately measure it using weighing scales and volumetric meters and record the data in digital format. The collection unit can also collect waste data using sensors and cameras. For example, it can collect waste data using infrared sensors and CCD cameras. Infrared sensors detect the temperature and shape of the waste, and CCD cameras capture images of the waste and save them as digital data. This allows the collection unit to understand the type and condition of the waste in detail. Furthermore, the collection unit can use IoT technology to track the location of waste generation and transportation routes in real time. For example, it can attach tracking devices equipped with GPS sensors to waste containers to monitor the movement route of the waste. This allows for understanding the entire process from the waste source to the final treatment facility, enabling efficient waste management. The collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection department to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses deep learning-based image recognition technology to analyze images of waste and identify recyclable resources. Specifically, it can analyze images of waste using CNNs (Convolutional Neural Networks) or RNNs (Recurrent Neural Networks). CNNs extract features from images of waste and identify recyclable resources with high accuracy. RNNs are suitable for analyzing time-series data and are used to analyze waste generation patterns and fluctuations. The analysis unit can also perform component analysis of waste to determine the proportion of recyclable resources in detail. Component analysis can utilize chemical or physical analysis. Chemical analysis examines the chemical composition of waste in detail to identify recyclable components. Physical analysis examines the physical properties of waste to identify recyclable resources. For example, spectroscopic analysis or mass spectrometry can be used to analyze the components of waste in detail. Furthermore, the analysis unit can integrate the collected data using AI to predict waste generation trends and the distribution of recyclable resources. This allows the analysis unit to quickly and accurately analyze collected data, contributing to the optimization of waste management. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on historical waste data, it can predict fluctuations in waste generation in specific areas or time periods and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0062] The proposal department proposes the optimal processing method based on the analysis results obtained by the analysis department. For example, the proposal department proposes the optimal processing method considering the type and quantity of waste, the proportion of recyclable resources, etc. Specifically, if there are many recyclable resources, it proposes transportation to a recycling facility, and if the waste is not recyclable, it proposes processing at an appropriate processing facility. The proposal department can also propose the optimal processing method considering the results of the waste's component analysis. Based on the component analysis results, it proposes a processing method that makes the most use of the recyclable components of the waste. For example, it proposes separating recyclable resources such as metals and plastics and reusing them at a recycling facility. Furthermore, the proposal department can propose different processing methods depending on the source of the waste. For example, since the processing methods for industrial waste generated from factories and general waste generated from households are different, it proposes processing methods appropriate for each. In addition, the proposal department can propose an optimal processing schedule according to the amount and type of waste generated. For example, if the amount of waste generated is large, it adjusts the processing schedule to maximize the utilization rate of the processing facility. In this way, the proposal department can achieve efficient and effective waste processing and support sustainable waste management. Furthermore, the proposal department can propose optimal waste treatment methods by considering the latest technologies and regulations related to waste treatment. For example, it can optimize waste treatment by taking into account new recycling technologies and environmental protection regulations. This allows the proposal department to always provide highly accurate proposals based on the latest information, thereby achieving sustainable waste management.

[0063] The collection unit can collect data on waste using sensors and cameras. For example, the collection unit can measure the temperature of waste using an infrared sensor and collect data. For example, the collection unit can take images of waste using a CCD camera and collect data. For example, the collection unit can measure the weight of waste using a weight sensor and collect data. This allows for accurate collection of waste data using sensors and cameras. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by sensors and cameras into a generating AI and have the generating AI perform data analysis.

[0064] The analysis unit can analyze images of waste using deep learning-based image recognition technology and identify recyclable resources. For example, the analysis unit can use a CNN (Convolutional Neural Network) to analyze images of waste and identify recyclable resources such as plastics, metals, and paper. For example, the analysis unit can use an RNN (Recurrent Neural Network) to analyze images of waste and identify recyclable resources. For example, the analysis unit can use deep learning-based image recognition technology to improve the accuracy of waste classification. As a result, by using deep learning, the accuracy of waste classification can be improved, and an improvement in the recycling rate can be expected. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input waste image data into a generating AI and have the generating AI perform the identification of recyclable resources.

[0065] The proposal unit can propose the optimal processing method by considering the type and quantity of waste, the proportion of recyclable resources, etc. For example, the proposal unit can propose the optimal processing method based on the type and quantity of waste. For example, the proposal unit can propose the optimal processing method based on the proportion of recyclable resources. For example, the proposal unit can propose the optimal processing method by considering the results of the waste component analysis. In this way, the optimal processing method can be proposed by considering the type and quantity of waste and the proportion of recyclable resources. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input waste data into a generating AI and have the generating AI execute a proposal for the optimal processing method.

[0066] The proposal unit can suggest transporting recyclable resources to a recycling facility if there are many recyclable resources, and suggest processing non-recyclable waste at an appropriate treatment facility. For example, if there are many recyclable resources, the proposal unit can suggest transporting them to a plastic recycling facility or a metal recycling facility. For example, if non-recyclable waste is to be processed at an incineration facility or a landfill facility, the proposal unit can suggest processing it at an incineration facility or a landfill facility. For example, the proposal unit can appropriately classify recyclable resources and non-recyclable waste and propose the most suitable processing method for each. This improves the efficiency of waste processing by appropriately processing recyclable resources and non-recyclable waste. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input waste data into a generating AI and have the generating AI classify recyclable resources and non-recyclable waste.

[0067] The analysis unit can optimize the processing plan in real time. For example, the analysis unit can dynamically adjust the processing plan according to the waste generation situation. For example, the analysis unit can optimize the processing plan based on the type and quantity of waste. For example, the analysis unit can apply different processing plans to each waste source. This improves the efficiency of waste processing by optimizing the processing plan in real time. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input waste data into a generating AI and have the generating AI perform real-time optimization of the processing plan.

[0068] The collection unit can estimate the user's emotions and adjust the timing of waste data collection based on the estimated emotions. For example, if the user is stressed, the collection unit can delay the collection timing to reduce the user's burden. For example, if the user is relaxed, the collection unit can speed up the collection timing to collect data efficiently. For example, if the user is in a hurry, the collection unit can adjust the collection timing to collect data quickly. In this way, the user's burden can be reduced by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0069] The collection unit can collect more detailed data using different sensors for each waste source. For example, the collection unit can use chemical sensors to analyze the components of waste from factories in detail. For example, the collection unit can use cameras to identify the type and quantity of waste from households. For example, the collection unit can use weight sensors to accurately measure the weight of waste from commercial facilities. This allows for the collection of detailed data by using different sensors for each source. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data acquired by different sensors for each waste source into a generating AI and have the generating AI perform data analysis.

[0070] The collection unit can apply different collection methods depending on the type of waste during waste collection. For example, the collection unit can separate and collect plastic waste using special collection bags. For example, the collection unit can automatically separate metal waste using magnetic sensors. For example, the collection unit can collect organic waste using special compost containers. This enables efficient collection by applying different collection methods to each type of waste. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input different collection methods for each type of waste into a generating AI and have the generating AI execute the application of the collection methods.

[0071] The data collection unit can estimate the user's emotions and determine the priority of waste data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can postpone the collection of less important data. For example, if the user is relaxed, the data collection unit can collect all data equally. For example, if the user is in a hurry, the data collection unit can prioritize the collection of highly important data. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0072] The collection unit can simultaneously collect environmental data of the waste generation site when collecting waste. For example, the collection unit can measure the temperature and humidity of the waste generation site with sensors and collect data. For example, the collection unit can measure the noise level of the waste generation site and collect data. For example, the collection unit can measure the air quality of the waste generation site with sensors and collect data. By simultaneously collecting environmental data of the waste generation site, more detailed data can be obtained. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input environmental data of the waste generation site into a generating AI and have the generating AI perform data analysis.

[0073] The collection unit can adjust the collection frequency when collecting waste, taking into account the time of day the waste is generated. For example, the collection unit can increase the collection frequency during times when there is a lot of waste generated. For example, the collection unit can decrease the collection frequency during times when there is little waste generated. For example, the collection unit can analyze the waste generation pattern and set the optimal collection frequency. This makes efficient collection possible by adjusting the collection frequency while taking into account the time of day the waste is generated. Some or all of the above-described processes in the collection unit may be performed using AI, for example, or without using AI. For example, the collection unit can input data on the time of day the waste is generated into a generating AI and have the generating AI adjust the collection frequency.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0075] The analysis unit can perform a component analysis of the waste during the analysis process and identify in detail the proportion of recyclable resources. For example, the analysis unit can chemically analyze the components of the waste to identify the proportion of recyclable resources. For example, the analysis unit can physically analyze the components of the waste to identify the proportion of recyclable resources. For example, the analysis unit can biologically analyze the components of the waste to identify the proportion of recyclable resources. In this way, by performing a component analysis of the waste, the proportion of recyclable resources can be identified in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input waste component data into a generating AI and have the generating AI perform the identification of the proportion of recyclable resources.

[0076] The analysis unit can apply different analysis algorithms to each waste source during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes chemical composition to waste from factories. For example, the analysis unit can apply an analysis algorithm that emphasizes type and quantity to waste from households. For example, the analysis unit can apply an analysis algorithm that emphasizes weight and volume to waste from commercial facilities. By applying different analysis algorithms to each source, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input different data for each waste source into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0077] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can postpone displaying analysis results of lower importance. For example, if the user is relaxed, the analysis unit can display all analysis results equally. For example, if the user is in a hurry, the analysis unit can prioritize displaying analysis results of higher importance. In this way, by determining the priority of analysis results according to the user's emotions, important information can be displayed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0078] The analysis unit can improve the accuracy of the analysis by considering environmental data of the waste generation site during the analysis. For example, the analysis unit can improve the accuracy of the analysis by considering the temperature and humidity of the waste generation site. For example, the analysis unit can improve the accuracy of the analysis by considering the noise level of the waste generation site. For example, the analysis unit can improve the accuracy of the analysis by considering the air quality of the waste generation site. In this way, the accuracy of the analysis is improved by considering environmental data of the generation site. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input environmental data of the waste generation site into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0079] The analysis unit can adjust the analysis algorithm during analysis, taking into account the time of waste generation. For example, the analysis unit can speed up the analysis algorithm during times when waste generation is high. For example, the analysis unit can refine the analysis algorithm during times when waste generation is low. For example, the analysis unit can analyze waste generation patterns and apply the optimal analysis algorithm. By adjusting the analysis algorithm to take into account the time of generation, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the time of waste generation into a generating AI and have the generating AI perform the adjustment of the analysis algorithm.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide a simple and easily understandable suggestion. If the user is relaxed, the suggestion unit can provide a suggestion that includes detailed information. If the user is in a hurry, the suggestion unit can provide a concise suggestion. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0081] The proposal unit can propose the optimal treatment method when making a proposal, taking into account the results of the waste's component analysis. For example, if the waste's component analysis results show a large amount of recyclable resources, the proposal unit can propose transport to a recycling facility. For example, if the waste's component analysis results show a large amount of non-recyclable waste, the proposal unit can propose treatment at an appropriate treatment facility. For example, if the waste's component analysis results show a large amount of a particular component, the proposal unit can propose a treatment method specifically for that component. In this way, the optimal treatment method can be proposed by taking into account the waste's component analysis results. Some or all of the above-mentioned processes in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input waste component data into a generating AI and have the generating AI propose the optimal treatment method.

[0082] The proposal unit can propose different treatment methods depending on the source of the waste. For example, the proposal unit can propose chemical treatment for waste from factories. For example, the proposal unit can propose recycling for waste from households. For example, the proposal unit can propose incineration for waste from commercial facilities. By proposing different treatment methods for each source, efficient waste treatment becomes possible. Some or all of the above-mentioned treatments in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data for each waste source into a generating AI and have the generating AI execute the proposal of treatment methods.

[0083] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can postpone displaying less important suggestions. For example, if the user is relaxed, the suggestion unit can display all suggestions equally. For example, if the user is in a hurry, the suggestion unit can prioritize displaying more important suggestions. In this way, important suggestions can be displayed preferentially by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0084] The proposal unit can propose the optimal treatment method by considering environmental data of the waste generation site when making a proposal. For example, the proposal unit can propose the optimal treatment method by considering the temperature and humidity of the waste generation site. For example, the proposal unit can propose the optimal treatment method by considering the noise level of the waste generation site. For example, the proposal unit can propose the optimal treatment method by considering the air quality of the waste generation site. In this way, the optimal treatment method can be proposed by considering environmental data of the generation site. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without using AI. For example, the proposal unit can input environmental data of the waste generation site into a generating AI and have the generating AI execute a proposal for the optimal treatment method.

[0085] The proposal unit can adjust the processing method when making a proposal, taking into account the time of waste generation. For example, the proposal unit can speed up the processing method during times when waste generation is high. For example, the proposal unit can refine the processing method during times when waste generation is low. For example, the proposal unit can analyze waste generation patterns and apply the optimal processing method. This makes efficient waste disposal possible by adjusting the processing method considering the time of generation. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input data on the time of waste generation into a generating AI and have the generating AI perform the adjustment of the processing method.

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

[0087] The collection unit can apply different data collection methods depending on the source of the waste when collecting waste data. For example, chemical sensors can be used to analyze the components of waste from factories. Cameras can be used to identify the type and quantity of waste from households. Weight sensors can be used to measure the exact weight of waste from commercial facilities. This allows for the collection of detailed data by using different sensors for each source. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input data acquired by different sensors for each waste source into a generating AI and have the generating AI perform data analysis.

[0088] The analysis unit can apply different analysis algorithms to each waste source when performing component analysis of waste. For example, an analysis algorithm that emphasizes chemical composition can be applied to waste from factories. An analysis algorithm that emphasizes type and quantity can be applied to waste from households. An analysis algorithm that emphasizes weight and volume can be applied to waste from commercial facilities. By applying different analysis algorithms to each source, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input different data for each waste source into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0089] The proposal unit can propose the optimal treatment method by considering the results of the waste's component analysis. For example, if the waste's component analysis results show a large amount of recyclable resources, it can propose transport to a recycling facility. For non-recyclable waste, it can propose treatment at an appropriate treatment facility. If a particular component is present in large quantities, it can propose a treatment method specifically tailored to that component. In this way, the optimal treatment method can be proposed by considering the waste's component analysis results. Some or all of the above-mentioned processes in the proposal unit may be performed using AI, or they may not be performed using AI. For example, the proposal unit can input waste component data into a generating AI and have the generating AI propose the optimal treatment method.

[0090] The collection unit can simultaneously collect environmental data from the waste generation site when collecting waste. For example, it can measure and collect data on the temperature and humidity of the waste generation site using sensors. It can also measure and collect data on the noise level of the waste generation site. It can also measure and collect data on the air quality of the waste generation site using sensors. By simultaneously collecting environmental data from the waste generation site, more detailed data can be obtained. Some or all of the above-described processes in the collection unit may be performed using AI or not. For example, the collection unit can input environmental data from the waste generation site into a generating AI and have the generating AI perform data analysis.

[0091] The analysis unit can improve the accuracy of the analysis by considering environmental data of the waste generation site. For example, it can improve the accuracy of the analysis by considering the temperature and humidity of the waste generation site. It can also improve the accuracy of the analysis by considering the noise level of the waste generation site. It can also improve the accuracy of the analysis by considering the air quality of the waste generation site. In this way, the accuracy of the analysis is improved by considering the environmental data of the generation site. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input environmental data of the waste generation site into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0092] The collection unit can estimate the user's emotions and adjust the timing of waste data collection based on the estimated emotions. For example, if the user is stressed, the collection timing can be delayed to reduce the user's burden. If the user is relaxed, the collection timing can be advanced to collect data efficiently. If the user is in a hurry, the collection timing can be adjusted to collect data quickly. In this way, the user's burden can be reduced by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method containing detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method according to the user's emotions, a display that is easy for the user to understand can be made possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible suggestion. If the user is relaxed, it can provide a suggestion that includes detailed information. If the user is in a hurry, it can provide a suggestion that gets straight to the point. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0095] The data collection unit can estimate the user's emotions and prioritize the collection of waste data based on the estimated emotions. For example, if the user is stressed, the collection of less important data can be postponed. If the user is relaxed, all data can be collected equally. If the user is in a hurry, highly important data can be prioritized. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data prioritization.

[0096] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, less important suggestions can be displayed later. If the user is relaxed, all suggestions can be displayed equally. If the user is in a hurry, highly important suggestions can be displayed preferentially. In this way, important suggestions can be displayed preferentially by determining the priority of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The collection unit collects waste data. The collection unit collects detailed data such as the type and quantity of waste and the location where it was generated. For example, the collection unit can measure the weight and volume of each type of waste at a waste treatment facility and collect that data. The collection unit can also collect waste data using sensors or cameras. For example, the collection unit can collect waste data using infrared sensors or CCD cameras. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes images of waste using, for example, deep learning-based image recognition technology to identify recyclable resources. The analysis unit can analyze images of waste using, for example, CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network). The analysis unit can also perform, for example, a component analysis of the waste to determine in detail the proportion of recyclable resources. The analysis unit can analyze the components of waste using, for example, chemical analysis or physical analysis. Step 3: The proposal unit proposes the optimal treatment method based on the analysis results obtained by the analysis unit. The proposal unit proposes the optimal treatment method considering, for example, the type and quantity of waste and the proportion of recyclable resources. For example, if there are many recyclable resources, the proposal unit proposes transport to a recycling facility, and if the waste is not recyclable, it proposes treatment at an appropriate treatment facility. The proposal unit can also propose the optimal treatment method considering, for example, the results of the waste component analysis. The proposal unit can also propose different treatment methods for each waste source.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect waste data using the sensors and cameras of the smart device 14. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify recyclable resources. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes the optimal processing method based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0104] As shown in Figure 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.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect waste data using the sensors and cameras of the smart glasses 214. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected data to identify recyclable resources. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and proposes the optimal processing method based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect waste data using the sensors and camera of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data to identify recyclable resources. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes the optimal processing method based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect waste data using the sensors and cameras of the robot 414. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the collected data to identify recyclable resources. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which proposes the optimal processing method based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The collection department collects waste data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes an optimal processing method based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect waste data using sensors and cameras. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using deep learning-based image recognition technology, we analyze images of waste and identify recyclable resources. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the optimal disposal method, taking into account the type and quantity of waste, the proportion of recyclable resources, and other factors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, If there are many recyclable resources, we will propose transporting them to a recycling facility; for non-recyclable waste, we will propose processing it at an appropriate treatment facility. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Optimize processing plans in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of waste data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is By using different sensors for each waste source, more detailed data can be collected. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting waste, different collection methods should be applied depending on the type of waste. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of waste data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting waste, simultaneously collect environmental data on the waste generation site. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting waste, adjust the collection frequency considering the time of day the waste is generated. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, a component analysis of the waste is performed to identify in detail the proportion of recyclable resources. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, different analysis algorithms are applied to each waste source. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, environmental data of the waste generation site is taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the analysis algorithm is adjusted to take into account the time of day when the waste was generated. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we will consider the results of the waste's component analysis and propose the most suitable treatment method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will suggest different treatment methods depending on the source of the waste. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we will consider environmental data from the waste generation site to suggest the most suitable treatment method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, adjust the processing method considering the time of day when the waste is generated. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects waste data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that proposes an optimal processing method based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect waste data using sensors and cameras. The system according to feature 1.

3. The aforementioned analysis unit, Using deep learning-based image recognition technology, we analyze images of waste and identify recyclable resources. The system according to feature 1.

4. The aforementioned proposal section is, We propose the optimal disposal method, taking into account the type and quantity of waste, the proportion of recyclable resources, and other factors. The system according to feature 1.

5. The aforementioned proposal section is, If there are many recyclable resources, we will propose transporting them to a recycling facility; for non-recyclable waste, we will propose processing it at an appropriate treatment facility. The system according to feature 1.

6. The aforementioned analysis unit, Optimize processing plans in real time. The system according to feature 1.

7. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of waste data collection based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is By using different sensors for each waste source, more detailed data can be collected. The system according to feature 1.

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