system

Abstract

The system according to this embodiment aims to track waste generation patterns and propose optimal waste management methods. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit tracks the patterns of waste generation. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal waste management method based on the analysis results obtained by the analysis unit. The provision unit provides the user with the information proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 prior art, there is a problem that it is difficult to efficiently track the generation pattern of waste and propose an optimal waste management method.

[0005] The system according to the embodiment aims to track the generation pattern of waste and propose an optimal waste management method.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit tracks the patterns of waste generation. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes an optimal waste management method based on the analysis results obtained by the analysis unit. The provision unit provides the user with the information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can track waste generation patterns and propose optimal waste management methods. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 environmental solution system according to an embodiment of the present invention is a comprehensive environmental solution system that revolutionizes waste management for individuals and businesses by utilizing cutting-edge AI technology. This environmental solution system works in conjunction with IoT sensors to track and analyze waste generation patterns in homes and offices in real time. For example, a dedicated smart trash can in the kitchen recognizes food items to be discarded, and the AI ​​analyzes trends in food waste to optimize shopping lists and suggest recipes. For businesses, it analyzes waste data from production lines and warehouses to suggest improvements in raw material utilization efficiency and inventory management. Furthermore, the AI ​​considers the processing capacity of local recycling facilities and the market demand for recycled materials to suggest the most effective waste sorting and recycling methods. Users can easily check waste sorting methods through a smartphone app, and "eco-points" earned through proper disposal are also visualized. These points can be used to donate to local environmental protection activities or exchanged for eco-products. It functions as a comprehensive platform that goes beyond a mere waste management tool, raising environmental awareness among individuals and businesses and encouraging behavioral change toward the realization of a circular economy. For example, IoT sensors are installed to track and analyze waste generation patterns in homes and offices in real time. For example, a dedicated smart trash can in the kitchen recognizes food waste, and AI analyzes trends in food loss. This allows users to receive optimized shopping lists and recipe suggestions. For instance, recipes to reduce frequently wasted ingredients and advice on adjusting purchase quantities are provided. For businesses, waste data from production lines and warehouses is analyzed to suggest improvements in raw material utilization efficiency and inventory management. For example, if a particular raw material is being used excessively, suggestions are made to optimize its usage. Advice is also provided to prevent inventory management errors and overproduction. This enables businesses to reduce waste and costs. Furthermore, the AI ​​considers the processing capacity of local recycling facilities and market demand for recycled materials to suggest the most effective waste sorting and recycling methods. For example, if a particular recycled material is in high demand, it is suggested to prioritize sorting that material. This improves recycling efficiency and reduces the environmental impact.Users can easily check waste sorting methods through a smartphone app. For example, they can check how specific types of waste should be sorted using the app. The app also visualizes "eco-points" earned through proper disposal. These points can be used to donate to local environmental protection activities or exchanged for eco-friendly products. For instance, accumulating a certain number of points allows users to donate to local cleanup activities or exchange them for eco-bags or recycled products. This system goes beyond a simple waste management tool, functioning as a comprehensive platform to raise environmental awareness among individuals and businesses and encourage behavioral change toward a circular economy. In this way, the environmental solution system can streamline waste management for individuals and businesses and raise environmental awareness.

[0029] The environmental solution system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit tracks waste generation patterns. For example, the collection unit tracks waste generation patterns in homes and offices in real time. The collection unit can detect waste generation patterns using IoT sensors. For example, the collection unit uses sensors installed in a dedicated smart trash can in the kitchen to recognize food waste. The collection unit collects waste generation patterns as data and transmits it to the analysis unit. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to analyze trends in food loss. The analysis unit can analyze the collected data in detail using AI. For example, the analysis unit classifies waste generation patterns by time of day, type, and location to understand trends. Based on the collected data, the analysis unit identifies the causes of food loss and proposes improvement measures. The proposal unit proposes the optimal waste management method based on the analysis results obtained by the analysis unit. For example, the proposal unit optimizes shopping lists and suggests cooking recipes. The Proposal Department can use AI to provide users with optimal suggestions. For example, it can offer recipes to reduce frequently wasted food and advice on adjusting purchase quantities. For businesses, the Proposal Department analyzes waste data from production lines and warehouses and presents solutions to improve raw material utilization efficiency and inventory management. The Proposal Department can use AI to propose ways to streamline waste management for businesses. For example, if a particular raw material is being used excessively, the Proposal Department will suggest ways to optimize its usage. The Proposal Department also provides advice to prevent inventory management errors and overproduction. The Service Department delivers the suggestions made by the Proposal Department to users. For example, the Service Department provides users with information on how to sort waste through a smartphone app. The Service Department makes it easy for users to check how to sort waste. For example, the Service Department allows users to check how specific types of waste should be sorted through the app. The Service Department also visualizes "eco-points" that can be earned through proper disposal, which can be used to donate to local environmental protection activities or exchanged for eco-friendly products.The service provider can provide information to raise users' environmental awareness. For example, by accumulating a certain number of points, users can make donations to participate in local cleanup activities or exchange them for eco-bags or recycled products. In this way, the environmental solution system according to the embodiment can streamline waste management for individuals and companies and raise environmental awareness.

[0030] The collection unit tracks waste generation patterns. For example, it tracks waste generation patterns in homes and offices in real time. The collection unit can detect waste generation patterns using IoT sensors. Specifically, it uses sensors installed in dedicated smart trash cans in kitchens to recognize discarded food items. These sensors combine weight sensors and image recognition technology to accurately determine the type and quantity of waste. For example, weight sensors measure the weight of waste placed in the trash can, and image recognition technology identifies the type of waste. This allows the collection unit to collect waste generation patterns as data and transmit it to the analysis unit. Furthermore, the collection unit can install sensors in other locations in homes and offices to gain a comprehensive understanding of waste generation. For example, it can install sensors in each room and common area of ​​an office to track waste generation patterns in detail. This allows the collection unit to accurately understand the sources and frequency of waste generation, supporting efficient waste management. The collection unit transmits this data to a cloud server for real-time data storage and management. This allows the collection unit to track waste generation patterns over the long term and understand trends. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data collected by the collection department. For example, the analysis department analyzes the collected data to analyze trends in food waste. The analysis department can use AI to analyze the collected data in detail. Specifically, it classifies waste generation patterns by time of day, type, and location to understand trends. For example, the AI ​​analyzes the time of day when waste is generated to identify the reasons why a large amount of waste is generated during specific time periods. It also classifies the data by type of waste to understand which type of waste is generated the most. Furthermore, it analyzes the data by location of waste generation to understand the amount of waste generated at specific locations. As a result, the analysis department can identify the causes of food waste and propose improvement measures based on the collected data. In addition, the analysis department can use historical data and statistical information to analyze long-term trends and patterns. For example, based on historical waste data, it can predict fluctuations in waste generation during specific seasons or events and plan future countermeasures. The analysis department can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0032] The Proposal Department proposes optimal waste management methods based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can optimize shopping lists and suggest cooking recipes. Using AI, the Proposal Department can provide optimal suggestions to users. Specifically, it can provide recipes to reduce frequently wasted ingredients and advice to adjust purchase quantities. For example, based on the user's past waste data, the Proposal Department can identify frequently wasted ingredients and suggest recipes to use those ingredients efficiently. It can also optimize the user's shopping list and adjust the amount of ingredients needed to reduce food waste. Furthermore, for businesses, the Proposal Department analyzes waste data from production lines and warehouses and presents measures to improve raw material utilization efficiency and inventory management. For example, if a particular raw material is being used excessively, the Proposal Department will suggest ways to optimize its usage. It also provides advice to prevent inventory management errors and overproduction. This allows the Proposal Department to streamline waste management for businesses, contributing to cost reduction and reduced environmental impact. Additionally, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with the most suitable waste management methods and raise environmental awareness.

[0033] The service provider will provide users with the content proposed by the proposal provider. For example, the service provider will provide users with information on how to sort waste through a smartphone app. The service provider will ensure that users can easily check how to sort waste. Specifically, the service provider will allow users to check how specific types of waste should be sorted through the app. For example, when a user enters the type of waste into the app, the appropriate sorting method will be displayed. The service provider will also visualize "eco points" that can be earned through proper processing, and allow users to use them to donate to local environmental protection activities or exchange them for eco-friendly products. For example, by accumulating a certain number of points, users can donate to participate in local cleanup activities or exchange them for eco-bags or recycled products. In this way, the service provider can provide information that raises users' environmental awareness. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the content provided. For example, based on user feedback, they can make the explanation of sorting methods easier to understand or review how to earn eco points. In addition, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, and to raise environmental awareness.

[0034] The collection unit can track waste generation patterns in homes and offices in real time. For example, the collection unit uses IoT sensors to track waste generation patterns in homes and offices in real time. The collection unit collects waste generation patterns as data and transmits it to the analysis unit. For example, the collection unit uses sensors installed in a dedicated smart trash can in the kitchen to recognize food waste. The collection unit classifies waste generation patterns by time of day, type, and location to understand trends. This improves the accuracy of waste management by tracking waste generation patterns in homes and offices in real time. 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 data acquired from IoT sensors into a generating AI and have the generating AI perform the tracking of waste generation patterns.

[0035] The analysis department can analyze collected data and analyze trends in food waste. For example, the analysis department can analyze collected data and analyze trends in food waste. The analysis department can use AI to analyze collected data in detail. For example, the analysis department can classify waste generation patterns by time of day, type, and location to understand trends. Based on the collected data, the analysis department can identify the causes of food waste and propose improvement measures. In this way, by analyzing collected data and analyzing trends in food waste, it contributes to reducing food waste. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform an analysis of food waste trends.

[0036] The suggestion unit can optimize shopping lists and suggest recipes. For example, the suggestion unit can optimize shopping lists and suggest recipes. The suggestion unit can use AI to provide optimal suggestions to the user. For example, the suggestion unit can provide recipes to reduce frequently wasted food and advice to adjust the amount purchased. This allows for reduced food waste and more efficient shopping by optimizing shopping lists and suggesting recipes. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input collected data into a generating AI and have the generating AI perform the optimization of shopping lists and suggest recipes.

[0037] The analysis department can analyze waste data from production lines and warehouses and propose improvements to raw material utilization efficiency and inventory management. For example, the analysis department can analyze waste data from production lines and warehouses and propose improvements to raw material utilization efficiency and inventory management. The analysis department can use AI to make suggestions for streamlining a company's waste management. For example, if a particular raw material is being used in excess, the analysis department will propose ways to optimize its usage. The analysis department will also provide advice to prevent inventory management errors and overproduction. In this way, by analyzing waste data from production lines and warehouses and proposing improvements to raw material utilization efficiency and inventory management, the analysis department streamlines a company's waste management. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input waste data into a generating AI and have the generating AI perform an analysis to propose improvements to raw material utilization efficiency and inventory management.

[0038] The proposal unit can propose the most effective waste sorting and recycling methods, taking into account the processing capacity of local recycling facilities and the market demand for recycled materials. For example, the proposal unit can propose the most effective waste sorting and recycling methods, considering the processing capacity of local recycling facilities and the market demand for recycled materials. The proposal unit can use AI to make suggestions to improve recycling efficiency. For example, if a particular recycled material is in high demand, the proposal unit can suggest prioritizing its sorting. This improves recycling efficiency by proposing the most effective waste sorting and recycling methods, taking into account the processing capacity of local recycling facilities and the market demand for recycled materials. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input data on the processing capacity of recycling facilities and market demand into a generating AI, which can then generate suggestions for the optimal sorting and recycling methods.

[0039] The service provider can provide users with information on how to sort waste through a smartphone app. For example, the service provider can provide users with information on how to sort waste through a smartphone app. The service provider makes it easy for users to check how to sort waste. For example, the service provider can allow users to check how specific types of waste should be sorted through the app. This allows users to easily check how to sort waste by providing information on how to sort waste through a smartphone app. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input information on how to sort waste into a generating AI and have the generating AI generate the information to be provided to the user.

[0040] The service provider can visualize "eco-points" earned through appropriate processing and make them usable for donations to local environmental protection activities or exchange for eco-friendly products. For example, the service provider can visualize "eco-points" earned through appropriate processing and make them usable for donations to local environmental protection activities or exchange for eco-friendly products. The service provider can provide information to raise users' environmental awareness. For example, the service provider can allow users to accumulate a certain number of points to make donations to participate in local cleanup activities or exchange them for eco-bags or recycled products. This raises users' environmental awareness by visualizing "eco-points" earned through appropriate processing and making them usable for donations to local environmental protection activities or exchange for eco-friendly products. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the calculation and visualization of eco-points.

[0041] The collection unit can select the optimal collection method by referring to the user's past waste data when collecting waste generation patterns. For example, the collection unit optimizes the collection method based on items that the user has frequently discarded in the past. The collection unit can concentrate collection on specific days of the week or time slots based on the user's past waste data. The collection unit can analyze the user's past waste data and propose the most efficient collection route. This enables efficient data collection by selecting the optimal collection method by referring to the user's past waste data. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past waste data into a generating AI and have the generating AI select the optimal collection method.

[0042] The collection unit can filter waste generation patterns based on the user's lifestyle and work content. For example, when a user collects waste from their home, the collection unit can filter the collected data based on their lifestyle. When a user collects waste from an office, the collection unit can filter the collected data based on their work content. The collection unit can determine the priority of the collected data according to the user's lifestyle and work content. This improves the accuracy of the collected data by filtering based on the user's lifestyle and work content. 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 data on the user's lifestyle and work content into a generating AI and have the generating AI perform the filtering.

[0043] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting waste generation patterns. For example, if the user is in a specific area, the data collection unit will prioritize the collection of waste data for that area. The data collection unit can filter highly relevant data based on the user's geographical location. If the user is on the move, the data collection unit can collect the most relevant data based on their current location. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0044] The collection unit can analyze users' social media activity and collect relevant data when collecting waste generation patterns. For example, the collection unit can predict waste generation patterns and collect data from users' social media posts. The collection unit can analyze users' social media activity and prioritize the collection of relevant waste data. The collection unit can collect relevant data by referring to the activities of users' social media followers and friends. This allows for a more accurate understanding of waste generation patterns by analyzing users' social media activity and collecting relevant data. 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 users' social media activity data into a generating AI and have the generating AI collect relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data when analyzing waste data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the collected data. 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 the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0046] The analysis unit can apply different analysis algorithms depending on the data category when analyzing waste data. For example, the analysis unit can apply an algorithm to analyze food loss trends to food waste data. For industrial waste data, it can apply an algorithm to analyze the efficiency of raw material use. For recyclable waste data, it can apply an algorithm to analyze recycling efficiency. By applying the most appropriate analysis algorithm according to the data category, it can provide highly accurate analysis results. 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 execute the application of analysis algorithms according to the category.

[0047] The analysis unit can determine the priority of analysis based on the data collection period when analyzing waste data. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can analyze current data while referring to past data. The analysis unit can adjust the order of analysis based on the data collection period. This enables efficient data analysis by determining the priority of analysis based on the data collection period. 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 collected data into a generating AI and have the generating AI determine the priority of analysis based on the collection period.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data when analyzing waste data. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0049] The proposal unit can adjust the level of detail of its proposals based on the importance of the waste management methods. For example, the proposal unit can provide detailed proposals for highly important waste management methods, and simplified proposals for less important methods. The proposal unit can also determine the priority of proposals according to the importance of the waste management methods. This allows for efficient proposals by adjusting the level of detail based on the importance of the waste management methods. 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 management method data into a generating AI and have the generating AI adjust the level of detail of proposals based on importance.

[0050] The proposal unit can apply different proposal algorithms depending on the waste category when making proposals. For example, the proposal unit can make proposals to reduce food loss for food waste. For industrial waste, it can make proposals to improve the efficiency of raw material use. For recyclable waste, it can make proposals to improve recycling efficiency. By applying the optimal proposal algorithm according to the waste category, it is possible to provide highly accurate proposals. 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 execute the application of a proposal algorithm according to the category.

[0051] The proposal unit can determine the priority of proposals based on the timing of waste data collection when making proposals. For example, the proposal unit can prioritize proposals based on recently collected data. The proposal unit can make proposals based on current data while referring to past data. The proposal unit can adjust the order of proposals based on the timing of data collection. This enables efficient proposals by determining the priority of proposals based on the timing of waste data collection. 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 collected data into a generating AI and have the generating AI determine the priority of proposals based on the collection timing.

[0052] The proposal unit can adjust the order of proposals based on the relevance of the waste data when making proposals. For example, the proposal unit can prioritize proposals based on highly relevant data. The proposal unit can postpone proposals based on less relevant data. The proposal unit can optimize the order of proposals based on the relevance of the data. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the waste data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0053] The information provider can select the optimal display method by referring to the user's past operation history when displaying the information to be provided. For example, the information provider can prioritize display methods that the user has previously preferred to use. The information provider can select the most efficient display method from the user's past operation history. The information provider can analyze the user's past operation history and propose the optimal display method. In this way, by selecting the optimal display method by referring to the user's past operation history, information suitable for the user can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0054] The information provider can select the optimal display method when displaying the information it provides, taking into account the user's device information. For example, if the user is using a smartphone, the provider can provide a display method that matches the screen size. If the user is using a tablet, the provider can provide a display method optimized for a larger screen. If the user is using a smartwatch, the provider can provide a concise and highly visible display method. In this way, by selecting the optimal display method considering the user's device information, the provider can provide information that is appropriate for the user. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0055] The information provider can select the optimal display method by referring to the user's calendar information when displaying the information it provides. For example, the information provider can prioritize displaying information related to important appointments based on the user's calendar. The information provider can select the optimal display timing based on the user's calendar information. The information provider can display relevant information by referring to the user's calendar information. In this way, by selecting the optimal display method by referring to the user's calendar information, it can provide information that is appropriate for the user. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's calendar information into a generating AI and have the generating AI perform the selection of the optimal display method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The collection unit can select the optimal collection method by referring to the user's past waste data when collecting waste generation patterns. For example, it can optimize the collection method based on items the user has frequently discarded in the past. Based on the user's past waste data, it can concentrate collection on specific days of the week or time slots. Furthermore, it can analyze the user's past waste data and propose the most efficient collection route. This enables efficient data collection by selecting the optimal collection method by referring to the user's past waste data. 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 past waste data into a generating AI and have the generating AI select the optimal collection method.

[0058] The collection unit can filter waste generation patterns based on the user's lifestyle and work content. For example, when a user collects waste from their home, the collected data can be filtered based on their lifestyle. When a user collects waste from an office, the collected data can be filtered based on their work content. Furthermore, the collection unit can determine the priority of the collected data according to the user's lifestyle and work content. This improves the accuracy of the collected data by filtering based on the user's lifestyle and work content. 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 data on the user's lifestyle and work content into a generating AI and have the generating AI perform the filtering.

[0059] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting waste generation patterns. For example, if the user is in a specific area, it can prioritize the collection of waste data for that area. It can filter highly relevant data based on the user's geographical location. Furthermore, if the user is on the move, it can collect the most relevant data based on their current location. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0060] The collection unit can analyze users' social media activity and collect relevant data when collecting waste generation patterns. For example, it can predict waste generation patterns from users' social media posts and collect data. By analyzing users' social media activity, it can prioritize the collection of relevant waste data. Furthermore, it can collect relevant data by referring to the activities of users' social media followers and friends. This allows for a more accurate understanding of waste generation patterns by analyzing users' social media activity and collecting relevant data. 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 social media activity data into a generating AI and have the generating AI collect relevant data.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data when analyzing waste data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit tracks waste generation patterns. For example, it tracks waste generation patterns in homes and offices in real time and detects waste generation patterns using IoT sensors. Specifically, it uses sensors installed in dedicated smart trash cans in kitchens to recognize food items that are being discarded. The collection unit collects waste generation patterns as data and transmits it to the analysis unit. Step 2: The analysis department analyzes the data collected by the collection department. For example, they analyze the collected data to analyze trends in food waste. The analysis department uses AI to analyze the collected data in detail, classifying waste generation patterns by time of day, type, and location to understand trends. Furthermore, based on the collected data, they identify the causes of food waste and propose improvement measures. Step 3: The proposal department proposes the optimal waste management method based on the analysis results obtained by the analysis department. For example, they may optimize shopping lists or suggest cooking recipes. The proposal department uses AI to provide optimal suggestions to users, offering recipes to reduce frequently wasted food and advice on adjusting purchase quantities. For businesses, they analyze waste data from production lines and warehouses and present solutions to improve raw material utilization efficiency and inventory management. Step 4: The provisioning department provides the content proposed by the proposaling department to the users. For example, they provide users with information on how to sort waste through a smartphone app, making it easy for users to check how to sort waste. Furthermore, they visualize "eco points" that can be earned through proper disposal, and allow users to use them for donations to local environmental protection activities or exchange them for eco-friendly products.

[0064] (Example of form 2) The environmental solution system according to an embodiment of the present invention is a comprehensive environmental solution system that revolutionizes waste management for individuals and businesses by utilizing cutting-edge AI technology. This environmental solution system works in conjunction with IoT sensors to track and analyze waste generation patterns in homes and offices in real time. For example, a dedicated smart trash can in the kitchen recognizes food items to be discarded, and the AI ​​analyzes trends in food waste to optimize shopping lists and suggest recipes. For businesses, it analyzes waste data from production lines and warehouses to suggest improvements in raw material utilization efficiency and inventory management. Furthermore, the AI ​​considers the processing capacity of local recycling facilities and the market demand for recycled materials to suggest the most effective waste sorting and recycling methods. Users can easily check waste sorting methods through a smartphone app, and "eco-points" earned through proper disposal are also visualized. These points can be used to donate to local environmental protection activities or exchanged for eco-products. It functions as a comprehensive platform that goes beyond a mere waste management tool, raising environmental awareness among individuals and businesses and encouraging behavioral change toward the realization of a circular economy. For example, IoT sensors are installed to track and analyze waste generation patterns in homes and offices in real time. For example, a dedicated smart trash can in the kitchen recognizes food waste, and AI analyzes trends in food loss. This allows users to receive optimized shopping lists and recipe suggestions. For instance, recipes to reduce frequently wasted ingredients and advice on adjusting purchase quantities are provided. For businesses, waste data from production lines and warehouses is analyzed to suggest improvements in raw material utilization efficiency and inventory management. For example, if a particular raw material is being used excessively, suggestions are made to optimize its usage. Advice is also provided to prevent inventory management errors and overproduction. This enables businesses to reduce waste and costs. Furthermore, the AI ​​considers the processing capacity of local recycling facilities and market demand for recycled materials to suggest the most effective waste sorting and recycling methods. For example, if a particular recycled material is in high demand, it is suggested to prioritize sorting that material. This improves recycling efficiency and reduces the environmental impact.Users can easily check waste sorting methods through a smartphone app. For example, they can check how specific types of waste should be sorted using the app. The app also visualizes "eco-points" earned through proper disposal. These points can be used to donate to local environmental protection activities or exchanged for eco-friendly products. For instance, accumulating a certain number of points allows users to donate to local cleanup activities or exchange them for eco-bags or recycled products. This system goes beyond a simple waste management tool, functioning as a comprehensive platform to raise environmental awareness among individuals and businesses and encourage behavioral change toward a circular economy. In this way, the environmental solution system can streamline waste management for individuals and businesses and raise environmental awareness.

[0065] The environmental solution system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit tracks waste generation patterns. For example, the collection unit tracks waste generation patterns in homes and offices in real time. The collection unit can detect waste generation patterns using IoT sensors. For example, the collection unit uses sensors installed in a dedicated smart trash can in the kitchen to recognize food waste. The collection unit collects waste generation patterns as data and transmits it to the analysis unit. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to analyze trends in food loss. The analysis unit can analyze the collected data in detail using AI. For example, the analysis unit classifies waste generation patterns by time of day, type, and location to understand trends. Based on the collected data, the analysis unit identifies the causes of food loss and proposes improvement measures. The proposal unit proposes the optimal waste management method based on the analysis results obtained by the analysis unit. For example, the proposal unit optimizes shopping lists and suggests cooking recipes. The Proposal Department can use AI to provide users with optimal suggestions. For example, it can offer recipes to reduce frequently wasted food and advice on adjusting purchase quantities. For businesses, the Proposal Department analyzes waste data from production lines and warehouses and presents solutions to improve raw material utilization efficiency and inventory management. The Proposal Department can use AI to propose ways to streamline waste management for businesses. For example, if a particular raw material is being used excessively, the Proposal Department will suggest ways to optimize its usage. The Proposal Department also provides advice to prevent inventory management errors and overproduction. The Service Department delivers the suggestions made by the Proposal Department to users. For example, the Service Department provides users with information on how to sort waste through a smartphone app. The Service Department makes it easy for users to check how to sort waste. For example, the Service Department allows users to check how specific types of waste should be sorted through the app. The Service Department also visualizes "eco-points" that can be earned through proper disposal, which can be used to donate to local environmental protection activities or exchanged for eco-friendly products.The service provider can provide information to raise users' environmental awareness. For example, by accumulating a certain number of points, users can make donations to participate in local cleanup activities or exchange them for eco-bags or recycled products. In this way, the environmental solution system according to the embodiment can streamline waste management for individuals and companies and raise environmental awareness.

[0066] The collection unit tracks waste generation patterns. For example, it tracks waste generation patterns in homes and offices in real time. The collection unit can detect waste generation patterns using IoT sensors. Specifically, it uses sensors installed in dedicated smart trash cans in kitchens to recognize discarded food items. These sensors combine weight sensors and image recognition technology to accurately determine the type and quantity of waste. For example, weight sensors measure the weight of waste placed in the trash can, and image recognition technology identifies the type of waste. This allows the collection unit to collect waste generation patterns as data and transmit it to the analysis unit. Furthermore, the collection unit can install sensors in other locations in homes and offices to gain a comprehensive understanding of waste generation. For example, it can install sensors in each room and common area of ​​an office to track waste generation patterns in detail. This allows the collection unit to accurately understand the sources and frequency of waste generation, supporting efficient waste management. The collection unit transmits this data to a cloud server for real-time data storage and management. This allows the collection unit to track waste generation patterns over the long term and understand trends. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis department analyzes data collected by the collection department. For example, the analysis department analyzes the collected data to analyze trends in food waste. The analysis department can use AI to analyze the collected data in detail. Specifically, it classifies waste generation patterns by time of day, type, and location to understand trends. For example, the AI ​​analyzes the time of day when waste is generated to identify the reasons why a large amount of waste is generated during specific time periods. It also classifies the data by type of waste to understand which type of waste is generated the most. Furthermore, it analyzes the data by location of waste generation to understand the amount of waste generated at specific locations. As a result, the analysis department can identify the causes of food waste and propose improvement measures based on the collected data. In addition, the analysis department can use historical data and statistical information to analyze long-term trends and patterns. For example, based on historical waste data, it can predict fluctuations in waste generation during specific seasons or events and plan future countermeasures. The analysis department can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0068] The Proposal Department proposes optimal waste management methods based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can optimize shopping lists and suggest cooking recipes. Using AI, the Proposal Department can provide optimal suggestions to users. Specifically, it can provide recipes to reduce frequently wasted ingredients and advice to adjust purchase quantities. For example, based on the user's past waste data, the Proposal Department can identify frequently wasted ingredients and suggest recipes to use those ingredients efficiently. It can also optimize the user's shopping list and adjust the amount of ingredients needed to reduce food waste. Furthermore, for businesses, the Proposal Department analyzes waste data from production lines and warehouses and presents measures to improve raw material utilization efficiency and inventory management. For example, if a particular raw material is being used excessively, the Proposal Department will suggest ways to optimize its usage. It also provides advice to prevent inventory management errors and overproduction. This allows the Proposal Department to streamline waste management for businesses, contributing to cost reduction and reduced environmental impact. Additionally, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the proposal department to provide users with the most suitable waste management methods and raise environmental awareness.

[0069] The service provider will provide users with the content proposed by the proposal provider. For example, the service provider will provide users with information on how to sort waste through a smartphone app. The service provider will ensure that users can easily check how to sort waste. Specifically, the service provider will allow users to check how specific types of waste should be sorted through the app. For example, when a user enters the type of waste into the app, the appropriate sorting method will be displayed. The service provider will also visualize "eco points" that can be earned through proper processing, and allow users to use them to donate to local environmental protection activities or exchange them for eco-friendly products. For example, by accumulating a certain number of points, users can donate to participate in local cleanup activities or exchange them for eco-bags or recycled products. In this way, the service provider can provide information that raises users' environmental awareness. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the content provided. For example, based on user feedback, they can make the explanation of sorting methods easier to understand or review how to earn eco points. In addition, the service provider can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the service provider to deliver information to users quickly and reliably, and to raise environmental awareness.

[0070] The collection unit can track waste generation patterns in homes and offices in real time. For example, the collection unit uses IoT sensors to track waste generation patterns in homes and offices in real time. The collection unit collects waste generation patterns as data and transmits it to the analysis unit. For example, the collection unit uses sensors installed in a dedicated smart trash can in the kitchen to recognize food waste. The collection unit classifies waste generation patterns by time of day, type, and location to understand trends. This improves the accuracy of waste management by tracking waste generation patterns in homes and offices in real time. 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 data acquired from IoT sensors into a generating AI and have the generating AI perform the tracking of waste generation patterns.

[0071] The analysis department can analyze collected data and analyze trends in food waste. For example, the analysis department can analyze collected data and analyze trends in food waste. The analysis department can use AI to analyze collected data in detail. For example, the analysis department can classify waste generation patterns by time of day, type, and location to understand trends. Based on the collected data, the analysis department can identify the causes of food waste and propose improvement measures. In this way, by analyzing collected data and analyzing trends in food waste, it contributes to reducing food waste. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input collected data into a generating AI and have the generating AI perform an analysis of food waste trends.

[0072] The suggestion unit can optimize shopping lists and suggest recipes. For example, the suggestion unit can optimize shopping lists and suggest recipes. The suggestion unit can use AI to provide optimal suggestions to the user. For example, the suggestion unit can provide recipes to reduce frequently wasted food and advice to adjust the amount purchased. This allows for reduced food waste and more efficient shopping by optimizing shopping lists and suggesting recipes. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input collected data into a generating AI and have the generating AI perform the optimization of shopping lists and suggest recipes.

[0073] The analysis department can analyze waste data from production lines and warehouses and propose improvements to raw material utilization efficiency and inventory management. For example, the analysis department can analyze waste data from production lines and warehouses and propose improvements to raw material utilization efficiency and inventory management. The analysis department can use AI to make suggestions for streamlining a company's waste management. For example, if a particular raw material is being used in excess, the analysis department will propose ways to optimize its usage. The analysis department will also provide advice to prevent inventory management errors and overproduction. In this way, by analyzing waste data from production lines and warehouses and proposing improvements to raw material utilization efficiency and inventory management, the analysis department streamlines a company's waste management. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input waste data into a generating AI and have the generating AI perform an analysis to propose improvements to raw material utilization efficiency and inventory management.

[0074] The proposal unit can propose the most effective waste sorting and recycling methods, taking into account the processing capacity of local recycling facilities and the market demand for recycled materials. For example, the proposal unit can propose the most effective waste sorting and recycling methods, considering the processing capacity of local recycling facilities and the market demand for recycled materials. The proposal unit can use AI to make suggestions to improve recycling efficiency. For example, if a particular recycled material is in high demand, the proposal unit can suggest prioritizing its sorting. This improves recycling efficiency by proposing the most effective waste sorting and recycling methods, taking into account the processing capacity of local recycling facilities and the market demand for recycled materials. Some or all of the above processes in the proposal unit may be performed using AI, or not. For example, the proposal unit can input data on the processing capacity of recycling facilities and market demand into a generating AI, which can then generate suggestions for the optimal sorting and recycling methods.

[0075] The service provider can provide users with information on how to sort waste through a smartphone app. For example, the service provider can provide users with information on how to sort waste through a smartphone app. The service provider makes it easy for users to check how to sort waste. For example, the service provider can allow users to check how specific types of waste should be sorted through the app. This allows users to easily check how to sort waste by providing information on how to sort waste through a smartphone app. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input information on how to sort waste into a generating AI and have the generating AI generate the information to be provided to the user.

[0076] The service provider can visualize "eco-points" earned through appropriate processing and make them usable for donations to local environmental protection activities or exchange for eco-friendly products. For example, the service provider can visualize "eco-points" earned through appropriate processing and make them usable for donations to local environmental protection activities or exchange for eco-friendly products. The service provider can provide information to raise users' environmental awareness. For example, the service provider can allow users to accumulate a certain number of points to make donations to participate in local cleanup activities or exchange them for eco-bags or recycled products. This raises users' environmental awareness by visualizing "eco-points" earned through appropriate processing and making them usable for donations to local environmental protection activities or exchange for eco-friendly products. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have a generating AI perform the calculation and visualization of eco-points.

[0077] The collection unit can estimate the user's emotions and adjust the timing of waste 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. If the user is relaxed, the collection unit can advance the collection timing to efficiently collect data. If the user is busy, the collection unit can adjust the collection timing to match the user's schedule. This reduces the user's burden and allows for efficient data collection by adjusting the collection timing based on 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 not using AI. For example, the collection unit can input user emotion data into the generative AI and have the generative AI adjust the collection timing.

[0078] The collection unit can select the optimal collection method by referring to the user's past waste data when collecting waste generation patterns. For example, the collection unit optimizes the collection method based on items that the user has frequently discarded in the past. The collection unit can concentrate collection on specific days of the week or time slots based on the user's past waste data. The collection unit can analyze the user's past waste data and propose the most efficient collection route. This enables efficient data collection by selecting the optimal collection method by referring to the user's past waste data. Some or all of the above processes in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past waste data into a generating AI and have the generating AI select the optimal collection method.

[0079] The collection unit can filter waste generation patterns based on the user's lifestyle and work content. For example, when a user collects waste from their home, the collection unit can filter the collected data based on their lifestyle. When a user collects waste from an office, the collection unit can filter the collected data based on their work content. The collection unit can determine the priority of the collected data according to the user's lifestyle and work content. This improves the accuracy of the collected data by filtering based on the user's lifestyle and work content. 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 data on the user's lifestyle and work content into a generating AI and have the generating AI perform the filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of waste data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. If the user is relaxed, the data collection unit can prioritize the collection of highly important data. If the user is busy, the data collection unit can adjust the priority of the data to be collected to collect data efficiently. This allows for efficient data collection by determining the priority of data to be collected based on 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data to be collected.

[0081] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting waste generation patterns. For example, if the user is in a specific area, the data collection unit will prioritize the collection of waste data for that area. The data collection unit can filter highly relevant data based on the user's geographical location. If the user is on the move, the data collection unit can collect the most relevant data based on their current location. This enables efficient data collection by prioritizing the collection of highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0082] The collection unit can analyze users' social media activity and collect relevant data when collecting waste generation patterns. For example, the collection unit can predict waste generation patterns and collect data from users' social media posts. The collection unit can analyze users' social media activity and prioritize the collection of relevant waste data. The collection unit can collect relevant data by referring to the activities of users' social media followers and friends. This allows for a more accurate understanding of waste generation patterns by analyzing users' social media activity and collecting relevant data. 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 users' social media activity data into a generating AI and have the generating AI collect relevant data.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis method of the waste data based on the estimated user emotions. For example, if the user is stressed, the analysis unit can apply a simple analysis method and provide results quickly. If the user is relaxed, the analysis unit can apply a detailed analysis method and provide comprehensive results. If the user is busy, the analysis unit can apply an analysis method that focuses on important data. This allows the analysis unit to provide analysis results that are appropriate for the user by adjusting the analysis method based on 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data when analyzing waste data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can determine the priority of the analysis according to the importance of the data. This enables efficient data analysis by adjusting the level of detail of the analysis based on the importance of the collected data. 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 the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0085] The analysis unit can apply different analysis algorithms depending on the data category when analyzing waste data. For example, the analysis unit can apply an algorithm to analyze food loss trends to food waste data. For industrial waste data, it can apply an algorithm to analyze the efficiency of raw material use. For recyclable waste data, it can apply an algorithm to analyze recycling efficiency. By applying the most appropriate analysis algorithm according to the data category, it can provide highly accurate analysis results. 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 execute the application of analysis algorithms according to the category.

[0086] 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. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, the analysis results can be provided that are appropriate for the user. 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 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.

[0087] The analysis unit can determine the priority of analysis based on the data collection period when analyzing waste data. For example, the analysis unit may prioritize the analysis of recently collected data. The analysis unit can analyze current data while referring to past data. The analysis unit can adjust the order of analysis based on the data collection period. This enables efficient data analysis by determining the priority of analysis based on the data collection period. 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 collected data into a generating AI and have the generating AI determine the priority of analysis based on the collection period.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data when analyzing waste data. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can optimize the order of analysis based on the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0089] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way it presents suggestions based on the user's emotions, it can provide suggestions that are appropriate for the user. 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 it presents suggestions.

[0090] The proposal unit can adjust the level of detail of its proposals based on the importance of the waste management methods. For example, the proposal unit can provide detailed proposals for highly important waste management methods, and simplified proposals for less important methods. The proposal unit can also determine the priority of proposals according to the importance of the waste management methods. This allows for efficient proposals by adjusting the level of detail based on the importance of the waste management methods. 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 management method data into a generating AI and have the generating AI adjust the level of detail of proposals based on importance.

[0091] The proposal unit can apply different proposal algorithms depending on the waste category when making proposals. For example, the proposal unit can make proposals to reduce food loss for food waste. For industrial waste, it can make proposals to improve the efficiency of raw material use. For recyclable waste, it can make proposals to improve recycling efficiency. By applying the optimal proposal algorithm according to the waste category, it is possible to provide highly accurate proposals. 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 execute the application of a proposal algorithm according to the category.

[0092] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide suggestions that can be quickly understood. In this way, by adjusting the length of suggestions based on the user's emotions, the suggestion unit can provide suggestions that are appropriate for the user. 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 adjust the length of suggestions.

[0093] The proposal unit can determine the priority of proposals based on the timing of waste data collection when making proposals. For example, the proposal unit can prioritize proposals based on recently collected data. The proposal unit can make proposals based on current data while referring to past data. The proposal unit can adjust the order of proposals based on the timing of data collection. This enables efficient proposals by determining the priority of proposals based on the timing of waste data collection. 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 collected data into a generating AI and have the generating AI determine the priority of proposals based on the collection timing.

[0094] The proposal unit can adjust the order of proposals based on the relevance of the waste data when making proposals. For example, the proposal unit can prioritize proposals based on highly relevant data. The proposal unit can postpone proposals based on less relevant data. The proposal unit can optimize the order of proposals based on the relevance of the data. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the waste data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0095] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. If the user is in a hurry, the service provider can provide a display method that gets straight to the point. In this way, by adjusting the display method based on the user's emotions, the service provider can provide information that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0096] The information provider can select the optimal display method by referring to the user's past operation history when displaying the information to be provided. For example, the information provider can prioritize display methods that the user has previously preferred to use. The information provider can select the most efficient display method from the user's past operation history. The information provider can analyze the user's past operation history and propose the optimal display method. In this way, by selecting the optimal display method by referring to the user's past operation history, information suitable for the user can be provided. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's operation history data into a generating AI and have the generating AI perform the selection of the optimal display method.

[0097] The information provider can select the optimal display method when displaying the information it provides, taking into account the user's device information. For example, if the user is using a smartphone, the provider can provide a display method that matches the screen size. If the user is using a tablet, the provider can provide a display method optimized for a larger screen. If the user is using a smartwatch, the provider can provide a concise and highly visible display method. In this way, by selecting the optimal display method considering the user's device information, the provider can provide information that is appropriate for the user. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0098] The service provider can estimate the user's emotions and adjust the instructions for operating the information provided based on the estimated emotions. For example, if the user is stressed, the service provider can simplify the instructions. If the user is relaxed, the service provider can provide detailed instructions. If the user is in a hurry, the service provider can provide instructions that allow for quick operation. In this way, by adjusting the instructions based on the user's emotions, information suitable for the user can be provided. 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the instructions.

[0099] The information provider can select the optimal display method by referring to the user's calendar information when displaying the information it provides. For example, the information provider can prioritize displaying information related to important appointments based on the user's calendar. The information provider can select the optimal display timing based on the user's calendar information. The information provider can display relevant information by referring to the user's calendar information. In this way, by selecting the optimal display method by referring to the user's calendar information, it can provide information that is appropriate for the user. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input the user's calendar information into a generating AI and have the generating AI perform the selection of the optimal display method.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The collection unit can estimate the user's emotions and adjust the timing of waste 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. Furthermore, if the user is busy, the collection timing can be adjusted to match the user's schedule. In this way, by adjusting the collection timing based on the user's emotions, the user's burden can be reduced and data can be collected efficiently. 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 not using AI. For example, the collection unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the collection timing.

[0102] The analysis unit can estimate the user's emotions and adjust the analysis method of waste data based on the estimated user emotions. For example, if the user is stressed, a simple analysis method can be applied to provide results quickly. If the user is relaxed, a detailed analysis method can be applied to provide comprehensive results. Furthermore, if the user is busy, an analysis method focusing on important data can be applied. In this way, by adjusting the analysis method based on the user's emotions, analysis results tailored to the user can be provided. 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 not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0103] 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 stressed, it can provide simple and easy-to-understand suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide concise suggestions. In this way, by adjusting the way suggestions are presented based on the user's emotions, the system can provide suggestions that are appropriate for the user. 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 using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the way suggestions are presented.

[0104] The service provider can estimate the user's emotions and adjust the way the information is displayed based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, 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 based on the user's emotions, information suitable for the user can be provided. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0105] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, it can provide short, concise suggestions. If the user is relaxed, it can provide detailed suggestions. Furthermore, if the user is in a hurry, it can provide suggestions that can be quickly understood. In this way, by adjusting the length of suggestions based on the user's emotions, it is possible to provide suggestions that are appropriate for the user. 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, for example, or not using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the length of the suggestions.

[0106] The collection unit can select the optimal collection method by referring to the user's past waste data when collecting waste generation patterns. For example, it can optimize the collection method based on items the user has frequently discarded in the past. Based on the user's past waste data, it can concentrate collection on specific days of the week or time slots. Furthermore, it can analyze the user's past waste data and propose the most efficient collection route. This enables efficient data collection by selecting the optimal collection method by referring to the user's past waste data. 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 past waste data into a generating AI and have the generating AI select the optimal collection method.

[0107] The collection unit can filter waste generation patterns based on the user's lifestyle and work content. For example, when a user collects waste from their home, the collected data can be filtered based on their lifestyle. When a user collects waste from an office, the collected data can be filtered based on their work content. Furthermore, the collection unit can determine the priority of the collected data according to the user's lifestyle and work content. This improves the accuracy of the collected data by filtering based on the user's lifestyle and work content. 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 data on the user's lifestyle and work content into a generating AI and have the generating AI perform the filtering.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting waste generation patterns. For example, if the user is in a specific area, it can prioritize the collection of waste data for that area. It can filter highly relevant data based on the user's geographical location. Furthermore, if the user is on the move, it can collect the most relevant data based on their current location. This enables efficient data collection by prioritizing the collection of highly relevant data while considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into a generating AI and have the generating AI prioritize the collection of highly relevant data.

[0109] The collection unit can analyze users' social media activity and collect relevant data when collecting waste generation patterns. For example, it can predict waste generation patterns from users' social media posts and collect data. By analyzing users' social media activity, it can prioritize the collection of relevant waste data. Furthermore, it can collect relevant data by referring to the activities of users' social media followers and friends. This allows for a more accurate understanding of waste generation patterns by analyzing users' social media activity and collecting relevant data. 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 social media activity data into a generating AI and have the generating AI collect relevant data.

[0110] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected data when analyzing waste data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, it can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the collected data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis based on importance.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit tracks waste generation patterns. For example, it tracks waste generation patterns in homes and offices in real time and detects waste generation patterns using IoT sensors. Specifically, it uses sensors installed in dedicated smart trash cans in kitchens to recognize food items that are being discarded. The collection unit collects waste generation patterns as data and transmits it to the analysis unit. Step 2: The analysis department analyzes the data collected by the collection department. For example, they analyze the collected data to analyze trends in food waste. The analysis department uses AI to analyze the collected data in detail, classifying waste generation patterns by time of day, type, and location to understand trends. Furthermore, based on the collected data, they identify the causes of food waste and propose improvement measures. Step 3: The proposal department proposes the optimal waste management method based on the analysis results obtained by the analysis department. For example, they may optimize shopping lists or suggest cooking recipes. The proposal department uses AI to provide optimal suggestions to users, offering recipes to reduce frequently wasted food and advice on adjusting purchase quantities. For businesses, they analyze waste data from production lines and warehouses and present solutions to improve raw material utilization efficiency and inventory management. Step 4: The provisioning department provides the content proposed by the proposaling department to the users. For example, they provide users with information on how to sort waste through a smartphone app, making it easy for users to check how to sort waste. Furthermore, they visualize "eco points" that can be earned through proper disposal, and allow users to use them for donations to local environmental protection activities or exchange them for eco-friendly products.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision 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 tracks waste generation patterns in real time using the sensors of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze trends in food loss. The proposal unit proposes an optimal waste management method using the specific processing unit 290 of the data processing unit 12. The provision unit provides the proposed content to the user using the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit tracks waste generation patterns in real time using the sensors of the smart glasses 214. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to analyze trends in food loss. The proposal unit proposes an optimal waste management method using the identification processing unit 290 of the data processing unit 12. The provision unit provides the proposed content to the user using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit tracks waste generation patterns in real time using the sensors of the headset terminal 314. The analysis unit analyzes the data collected by the identification processing unit 290 of the data processing unit 12 to analyze trends in food loss. The proposal unit proposes an optimal waste management method using the identification processing unit 290 of the data processing unit 12. The provision unit provides the proposed content to the user using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit tracks waste generation patterns in real time using the sensors of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing unit 12 to analyze trends in food loss. The proposal unit proposes an optimal waste management method using the specific processing unit 290 of the data processing unit 12. The provision unit provides the proposed content to the user using the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A collection unit that tracks the patterns of waste generation, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal waste management method. The system comprises a provisioning unit that provides the user with the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Track waste generation patterns in homes and offices in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is We will analyze the collected data to analyze trends in food waste. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Optimize shopping lists and suggest recipes. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is We analyze waste data from production lines and warehouses to propose improvements to raw material utilization efficiency and inventory management. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the most effective waste sorting and recycling methods, taking into account the processing capacity of local recycling facilities and the market demand for recycled materials. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, Providing users with information on how to sort waste through a smartphone app. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, The "eco-points" earned through proper processing will be visualized and made available for use in donations to local environmental protection activities or in exchange for eco-friendly products. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates user emotions and adjusts the timing of waste collection based on the estimated user emotions and waste generation patterns. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting waste generation patterns, the system selects the optimal collection method by referring to the user's past waste data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting waste generation patterns, filtering is performed based on the user's lifestyle and work content. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is When collecting waste generation patterns, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting waste generation patterns, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate user sentiment and adjust the waste data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing waste data, adjust the level of detail of the analysis based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When analyzing waste data, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When analyzing waste data, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing waste data, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the waste management method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the waste category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of waste data collection. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the waste data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When displaying the information provided, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When displaying the information provided, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts the instructions for interacting with the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When displaying the information provided, the system references the user's calendar information to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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. A collection unit that tracks the patterns of waste generation, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal waste management method. The system comprises a provisioning unit that provides the user with the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Track waste generation patterns in homes and offices in real time. The system according to feature 1.

3. The aforementioned analysis unit is We will analyze the collected data to analyze trends in food waste. The system according to feature 1.

4. The aforementioned proposal section is, Optimize shopping lists and suggest recipes. The system according to feature 1.

5. The aforementioned analysis unit is We analyze waste data from production lines and warehouses to propose improvements to raw material utilization efficiency and inventory management. The system according to feature 1.

6. The aforementioned proposal section is, We propose the most effective waste sorting and recycling methods, taking into account the processing capacity of local recycling facilities and the market demand for recycled materials. The system according to feature 1.

7. The aforementioned supply unit is, Providing users with information on how to sort waste through a smartphone app. The system according to feature 1.

8. The aforementioned supply unit is, The eco-points earned through proper processing will be visualized and made available for use in donations to local environmental protection activities or in exchange for eco-friendly products. The system according to feature 1.

9. The aforementioned collection unit is The system estimates user emotions and adjusts the timing of waste collection based on the estimated user emotions and waste generation patterns. The system according to feature 1.

10. The aforementioned collection unit is When collecting waste generation patterns, the system selects the optimal collection method by referring to the user's past waste data. The system according to feature 1.

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