System

Abstract

An object of the system according to the embodiment is to propose reduction and reuse / recycling of waste.SOLUTION: A system includes a waste data collection part, an analysis part, a proposal part, and a monitoring part. The waste data collection part collects waste data. The analysis unit analyzes the waste data collected by the waste data collection unit. The proposal part proposes a waste reduction method or reuse / recycling on the basis of a result analyzed by the analysis part. The monitoring unit monitors a use-by date and a degree of damage of food based on data acquired from the sensor device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose waste reduction, reuse, and recycling, and there is room for improvement.

[0005] The system according to the embodiment aims to propose ways to reduce waste and reuse and recycle. [Means for solving the problem]

[0006] The system according to the embodiment includes a waste data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The waste data collection unit collects waste data. The analysis unit analyzes the waste data collected by the waste data collection unit. The proposal unit proposes methods for reducing waste or reuse / recycling based on the results of the analysis by the analysis unit. The monitoring unit monitors the expiration date and spoilage of food based on data acquired from sensor devices. [Effects of the Invention]

[0007] The system according to the embodiment can propose ways to reduce waste and reuse and recycle. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The waste reduction system according to an embodiment of the present invention analyzes patterns and trends in waste generated by individuals and businesses, proposes methods for reducing waste and for reuse and recycling, monitors food expiration dates and spoilage, and makes suggestions for reducing food waste. This allows individuals and businesses to efficiently manage waste and reduce the burden on the environment.

[0029] A waste reduction system according to an embodiment includes a waste data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The waste data collection unit collects waste data. For example, it collects information such as the type and amount of waste generated in households and the frequency of waste generation in businesses. The waste data collection unit can also collect waste data in real time using sensor devices. For example, sensor devices are attached to trash cans and automatically record the type and amount of waste. The analysis unit analyzes the waste data collected by the waste data collection unit. For example, a generation AI analyzes waste patterns and trends to identify the causes of waste generation. The analysis unit can also analyze the waste data using a machine learning algorithm to predict future waste generation. For example, the generation AI predicts waste generation trends based on past data and proposes appropriate countermeasures. The proposal unit proposes waste reduction methods, reuse, and recycling based on the results of the analysis by the analysis unit. For example, the generation AI proposes storage methods to reduce food waste in households. The proposal unit can also propose paper reuse methods and recycling procedures in businesses. For example, the generation AI analyzes a business's waste data and proposes optimal recycling methods. The monitoring unit monitors food expiration dates and spoilage levels based on data obtained from sensor devices. For example, sensor devices are attached to food in a refrigerator, and the generation AI analyzes the data to monitor the condition of the food. The monitoring unit can also send notifications when food is approaching its expiration date. For example, the generation AI could monitor food expiration dates and send a notification to the user when the expiration date is approaching. This allows the waste reduction system to enable individuals and businesses to efficiently manage waste and reduce the burden on the environment. For example, reducing food waste at home can save on food costs, and reducing waste at businesses can be expected to reduce costs. Proper waste management can also contribute to environmental protection.

[0030] The analysis unit can analyze seasonal patterns of waste generation based on local climate data and seasonal fluctuations. The analysis unit, for example, collects local climate data and analyzes seasonal patterns of waste generation. For example, it analyzes the tendency for food waste to increase in the summer and identifies the cause. The analysis unit can also predict waste generation taking seasonal fluctuations into account. For example, it predicts the type and amount of waste generated due to increased use of heating appliances in the winter. In this way, by taking local climate data and seasonal fluctuations into account, it is possible to analyze seasonal patterns of waste generation and propose appropriate measures.

[0031] The suggestion unit can propose an individually customized waste reduction plan by referring to the user's lifestyle or purchase history. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and propose an individually customized waste reduction plan. For example, the suggestion unit can propose optimal waste reduction methods based on the household configuration and lifestyle habits. The suggestion unit can also propose a waste reduction plan by referring to the user's purchase history. For example, the suggestion unit can recommend the purchase of reusable or recyclable products based on past purchase data. This makes it possible to propose an individually optimized waste reduction plan based on the user's lifestyle and purchase history.

[0032] The analysis unit can analyze waste patterns and trends not only within households but also at the regional or city level, and formulate waste reduction strategies for each region. For example, the analysis unit collects waste data for the entire region, and the generation AI formulates a waste reduction strategy for each region. For example, it identifies the types of waste that are generated frequently in a particular region and proposes ways to reduce them. The analysis unit can also analyze waste data at the city level and formulate a waste reduction strategy for the entire city. For example, it can propose infrastructure development to reduce waste generation based on urban planning data. This makes it possible to analyze waste patterns and trends not only within households but also at the regional or city level, and formulate a waste reduction strategy for each region.

[0033] The analysis unit can be applied to different industries or business types and propose industry-specific waste reduction methods. For example, the analysis unit collects waste data from different industries, and the generation AI proposes industry-specific waste reduction methods. For example, different waste reduction methods are proposed for the manufacturing industry and the service industry. The analysis unit can also analyze waste data for each industry and propose industry-specific waste reduction methods. For example, different waste reduction methods are proposed for the food industry and the chemical industry. This makes it possible to apply the method to different industries or business types and propose industry-specific waste reduction methods.

[0034] The suggestion unit can refer to the user's past behavioral data and customize and suggest the most effective waste reduction method. For example, the suggestion unit uses a generation AI to analyze the user's past behavioral data and customize and suggest the most effective waste reduction method. For example, it makes new suggestions based on methods that have been successful in the past. The suggestion unit can also refer to the user's past behavioral data and customize the waste reduction method. For example, it suggests the optimal waste reduction method for the user based on the past data. This makes it possible to customize and suggest the most effective waste reduction method based on the user's past behavioral data.

[0035] The suggestion unit can propose individually optimized waste reduction methods taking into account the user's lifestyle habits or family composition. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and propose individually optimized waste reduction methods. For example, it proposes recycling methods that suit the family composition and lifestyle rhythm. The suggestion unit can also propose waste reduction methods taking into account the user's family composition. For example, it proposes the optimal waste reduction method based on the number of family members and age composition. This makes it possible to propose individually optimized waste reduction methods based on the user's lifestyle habits and family composition.

[0036] The suggestion unit can be applied not only within a household but also to the entire community, such as a workplace or school, to promote collective waste reduction. For example, the generative AI in the suggestion unit suggests ways to reduce waste throughout the entire community, such as a workplace or school. For example, it could suggest going paperless in the workplace or a recycling program at school. The suggestion unit can also make suggestions to promote waste reduction throughout the entire community, not just within a household. For example, it could suggest a waste reduction campaign in a local community. This can promote waste reduction throughout the entire community, not just within a household, such as a workplace or school.

[0037] The suggestion unit can propose waste reduction methods that are applicable to different cultural areas or countries and take cultural backgrounds into consideration. For example, the generation AI proposes waste reduction methods for different cultural areas or countries. For example, it proposes recycling methods that are suitable for a specific culture. The suggestion unit can also propose waste reduction methods taking cultural backgrounds into consideration. For example, it proposes the optimal waste reduction method based on local culture and customs. This makes it possible to propose waste reduction methods that are applicable to different cultural areas or countries and take cultural backgrounds into consideration.

[0038] The monitoring unit monitors not only food expiration dates but also fluctuations in nutritional value based on data obtained from sensor devices, and can suggest the optimal timing for consumption. For example, the monitoring unit uses the generation AI to monitor food expiration dates and fluctuations in nutritional value based on data obtained from sensor devices. For example, it analyzes changes in food freshness and nutritional value in real time and suggests the optimal timing for consumption. The monitoring unit can also suggest consuming food before its nutritional value decreases. For example, the generation AI monitors fluctuations in food nutritional value and notifies the optimal timing for consumption. This makes it possible to monitor not only food expiration dates but also fluctuations in nutritional value and suggest the optimal timing for consumption.

[0039] The monitoring unit can use the generation AI to make individually customized suggestions by taking into account the user's eating patterns and preferences when monitoring the spoilage of food. For example, the generation AI analyzes the user's eating pattern data and makes individually customized suggestions when monitoring the spoilage of food. For example, it can suggest consumption timing that suits the user's preferences. The monitoring unit can also suggest how to consume food by taking into account the user's eating patterns. For example, the generation AI can analyze the user's eating patterns and suggest the optimal consumption method. This makes it possible to make individually customized suggestions based on the user's eating patterns and preferences.

[0040] The monitoring unit can be applied not only in homes but also in commercial environments such as restaurants or food factories to reduce food waste. For example, the monitoring unit can be applied to a restaurant by combining sensor devices and generative AI to reduce food waste. For example, it can monitor the expiration dates of food in the restaurant's refrigerator and suggest the best time to consume it. The monitoring unit can also reduce food waste in food factories by using sensor devices and generative AI. For example, it can monitor waste generated on a food factory's production line in real time and suggest ways to reduce it. This makes it possible to reduce food waste not only in homes but also in commercial environments such as restaurants and food factories.

[0041] The monitoring unit can be applied to different food categories and propose the optimal monitoring method for each. For example, the monitoring unit applies sensor devices and generation AI to fresh foods and proposes the optimal monitoring method. For example, it monitors the freshness of vegetables and fruits in real time and proposes the optimal timing for consumption. The monitoring unit can also propose monitoring methods for processed foods using sensor devices and generation AI. For example, it monitors the storage status of canned and frozen foods and proposes the optimal timing for consumption. This makes it possible to apply the system to different food categories and propose the optimal monitoring method for each.

[0042] The suggestion unit can refer to the user's past behavioral data and customize and provide the most effective waste disposal method or recycling procedure. The suggestion unit, for example, uses a generation AI to analyze the user's past behavioral data and customize and provide the most effective waste disposal method or recycling procedure. For example, it makes new suggestions based on methods that have been successful in the past. The suggestion unit can also refer to the user's past behavioral data and customize the waste disposal method or recycling procedure. For example, it suggests the optimal waste disposal method or recycling procedure for the user based on past data. This makes it possible to customize and provide the most effective waste disposal method or recycling procedure based on the user's past behavioral data.

[0043] The suggestion unit can provide individually optimized waste disposal methods and recycling procedures by taking into account the user's lifestyle habits or family composition. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and provide individually optimized waste disposal methods and recycling procedures. For example, it can suggest recycling methods that suit the family composition and lifestyle rhythm. The suggestion unit can also suggest waste disposal methods and recycling procedures by taking into account the user's family composition. For example, it can suggest optimal waste disposal methods and recycling procedures based on the number of family members and age composition. This makes it possible to provide individually optimized waste disposal methods and recycling procedures based on the user's lifestyle habits and family composition.

[0044] The suggestion unit can be applied not only within a household but also to the entire community, such as a workplace or school, to promote collective waste management. For example, the generative AI provides waste disposal methods and recycling procedures for the entire community, such as a workplace or school. For example, it can suggest going paperless at work or a recycling program at school. The suggestion unit can also make suggestions to promote waste management not only within a household but also throughout the community. For example, it can suggest a waste management campaign in a local community. This can promote waste management not only within a household but throughout the entire community, such as a workplace or school.

[0045] The suggestion unit can provide waste disposal methods and recycling procedures that are applicable to different cultural areas or countries and take cultural backgrounds into consideration. For example, the generation AI provides waste disposal methods and recycling procedures for different cultural areas or countries. For example, it proposes a recycling method that is suitable for a particular culture. The suggestion unit can also propose waste disposal methods and recycling procedures that take cultural backgrounds into consideration. For example, it proposes the optimal waste disposal method based on local culture and customs. This makes it possible to provide waste disposal methods and recycling procedures that are applicable to different cultural areas or countries and take cultural backgrounds into consideration.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The waste reduction system can also collect energy consumption data and make suggestions to simultaneously achieve waste reduction and improved energy efficiency. For example, it can analyze household electricity consumption data and recommend the use of energy-efficient home appliances. It can also use corporate energy consumption data to suggest ways to achieve both improved energy efficiency and waste reduction. This makes it possible to simultaneously achieve waste reduction and improved energy efficiency.

[0048] The waste reduction system can also collect user health data and suggest waste reduction methods based on the user's health status. For example, it can analyze the user's food records and exercise data to suggest ways to reduce food waste while maintaining a healthy diet. It can also suggest ways to achieve both health promotion and waste reduction based on the health data of company employees. This makes it possible to suggest waste reduction methods based on the user's health status.

[0049] The waste reduction system can also collect user purchasing data and suggest waste reduction methods based on purchasing behavior. For example, it can analyze past purchasing data and recommend purchasing reusable or recyclable products. It can also suggest waste-efficient purchasing plans based on the user's purchasing patterns. This makes it possible to suggest waste reduction methods based on purchasing behavior.

[0050] The waste reduction system can also collect lifestyle data about the user and suggest waste reduction methods based on that lifestyle. For example, it can suggest optimal waste reduction methods based on the user's lifestyle habits and hobbies. It can also suggest recycling and reuse methods that fit the user's lifestyle. This makes it possible to suggest waste reduction methods based on the user's lifestyle.

[0051] The waste reduction system can also collect the user's social network data and utilize social influence to suggest waste reduction methods. For example, the system can refer to the waste reduction activities of the user's friends and family to suggest the most suitable method for the user. The system can also utilize the user's social network to suggest ways to jointly work on waste reduction. In this way, it can suggest waste reduction methods by utilizing social influence.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The waste data collection unit collects waste data. For example, it collects the type and amount of waste in households, and the frequency of waste generation in businesses. The waste data collection unit can also collect waste data in real time using sensor devices. For example, a sensor device is attached to a trash can and automatically records the type and amount of waste. Step 2: The analysis unit analyzes the waste data collected by the waste data collection unit. For example, the generation AI analyzes waste patterns and trends and identifies the causes of waste generation. The analysis unit can also analyze the waste data using machine learning algorithms to predict future waste generation. For example, the generation AI predicts waste generation trends based on past data and proposes appropriate countermeasures. Step 3: The proposal unit makes proposals for waste reduction methods and reuse / recycling based on the results of the analysis by the analysis unit. For example, the generation AI would suggest storage methods to reduce food waste at home. The proposal unit could also suggest paper reuse methods and recycling procedures for businesses. For example, the generation AI would analyze a company's waste data and propose the optimal recycling method. Step 4: The monitoring unit monitors the food's expiration date and degree of spoilage based on data obtained from the sensor device. For example, a sensor device is attached to food in the refrigerator, and the generation AI analyzes the data to monitor the food's condition. The monitoring unit can also send notifications when the food's expiration date approaches. For example, the generation AI monitors the food's expiration date and sends a notification to the user when the expiration date approaches.

[0054] (Example 2) The waste reduction system according to an embodiment of the present invention analyzes patterns and trends in waste generated by individuals and businesses, proposes methods for reducing waste and for reuse and recycling, monitors food expiration dates and spoilage, and makes suggestions for reducing food waste. This allows individuals and businesses to efficiently manage waste and reduce the burden on the environment.

[0055] A waste reduction system according to an embodiment includes a waste data collection unit, an analysis unit, a proposal unit, and a monitoring unit. The waste data collection unit collects waste data. For example, it collects information such as the type and amount of waste generated in households and the frequency of waste generation in businesses. The waste data collection unit can also collect waste data in real time using sensor devices. For example, sensor devices are attached to trash cans and automatically record the type and amount of waste. The analysis unit analyzes the waste data collected by the waste data collection unit. For example, a generation AI analyzes waste patterns and trends to identify the causes of waste generation. The analysis unit can also analyze the waste data using a machine learning algorithm to predict future waste generation. For example, the generation AI predicts waste generation trends based on past data and proposes appropriate countermeasures. The proposal unit proposes waste reduction methods, reuse, and recycling based on the results of the analysis by the analysis unit. For example, the generation AI proposes storage methods to reduce food waste in households. The proposal unit can also propose paper reuse methods and recycling procedures in businesses. For example, the generation AI analyzes a business's waste data and proposes optimal recycling methods. The monitoring unit monitors food expiration dates and spoilage levels based on data obtained from sensor devices. For example, sensor devices are attached to food in a refrigerator, and the generation AI analyzes the data to monitor the condition of the food. The monitoring unit can also send notifications when food is approaching its expiration date. For example, the generation AI could monitor food expiration dates and send a notification to the user when the expiration date is approaching. This allows the waste reduction system to enable individuals and businesses to efficiently manage waste and reduce the burden on the environment. For example, reducing food waste at home can save on food costs, and reducing waste at businesses can be expected to reduce costs. Proper waste management can also contribute to environmental protection.

[0056] The analysis unit can perform sentiment analysis and identify emotional factors that influence a user's consumption behavior. For example, the analysis unit can use generative AI to analyze a user's emotions when generating waste and identify emotional factors that influence consumption behavior. For example, it can analyze emotions such as stress and satisfaction and identify emotions related to waste generation. The analysis unit can also use natural language processing technology to analyze a user's emotions. For example, it can analyze a user's statements and behavioral data to identify emotional factors. By identifying emotional factors that influence a user's consumption behavior, it becomes possible to make effective suggestions for reducing waste.

[0057] The analysis unit can analyze seasonal patterns of waste generation based on local climate data and seasonal fluctuations. The analysis unit, for example, collects local climate data and analyzes seasonal patterns of waste generation. For example, it analyzes the tendency for food waste to increase in the summer and identifies the cause. The analysis unit can also predict waste generation taking seasonal fluctuations into account. For example, it predicts the type and amount of waste generated due to increased use of heating appliances in the winter. In this way, by taking local climate data and seasonal fluctuations into account, it is possible to analyze seasonal patterns of waste generation and propose appropriate measures.

[0058] The suggestion unit can propose an individually customized waste reduction plan by referring to the user's lifestyle or purchase history. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and propose an individually customized waste reduction plan. For example, the suggestion unit can propose optimal waste reduction methods based on the household configuration and lifestyle habits. The suggestion unit can also propose a waste reduction plan by referring to the user's purchase history. For example, the suggestion unit can recommend the purchase of reusable or recyclable products based on past purchase data. This makes it possible to propose an individually optimized waste reduction plan based on the user's lifestyle and purchase history.

[0059] The analysis unit can analyze waste patterns and trends not only within households but also at the regional or city level, and formulate waste reduction strategies for each region. For example, the analysis unit collects waste data for the entire region, and the generation AI formulates a waste reduction strategy for each region. For example, it identifies the types of waste that are generated frequently in a particular region and proposes ways to reduce them. The analysis unit can also analyze waste data at the city level and formulate a waste reduction strategy for the entire city. For example, it can propose infrastructure development to reduce waste generation based on urban planning data. This makes it possible to analyze waste patterns and trends not only within households but also at the regional or city level, and formulate a waste reduction strategy for each region.

[0060] The analysis unit can be applied to different industries or business types and propose industry-specific waste reduction methods. For example, the analysis unit collects waste data from different industries, and the generation AI proposes industry-specific waste reduction methods. For example, different waste reduction methods are proposed for the manufacturing industry and the service industry. The analysis unit can also analyze waste data for each industry and propose industry-specific waste reduction methods. For example, different waste reduction methods are proposed for the food industry and the chemical industry. This makes it possible to apply the method to different industries or business types and propose industry-specific waste reduction methods.

[0061] The analysis unit can use the emotion estimation function to analyze how the user feels about waste reduction and make suggestions to increase motivation based on the emotions. The analysis unit, for example, uses the emotion estimation function to analyze how the user feels about waste reduction. For example, it can make suggestions to further increase motivation for a user who has strong positive emotions. The analysis unit can also use the emotion estimation function to analyze the user's emotion data and make suggestions to increase motivation. For example, it can provide positive feedback to a user who has negative emotions. This makes it possible to make suggestions to increase motivation for waste reduction based on the user's emotions.

[0062] The suggestion unit can use the generation AI to analyze the user's emotions and suggest waste reduction methods that elicit positive emotions. For example, the suggestion unit uses the generation AI to analyze the user's emotional data and suggest waste reduction methods that elicit positive emotions. For example, it can suggest recycling methods that the user can enjoy. The suggestion unit can also use the generation AI to analyze the user's emotions and make suggestions to elicit positive emotions. For example, it can suggest waste reduction methods that will give the user a sense of satisfaction. In this way, it is possible to analyze the user's emotions and suggest waste reduction methods that elicit positive emotions.

[0063] The suggestion unit can refer to the user's past behavioral data and customize and suggest the most effective waste reduction method. For example, the suggestion unit uses a generation AI to analyze the user's past behavioral data and customize and suggest the most effective waste reduction method. For example, it makes new suggestions based on methods that have been successful in the past. The suggestion unit can also refer to the user's past behavioral data and customize the waste reduction method. For example, it suggests the optimal waste reduction method for the user based on the past data. This makes it possible to customize and suggest the most effective waste reduction method based on the user's past behavioral data.

[0064] The suggestion unit can propose individually optimized waste reduction methods taking into account the user's lifestyle habits or family composition. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and propose individually optimized waste reduction methods. For example, it proposes recycling methods that suit the family composition and lifestyle rhythm. The suggestion unit can also propose waste reduction methods taking into account the user's family composition. For example, it proposes the optimal waste reduction method based on the number of family members and age composition. This makes it possible to propose individually optimized waste reduction methods based on the user's lifestyle habits and family composition.

[0065] The suggestion unit can be applied not only within a household but also to the entire community, such as a workplace or school, to promote collective waste reduction. For example, the generative AI in the suggestion unit suggests ways to reduce waste throughout the entire community, such as a workplace or school. For example, it could suggest going paperless in the workplace or a recycling program at school. The suggestion unit can also make suggestions to promote waste reduction throughout the entire community, not just within a household. For example, it could suggest a waste reduction campaign in a local community. This can promote waste reduction throughout the entire community, not just within a household, such as a workplace or school.

[0066] The suggestion unit can propose waste reduction methods that are applicable to different cultural areas or countries and take cultural backgrounds into consideration. For example, the generation AI proposes waste reduction methods for different cultural areas or countries. For example, it proposes recycling methods that are suitable for a specific culture. The suggestion unit can also propose waste reduction methods taking cultural backgrounds into consideration. For example, it proposes the optimal waste reduction method based on local culture and customs. This makes it possible to propose waste reduction methods that are applicable to different cultural areas or countries and take cultural backgrounds into consideration.

[0067] The suggestion unit can use the emotion estimation function to analyze how the user feels about waste reduction and make suggestions to increase motivation based on the emotions. The suggestion unit, for example, uses the emotion estimation function to analyze how the user feels about waste reduction. For example, the suggestion unit can make suggestions to further increase motivation for a user who has strong positive emotions. The suggestion unit can also use the emotion estimation function to analyze the user's emotion data and make suggestions to increase motivation. For example, the suggestion unit can provide positive feedback to a user who has negative emotions. This makes it possible to make suggestions to increase motivation for waste reduction based on the user's emotions.

[0068] The monitoring unit monitors not only food expiration dates but also fluctuations in nutritional value based on data obtained from sensor devices, and can suggest the optimal timing for consumption. For example, the monitoring unit uses the generation AI to monitor food expiration dates and fluctuations in nutritional value based on data obtained from sensor devices. For example, it analyzes changes in food freshness and nutritional value in real time and suggests the optimal timing for consumption. The monitoring unit can also suggest consuming food before its nutritional value decreases. For example, the generation AI monitors fluctuations in food nutritional value and notifies the optimal timing for consumption. This makes it possible to monitor not only food expiration dates but also fluctuations in nutritional value and suggest the optimal timing for consumption.

[0069] The monitoring unit can use the generation AI to make individually customized suggestions by taking into account the user's eating patterns and preferences when monitoring the spoilage of food. For example, the generation AI analyzes the user's eating pattern data and makes individually customized suggestions when monitoring the spoilage of food. For example, it can suggest consumption timing that suits the user's preferences. The monitoring unit can also suggest how to consume food by taking into account the user's eating patterns. For example, the generation AI can analyze the user's eating patterns and suggest the optimal consumption method. This makes it possible to make individually customized suggestions based on the user's eating patterns and preferences.

[0070] The monitoring unit can use the emotion estimation function to analyze how the user feels about food waste and make suggestions for reducing food waste based on the emotions. The monitoring unit, for example, uses the emotion estimation function to analyze how the user feels about food waste. For example, it provides positive feedback to a user who has negative emotions. The monitoring unit can also use the emotion estimation function to analyze the user's emotion data and make suggestions for reducing food waste. For example, it makes a suggestion to encourage the user to have positive emotions about food waste. This makes it possible to make suggestions for reducing food waste based on the user's emotions.

[0071] The monitoring unit can be applied not only in homes but also in commercial environments such as restaurants or food factories to reduce food waste. For example, the monitoring unit can be applied to a restaurant by combining sensor devices and generative AI to reduce food waste. For example, it can monitor the expiration dates of food in the restaurant's refrigerator and suggest the best time to consume it. The monitoring unit can also reduce food waste in food factories by using sensor devices and generative AI. For example, it can monitor waste generated on a food factory's production line in real time and suggest ways to reduce it. This makes it possible to reduce food waste not only in homes but also in commercial environments such as restaurants and food factories.

[0072] The monitoring unit can be applied to different food categories and propose the optimal monitoring method for each. For example, the monitoring unit applies sensor devices and generation AI to fresh foods and proposes the optimal monitoring method. For example, it monitors the freshness of vegetables and fruits in real time and proposes the optimal timing for consumption. The monitoring unit can also propose monitoring methods for processed foods using sensor devices and generation AI. For example, it monitors the storage status of canned and frozen foods and proposes the optimal timing for consumption. This makes it possible to apply the system to different food categories and propose the optimal monitoring method for each.

[0073] The monitoring unit can use the emotion estimation function to analyze how the user feels about food waste and make suggestions for reducing food waste based on the emotions. The monitoring unit, for example, uses the emotion estimation function to analyze how the user feels about food waste. For example, it provides positive feedback to a user who has negative emotions. The monitoring unit can also use the emotion estimation function to analyze the user's emotion data and make suggestions for reducing food waste. For example, it makes a suggestion to encourage the user to have positive emotions about food waste. This makes it possible to make suggestions for reducing food waste based on the user's emotions.

[0074] The suggestion unit can use the generation AI to analyze the user's emotions and provide information that elicits positive emotions when providing waste disposal methods and recycling procedures. For example, the suggestion unit uses the generation AI to analyze the user's emotional data and provide waste disposal methods and recycling procedures that elicit positive emotions. For example, it can suggest recycling methods that the user can enjoy. The suggestion unit can also use the generation AI to analyze the user's emotions and provide information that elicits positive emotions. For example, it can suggest waste disposal methods that give the user a sense of satisfaction. This makes it possible to analyze the user's emotions and provide information on waste disposal methods and recycling procedures that elicit positive emotions.

[0075] The suggestion unit can refer to the user's past behavioral data and customize and provide the most effective waste disposal method or recycling procedure. The suggestion unit, for example, uses a generation AI to analyze the user's past behavioral data and customize and provide the most effective waste disposal method or recycling procedure. For example, it makes new suggestions based on methods that have been successful in the past. The suggestion unit can also refer to the user's past behavioral data and customize the waste disposal method or recycling procedure. For example, it suggests the optimal waste disposal method or recycling procedure for the user based on past data. This makes it possible to customize and provide the most effective waste disposal method or recycling procedure based on the user's past behavioral data.

[0076] The suggestion unit can provide individually optimized waste disposal methods and recycling procedures by taking into account the user's lifestyle habits or family composition. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and provide individually optimized waste disposal methods and recycling procedures. For example, it can suggest recycling methods that suit the family composition and lifestyle rhythm. The suggestion unit can also suggest waste disposal methods and recycling procedures by taking into account the user's family composition. For example, it can suggest optimal waste disposal methods and recycling procedures based on the number of family members and age composition. This makes it possible to provide individually optimized waste disposal methods and recycling procedures based on the user's lifestyle habits and family composition.

[0077] The suggestion unit can be applied not only within a household but also to the entire community, such as a workplace or school, to promote collective waste management. For example, the generative AI provides waste disposal methods and recycling procedures for the entire community, such as a workplace or school. For example, it can suggest going paperless at work or a recycling program at school. The suggestion unit can also make suggestions to promote waste management not only within a household but also throughout the community. For example, it can suggest a waste management campaign in a local community. This can promote waste management not only within a household but throughout the entire community, such as a workplace or school.

[0078] The suggestion unit can provide waste disposal methods and recycling procedures that are applicable to different cultural areas or countries and take cultural backgrounds into consideration. For example, the generation AI provides waste disposal methods and recycling procedures for different cultural areas or countries. For example, it proposes a recycling method that is suitable for a particular culture. The suggestion unit can also propose waste disposal methods and recycling procedures that take cultural backgrounds into consideration. For example, it proposes the optimal waste disposal method based on local culture and customs. This makes it possible to provide waste disposal methods and recycling procedures that are applicable to different cultural areas or countries and take cultural backgrounds into consideration.

[0079] The suggestion unit can use the emotion estimation function to analyze how a user feels about waste disposal and recycling, and provide information that increases motivation based on the emotion. The suggestion unit, for example, uses the emotion estimation function to analyze how a user feels about waste disposal and recycling. For example, it provides information that further increases motivation to a user who has strong positive emotions. The suggestion unit can also use the emotion estimation function to analyze the user's emotion data and provide information to increase motivation. For example, it provides positive feedback to a user who has negative emotions. This makes it possible to provide information that increases motivation for waste disposal and recycling based on the user's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The waste reduction system can also collect energy consumption data and make suggestions to simultaneously achieve waste reduction and improved energy efficiency. For example, it can analyze household electricity consumption data and recommend the use of energy-efficient home appliances. It can also use corporate energy consumption data to suggest ways to achieve both improved energy efficiency and waste reduction. This makes it possible to simultaneously achieve waste reduction and improved energy efficiency.

[0082] The waste reduction system can also collect user health data and suggest waste reduction methods based on the user's health status. For example, it can analyze the user's food records and exercise data to suggest ways to reduce food waste while maintaining a healthy diet. It can also suggest ways to achieve both health promotion and waste reduction based on the health data of company employees. This makes it possible to suggest waste reduction methods based on the user's health status.

[0083] The waste reduction system can further estimate the user's emotions and provide gamified suggestions for waste reduction based on their emotions. For example, it can suggest a waste reduction challenge that the user can enjoy. The emotion estimation function can also be used to introduce a reward system based on the user's emotions to increase motivation for waste reduction. This enables gamified suggestions for waste reduction based on emotions.

[0084] The waste reduction system can also collect user purchasing data and suggest waste reduction methods based on purchasing behavior. For example, it can analyze past purchasing data and recommend purchasing reusable or recyclable products. It can also suggest waste-efficient purchasing plans based on the user's purchasing patterns. This makes it possible to suggest waste reduction methods based on purchasing behavior.

[0085] The waste reduction system can further estimate the user's emotions and suggest community activities for waste reduction based on the emotions. For example, the emotion estimation function can be used to suggest waste reduction events that the user would like to participate in. Also, based on the emotions, a platform can be provided for the user to collaborate with others to work on waste reduction. This makes it possible to suggest community activities for waste reduction based on the emotions.

[0086] The waste reduction system can also collect lifestyle data about the user and suggest waste reduction methods based on that lifestyle. For example, it can suggest optimal waste reduction methods based on the user's lifestyle habits and hobbies. It can also suggest recycling and reuse methods that fit the user's lifestyle. This makes it possible to suggest waste reduction methods based on the user's lifestyle.

[0087] The waste reduction system can further estimate the user's emotions and suggest educational programs for waste reduction based on the emotions. For example, the emotion estimation function can be used to provide educational content related to waste reduction that the user is interested in. Also, based on the emotions, it can suggest workshops and seminars that the user would be willing to participate in. This makes it possible to suggest educational programs for waste reduction based on emotions.

[0088] The waste reduction system can further estimate the user's emotions and provide personalized advice for waste reduction based on the emotions. For example, the emotion estimation function can be used to suggest waste reduction methods that the user can undertake without feeling stressed. It can also suggest waste reduction methods that will give the user a sense of satisfaction based on the emotions. This makes it possible to provide personalized waste reduction advice based on emotions.

[0089] The waste reduction system can further estimate the user's emotions and propose an incentive program for waste reduction based on the emotions. For example, the emotion estimation function can be used to provide an incentive that gives the user a sense of accomplishment. Also, based on the emotions, an incentive program that the user can enjoy participating in can be proposed. In this way, an incentive program for waste reduction can be proposed based on the emotions.

[0090] The waste reduction system can also collect the user's social network data and utilize social influence to suggest waste reduction methods. For example, the system can refer to the waste reduction activities of the user's friends and family to suggest the most suitable method for the user. The system can also utilize the user's social network to suggest ways to jointly work on waste reduction. In this way, it can suggest waste reduction methods by utilizing social influence.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The waste data collection unit collects waste data. For example, it collects the type and amount of waste in households, and the frequency of waste generation in businesses. The waste data collection unit can also collect waste data in real time using sensor devices. For example, a sensor device is attached to a trash can and automatically records the type and amount of waste. Step 2: The analysis unit analyzes the waste data collected by the waste data collection unit. For example, the generation AI analyzes waste patterns and trends and identifies the causes of waste generation. The analysis unit can also analyze the waste data using machine learning algorithms to predict future waste generation. For example, the generation AI predicts waste generation trends based on past data and proposes appropriate countermeasures. Step 3: The proposal unit makes proposals for waste reduction methods and reuse / recycling based on the results of the analysis by the analysis unit. For example, the generation AI would suggest storage methods to reduce food waste at home. The proposal unit could also suggest paper reuse methods and recycling procedures for businesses. For example, the generation AI would analyze a company's waste data and propose the optimal recycling method. Step 4: The monitoring unit monitors the food's expiration date and degree of spoilage based on data obtained from the sensor device. For example, a sensor device is attached to food in the refrigerator, and the generation AI analyzes the data to monitor the food's condition. The monitoring unit can also send notifications when the food's expiration date approaches. For example, the generation AI monitors the food's expiration date and sends a notification to the user when the expiration date approaches.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a waste data collection unit that collects waste data; an analysis unit that analyzes the waste data collected by the waste data collection unit; a proposal unit that proposes a method for reducing waste or a method for reuse or recycling based on the results of the analysis by the analysis unit; A monitoring unit that monitors the expiration date and degree of spoilage of food based on data acquired from the sensor device. A system characterized by:

2. The analysis unit Analyze seasonal patterns of waste generation based on local climate data and seasonal variations 2. The system of claim 1.

3. The proposal unit Referencing the user's lifestyle or purchasing history to propose an individually customized waste reduction plan 2. The system of claim 1.

4. The analysis unit Analyze waste patterns and trends not only within households but also at the regional or city level to develop local waste reduction strategies 2. The system of claim 1.

5. The analysis unit Using an emotion estimation function, the user's emotions regarding waste reduction are analyzed, and the suggestion to increase motivation is made based on the emotions.

2. The system of claim 1.

6. The monitoring unit Based on the data acquired from the sensor device, not only the expiration date of the food but also fluctuations in nutritional value are monitored, and the optimal timing for consumption is suggested.

2. The system of claim 1.

7. The proposal unit Using generative AI, the system analyzes user emotions when providing waste disposal methods and recycling procedures, and provides information that elicits positive emotions.

2. The system of claim 1.

8. The proposal unit Using the emotion estimation function, the user's feelings regarding waste disposal or recycling are analyzed, and information that increases motivation is provided based on the user's feelings.

2. The system of claim 1.

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