System

Abstract

An object of a system according to an embodiment is to enable a user to quickly and easily understand a waste sorting rule.SOLUTION: A system according to an embodiment includes a rule learning unit, a question reception unit, and an answer generation unit. The rule learning unit learns a waste sorting rule for each area. The question reception unit receives a question in a free comment format from a user. The answer generation unit generates an answer based on the question received by the question reception unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that the rules for separating garbage are complicated, making it difficult for users to quickly obtain information for separating garbage appropriately.

[0005] The system according to the embodiment aims to enable users to quickly and easily understand the rules for separating garbage. [Means for solving the problem]

[0006] The system according to the embodiment includes a rule learning unit, a question receiving unit, and an answer generating unit. The rule learning unit learns the waste sorting rules of each region. The question receiving unit receives questions in the form of free comments from users. The answer generating unit generates answers based on the questions received by the question receiving unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable users to quickly and easily understand the rules for separating garbage. [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 AI ​​chat system according to an embodiment of the present invention provides information about waste sorting. This system allows users to ask questions in the form of free comments using a generation AI that has learned the waste sorting rules of each region. This allows users to easily obtain information about waste sorting from the AI ​​chat system.

[0029] The AI ​​chat system according to the embodiment includes a rule learning unit, a question receiving unit, and an answer generating unit. The rule learning unit learns the garbage sorting rules of each region. For example, the generation AI performs learning based on garbage sorting guidelines and regulations provided by local governments. The generation AI learns detailed local rules, such as regions where plastic products are treated as combustible garbage and regions where they are treated as non-combustible garbage. The question receiving unit receives questions in the form of free comments from users. For example, users can input specific questions such as, "What type of garbage is this plastic bottle cap?" The answer generating unit generates answers based on the questions received by the question receiving unit. For example, the generation AI analyzes the user's questions and generates appropriate answers based on the garbage sorting rules of each region. As a result, the AI ​​chat system according to the embodiment allows users to ask questions in the form of free comments and provides accurate information based on the garbage sorting rules of each region.

[0030] The rule learning unit learns not only garbage sorting rules but also seasonal variations in garbage sorting rules, making it possible to provide a garbage sorting method suited to the season. For example, the rule learning unit learns not only garbage sorting rules for each region, but also seasonal variations in garbage sorting rules. For example, during the Christmas season, there is an increase in the amount of specific wrapping paper and decorations, so a method for sorting those items is provided. This makes it possible to provide a garbage sorting method suited to the season.

[0031] The rule learning unit can incorporate past violation cases and penalty information when learning the garbage sorting rules and can warn the user. For example, the rule learning unit incorporates past violation cases and penalty information when learning the garbage sorting rules. For example, if there are frequent cases of improper sorting of a particular type of garbage, the rule learning unit can warn the user based on those cases. This makes it possible to warn the user based on past violation cases and penalty information.

[0032] The rule learning unit incorporates the sorting rules of other countries when learning garbage sorting rules, making it possible to provide advice from an international perspective.The rule learning unit incorporates the sorting rules of other countries when learning garbage sorting rules.For example, in addition to Japanese rules, it can learn the sorting rules of the United States and Europe and provide advice from an international perspective.This makes it possible to provide advice from an international perspective.

[0033] The rule learning unit incorporates the opinions of recycling companies and waste disposal companies when learning the garbage sorting rules, and is able to provide the optimal sorting method for actual disposal sites. For example, the rule learning unit incorporates the opinions of recycling companies and waste disposal companies when learning the garbage sorting rules. For example, based on the opinions of the companies, it provides the optimal sorting method for actual disposal sites. This makes it possible to provide the optimal sorting method for actual disposal sites.

[0034] The question receiving unit can provide a more personalized answer to a free comment-style question by referring to the user's past question history. For example, the question receiving unit can provide a more personalized answer to a free comment-style question by referring to the user's past question history. For example, advice based on previous answers is provided to a user who has asked the same question in the past. This makes it possible to provide a more personalized answer by referring to the user's past question history.

[0035] The question receiving unit can automatically acquire the user's location information when receiving a question and provide an answer that conforms to the rules of the area. For example, when a question is received, the question receiving unit can automatically acquire the user's location information and provide an answer that conforms to the rules of the area. For example, if the user is in a specific area, an answer based on the garbage sorting rules of that area is provided. This makes it possible to automatically acquire the user's location information and provide an answer that conforms to the rules of that area.

[0036] The question receiving unit can also receive voice input for questions in the free comment format and generate an answer using voice recognition technology. The question receiving unit can also receive voice input for questions in the free comment format and generate an answer using voice recognition technology. For example, a user can input a question by voice, and the voice can be converted into text to generate an answer. This makes it possible to receive voice input and generate an answer using voice recognition technology.

[0037] The question receiving unit automatically completes keywords when a user inputs a question when receiving a question, thereby reducing the effort required for input. For example, when a user starts to input "plastic bottle," the question receiving unit automatically completes keywords to "What type of garbage are plastic bottle caps?" This automatically completes keywords when a user inputs a question, thereby reducing the effort required for input.

[0038] The answer generation unit can provide answers regarding garbage sorting that include not only specific sorting methods but also the benefits of recycling and its impact on the environment. For example, the answer generation unit can provide answers regarding garbage sorting that include not only specific sorting methods but also the benefits of recycling and its impact on the environment. For example, the answer generation unit can explain the positive impact that recycling plastic waste has on the environment. This makes it possible to provide not only specific sorting methods but also the benefits of recycling and its impact on the environment.

[0039] The answer generation unit can show specific steps and points to note for the user to perform sorting using videos and images when providing an answer. For example, the answer generation unit shows specific steps and points to note for the user to perform sorting using videos and images when providing an answer. For example, the answer generation unit explains the steps to remove the cap of a plastic bottle using a video. This makes it possible to show specific steps and points to note for the user to perform sorting using videos and images.

[0040] The answer generation unit provides answers regarding garbage sorting in stages according to the user's level of understanding, making it possible to accommodate users from beginners to advanced users.The answer generation unit provides answers regarding garbage sorting in stages according to the user's level of understanding, for example, providing a basic sorting method to beginners and a detailed sorting method to advanced users.In this way, answers are provided in stages according to the user's level of understanding, making it possible to accommodate users from beginners to advanced users.

[0041] The answer generation unit can include advice that takes into account success stories and feedback from other users when providing an answer. For example, the answer generation unit can include advice that takes into account success stories and feedback from other users when providing an answer. For example, the answer generation unit can introduce success stories from other users regarding a specific method of sorting garbage. This makes it possible to include advice that takes into account success stories and feedback from other users.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The AI ​​chat system can also be equipped with an evaluation unit that evaluates the user's knowledge level regarding garbage sorting. The evaluation unit evaluates the user's knowledge level based on the content of the user's past questions and answers, and provides information at an appropriate level. For example, it can provide beginners with basic garbage sorting methods, while advanced users can receive detailed garbage sorting methods and the benefits of recycling. This makes it possible to provide information tailored to the user's knowledge level, supporting more effective learning.

[0044] The AI ​​chat system can further include a tracking unit that tracks the user's garbage sorting behavior. The tracking unit records the garbage sorting behavior that the user actually performs and provides appropriate feedback. For example, if the user sorts their garbage correctly, a message praising them may be displayed, and if they sort their garbage incorrectly, the correct method may be explained again. In this way, by tracking the user's behavior and providing appropriate feedback, it is possible to promote proper garbage sorting habits.

[0045] The AI ​​chat system can also be equipped with an education section to raise users' awareness of garbage sorting. The education section can provide information about the importance of garbage sorting and its impact on the environment to raise users' awareness. For example, it can provide videos and articles explaining how garbage sorting contributes to environmental protection. This can raise users' awareness of garbage sorting and encourage them to take positive action.

[0046] The AI ​​chat system can also be equipped with a community function related to users' garbage sorting. The community function provides a forum for users to share information and help each other. For example, a bulletin board can be set up where users can ask other users questions about how to sort specific types of garbage. This allows users to share information and help each other, enabling more effective garbage sorting.

[0047] The AI ​​chat system can also be equipped with a reward module to increase users' motivation to separate their trash. The reward module can increase motivation by awarding points or badges when users separate their trash correctly. For example, a system can be provided where users can exchange a certain number of points for eco-friendly goods. This can increase users' motivation and promote the habit of separating trash correctly.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The rule learning unit learns the garbage sorting rules for each region. For example, the generation AI learns based on the garbage sorting guidelines and rules provided by each local government. The generation AI learns in detail the rules for each region, such as which regions treat plastic products as burnable garbage and which regions treat them as non-burnable garbage. Step 2: The question receiving unit receives questions in the form of free comments from the user. For example, the user can input a specific question such as, "What type of garbage is this plastic bottle cap?" Step 3: The answer generation unit generates an answer based on the question received by the question reception unit. For example, the generation AI analyzes the user's question and generates an appropriate answer based on the garbage sorting rules of each region.

[0050] (Example 2) The AI ​​chat system according to an embodiment of the present invention provides information about waste sorting. This system allows users to ask questions in the form of free comments using a generation AI that has learned the waste sorting rules of each region. This allows users to easily obtain information about waste sorting from the AI ​​chat system.

[0051] The AI ​​chat system according to the embodiment includes a rule learning unit, a question receiving unit, and an answer generating unit. The rule learning unit learns the garbage sorting rules of each region. For example, the generation AI performs learning based on garbage sorting guidelines and regulations provided by local governments. The generation AI learns detailed local rules, such as regions where plastic products are treated as combustible garbage and regions where they are treated as non-combustible garbage. The question receiving unit receives questions in the form of free comments from users. For example, users can input specific questions such as, "What type of garbage is this plastic bottle cap?" The answer generating unit generates answers based on the questions received by the question receiving unit. For example, the generation AI analyzes the user's questions and generates appropriate answers based on the garbage sorting rules of each region. As a result, the AI ​​chat system according to the embodiment allows users to ask questions in the form of free comments and provides accurate information based on the garbage sorting rules of each region.

[0052] The rule learning unit learns not only garbage sorting rules but also seasonal variations in garbage sorting rules, making it possible to provide a garbage sorting method suited to the season. For example, the rule learning unit learns not only garbage sorting rules for each region, but also seasonal variations in garbage sorting rules. For example, during the Christmas season, there is an increase in the amount of specific wrapping paper and decorations, so a method for sorting those items is provided. This makes it possible to provide a garbage sorting method suited to the season.

[0053] The rule learning unit can incorporate past violation cases and penalty information when learning the garbage sorting rules and can warn the user. For example, the rule learning unit incorporates past violation cases and penalty information when learning the garbage sorting rules. For example, if there are frequent cases of improper sorting of a particular type of garbage, the rule learning unit can warn the user based on those cases. This makes it possible to warn the user based on past violation cases and penalty information.

[0054] The rule learning unit can use the emotion estimation function to provide advice to reduce the stress and anxiety the user feels when sorting garbage. The rule learning unit, for example, uses the emotion estimation function to provide advice to reduce the stress and anxiety the user feels when sorting garbage. For example, the rule learning unit analyzes the user's emotional state and displays advice to help the user relax. This makes it possible to provide advice to reduce the user's stress and anxiety.

[0055] The rule learning unit incorporates the sorting rules of other countries when learning garbage sorting rules, making it possible to provide advice from an international perspective.The rule learning unit incorporates the sorting rules of other countries when learning garbage sorting rules.For example, in addition to Japanese rules, it can learn the sorting rules of the United States and Europe and provide advice from an international perspective.This makes it possible to provide advice from an international perspective.

[0056] The rule learning unit incorporates the opinions of recycling companies and waste disposal companies when learning the garbage sorting rules, and is able to provide the optimal sorting method for actual disposal sites. For example, the rule learning unit incorporates the opinions of recycling companies and waste disposal companies when learning the garbage sorting rules. For example, based on the opinions of the companies, it provides the optimal sorting method for actual disposal sites. This makes it possible to provide the optimal sorting method for actual disposal sites.

[0057] The rule learning unit can use the emotion inference function to suggest eco-friendly activities that elicit positive emotions felt by the user regarding trash sorting. For example, the rule learning unit can use the emotion inference function to suggest eco-friendly activities that elicit positive emotions felt by the user regarding trash sorting. For example, the rule learning unit can suggest participating in recycling activities or local cleanup activities. This makes it possible to suggest eco-friendly activities that elicit positive emotions felt by the user.

[0058] The question receiving unit can provide a more personalized answer to a free comment-style question by referring to the user's past question history. For example, the question receiving unit can provide a more personalized answer to a free comment-style question by referring to the user's past question history. For example, advice based on previous answers is provided to a user who has asked the same question in the past. This makes it possible to provide a more personalized answer by referring to the user's past question history.

[0059] The question receiving unit can automatically acquire the user's location information when receiving a question and provide an answer that conforms to the rules of the area. For example, when a question is received, the question receiving unit can automatically acquire the user's location information and provide an answer that conforms to the rules of the area. For example, if the user is in a specific area, an answer based on the garbage sorting rules of that area is provided. This makes it possible to automatically acquire the user's location information and provide an answer that conforms to the rules of that area.

[0060] The question receiving unit can use the emotion estimation function to estimate the emotion from the content of the user's question and provide an answer in an appropriate tone. For example, the question receiving unit can use the emotion estimation function to estimate the emotion from the content of the user's question and provide an answer in an appropriate tone. For example, if the user is feeling anxious, the answer can be given in a gentle tone. This makes it possible to estimate the user's emotion and provide an answer in an appropriate tone.

[0061] The question receiving unit can also receive voice input for questions in the free comment format and generate an answer using voice recognition technology. The question receiving unit can also receive voice input for questions in the free comment format and generate an answer using voice recognition technology. For example, a user can input a question by voice, and the voice can be converted into text to generate an answer. This makes it possible to receive voice input and generate an answer using voice recognition technology.

[0062] The question receiving unit automatically completes keywords when a user inputs a question when receiving a question, thereby reducing the effort required for input. For example, when a user starts to input "plastic bottle," the question receiving unit automatically completes keywords to "What type of garbage are plastic bottle caps?" This automatically completes keywords when a user inputs a question, thereby reducing the effort required for input.

[0063] The question receiving unit can use the emotion estimation function to monitor in real time the emotion a user feels when inputting a question and make suggestions to elicit positive emotions. For example, the question receiving unit can use the emotion estimation function to monitor in real time the emotion a user feels when inputting a question and make suggestions to elicit positive emotions. For example, if the user is feeling stressed, the question receiving unit can make suggestions to help the user relax. This allows the emotion a user feels when inputting a question to be monitored in real time and make suggestions to elicit positive emotions.

[0064] The answer generation unit can provide answers regarding garbage sorting that include not only specific sorting methods but also the benefits of recycling and its impact on the environment. For example, the answer generation unit can provide answers regarding garbage sorting that include not only specific sorting methods but also the benefits of recycling and its impact on the environment. For example, the answer generation unit can explain the positive impact that recycling plastic waste has on the environment. This makes it possible to provide not only specific sorting methods but also the benefits of recycling and its impact on the environment.

[0065] The answer generation unit can show specific steps and points to note for the user to perform sorting using videos and images when providing an answer. For example, the answer generation unit shows specific steps and points to note for the user to perform sorting using videos and images when providing an answer. For example, the answer generation unit explains the steps to remove the cap of a plastic bottle using a video. This makes it possible to show specific steps and points to note for the user to perform sorting using videos and images.

[0066] The answer generation unit can use the emotion estimation function to provide additional information to resolve any doubts or anxieties the user may have about sorting. The answer generation unit, for example, uses the emotion estimation function to provide additional information to resolve any doubts or anxieties the user may have about sorting. For example, if the user is feeling anxious, a detailed explanation or a reference link can be provided. This makes it possible to provide additional information to resolve any doubts or anxieties the user may have about sorting.

[0067] The answer generation unit provides answers regarding garbage sorting in stages according to the user's level of understanding, making it possible to accommodate users from beginners to advanced users.The answer generation unit provides answers regarding garbage sorting in stages according to the user's level of understanding, for example, providing a basic sorting method to beginners and a detailed sorting method to advanced users.In this way, answers are provided in stages according to the user's level of understanding, making it possible to accommodate users from beginners to advanced users.

[0068] The answer generation unit can include advice that takes into account success stories and feedback from other users when providing an answer. For example, the answer generation unit can include advice that takes into account success stories and feedback from other users when providing an answer. For example, the answer generation unit can introduce success stories from other users regarding a specific method of sorting garbage. This makes it possible to include advice that takes into account success stories and feedback from other users.

[0069] The answer generation unit can use the emotion estimation function to suggest eco-friendly activities to enhance the positive emotions the user feels about sorting. The answer generation unit, for example, uses the emotion estimation function to suggest eco-friendly activities to enhance the positive emotions the user feels about sorting. For example, the answer generation unit can suggest participating in recycling activities or local cleanup activities. This makes it possible to suggest eco-friendly activities to enhance the positive emotions the user feels about sorting.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The AI ​​chat system can also be equipped with an evaluation unit that evaluates the user's knowledge level regarding garbage sorting. The evaluation unit evaluates the user's knowledge level based on the content of the user's past questions and answers, and provides information at an appropriate level. For example, it can provide beginners with basic garbage sorting methods, while advanced users can receive detailed garbage sorting methods and the benefits of recycling. This makes it possible to provide information tailored to the user's knowledge level, supporting more effective learning.

[0072] The AI ​​chat system can further include a tracking unit that tracks the user's garbage sorting behavior. The tracking unit records the garbage sorting behavior that the user actually performs and provides appropriate feedback. For example, if the user sorts their garbage correctly, a message praising them may be displayed, and if they sort their garbage incorrectly, the correct method may be explained again. In this way, by tracking the user's behavior and providing appropriate feedback, it is possible to promote proper garbage sorting habits.

[0073] The AI ​​chat system can also be equipped with an education section to raise users' awareness of garbage sorting. The education section can provide information about the importance of garbage sorting and its impact on the environment to raise users' awareness. For example, it can provide videos and articles explaining how garbage sorting contributes to environmental protection. This can raise users' awareness of garbage sorting and encourage them to take positive action.

[0074] The AI ​​chat system can also be equipped with a community function related to users' garbage sorting. The community function provides a forum for users to share information and help each other. For example, a bulletin board can be set up where users can ask other users questions about how to sort specific types of garbage. This allows users to share information and help each other, enabling more effective garbage sorting.

[0075] The AI ​​chat system can also be equipped with a reward module to increase users' motivation to separate their trash. The reward module can increase motivation by awarding points or badges when users separate their trash correctly. For example, a system can be provided where users can exchange a certain number of points for eco-friendly goods. This can increase users' motivation and promote the habit of separating trash correctly.

[0076] The AI ​​chat system can further include an emotion advice unit that estimates the user's emotions and provides advice on garbage sorting based on the estimated emotions. The emotion advice unit provides advice to reduce the stress and anxiety the user feels when sorting garbage. For example, if the user is feeling stressed, it displays relaxation techniques and encouraging messages. This allows the system to provide advice based on the user's emotions and reduce stress and anxiety about garbage sorting.

[0077] The AI ​​chat system can further include an emotion feedback unit that estimates the user's emotions and provides positive feedback regarding trash sorting based on the estimated emotions. The emotion feedback unit provides feedback to reinforce the positive emotions the user feels about trash sorting. For example, when the user correctly sorts their trash, the unit may send a praising message or suggest participating in eco-friendly activities. This reinforces the user's positive emotions and increases their motivation to sort trash.

[0078] The AI ​​chat system can further include an emotional education unit that estimates the user's emotions and provides educational content about garbage sorting based on the estimated emotions. The emotional education unit provides educational content to alleviate any doubts or anxieties the user may have about garbage sorting. For example, if the user feels anxious, the emotional education unit can provide detailed explanations and reference links to alleviate the doubts. This allows the system to provide educational content that matches the user's emotions and deepen their understanding of garbage sorting.

[0079] The AI ​​chat system can further include an emotion community unit that estimates the user's emotions and suggests community activities related to garbage sorting based on the estimated emotions. The emotion community unit suggests community activities to elicit the positive emotions the user feels about garbage sorting. For example, it suggests participating in recycling activities or local cleanup activities to elicit the user's positive emotions. This makes it possible to suggest community activities that correspond to the user's emotions and raise awareness of garbage sorting.

[0080] The AI ​​chat system can further include an emotion reward unit that estimates the user's emotions and provides rewards for sorting trash based on the estimated emotions. The emotion reward unit provides rewards to reinforce the positive emotions the user feels about sorting trash. For example, when the user sorts trash correctly, points or badges can be awarded to increase motivation. This reinforces the user's positive emotions and promotes good trash sorting habits.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The rule learning unit learns the garbage sorting rules for each region. For example, the generation AI learns based on the garbage sorting guidelines and rules provided by each local government. The generation AI learns in detail the rules for each region, such as which regions treat plastic products as burnable garbage and which regions treat them as non-burnable garbage. Step 2: The question receiving unit receives questions in the form of free comments from the user. For example, the user can input a specific question such as, "What type of garbage is this plastic bottle cap?" Step 3: The answer generation unit generates an answer based on the question received by the question reception unit. For example, the generation AI analyzes the user's question and generates an appropriate answer based on the garbage sorting rules of each region.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 rule learning section where children learn the rules for separating garbage in each region, a question receiving unit that receives questions in the form of free comments from users; an answer generation unit that generates an answer based on the question received by the question reception unit; A system characterized by:

2. The rule learning unit In addition to the garbage sorting rules, the system also learns seasonal variations in garbage sorting rules and provides a seasonal sorting method.

2. The system of claim 1.

3. The rule learning unit In addition to learning about the waste sorting rules, we will also incorporate the rules of other countries and provide advice from an international perspective.

2. The system of claim 1.

4. The question receiving unit In response to the free-comment questions, the system references the user's past question history and provides more personalized answers.

2. The system of claim 1.

5. The answer generation unit In addition to providing specific methods for separating garbage, we will also provide information on the benefits of recycling and its impact on the environment.

2. The system of claim 1.

6. The rule learning unit Providing advice to reduce the stress and anxiety users feel about sorting their trash 2. The system of claim 1.

7. The question receiving unit Inferring the user's emotions from the content of the question and providing an answer in an appropriate tone 2. The system of claim 1.

8. The answer generation unit Providing additional information to resolve any doubts or concerns the user may have about sorting 2. The system of claim 1.

Images