System

Abstract

An object of a system according to an embodiment is to easily perform a bulky waste disposal procedure.SOLUTION: A system includes an image acquisition part, a type determination part, an application procedure part, a payment proposal part, and a delivery method guide part. The image acquisition unit acquires an image of the coarse dust captured by the smartphone. The type determination part analyzes the image acquired by the image acquisition part to determine the type of the coarse dust. The application procedure unit automates an application procedure on the basis of the type of the bulky waste determined by the type determination unit. The payment proposal unit proposes a payment method after the application procedure is completed by the application procedure unit. After the payment procedure is completed by the payment proposal part, the delivery method guide part guides an optimum delivery method.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 technology has had the problem that the procedures for disposing of bulky waste are complicated and time-consuming for users.

[0005] The system according to the embodiment aims to simplify the procedure for disposing of bulky waste. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, a type determination unit, an application procedure unit, a payment proposal unit, and a disposal method guidance unit. The image acquisition unit acquires an image of the bulky waste taken with a smartphone. The type determination unit analyzes the image acquired by the image acquisition unit to determine the type of bulky waste. The application procedure unit automates the application procedure based on the type of bulky waste determined by the type determination unit. The payment proposal unit proposes a payment method after the application procedure unit has completed the application procedure. The disposal method guidance unit provides guidance on the optimal disposal method after the payment proposal unit has completed the payment procedure. [Effects of the Invention]

[0007] The system according to the embodiment can easily carry out the procedure for disposing of bulky waste. [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 bulky waste disposal system according to an embodiment of the present invention acquires images of bulky waste taken with a smartphone, and a generating AI analyzes the images to determine the type of bulky waste, automates the application process, suggests payment methods, and provides guidance on the optimal disposal method. As a result, the bulky waste disposal system allows users to complete the process of type determination, application, payment, and disposal method all in one stop, simply by taking a photo of the bulky waste with their smartphone.

[0029] The bulky waste disposal system according to the embodiment includes an image acquisition unit, a type determination unit, an application procedure unit, a payment proposal unit, and a disposal guidance unit. The image acquisition unit acquires images of bulky waste taken with a smartphone. For example, a user takes a photo of the bulky waste with their smartphone and uploads the image to the system. The image acquisition unit can also automatically adjust the image resolution and format. For example, the image acquisition unit accepts images in JPEG or PNG format and converts them to an appropriate resolution. The type determination unit analyzes the images acquired by the image acquisition unit to determine the type of bulky waste. For example, the generation AI determines the type of bulky waste using image recognition technology. The generation AI can analyze images using deep learning, for example, to identify the type of bulky waste. The generation AI can also extract features within the image and determine the type of bulky waste using computer vision technology. The application procedure unit automates the application procedure based on the type of bulky waste determined by the type determination unit. For example, the generation AI automates the application procedure using natural language processing technology. For example, the generation AI collects information required by the user and proceeds with the application procedure. The generation AI can also automatically generate application forms based on information entered by the user. The payment suggestion unit suggests payment methods after the application procedure unit has completed the application procedure. For example, the generation AI may suggest to the user that payment is possible by credit card, electronic money, or bank transfer. The generation AI can also refer to the user's past payment history and suggest the optimal payment method. The generation AI can also automatically detect errors that occur during the payment procedure and suggest solutions. The disposal guidance unit guides the user on the optimal disposal method after the payment procedure is completed by the payment suggestion unit. For example, the generation AI guides the user on the optimal disposal method based on the user's address information. The generation AI can automatically refer to local regulations and rules, for example, and provide appropriate guidance to the user. The generation AI can also analyze the user's emotions when disposing of bulky waste and provide advice to reduce stress.As a result, the bulky waste disposal system according to the embodiment allows users to complete all procedures, from type determination, application, payment, and disposal instructions, all in one place, simply by taking a photo of the bulky waste with their smartphone. For example, if a user wants to dispose of an old sofa, they can simply take a photo of the sofa with their smartphone and input it into the generating AI, and all procedures can be automated and completed. This makes the disposal of bulky waste simple and efficient.

[0030] The type determination unit uses image recognition technology to determine the material and condition of the bulky waste and identify the recyclable parts. For example, the generation AI uses image recognition technology to determine the material of the bulky waste. For example, it identifies materials such as plastic, metal, and wood and evaluates the recyclability of each. The type determination unit also uses image recognition technology to determine the condition of the bulky waste. For example, it analyzes damage, dirt, and usable condition to identify the recyclable parts. The type determination unit also uses image recognition technology to identify the recyclable parts of the bulky waste. For example, it extracts reusable parts and recyclable materials. This makes it possible to determine the material and condition of the bulky waste and identify the recyclable parts.

[0031] The type determination unit can analyze multiple images and estimate the size and weight of bulky waste. In the type determination unit, for example, the generation AI analyzes multiple images and estimates the size of the bulky waste. For example, it generates a three-dimensional model based on images taken from different angles and calculates the size. In addition, the type determination unit analyzes multiple images and estimates the weight of the bulky waste. For example, it calculates the weight based on the size and shape of objects in the image. In addition, the type determination unit analyzes multiple images and comprehensively evaluates the size and weight of the bulky waste. For example, it integrates information obtained from multiple images and estimates the accurate size and weight. This makes it possible to analyze multiple images and estimate the size and weight of bulky waste.

[0032] The type determination unit uses image recognition technology to identify the brand and model of bulky waste and can provide appropriate information to recyclers. For example, the generation AI uses image recognition technology to identify the brand of bulky waste. For example, it analyzes the logo and model number of a home appliance and identifies the brand name. The type determination unit also uses image recognition technology to identify the model of bulky waste. For example, it analyzes the product number and external features and identifies the model name. The generation AI also uses image recognition technology to provide appropriate information to recyclers. For example, it provides information on recyclable materials and parts, allowing recyclers to process them efficiently. This makes it possible to identify the brand and model of bulky waste and provide appropriate information to recyclers.

[0033] The type determination unit uses image recognition technology to determine whether the bulky waste can be repaired and can suggest repair services. For example, the generation AI uses image recognition technology to determine whether the bulky waste can be repaired. For example, it analyzes the damaged areas and state of deterioration and evaluates whether repair is possible. The type determination unit also uses image recognition technology to suggest repair services. For example, it selects a repair company and provides an estimate of the repair costs. The type determination unit also uses image recognition technology to identify repairable parts and materials and suggest repair services. For example, it extracts reusable parts and repairable materials and provides them to repair companies. This makes it possible to determine whether the bulky waste can be repaired and suggest repair services.

[0034] The application procedure unit can use natural language processing technology to refer to the user's past application history and propose the optimal application procedure. In the application procedure unit, for example, the generation AI uses natural language processing technology to analyze the user's past application history and propose the optimal application procedure. For example, the procedure can be simplified based on a history of similar bulky waste disposal in the past. In addition, the application procedure unit can use natural language processing technology to automatically refer to the user's application history and propose the optimal procedure. For example, the optimal procedure can be proposed based on past application content and application date and time. In addition, the application procedure unit can use natural language processing technology to optimize the procedure flow based on the user's application history. For example, the application procedure unit can automatically enter necessary information based on the past application history to quickly proceed with the procedure. This makes it possible to refer to the user's past application history and propose the optimal application procedure.

[0035] The application procedure unit uses natural language processing technology to process the application procedures of multiple users simultaneously, improving efficiency. For example, the application procedure unit uses natural language processing technology to build a system that processes the application procedures of multiple users simultaneously. For example, parallel processing is used to proceed with multiple applications simultaneously. The application procedure unit also uses natural language processing technology to efficiently manage the application procedures of multiple users. For example, the application status of each user is monitored in real time to proceed with the procedure efficiently. The application procedure unit also uses natural language processing technology to optimize the application procedures of multiple users. For example, the application details of each user are analyzed and the optimal procedure is proposed. This allows the application procedures of multiple users to be processed simultaneously, improving efficiency.

[0036] The application procedure unit can add a function to notify the user of the progress of the application procedure in real time using natural language processing technology. For example, the application procedure unit provides a function in which the generation AI uses natural language processing technology to notify the user of the progress of the application procedure in real time. For example, a notification is sent each time each step of the procedure is completed. The application procedure unit also uses natural language processing technology to automatically update the progress of the application procedure and notify the user. For example, the progress of the procedure is monitored in real time and the latest information is provided to the user. The application procedure unit also uses natural language processing technology to visualize the progress of the application procedure and provide it to the user. For example, the progress of the procedure is displayed in graphs or charts so that the user can understand it visually. This allows for the addition of a function to notify the user of the progress of the application procedure in real time.

[0037] The payment proposal unit can refer to the user's past payment history and propose the optimal payment method. In the payment proposal unit, for example, the generation AI analyzes the user's past payment history and proposes the optimal payment method. For example, the optimal option is presented to the user based on payment methods used in the past. In addition, the payment proposal unit can automatically refer to the user's payment history and propose the optimal payment method. For example, the optimal payment method is proposed based on past payment details and payment dates and times. In addition, the payment proposal unit can optimize the payment method options based on the user's payment history. For example, the most advantageous payment method is proposed for the user based on past payment history. This makes it possible to refer to the user's past payment history and propose the optimal payment method.

[0038] The payment proposal unit can add a function that automatically detects errors that occur during the payment procedure and proposes a solution. For example, the generation AI automatically detects errors that occur during the payment procedure and proposes a solution. For example, if a credit card authentication error occurs, the payment proposal unit proposes an alternative payment method. The payment proposal unit also monitors errors that occur during the payment procedure in real time and provides solutions. For example, when an error occurs, the payment proposal unit presents a specific solution to the user. The payment proposal unit also allows the generation AI to automatically correct errors that occur during the payment procedure and proceed with the procedure. For example, the generation AI automatically corrects input errors and system errors and completes the procedure. This allows the addition of a function that automatically detects errors that occur during the payment procedure and proposes a solution.

[0039] The payment proposal unit can combine multiple payment methods to propose the most advantageous payment plan to the user. For example, the generation AI in the payment proposal unit combines multiple payment methods to propose the most advantageous payment plan to the user. For example, by combining credit cards and electronic money, the point redemption rate can be maximized. The payment proposal unit also combines multiple payment methods to propose the optimal payment plan to the user. For example, by combining installment payments and revolving payments, the payment burden can be reduced. The payment proposal unit also combines multiple payment methods to provide the most advantageous payment plan to the user. For example, by combining multiple payment methods, fees can be minimized. This allows the generation AI to combine multiple payment methods to propose the most advantageous payment plan to the user.

[0040] The payment proposal unit can add a function to notify the user of the progress of the payment procedure in real time. For example, the payment proposal unit provides a function in which the generation AI notifies the user of the progress of the payment procedure in real time. For example, a notification is sent each time a payment is completed. The payment proposal unit also allows the generation AI to automatically update the progress of the payment procedure and notify the user. For example, the generation AI provides the user with the latest information each time each payment step is completed. The payment proposal unit also allows the generation AI to visualize the progress of the payment procedure and provide it to the user. For example, the payment progress can be displayed in graphs or charts so that the user can understand it visually. This allows the generation AI to add a function to notify the user of the progress of the payment procedure in real time.

[0041] The disposal guidance unit can automatically refer to local regulations and rules regarding how to dispose of bulky waste and provide appropriate guidance to the user. For example, the generation AI can automatically refer to local regulations and rules regarding how to dispose of bulky waste and provide appropriate guidance to the user. For example, it can provide collection days and disposal rules for each region. The generation AI can also automatically update local regulations and rules to provide the user with the latest information. For example, it can notify the user if local waste disposal regulations change. The generation AI can also provide specific disposal guidance to the user based on local regulations and rules. For example, it can guide the user to dispose of bulky waste at a specified location and date and time. This allows the generation AI to automatically refer to local regulations and rules regarding how to dispose of bulky waste and provide appropriate guidance to the user.

[0042] The disposal guidance unit can provide a video tutorial on how to dispose of oversized waste, allowing the user to understand visually. In the disposal guidance unit, for example, the generation AI provides a video tutorial on how to dispose of oversized waste. For example, specific steps are explained in a video, allowing the user to understand visually. In addition, the disposal guidance unit automatically updates the video tutorial to provide the latest information. For example, the video tutorial is updated when local regulations or rules change. In addition, the disposal guidance unit improves the video tutorial based on user feedback. For example, the generation AI reflects the user's opinions and requests and provides a video tutorial that is easier to understand. In this way, a video tutorial on how to dispose of oversized waste can be provided, allowing the user to understand visually.

[0043] The disposal guidance unit can collect user feedback on how to dispose of bulky waste and use it to improve the service. In the disposal guidance unit, for example, the generation AI collects user feedback on how to dispose of bulky waste and uses it to improve the service. For example, it collects opinions and requests on how to dispose of it. In addition, the disposal guidance unit automatically analyzes user feedback and reflects it in improving the service. For example, it improves the disposal guidance based on user feedback. In addition, the disposal guidance unit suggests new functions and services based on user feedback. For example, it provides new disposal guidance methods in response to user requests. In this way, user feedback on how to dispose of bulky waste can be collected and used to improve the service.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The bulky waste disposal system can also include a location information providing unit that acquires the user's location information and provides information on the nearest recycling center or waste disposal facility. For example, when a user enables the GPS function on their smartphone, the location information providing unit identifies the user's current location and displays the address and contact information of the nearest recycling center. The location information providing unit can also provide information on the recycling center's business hours and the types of bulky waste that can be accepted. Furthermore, the location information providing unit can provide the user with a function to navigate the route to the recycling center. This allows the user to easily find the nearest recycling center and efficiently dispose of their bulky waste.

[0046] The bulky waste disposal system can also be equipped with a history reference unit that references the user's past bulky waste disposal history and suggests ways to reuse recyclable materials and parts. For example, based on data on the bulky waste the user has disposed of in the past, the system can identify reusable materials and parts and provide ideas for DIY projects using them. The history reference unit can also display the recycling rate of the bulky waste the user has disposed of in the past, visualizing the user's contribution to the environment. Furthermore, the history reference unit can provide information on workshops and events that help users utilize reusable materials. This allows users to learn how to reuse bulky waste and practice environmentally friendly living.

[0047] The bulky waste disposal system may also include a history reference unit that references the user's past bulky waste disposal history and suggests the optimal disposal method. For example, the system may suggest a disposal method for similar bulky waste based on data on the bulky waste the user has disposed of in the past. The history reference unit may also provide information on recycling companies and disposal facilities that the user has used in the past. Furthermore, the history reference unit may display the recycling rate and disposal costs for the bulky waste the user has disposed of in the past, providing reference information for selecting the optimal disposal method. This allows the user to select the optimal disposal method based on their past history and dispose of their bulky waste efficiently.

[0048] The bulky waste disposal system can also include a location information providing unit that acquires the user's location information and provides information on the nearest recycling shop or second-hand goods store. For example, when the user enables the GPS function on their smartphone, the location information providing unit identifies the user's current location and displays the address and contact information of the nearest recycling shop. The location information providing unit can also provide information on the recycling shop's business hours and items sold. Furthermore, the location information providing unit can provide the user with a function to navigate the route to the recycling shop. This allows the user to easily find the nearest recycling shop and reuse unwanted bulky waste.

[0049] The bulky waste disposal system can also be equipped with a history reference unit that references the user's past bulky waste disposal history and suggests ways to reuse recyclable materials and parts. For example, based on data on the bulky waste the user has disposed of in the past, the system can identify reusable materials and parts and provide ideas for DIY projects using them. The history reference unit can also display the recycling rate of the bulky waste the user has disposed of in the past, visualizing the user's contribution to the environment. Furthermore, the history reference unit can provide information on workshops and events that help users utilize reusable materials. This allows users to learn how to reuse bulky waste and practice environmentally friendly living.

[0050] The bulky waste disposal system can also include a location information providing unit that acquires the user's location information and provides information on the nearest recycling center or waste disposal facility. For example, when a user enables the GPS function on their smartphone, the location information providing unit identifies the user's current location and displays the address and contact information of the nearest recycling center. The location information providing unit can also provide information on the recycling center's business hours and the types of bulky waste that can be accepted. Furthermore, the location information providing unit can provide the user with a function to navigate the route to the recycling center. This allows the user to easily find the nearest recycling center and efficiently dispose of their bulky waste.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The image acquisition unit acquires images of bulky waste taken with a smartphone. For example, a user takes a photo of the bulky waste with their smartphone and uploads the image to the system. The image acquisition unit can also automatically adjust the image resolution and format. For example, it accepts images in JPEG or PNG format and converts them to the appropriate resolution. Step 2: The type determination unit analyzes the image acquired by the image acquisition unit to determine the type of bulky waste. For example, the generation AI uses image recognition technology to determine the type of bulky waste. The generation AI analyzes the image using deep learning and computer vision technology to identify the type of bulky waste. Step 3: The application procedure unit automates the application procedure based on the type of bulky waste determined by the type determination unit. For example, the generation AI automates the application procedure using natural language processing technology, collects the information required by the user, and automatically generates application documents. Step 4: After the application procedure has been completed by the application procedure section, the payment suggestion section suggests a payment method. For example, the generation AI may suggest to the user that payment can be made by credit card, electronic money, or bank transfer. The generation AI may also refer to the user's past payment history to suggest the optimal payment method. Step 5: After the payment procedure is completed by the payment suggestion unit, the disposal guidance unit provides guidance on the optimal disposal method. For example, the generation AI can automatically refer to local regulations and rules based on the user's address information and provide appropriate guidance to the user. It can also analyze the user's emotions when disposing of bulky waste and provide advice to reduce stress.

[0053] (Example 2) The bulky waste disposal system according to an embodiment of the present invention acquires images of bulky waste taken with a smartphone, and a generating AI analyzes the images to determine the type of bulky waste, automates the application process, suggests payment methods, and provides guidance on the optimal disposal method. As a result, the bulky waste disposal system allows users to complete the process of type determination, application, payment, and disposal method all in one stop, simply by taking a photo of the bulky waste with their smartphone.

[0054] The bulky waste disposal system according to the embodiment includes an image acquisition unit, a type determination unit, an application procedure unit, a payment proposal unit, and a disposal guidance unit. The image acquisition unit acquires images of bulky waste taken with a smartphone. For example, a user takes a photo of the bulky waste with their smartphone and uploads the image to the system. The image acquisition unit can also automatically adjust the image resolution and format. For example, the image acquisition unit accepts images in JPEG or PNG format and converts them to an appropriate resolution. The type determination unit analyzes the images acquired by the image acquisition unit to determine the type of bulky waste. For example, the generation AI determines the type of bulky waste using image recognition technology. The generation AI can analyze images using deep learning, for example, to identify the type of bulky waste. The generation AI can also extract features within the image and determine the type of bulky waste using computer vision technology. The application procedure unit automates the application procedure based on the type of bulky waste determined by the type determination unit. For example, the generation AI automates the application procedure using natural language processing technology. For example, the generation AI collects information required by the user and proceeds with the application procedure. The generation AI can also automatically generate application forms based on information entered by the user. The payment suggestion unit suggests payment methods after the application procedure unit has completed the application procedure. For example, the generation AI may suggest to the user that payment is possible by credit card, electronic money, or bank transfer. The generation AI can also refer to the user's past payment history and suggest the optimal payment method. The generation AI can also automatically detect errors that occur during the payment procedure and suggest solutions. The disposal guidance unit guides the user on the optimal disposal method after the payment procedure is completed by the payment suggestion unit. For example, the generation AI guides the user on the optimal disposal method based on the user's address information. The generation AI can automatically refer to local regulations and rules, for example, and provide appropriate guidance to the user. The generation AI can also analyze the user's emotions when disposing of bulky waste and provide advice to reduce stress.As a result, the bulky waste disposal system according to the embodiment allows users to complete all procedures, from type determination, application, payment, and disposal instructions, all in one place, simply by taking a photo of the bulky waste with their smartphone. For example, if a user wants to dispose of an old sofa, they can simply take a photo of the sofa with their smartphone and input it into the generating AI, and all procedures can be automated and completed. This makes the disposal of bulky waste simple and efficient.

[0055] The type determination unit uses image recognition technology to determine the material and condition of the bulky waste and identify the recyclable parts. For example, the generation AI uses image recognition technology to determine the material of the bulky waste. For example, it identifies materials such as plastic, metal, and wood and evaluates the recyclability of each. The type determination unit also uses image recognition technology to determine the condition of the bulky waste. For example, it analyzes damage, dirt, and usable condition to identify the recyclable parts. The type determination unit also uses image recognition technology to identify the recyclable parts of the bulky waste. For example, it extracts reusable parts and recyclable materials. This makes it possible to determine the material and condition of the bulky waste and identify the recyclable parts.

[0056] The type determination unit can analyze multiple images and estimate the size and weight of bulky waste. In the type determination unit, for example, the generation AI analyzes multiple images and estimates the size of the bulky waste. For example, it generates a three-dimensional model based on images taken from different angles and calculates the size. In addition, the type determination unit analyzes multiple images and estimates the weight of the bulky waste. For example, it calculates the weight based on the size and shape of objects in the image. In addition, the type determination unit analyzes multiple images and comprehensively evaluates the size and weight of the bulky waste. For example, it integrates information obtained from multiple images and estimates the accurate size and weight. This makes it possible to analyze multiple images and estimate the size and weight of bulky waste.

[0057] The type determination unit uses the emotion estimation function to analyze the emotion of the user when taking pictures of bulky waste and can provide advice to reduce stress. The type determination unit, for example, uses the emotion estimation function to analyze the emotion of the user when taking pictures of bulky waste in real time. For example, it analyzes the user's facial expression from camera footage and calculates an emotion score. The type determination unit also uses the emotion estimation function to analyze the user's voice and estimate the emotion. For example, it analyzes the tone and speed of the voice and calculates the emotion score. The type determination unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate the emotion. For example, it calculates the emotion score based on fluctuations in heart rate. This makes it possible to analyze the emotion of the user when taking pictures of bulky waste and provide advice to reduce stress.

[0058] The type determination unit uses image recognition technology to identify the brand and model of bulky waste and can provide appropriate information to recyclers. For example, the generation AI uses image recognition technology to identify the brand of bulky waste. For example, it analyzes the logo and model number of a home appliance and identifies the brand name. The type determination unit also uses image recognition technology to identify the model of bulky waste. For example, it analyzes the product number and external features and identifies the model name. The generation AI also uses image recognition technology to provide appropriate information to recyclers. For example, it provides information on recyclable materials and parts, allowing recyclers to process them efficiently. This makes it possible to identify the brand and model of bulky waste and provide appropriate information to recyclers.

[0059] The type determination unit uses image recognition technology to determine whether the bulky waste can be repaired and can suggest repair services. For example, the generation AI uses image recognition technology to determine whether the bulky waste can be repaired. For example, it analyzes the damaged areas and state of deterioration and evaluates whether repair is possible. The type determination unit also uses image recognition technology to suggest repair services. For example, it selects a repair company and provides an estimate of the repair costs. The type determination unit also uses image recognition technology to identify repairable parts and materials and suggest repair services. For example, it extracts reusable parts and repairable materials and provides them to repair companies. This makes it possible to determine whether the bulky waste can be repaired and suggest repair services.

[0060] The type determination unit uses the emotion estimation function to analyze the emotion a user has when taking a photo of bulky waste, and can display a message to elicit positive emotions. The type determination unit, for example, uses the emotion estimation function to analyze the emotion a user has when taking a photo of bulky waste in real time. For example, it analyzes the user's facial expression from camera footage and calculates an emotion score. The type determination unit also uses the emotion estimation function to analyze the user's voice and estimate their emotion. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The type determination unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate their emotion. For example, it calculates an emotion score based on heart rate fluctuations. This makes it possible to analyze the emotion a user has when taking a photo of bulky waste, and display a message to elicit positive emotions.

[0061] The application procedure unit can use natural language processing technology to refer to the user's past application history and propose the optimal application procedure. In the application procedure unit, for example, the generation AI uses natural language processing technology to analyze the user's past application history and propose the optimal application procedure. For example, the procedure can be simplified based on a history of similar bulky waste disposal in the past. In addition, the application procedure unit can use natural language processing technology to automatically refer to the user's application history and propose the optimal procedure. For example, the optimal procedure can be proposed based on past application content and application date and time. In addition, the application procedure unit can use natural language processing technology to optimize the procedure flow based on the user's application history. For example, the application procedure unit can automatically enter necessary information based on the past application history to quickly proceed with the procedure. This makes it possible to refer to the user's past application history and propose the optimal application procedure.

[0062] The application procedure unit can use the emotion estimation function to analyze the user's emotions during the application procedure and provide support to reduce stress. The application procedure unit, for example, uses the emotion estimation function to analyze the user's emotions during the application procedure in real time. For example, it analyzes the user's facial expressions from camera footage and calculates an emotion score. The application procedure unit also uses the emotion estimation function to analyze the user's voice and estimate their emotions. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The application procedure unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate their emotions. For example, it calculates an emotion score based on fluctuations in heart rate. This makes it possible to analyze the user's emotions during the application procedure and provide support to reduce stress.

[0063] The application procedure unit uses natural language processing technology to process the application procedures of multiple users simultaneously, improving efficiency. For example, the application procedure unit uses natural language processing technology to build a system that processes the application procedures of multiple users simultaneously. For example, parallel processing is used to proceed with multiple applications simultaneously. The application procedure unit also uses natural language processing technology to efficiently manage the application procedures of multiple users. For example, the application status of each user is monitored in real time to proceed with the procedure efficiently. The application procedure unit also uses natural language processing technology to optimize the application procedures of multiple users. For example, the application details of each user are analyzed and the optimal procedure is proposed. This allows the application procedures of multiple users to be processed simultaneously, improving efficiency.

[0064] The application procedure unit can add a function to notify the user of the progress of the application procedure in real time using natural language processing technology. For example, the application procedure unit provides a function in which the generation AI uses natural language processing technology to notify the user of the progress of the application procedure in real time. For example, a notification is sent each time each step of the procedure is completed. The application procedure unit also uses natural language processing technology to automatically update the progress of the application procedure and notify the user. For example, the progress of the procedure is monitored in real time and the latest information is provided to the user. The application procedure unit also uses natural language processing technology to visualize the progress of the application procedure and provide it to the user. For example, the progress of the procedure is displayed in graphs or charts so that the user can understand it visually. This allows for the addition of a function to notify the user of the progress of the application procedure in real time.

[0065] The application procedure unit uses the emotion estimation function to analyze the user's emotions during the application procedure and can provide incentives to elicit positive emotions. The application procedure unit, for example, uses the emotion estimation function to analyze the user's emotions during the application procedure in real time. For example, it analyzes the user's facial expressions from camera footage and calculates an emotion score. The application procedure unit also uses the emotion estimation function to analyze the user's voice and estimate emotions. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The application procedure unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate emotions. For example, it calculates an emotion score based on heart rate fluctuations. This makes it possible to analyze the user's emotions during the application procedure and provide incentives to elicit positive emotions.

[0066] The payment proposal unit can refer to the user's past payment history and propose the optimal payment method. In the payment proposal unit, for example, the generation AI analyzes the user's past payment history and proposes the optimal payment method. For example, the optimal option is presented to the user based on payment methods used in the past. In addition, the payment proposal unit can automatically refer to the user's payment history and propose the optimal payment method. For example, the optimal payment method is proposed based on past payment details and payment dates and times. In addition, the payment proposal unit can optimize the payment method options based on the user's payment history. For example, the most advantageous payment method is proposed for the user based on past payment history. This makes it possible to refer to the user's past payment history and propose the optimal payment method.

[0067] The payment proposal unit can add a function that automatically detects errors that occur during the payment procedure and proposes a solution. For example, the generation AI automatically detects errors that occur during the payment procedure and proposes a solution. For example, if a credit card authentication error occurs, the payment proposal unit proposes an alternative payment method. The payment proposal unit also monitors errors that occur during the payment procedure in real time and provides solutions. For example, when an error occurs, the payment proposal unit presents a specific solution to the user. The payment proposal unit also allows the generation AI to automatically correct errors that occur during the payment procedure and proceed with the procedure. For example, the generation AI automatically corrects input errors and system errors and completes the procedure. This allows the addition of a function that automatically detects errors that occur during the payment procedure and proposes a solution.

[0068] The payment proposal unit can use the emotion estimation function to analyze the user's emotions during the payment process and provide support to reduce stress. The payment proposal unit, for example, uses the emotion estimation function to analyze the user's emotions during the payment process in real time. For example, it analyzes the user's facial expressions from camera footage and calculates an emotion score. The payment proposal unit also uses the emotion estimation function to analyze the user's voice and estimate their emotions. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The payment proposal unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate their emotions. For example, it calculates an emotion score based on heart rate fluctuations. This makes it possible to analyze the user's emotions during the payment process and provide support to reduce stress.

[0069] The payment proposal unit can combine multiple payment methods to propose the most advantageous payment plan to the user. For example, the generation AI in the payment proposal unit combines multiple payment methods to propose the most advantageous payment plan to the user. For example, by combining credit cards and electronic money, the point redemption rate can be maximized. The payment proposal unit also combines multiple payment methods to propose the optimal payment plan to the user. For example, by combining installment payments and revolving payments, the payment burden can be reduced. The payment proposal unit also combines multiple payment methods to provide the most advantageous payment plan to the user. For example, by combining multiple payment methods, fees can be minimized. This allows the generation AI to combine multiple payment methods to propose the most advantageous payment plan to the user.

[0070] The payment proposal unit can add a function to notify the user of the progress of the payment procedure in real time. For example, the payment proposal unit provides a function in which the generation AI notifies the user of the progress of the payment procedure in real time. For example, a notification is sent each time a payment is completed. The payment proposal unit also allows the generation AI to automatically update the progress of the payment procedure and notify the user. For example, the generation AI provides the user with the latest information each time each payment step is completed. The payment proposal unit also allows the generation AI to visualize the progress of the payment procedure and provide it to the user. For example, the payment progress can be displayed in graphs or charts so that the user can understand it visually. This allows the generation AI to add a function to notify the user of the progress of the payment procedure in real time.

[0071] The payment proposal unit can use the emotion estimation function to analyze the user's emotions during the payment process and display a message to elicit positive emotions. The payment proposal unit, for example, uses the emotion estimation function to analyze the user's emotions during the payment process in real time. For example, it analyzes the user's facial expressions from camera footage and calculates an emotion score. The payment proposal unit also uses the emotion estimation function to analyze the user's voice and estimate their emotions. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The payment proposal unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate their emotions. For example, it calculates an emotion score based on heart rate fluctuations. This makes it possible to analyze the user's emotions during the payment process and display a message to elicit positive emotions.

[0072] The disposal guidance unit can automatically refer to local regulations and rules regarding how to dispose of bulky waste and provide appropriate guidance to the user. For example, the generation AI can automatically refer to local regulations and rules regarding how to dispose of bulky waste and provide appropriate guidance to the user. For example, it can provide collection days and disposal rules for each region. The generation AI can also automatically update local regulations and rules to provide the user with the latest information. For example, it can notify the user if local waste disposal regulations change. The generation AI can also provide specific disposal guidance to the user based on local regulations and rules. For example, it can guide the user to dispose of bulky waste at a specified location and date and time. This allows the generation AI to automatically refer to local regulations and rules regarding how to dispose of bulky waste and provide appropriate guidance to the user.

[0073] The disposal guidance unit uses the emotion estimation function to analyze the emotions of the user when disposing of bulky waste and can provide advice to reduce stress. The disposal guidance unit, for example, uses the emotion estimation function to analyze the emotions of the user when disposing of bulky waste in real time. For example, it analyzes the user's facial expression from camera footage and calculates an emotion score. The disposal guidance unit also uses the emotion estimation function to analyze the user's voice and estimate their emotion. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The disposal guidance unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate their emotion. For example, it calculates an emotion score based on fluctuations in heart rate. This makes it possible to analyze the emotions of the user when disposing of bulky waste and provide advice to reduce stress.

[0074] The disposal guidance unit can provide a video tutorial on how to dispose of oversized waste, allowing the user to understand visually. In the disposal guidance unit, for example, the generation AI provides a video tutorial on how to dispose of oversized waste. For example, specific steps are explained in a video, allowing the user to understand visually. In addition, the disposal guidance unit automatically updates the video tutorial to provide the latest information. For example, the video tutorial is updated when local regulations or rules change. In addition, the disposal guidance unit improves the video tutorial based on user feedback. For example, the generation AI reflects the user's opinions and requests and provides a video tutorial that is easier to understand. In this way, a video tutorial on how to dispose of oversized waste can be provided, allowing the user to understand visually.

[0075] The disposal guidance unit can collect user feedback on how to dispose of bulky waste and use it to improve the service. In the disposal guidance unit, for example, the generation AI collects user feedback on how to dispose of bulky waste and uses it to improve the service. For example, it collects opinions and requests on how to dispose of it. In addition, the disposal guidance unit automatically analyzes user feedback and reflects it in improving the service. For example, it improves the disposal guidance based on user feedback. In addition, the disposal guidance unit suggests new functions and services based on user feedback. For example, it provides new disposal guidance methods in response to user requests. In this way, user feedback on how to dispose of bulky waste can be collected and used to improve the service.

[0076] The disposal guidance unit uses the emotion estimation function to analyze the emotions of the user when disposing of bulky waste and can display a message to elicit positive emotions. The disposal guidance unit, for example, uses the emotion estimation function to analyze the emotions of the user when disposing of bulky waste in real time. For example, it analyzes the user's facial expression from camera footage and calculates an emotion score. The disposal guidance unit also uses the emotion estimation function to analyze the user's voice and estimate emotions. For example, it analyzes the tone and speed of voice and calculates an emotion score. The disposal guidance unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and electrodermal activity) and estimate emotions. For example, it calculates an emotion score based on heart rate fluctuations. This makes it possible to analyze the emotions of the user when disposing of bulky waste and display a message to elicit positive emotions.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The bulky waste disposal system can also include a location information providing unit that acquires the user's location information and provides information on the nearest recycling center or waste disposal facility. For example, when a user enables the GPS function on their smartphone, the location information providing unit identifies the user's current location and displays the address and contact information of the nearest recycling center. The location information providing unit can also provide information on the recycling center's business hours and the types of bulky waste that can be accepted. Furthermore, the location information providing unit can provide the user with a function to navigate the route to the recycling center. This allows the user to easily find the nearest recycling center and efficiently dispose of their bulky waste.

[0079] The bulky waste disposal system can also be equipped with a history reference unit that references the user's past bulky waste disposal history and suggests ways to reuse recyclable materials and parts. For example, based on data on the bulky waste the user has disposed of in the past, the system can identify reusable materials and parts and provide ideas for DIY projects using them. The history reference unit can also display the recycling rate of the bulky waste the user has disposed of in the past, visualizing the user's contribution to the environment. Furthermore, the history reference unit can provide information on workshops and events that help users utilize reusable materials. This allows users to learn how to reuse bulky waste and practice environmentally friendly living.

[0080] The bulky waste disposal system can further estimate the user's emotions and provide an FAQ section to resolve any doubts or concerns about bulky waste disposal. For example, if the emotion estimation function is used and the user has concerns or questions about bulky waste disposal, the FAQ section can be displayed and provide frequently asked questions and their answers. The FAQ section can also have a function that allows the user to search for related answers by entering a specific question. Furthermore, the FAQ section can prioritize the display of particularly useful information according to the user's emotions. This will resolve the user's doubts and concerns about bulky waste disposal and allow them to proceed with the procedure with peace of mind.

[0081] The bulky waste disposal system can further estimate the user's emotions and introduce gamification elements to increase motivation as the user goes through the bulky waste disposal procedures. For example, the emotion estimation function can be used to analyze the user's emotions as they go through the procedures and award badges or points that give the user a sense of accomplishment. The gamification element can also provide a system in which the user levels up each time they complete a certain procedure. Furthermore, the gamification element can also include a ranking function that allows users to compete with other users. This allows the user to go through the bulky waste disposal procedures while having fun, thereby increasing their motivation.

[0082] The bulky waste disposal system can also estimate the user's emotions and provide relaxing music or environmental sounds as the user goes through the bulky waste disposal process. For example, if the user is feeling stressed, the emotion estimation function can be used to play relaxing music or natural environmental sounds. The music and environmental sounds can also be customized according to the user's preferences. Furthermore, the music and environmental sounds can be changed depending on the progress of the process. This allows the user to relax while going through the bulky waste disposal process, reducing stress.

[0083] The bulky waste disposal system can further estimate the user's emotions and provide incentives to elicit positive emotions when the user goes through the bulky waste disposal process. For example, the emotion estimation function can be used to analyze the user's emotions when the user goes through the process and provide coupons or discounts to elicit positive emotions. Incentives can also be provided as points that the user can earn each time the user completes the process. Furthermore, incentives can be provided as rewards that the user can share with friends and family. This allows the user to go through the bulky waste disposal process with positive emotions, thereby increasing the user's motivation to go through the process.

[0084] The bulky waste disposal system may also include a history reference unit that references the user's past bulky waste disposal history and suggests the optimal disposal method. For example, the system may suggest a disposal method for similar bulky waste based on data on the bulky waste the user has disposed of in the past. The history reference unit may also provide information on recycling companies and disposal facilities that the user has used in the past. Furthermore, the history reference unit may display the recycling rate and disposal costs for the bulky waste the user has disposed of in the past, providing reference information for selecting the optimal disposal method. This allows the user to select the optimal disposal method based on their past history and dispose of their bulky waste efficiently.

[0085] The bulky waste disposal system can also include a location information providing unit that acquires the user's location information and provides information on the nearest recycling shop or second-hand goods store. For example, when the user enables the GPS function on their smartphone, the location information providing unit identifies the user's current location and displays the address and contact information of the nearest recycling shop. The location information providing unit can also provide information on the recycling shop's business hours and items sold. Furthermore, the location information providing unit can provide the user with a function to navigate the route to the recycling shop. This allows the user to easily find the nearest recycling shop and reuse unwanted bulky waste.

[0086] The bulky waste disposal system can also be equipped with a history reference unit that references the user's past bulky waste disposal history and suggests ways to reuse recyclable materials and parts. For example, based on data on the bulky waste the user has disposed of in the past, the system can identify reusable materials and parts and provide ideas for DIY projects using them. The history reference unit can also display the recycling rate of the bulky waste the user has disposed of in the past, visualizing the user's contribution to the environment. Furthermore, the history reference unit can provide information on workshops and events that help users utilize reusable materials. This allows users to learn how to reuse bulky waste and practice environmentally friendly living.

[0087] The bulky waste disposal system can also include a location information providing unit that acquires the user's location information and provides information on the nearest recycling center or waste disposal facility. For example, when a user enables the GPS function on their smartphone, the location information providing unit identifies the user's current location and displays the address and contact information of the nearest recycling center. The location information providing unit can also provide information on the recycling center's business hours and the types of bulky waste that can be accepted. Furthermore, the location information providing unit can provide the user with a function to navigate the route to the recycling center. This allows the user to easily find the nearest recycling center and efficiently dispose of their bulky waste.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The image acquisition unit acquires images of bulky waste taken with a smartphone. For example, a user takes a photo of the bulky waste with their smartphone and uploads the image to the system. The image acquisition unit can also automatically adjust the image resolution and format. For example, it accepts images in JPEG or PNG format and converts them to the appropriate resolution. Step 2: The type determination unit analyzes the image acquired by the image acquisition unit to determine the type of bulky waste. For example, the generation AI uses image recognition technology to determine the type of bulky waste. The generation AI analyzes the image using deep learning and computer vision technology to identify the type of bulky waste. Step 3: The application procedure unit automates the application procedure based on the type of bulky waste determined by the type determination unit. For example, the generation AI automates the application procedure using natural language processing technology, collects the information required by the user, and automatically generates application documents. Step 4: After the application procedure has been completed by the application procedure section, the payment suggestion section suggests a payment method. For example, the generation AI may suggest to the user that payment can be made by credit card, electronic money, or bank transfer. The generation AI may also refer to the user's past payment history to suggest the optimal payment method. Step 5: After the payment procedure is completed by the payment suggestion unit, the disposal guidance unit provides guidance on the optimal disposal method. For example, the generation AI can automatically refer to local regulations and rules based on the user's address information and provide appropriate guidance to the user. It can also analyze the user's emotions when disposing of bulky waste and provide advice to reduce stress.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 AI 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.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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 AI 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an image acquisition unit that acquires an image of bulky waste taken with a smartphone; a type determination unit that analyzes the image acquired by the image acquisition unit and determines the type of bulky waste; an application procedure unit that automates application procedures based on the type of bulky waste determined by the type determination unit; a payment proposal unit that proposes a payment method after the application procedure unit completes the application procedure; and a payment method guidance unit that provides guidance on the optimal payment method after the payment procedure is completed by the payment proposal unit. A system characterized by:

2. The type determination unit Image recognition technology is used to determine the material and condition of the bulky waste and identify recyclable parts.

2. The system of claim 1.

3. The type determination unit Image recognition technology is used to identify the brand and model of the bulky waste and provide the appropriate information to recyclers.

2. The system of claim 1.

4. The application procedure unit Using natural language processing technology, the system references the user's past application history and suggests the optimal application procedure.

2. The system of claim 1.

5. The payment proposal unit: Refer to the user's past payment history and suggest the most suitable payment method.

2. The system of claim 1.

6. The dispensing guidance unit Based on the user's address information, the system not only suggests the optimal disposal method but also the optimal collection company.

2. The system of claim 1.

7. The type determination unit Analyze the emotions the user feels when photographing the bulky trash and provide advice to reduce stress 2. The system of claim 1.

8. The application procedure unit Analyze the user's emotions during the application process and provide support to reduce stress 2. The system of claim 1.

Images