System

Abstract

An object of the system according to the embodiment is to efficiently propose a method for utilizing waste or a disused article.SOLUTION: A system includes an imaging unit, an analysis unit, and a proposal unit. The imaging unit acquires an image of the trash or the disused article. The analysis unit analyzes the image acquired by the imaging unit. The proposal unit proposes a utilization method on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult to efficiently suggest ways to utilize garbage and unwanted items, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently propose ways to utilize garbage and unwanted items. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a proposal unit. The photographing unit captures images of trash and unwanted items. The analysis unit analyzes the images captured by the photographing unit. The proposal unit proposes a utilization method based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently suggest ways to utilize garbage and unwanted items. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 utilization suggestion system according to an embodiment of the present invention is a system in which AI suggests ways to utilize garbage or unwanted items simply by taking a photo of the item. This makes it possible to reduce garbage, promote recycling, and even use it as an educational tool for children.

[0029] The utilization suggestion system according to the embodiment includes a photographing unit, an analysis unit, and a suggestion unit. The photographing unit acquires images of garbage or unwanted items. For example, a user photographs garbage or unwanted items using a smartphone camera. The photographing unit can also acquire high-resolution images using a digital camera. The photographing unit can also acquire images in real time using a webcam. For example, a smartphone camera is easily portable and can be used to take photos anytime, anywhere. The high-resolution images acquired by a digital camera allow for detailed analysis. The real-time image acquisition by a webcam is suitable for online use. The analysis unit analyzes the images acquired by the photographing unit. For example, the generation AI analyzes the type and condition of garbage or unwanted items using an image recognition algorithm. The generation AI can also extract important information from images using feature extraction technology. The generation AI can also improve the accuracy of image analysis using deep learning. For example, an image recognition algorithm identifies the type of object based on its shape and color. Feature extraction technology extracts important parts from images and uses them for analysis. Deep learning improves the accuracy of analysis by learning from large amounts of data. The suggestion unit suggests utilization methods based on the results of the analysis by the analysis unit. For example, the generation AI suggests selling the items at a nearby recycle shop or online marketplace. The generation AI can also suggest taking the items to a recycling center. The generation AI can also suggest ways to make crafts for children. For example, recycle shops buy unwanted items, making recycling easy. Online marketplaces allow items to be sold over the Internet, so a wide range of customers can be reached. Recycling centers are environmentally friendly because they handle specialized recycling processes. As a result, the utilization suggestion system according to the embodiment contributes to promoting recycling and reducing waste by suggesting ways to utilize trash and unwanted items. For example, by implementing the suggested utilization methods, users can reduce waste and contribute to environmental protection.Children can also develop their creativity and get involved in recycling by making the suggested crafts.

[0030] The analysis unit can automatically generate a 3D model of an object and perform a more detailed analysis. For example, the analysis unit uses a generative AI to automatically generate a 3D model based on images of garbage or unwanted items taken by the user. For example, it can integrate images taken from multiple angles to recreate the three-dimensional shape of an object. The analysis unit can also generate a 3D model of an object using a 3D scanner. For example, a 3D scanner can scan the surface of an object and generate a detailed 3D model. The analysis unit can also analyze the internal structure of an object based on the 3D model. For example, it can use CT scanning technology to scan the inside of an object and analyze the internal structure. This enables a detailed analysis of the object and suggests more appropriate ways to use it. For example, a user can learn how to repair an object based on the 3D model. It can also suggest ways to reuse the object based on the 3D model.

[0031] The photographing unit can evaluate the condition of an object in real time and instruct the user on how to adjust the shooting angle and lighting. For example, when a user photographs trash or unwanted items, the generation AI evaluates the condition of the object in real time and instructs the user on how to adjust the optimal shooting angle and lighting. For example, it adjusts the direction of the light to avoid shadows. The photographing unit can also evaluate the condition of the object and instruct the user on an appropriate shooting distance. For example, if the object is small, it instructs the user to shoot at a close distance. The photographing unit can also evaluate the condition of the object and instruct the user on an appropriate background for shooting. For example, if the background is cluttered, it instructs the user to choose a simple background. This provides optimal shooting conditions and improves analysis accuracy. For example, by taking photos according to the generation AI's instructions, the user can obtain more accurate analysis results. Furthermore, providing optimal shooting conditions allows the user to obtain detailed information about the object.

[0032] The analysis unit can automatically remove the background from an image and extract only the object. For example, in the case of an image of garbage or unwanted items taken by a user, the analysis unit uses a generation AI to automatically remove the background and extract only the object. For example, unnecessary parts of the background are automatically deleted. The analysis unit can also separate the foreground and background of an image and extract only the object. For example, a segmentation algorithm is used to separate the foreground and background. The analysis unit can also remove the background using chromakey technology. For example, a specific color is set as the background and then removed. This improves the accuracy of the analysis by extracting only the object. For example, a user can perform a detailed analysis of the object based on an image with the background removed. Furthermore, extracting only the object improves the accuracy of the analysis results.

[0033] The analysis unit can automatically search for information about the object's history and manufacturer and provide it to the user. For example, when a user takes a photo of trash or unwanted items, the analysis unit's generation AI automatically searches for information about the object's history and manufacturer and provides it to the user. For example, it displays information about the object's year of manufacture and manufacturer. The analysis unit can also provide information about the object's manufacturing process and how to use it. For example, it displays the object's manufacturing process and precautions for use. The analysis unit can also provide information about the object's historical background and cultural value. For example, it can explain the object's historical background and its cultural value. Providing detailed information about the object deepens the user's understanding. For example, knowing the object's history and manufacturer information can increase a user's interest in the object. Furthermore, knowing detailed information about an object can help users find appropriate ways to use it.

[0034] The suggestion unit can expand the scope of reuse by including methods for repairing or modifying an object. For example, the suggestion unit includes methods for repairing an object in the utilization methods proposed by the generative AI. For example, it may suggest the steps to repair a broken toy and the tools needed. The suggestion unit can also suggest methods for modifying an object. For example, it may suggest a way to remake old furniture into new furniture. The suggestion unit can also suggest materials and parts needed to repair or modify an object. For example, it may suggest the screws and adhesives needed for repair, or the paint and fabric needed for modification. In this way, suggesting methods for repairing or modifying an object expands the scope of reuse. For example, a user can reuse a broken object by implementing the proposed repair method. Also, an old object can be put to new use by implementing the proposed modification method.

[0035] The suggestion unit collects user feedback on the proposed use methods, and the generation AI learns to improve the accuracy of the suggestions. For example, the suggestion unit collects user feedback on the proposed use methods, and the generation AI learns based on that data to improve the accuracy of the suggestions. For example, the suggestion unit provides feedback on the results of the user implementing the suggestions. The suggestion unit can also improve the content of the suggestions based on user feedback. For example, it can simplify the steps to make it easier for the user to implement the proposed methods. The suggestion unit can also add new suggestions based on user feedback. For example, it can suggest new recycling and reuse methods in response to user requests. This improves the accuracy of the suggestions based on user feedback. For example, by receiving more appropriate suggestions, the user will be more satisfied with how the object is used. Furthermore, as the generation AI learns, the accuracy of the suggestions improves, enabling more effective recycling and reuse.

[0036] The suggestion unit can include ways of use in local community events and workshops. For example, the suggestion unit includes ways of use in local community events in the ways of use suggested by the generation AI. For example, it suggests ways of use in local recycling events. The suggestion unit can also suggest ways of use in local workshops. For example, it suggests ways of use in local craft workshops for children. The suggestion unit can also suggest ways of participating in local community activities. For example, it suggests ways of participating in local cleanup activities and recycling activities. In this way, by suggesting ways of use in local community events and workshops, ties with the local community are strengthened. For example, by participating in local events and workshops, a user can deepen their ties with the local community. Furthermore, by participating in local community activities, they can contribute to environmental protection.

[0037] The suggestion unit can add ideas for reusing objects as artworks. For example, the suggestion unit can add ideas for reusing objects as artworks to the utilization methods suggested by the generative AI. For example, the suggestion unit can suggest ways to remake old furniture into artworks. The suggestion unit can also suggest ideas for art projects using objects. For example, the suggestion unit can suggest ways to create sculptures or installations using recycled materials. The suggestion unit can also suggest ways to hold art workshops using objects. For example, the suggestion unit can suggest ways to hold workshops at local art events. In this way, creative reuse is promoted by reusing objects as artworks. For example, by carrying out the suggested art projects, a user can reuse the object in a new way. Furthermore, reusing the object as an artwork can increase the value of the object.

[0038] The suggestion unit can include a delivery method or a packaging method for the object. For example, the suggestion unit includes a delivery method for the object in the sales or recycling path suggested by the generation AI. For example, it suggests an appropriate delivery company or delivery procedure. The suggestion unit can also suggest a packaging method for the object. For example, it suggests appropriate packaging materials and packaging procedures. The suggestion unit can also provide points to note regarding the delivery of the object. For example, it suggests how to handle fragile objects. In this way, by suggesting a delivery or packaging method for the object, the user can easily sell or recycle the object. For example, by implementing the suggested delivery method, the user can deliver the object safely. Also, by implementing the suggested packaging method, the user can package the object appropriately.

[0039] The suggestion unit can analyze the user's usage history for the proposed lead and automatically customize the optimal lead. The suggestion unit, for example, analyzes the user's usage history for the proposed lead and automatically customizes the optimal lead. For example, the suggestion unit suggests an optimal sales channel based on the past usage history. The suggestion unit can also improve the content of the lead based on the user's usage history. For example, the suggestion unit simplifies the steps of the lead to make it easier for the user to use. The suggestion unit can also add a new lead based on the user's usage history. For example, the suggestion unit suggests a new sales channel or recycling method in response to the user's request. In this way, the user experience is improved by suggesting the optimal lead based on the user's usage history. For example, the user can easily implement the lead by receiving suggestions based on the past usage history. Furthermore, the suggestion of the optimal lead can efficiently sell and recycle objects.

[0040] The suggestion unit can add options for exchanging or donating objects. For example, the suggestion unit adds an option for exchanging objects to the sales or recycling paths suggested by the generation AI. For example, it can suggest a platform where objects can be exchanged for objects of the same value. The suggestion unit can also suggest options for donating objects. For example, it can suggest appropriate donation recipients and donation procedures. The suggestion unit can also provide points to note regarding exchanging or donating objects. For example, it can suggest exchange conditions and how to select a donation recipient. In this way, adding options for exchanging or donating objects expands the scope of reuse. For example, by using the suggested exchange option, a user can make effective use of unwanted objects. Also, by using the suggested donation option, the user can donate the object to someone in need.

[0041] The suggestion unit can provide a video link that visually explains the recycling process of an object. For example, the suggestion unit provides a video link that visually explains the recycling process of an object to the sales and recycling flow suggested by the generation AI. For example, the recycling procedure is explained in a video. The suggestion unit can also provide a video link that explains in detail each step of the recycling process. For example, sorting methods and processing methods are explained in a video. The suggestion unit can also provide a video link that explains points to note in the recycling process. For example, safety measures and environmental considerations when recycling are explained in a video. This visual explanation of the recycling process deepens the user's understanding. For example, by watching the video, the user can accurately understand the recycling procedure. Furthermore, the visual explanation allows the user to realize the importance and effects of recycling.

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

[0043] The analysis unit can analyze the components of an object and identify reusable materials. For example, it can analyze the components of metals and plastics to extract recyclable materials. The analysis unit can also analyze the chemical components of an object and identify substances that are harmful to the environment. For example, it can detect harmful substances such as lead and mercury and suggest appropriate disposal methods. The analysis unit can also suggest methods for separating reusable materials based on the results of the component analysis of an object. For example, it can suggest a procedure for separating metals and plastics. In this way, analyzing the components of an object makes it possible to identify and separate reusable materials, improving recycling efficiency. For example, a user can select an appropriate recycling method based on the results of the component analysis. Component analysis also enables the appropriate disposal of substances that are harmful to the environment.

[0044] The suggestion unit can suggest ways to improve energy efficiency in addition to ways to recycle objects. For example, it can suggest ways to improve the energy efficiency of old home appliances. The suggestion unit can also suggest parts and materials necessary to improve energy efficiency. For example, it can suggest energy-saving parts and insulation materials. The suggestion unit can also provide information on improving energy efficiency. For example, it can explain the environmental impact and cost reduction effects of improving energy efficiency. In this way, suggesting ways to improve energy efficiency in addition to ways to recycle objects contributes to both environmental protection and cost reduction. For example, by implementing the suggested ways to improve energy efficiency, a user can reduce energy consumption and contribute to environmental protection. Furthermore, improving energy efficiency can lead to long-term cost reductions.

[0045] The suggestion unit can suggest ways to use the device as part of environmental education, in addition to how to recycle objects. For example, it can suggest ways to use the device in environmental education programs at schools or in local areas. The suggestion unit can also suggest teaching materials and resources necessary for environmental education. For example, it can provide information on the importance of recycling and environmental protection. The suggestion unit can also suggest activities to enhance the effectiveness of environmental education. For example, it can suggest recycling crafts and games related to environmental protection. In this way, environmental awareness can be improved by suggesting ways to use the device as part of environmental education, in addition to how to recycle objects. For example, through the proposed environmental education program, the user can learn the importance of recycling and increase their awareness of environmental protection. Furthermore, environmental education is expected to improve the environmental awareness of the next generation.

[0046] The suggestion unit can suggest ways to use an object as a DIY project in addition to how to recycle it. For example, it can suggest ways to remake old furniture into new furniture. The suggestion unit can also suggest tools and materials needed for the DIY project. For example, it can suggest paints and fabrics needed for the remake. The suggestion unit can also provide detailed instructions for the DIY project. For example, it can provide a step-by-step guide. This promotes creative reuse by suggesting ways to use an object as a DIY project in addition to how to recycle it. For example, by carrying out the suggested DIY project, the user can reuse the object in a new way. Furthermore, the value of the object can be increased through the DIY project.

[0047] The suggestion unit can suggest health and wellness-related uses in addition to methods for recycling the object. For example, it can suggest methods for making exercise equipment using old furniture. The suggestion unit can also provide information necessary for health and wellness. For example, it can provide advice on the effects of exercise and how to maintain good health. The suggestion unit can also suggest health and wellness-related activities. For example, it can suggest methods for making a yoga mat using recycled materials. In this way, by suggesting health and wellness-related uses in addition to methods for recycling the object, the user's health awareness is improved. For example, the user can maintain their health by using the suggested exercise equipment. The user's quality of life is also improved through information related to health and wellness.

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

[0049] Step 1: The camera acquires images of trash and unwanted items. For example, a user takes a photo of trash or unwanted items using a smartphone camera. High-resolution images can also be acquired using a digital camera, and images can also be acquired in real time using a webcam. Smartphone cameras are easily portable and can be used to take photos anytime, anywhere. Digital cameras acquire high-resolution images, which allow for detailed analysis. Webcams can acquire images in real time, making them suitable for online use. Step 2: The analysis unit analyzes the images captured by the photography unit. For example, the generative AI uses an image recognition algorithm to analyze the type and condition of trash or unwanted items. It then uses feature extraction technology to extract important information from the images, and deep learning to improve the accuracy of the image analysis. The image recognition algorithm identifies the type of object based on its shape and color, and feature extraction technology extracts important parts from the image and uses them for analysis. Deep learning improves the accuracy of analysis by learning from large amounts of data. Step 3: The suggestion unit suggests ways to use the items based on the results of the analysis by the analysis unit. For example, the generation AI might suggest selling the items at a nearby recycling shop or online marketplace. It could also suggest taking the items to a recycling center or creating crafts for children. Recycling shops buy unwanted items, making recycling easy, while online marketplaces allow items to be sold over the internet, reaching a wide range of customers. Recycling centers handle professional recycling processes, making them environmentally friendly. By implementing the suggested ways to use the items, users can reduce waste and contribute to environmental protection. Children can also develop their creativity while recycling by creating the suggested crafts.

[0050] (Example 2) The utilization suggestion system according to an embodiment of the present invention is a system in which AI suggests ways to utilize garbage or unwanted items simply by taking a photo of the item. This makes it possible to reduce garbage, promote recycling, and even use it as an educational tool for children.

[0051] The utilization suggestion system according to the embodiment includes a photographing unit, an analysis unit, and a suggestion unit. The photographing unit acquires images of garbage or unwanted items. For example, a user photographs garbage or unwanted items using a smartphone camera. The photographing unit can also acquire high-resolution images using a digital camera. The photographing unit can also acquire images in real time using a webcam. For example, a smartphone camera is easily portable and can be used to take photos anytime, anywhere. The high-resolution images acquired by a digital camera allow for detailed analysis. The real-time image acquisition by a webcam is suitable for online use. The analysis unit analyzes the images acquired by the photographing unit. For example, the generation AI analyzes the type and condition of garbage or unwanted items using an image recognition algorithm. The generation AI can also extract important information from images using feature extraction technology. The generation AI can also improve the accuracy of image analysis using deep learning. For example, an image recognition algorithm identifies the type of object based on its shape and color. Feature extraction technology extracts important parts from images and uses them for analysis. Deep learning improves the accuracy of analysis by learning from large amounts of data. The suggestion unit suggests utilization methods based on the results of the analysis by the analysis unit. For example, the generation AI suggests selling the items at a nearby recycle shop or online marketplace. The generation AI can also suggest taking the items to a recycling center. The generation AI can also suggest ways to make crafts for children. For example, recycle shops buy unwanted items, making recycling easy. Online marketplaces allow items to be sold over the Internet, so a wide range of customers can be reached. Recycling centers are environmentally friendly because they handle specialized recycling processes. As a result, the utilization suggestion system according to the embodiment contributes to promoting recycling and reducing waste by suggesting ways to utilize trash and unwanted items. For example, by implementing the suggested utilization methods, users can reduce waste and contribute to environmental protection.Children can also develop their creativity and get involved in recycling by making the suggested crafts.

[0052] The analysis unit can automatically generate a 3D model of an object and perform a more detailed analysis. For example, the analysis unit uses a generative AI to automatically generate a 3D model based on images of garbage or unwanted items taken by the user. For example, it can integrate images taken from multiple angles to recreate the three-dimensional shape of an object. The analysis unit can also generate a 3D model of an object using a 3D scanner. For example, a 3D scanner can scan the surface of an object and generate a detailed 3D model. The analysis unit can also analyze the internal structure of an object based on the 3D model. For example, it can use CT scanning technology to scan the inside of an object and analyze the internal structure. This enables a detailed analysis of the object and suggests more appropriate ways to use it. For example, a user can learn how to repair an object based on the 3D model. It can also suggest ways to reuse the object based on the 3D model.

[0053] The photographing unit can evaluate the condition of an object in real time and instruct the user on how to adjust the shooting angle and lighting. For example, when a user photographs trash or unwanted items, the generation AI evaluates the condition of the object in real time and instructs the user on how to adjust the optimal shooting angle and lighting. For example, it adjusts the direction of the light to avoid shadows. The photographing unit can also evaluate the condition of the object and instruct the user on an appropriate shooting distance. For example, if the object is small, it instructs the user to shoot at a close distance. The photographing unit can also evaluate the condition of the object and instruct the user on an appropriate background for shooting. For example, if the background is cluttered, it instructs the user to choose a simple background. This provides optimal shooting conditions and improves analysis accuracy. For example, by taking photos according to the generation AI's instructions, the user can obtain more accurate analysis results. Furthermore, providing optimal shooting conditions allows the user to obtain detailed information about the object.

[0054] The photography unit can use the emotion estimation function to analyze the user's emotions and provide photography guidance to elicit positive emotions. For example, when a user takes a photo of trash or unwanted items, the photography unit uses the emotion estimation function to analyze the user's emotions and provide photography guidance to elicit positive emotions. For example, the photography unit can display an encouraging message to help the user enjoy photography. The photography unit can also analyze the user's emotions and provide a relaxing photography environment. For example, if the user is nervous, the photography unit can play relaxing music. The photography unit can also analyze the user's emotions and provide positive feedback during photography. For example, if the user takes a good photo, the photography unit can display a praising message. This allows the user to enjoy photography and improves the user experience. For example, by taking photos with positive emotions, the user can take better photos. Furthermore, eliciting positive emotions increases motivation for photography.

[0055] The analysis unit can automatically remove the background from an image and extract only the object. For example, in the case of an image of garbage or unwanted items taken by a user, the analysis unit uses a generation AI to automatically remove the background and extract only the object. For example, unnecessary parts of the background are automatically deleted. The analysis unit can also separate the foreground and background of an image and extract only the object. For example, a segmentation algorithm is used to separate the foreground and background. The analysis unit can also remove the background using chromakey technology. For example, a specific color is set as the background and then removed. This improves the accuracy of the analysis by extracting only the object. For example, a user can perform a detailed analysis of the object based on an image with the background removed. Furthermore, extracting only the object improves the accuracy of the analysis results.

[0056] The analysis unit can automatically search for information about the object's history and manufacturer and provide it to the user. For example, when a user takes a photo of trash or unwanted items, the analysis unit's generation AI automatically searches for information about the object's history and manufacturer and provides it to the user. For example, it displays information about the object's year of manufacture and manufacturer. The analysis unit can also provide information about the object's manufacturing process and how to use it. For example, it displays the object's manufacturing process and precautions for use. The analysis unit can also provide information about the object's historical background and cultural value. For example, it can explain the object's historical background and its cultural value. Providing detailed information about the object deepens the user's understanding. For example, knowing the object's history and manufacturer information can increase a user's interest in the object. Furthermore, knowing detailed information about an object can help users find appropriate ways to use it.

[0057] The analysis unit can use the emotion estimation function to analyze the user's emotion toward an object photographed by the user and make customized suggestions based on the emotion. For example, the analysis unit can use the emotion estimation function to analyze the user's emotion toward an object photographed by the user and make customized suggestions based on the emotion. For example, the analysis unit can suggest methods for preserving or reusing an object to which the user is attached. The analysis unit can also suggest methods for repairing or modifying an object based on the user's emotion. For example, the analysis unit can suggest repair procedures and necessary tools for an object that the user cherishes. The analysis unit can also suggest places to donate or exchange an object based on the user's emotion. For example, the analysis unit can suggest appropriate places to donate or exchange an object that the user no longer needs. In this way, suggestions based on the user's emotion improve the user experience. For example, by receiving suggestions that match their emotion, the user's satisfaction with how the object is used increases. Furthermore, suggestions based on the user's emotion promote the reuse and recycling of objects.

[0058] The suggestion unit can expand the scope of reuse by including methods for repairing or modifying an object. For example, the suggestion unit includes methods for repairing an object in the utilization methods proposed by the generative AI. For example, it may suggest the steps to repair a broken toy and the tools needed. The suggestion unit can also suggest methods for modifying an object. For example, it may suggest a way to remake old furniture into new furniture. The suggestion unit can also suggest materials and parts needed to repair or modify an object. For example, it may suggest the screws and adhesives needed for repair, or the paint and fabric needed for modification. In this way, suggesting methods for repairing or modifying an object expands the scope of reuse. For example, a user can reuse a broken object by implementing the proposed repair method. Also, an old object can be put to new use by implementing the proposed modification method.

[0059] The suggestion unit collects user feedback on the proposed use methods, and the generation AI learns to improve the accuracy of the suggestions. For example, the suggestion unit collects user feedback on the proposed use methods, and the generation AI learns based on that data to improve the accuracy of the suggestions. For example, the suggestion unit provides feedback on the results of the user implementing the suggestions. The suggestion unit can also improve the content of the suggestions based on user feedback. For example, it can simplify the steps to make it easier for the user to implement the proposed methods. The suggestion unit can also add new suggestions based on user feedback. For example, it can suggest new recycling and reuse methods in response to user requests. This improves the accuracy of the suggestions based on user feedback. For example, by receiving more appropriate suggestions, the user will be more satisfied with how the object is used. Furthermore, as the generation AI learns, the accuracy of the suggestions improves, enabling more effective recycling and reuse.

[0060] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed utilization method and prioritize suggestions that elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotions the user feels toward the proposed utilization method and prioritize suggestions that elicit positive emotions. For example, it prioritizes suggestions that the user can enjoy implementing. The suggestion unit can also customize the content of the suggestion based on the user's emotions. For example, it makes suggestions related to areas that the user is interested in. The suggestion unit can also adjust the timing of the suggestion based on the user's emotions. For example, it makes suggestions when the user is relaxed. This improves the user experience by prioritizing suggestions that elicit positive emotions. For example, when a user receives a suggestion while feeling positive emotions, their satisfaction with the suggestion content increases. Furthermore, making suggestions that elicit positive emotions increases the rate at which the suggestion is implemented.

[0061] The suggestion unit can include ways of use in local community events and workshops. For example, the suggestion unit includes ways of use in local community events in the ways of use suggested by the generation AI. For example, it suggests ways of use in local recycling events. The suggestion unit can also suggest ways of use in local workshops. For example, it suggests ways of use in local craft workshops for children. The suggestion unit can also suggest ways of participating in local community activities. For example, it suggests ways of participating in local cleanup activities and recycling activities. In this way, by suggesting ways of use in local community events and workshops, ties with the local community are strengthened. For example, by participating in local events and workshops, a user can deepen their ties with the local community. Furthermore, by participating in local community activities, they can contribute to environmental protection.

[0062] The suggestion unit can add ideas for reusing objects as artworks. For example, the suggestion unit can add ideas for reusing objects as artworks to the utilization methods suggested by the generative AI. For example, the suggestion unit can suggest ways to remake old furniture into artworks. The suggestion unit can also suggest ideas for art projects using objects. For example, the suggestion unit can suggest ways to create sculptures or installations using recycled materials. The suggestion unit can also suggest ways to hold art workshops using objects. For example, the suggestion unit can suggest ways to hold workshops at local art events. In this way, creative reuse is promoted by reusing objects as artworks. For example, by carrying out the suggested art projects, a user can reuse the object in a new way. Furthermore, reusing the object as an artwork can increase the value of the object.

[0063] The suggestion unit can use the emotion estimation function to identify a utilization method that the user is most interested in and provide additional information related to that method. For example, the suggestion unit can use the emotion estimation function to identify a utilization method that the user is most interested in and provide additional information related to that method. For example, detailed information about a recycling method that the user is interested in can be provided. The suggestion unit can also provide related articles and videos based on the user's interests. For example, links to articles and videos related to fields that the user is interested in can be provided. The suggestion unit can also make new suggestions based on the user's interests. For example, new recycling and reuse methods that the user is interested in can be proposed. This improves the user experience by providing information related to the utilization method that the user is most interested in. For example, by obtaining information that interests the user, the user can deepen their understanding of the proposed content. Furthermore, providing information that interests the user can improve the implementation rate of suggestions.

[0064] The suggestion unit can include a delivery method or a packaging method for the object. For example, the suggestion unit includes a delivery method for the object in the sales or recycling path suggested by the generation AI. For example, it suggests an appropriate delivery company or delivery procedure. The suggestion unit can also suggest a packaging method for the object. For example, it suggests appropriate packaging materials and packaging procedures. The suggestion unit can also provide points to note regarding the delivery of the object. For example, it suggests how to handle fragile objects. In this way, by suggesting a delivery or packaging method for the object, the user can easily sell or recycle the object. For example, by implementing the suggested delivery method, the user can deliver the object safely. Also, by implementing the suggested packaging method, the user can package the object appropriately.

[0065] The suggestion unit can analyze the user's usage history for the proposed lead and automatically customize the optimal lead. The suggestion unit, for example, analyzes the user's usage history for the proposed lead and automatically customizes the optimal lead. For example, the suggestion unit suggests an optimal sales channel based on the past usage history. The suggestion unit can also improve the content of the lead based on the user's usage history. For example, the suggestion unit simplifies the steps of the lead to make it easier for the user to use. The suggestion unit can also add a new lead based on the user's usage history. For example, the suggestion unit suggests a new sales channel or recycling method in response to the user's request. In this way, the user experience is improved by suggesting the optimal lead based on the user's usage history. For example, the user can easily implement the lead by receiving suggestions based on the past usage history. Furthermore, the suggestion of the optimal lead can efficiently sell and recycle objects.

[0066] The suggestion unit can use the emotion estimation function to analyze the emotion the user feels toward the proposed lead line and prioritize lead lines that elicit positive emotions. The suggestion unit, for example, uses the emotion estimation function to analyze the emotion the user feels toward the proposed lead line and prioritize lead lines that elicit positive emotions. For example, the suggestion unit can suggest recycling centers that the user can use with peace of mind. The suggestion unit can also customize the content of the lead line based on the user's emotions. For example, the suggestion unit can suggest lead lines that the user can use while relaxing. The suggestion unit can also adjust the timing of the lead line based on the user's emotions. For example, the suggestion unit can suggest lead lines when the user is not feeling stressed. This improves the user experience by prioritizing lead lines that the user feels positive about. For example, the user can easily execute the lead line by executing the lead line while feeling positive emotions. The suggestion unit can also improve the execution rate of the lead line by suggesting lead lines that elicit positive emotions.

[0067] The suggestion unit can add options for exchanging or donating objects. For example, the suggestion unit adds an option for exchanging objects to the sales or recycling paths suggested by the generation AI. For example, it can suggest a platform where objects can be exchanged for objects of the same value. The suggestion unit can also suggest options for donating objects. For example, it can suggest appropriate donation recipients and donation procedures. The suggestion unit can also provide points to note regarding exchanging or donating objects. For example, it can suggest exchange conditions and how to select a donation recipient. In this way, adding options for exchanging or donating objects expands the scope of reuse. For example, by using the suggested exchange option, a user can make effective use of unwanted objects. Also, by using the suggested donation option, the user can donate the object to someone in need.

[0068] The suggestion unit can provide a video link that visually explains the recycling process of an object. For example, the suggestion unit provides a video link that visually explains the recycling process of an object to the sales and recycling flow suggested by the generation AI. For example, the recycling procedure is explained in a video. The suggestion unit can also provide a video link that explains in detail each step of the recycling process. For example, sorting methods and processing methods are explained in a video. The suggestion unit can also provide a video link that explains points to note in the recycling process. For example, safety measures and environmental considerations when recycling are explained in a video. This visual explanation of the recycling process deepens the user's understanding. For example, by watching the video, the user can accurately understand the recycling procedure. Furthermore, the visual explanation allows the user to realize the importance and effects of recycling.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The analysis unit can analyze the components of an object and identify reusable materials. For example, it can analyze the components of metals and plastics to extract recyclable materials. The analysis unit can also analyze the chemical components of an object and identify substances that are harmful to the environment. For example, it can detect harmful substances such as lead and mercury and suggest appropriate disposal methods. The analysis unit can also suggest methods for separating reusable materials based on the results of the component analysis of an object. For example, it can suggest a procedure for separating metals and plastics. In this way, analyzing the components of an object makes it possible to identify and separate reusable materials, improving recycling efficiency. For example, a user can select an appropriate recycling method based on the results of the component analysis. Component analysis also enables the appropriate disposal of substances that are harmful to the environment.

[0071] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed recycling methods and prioritize recycling methods that elicit positive emotions. For example, it prioritizes recycling methods that the user can enjoy performing. The suggestion unit can also customize recycling methods based on the user's emotions. For example, it can suggest recycling methods related to areas that the user is interested in. The suggestion unit can also adjust the timing of recycling methods based on the user's emotions. For example, it can suggest recycling methods when the user is relaxed. This improves the user experience by prioritizing recycling methods that elicit positive emotions. For example, when a user performs a recycling method while feeling positive emotions, the rate at which the user performs recycling increases. Furthermore, suggesting recycling methods that elicit positive emotions increases the effectiveness of recycling.

[0072] The suggestion unit can suggest ways to improve energy efficiency in addition to ways to recycle objects. For example, it can suggest ways to improve the energy efficiency of old home appliances. The suggestion unit can also suggest parts and materials necessary to improve energy efficiency. For example, it can suggest energy-saving parts and insulation materials. The suggestion unit can also provide information on improving energy efficiency. For example, it can explain the environmental impact and cost reduction effects of improving energy efficiency. In this way, suggesting ways to improve energy efficiency in addition to ways to recycle objects contributes to both environmental protection and cost reduction. For example, by implementing the suggested ways to improve energy efficiency, a user can reduce energy consumption and contribute to environmental protection. Furthermore, improving energy efficiency can lead to long-term cost reductions.

[0073] The analysis unit can use the emotion estimation function to analyze the user's emotions toward the object photographed and suggest a customized repair method based on that emotion. For example, it can suggest a preservation method or repair procedure for an object that the user is attached to. The analysis unit can also suggest tools and materials needed for repair based on the user's emotions. For example, it can suggest parts and adhesives needed for repair for an object that the user cherishes. The analysis unit can also adjust the timing of repair based on the user's emotions. For example, it can suggest a repair method when the user is relaxed. This improves the user experience by suggesting a repair method that matches the user's emotions. For example, a user's satisfaction with the object repair increases when a repair method that matches their emotions is suggested. Furthermore, suggesting a repair method based on the user's emotions promotes object repair.

[0074] The suggestion unit can suggest ways to use the device as part of environmental education, in addition to how to recycle objects. For example, it can suggest ways to use the device in environmental education programs at schools or in local areas. The suggestion unit can also suggest teaching materials and resources necessary for environmental education. For example, it can provide information on the importance of recycling and environmental protection. The suggestion unit can also suggest activities to enhance the effectiveness of environmental education. For example, it can suggest recycling crafts and games related to environmental protection. In this way, environmental awareness can be improved by suggesting ways to use the device as part of environmental education, in addition to how to recycle objects. For example, through the proposed environmental education program, the user can learn the importance of recycling and increase their awareness of environmental protection. Furthermore, environmental education is expected to improve the environmental awareness of the next generation.

[0075] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed repair methods and prioritize repair methods that elicit positive emotions. For example, it prioritizes repair methods that the user can enjoy performing. The suggestion unit can also customize repair methods based on the user's emotions. For example, it can suggest repair methods related to areas that the user is interested in. The suggestion unit can also adjust the timing of repair methods based on the user's emotions. For example, it can suggest repair methods when the user is relaxed. This improves the user experience by prioritizing repair methods that the user feels positive about. For example, when a user performs a repair method while feeling positive about it, the repair execution rate increases. Furthermore, suggesting repair methods that elicit positive emotions increases the effectiveness of repairs.

[0076] The suggestion unit can suggest ways to use an object as a DIY project in addition to how to recycle it. For example, it can suggest ways to remake old furniture into new furniture. The suggestion unit can also suggest tools and materials needed for the DIY project. For example, it can suggest paints and fabrics needed for the remake. The suggestion unit can also provide detailed instructions for the DIY project. For example, it can provide a step-by-step guide. This promotes creative reuse by suggesting ways to use an object as a DIY project in addition to how to recycle it. For example, by carrying out the suggested DIY project, the user can reuse the object in a new way. Furthermore, the value of the object can be increased through the DIY project.

[0077] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed DIY projects and prioritize DIY projects that elicit positive emotions. For example, it prioritizes DIY projects that the user can enjoy completing. The suggestion unit can also customize DIY projects based on the user's emotions. For example, it can suggest DIY projects related to areas of interest to the user. The suggestion unit can also adjust the timing of DIY projects based on the user's emotions. For example, it can suggest DIY projects when the user is relaxing. This improves the user experience by prioritizing DIY projects that elicit positive emotions. For example, when a user performs a DIY project while feeling positive emotions, the project completion rate increases. Furthermore, suggesting DIY projects that elicit positive emotions increases the effectiveness of the project.

[0078] The suggestion unit can suggest health and wellness-related uses in addition to methods for recycling the object. For example, it can suggest methods for making exercise equipment using old furniture. The suggestion unit can also provide information necessary for health and wellness. For example, it can provide advice on the effects of exercise and how to maintain good health. The suggestion unit can also suggest health and wellness-related activities. For example, it can suggest methods for making a yoga mat using recycled materials. In this way, by suggesting health and wellness-related uses in addition to methods for recycling the object, the user's health awareness is improved. For example, the user can maintain their health by using the suggested exercise equipment. The user's quality of life is also improved through information related to health and wellness.

[0079] The suggestion unit can use the emotion estimation function to analyze the emotions the user feels toward the proposed health- and wellness-related usage methods and prioritize usage methods that elicit positive emotions. For example, it can prioritize exercise methods that the user can enjoy. The suggestion unit can also customize health- and wellness-related usage methods based on the user's emotions. For example, it can suggest exercise methods related to areas of interest to the user. The suggestion unit can also adjust the timing of exercise based on the user's emotions. For example, it can suggest exercise methods when the user is relaxed. This improves the user experience by prioritizing health- and wellness-related usage methods that elicit positive emotions. For example, if a user exercises while feeling positive emotions, the user's exercise completion rate will increase. Furthermore, suggesting exercise methods that elicit positive emotions will increase the effectiveness of exercise.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The camera acquires images of trash and unwanted items. For example, a user takes a photo of trash or unwanted items using a smartphone camera. High-resolution images can also be acquired using a digital camera, and images can also be acquired in real time using a webcam. Smartphone cameras are easily portable and can be used to take photos anytime, anywhere. Digital cameras acquire high-resolution images, which allow for detailed analysis. Webcams can acquire images in real time, making them suitable for online use. Step 2: The analysis unit analyzes the images captured by the photography unit. For example, the generative AI uses an image recognition algorithm to analyze the type and condition of trash or unwanted items. It then uses feature extraction technology to extract important information from the images, and deep learning to improve the accuracy of the image analysis. The image recognition algorithm identifies the type of object based on its shape and color, and feature extraction technology extracts important parts from the image and uses them for analysis. Deep learning improves the accuracy of analysis by learning from large amounts of data. Step 3: The suggestion unit suggests ways to use the items based on the results of the analysis by the analysis unit. For example, the generation AI might suggest selling the items at a nearby recycling shop or online marketplace. It could also suggest taking the items to a recycling center or creating crafts for children. Recycling shops buy unwanted items, making recycling easy, while online marketplaces allow items to be sold over the internet, reaching a wide range of customers. Recycling centers handle professional recycling processes, making them environmentally friendly. By implementing the suggested ways to use the items, users can reduce waste and contribute to environmental protection. Children can also develop their creativity while recycling by creating the suggested crafts.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photographing unit that captures images of garbage and unwanted items; an analysis unit that analyzes the image acquired by the imaging unit; a proposal unit that proposes a utilization method based on the results of the analysis by the analysis unit. A system characterized by:

2. The analysis unit Automatically generate 3D models of objects for more detailed analysis 2. The system of claim 1.

3. The analysis unit The background is automatically removed from the image and only the object is extracted.

2. The system of claim 1.

4. The proposal unit Expanding reuse by including ways to repair and modify objects 2. The system of claim 1.

5. The proposal unit Include how the object will be shipped and packaged 2. The system of claim 1.

6. The imaging unit is Analyzes the user's emotions using emotion estimation functionality and provides a shooting guide to elicit positive emotions 2. The system of claim 1.

7. The proposal unit Using emotion estimation, we analyze the emotions users feel about proposed usage methods and prioritize suggestions that evoke positive emotions.

2. The system of claim 1.

8. The proposal unit Using emotion estimation functionality, the emotions felt by the user regarding the proposed paths are analyzed, and the paths that elicit positive emotions are prioritized.

2. The system of claim 1.

Images