system

Abstract

The system according to the embodiment aims to quickly propose an optimal method for disposing of unwanted items. [Solution] A system according to an embodiment includes a photographing unit, an analysis unit, a presentation unit, and a procedure provision unit. The photographing unit takes and uploads photos of unwanted items. The analysis unit analyzes the photos uploaded by the photographing unit and proposes an appropriate solution from three options: reuse, recycle, and reduce. The presentation unit presents the solution proposed by the analysis unit to the user. The procedure provision unit provides a procedure based on the solution proposed by the presentation unit.

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] Previous technology had the problem of making it difficult to determine how to dispose of unwanted items, and it took time to find an appropriate solution.

[0005] The system according to the embodiment aims to quickly propose an optimal method for disposing of unwanted items. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, a presentation unit, and a procedure providing unit. The photographing unit takes and uploads photos of unwanted items. The analysis unit analyzes the photos uploaded by the photographing unit and proposes an appropriate solution from three options: reuse, recycle, and reduce. The presentation unit presents the solution proposed by the analysis unit to the user. The procedure providing unit provides a procedure based on the solution proposed by the presentation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly propose an optimal method for disposing of 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 non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) An AI app according to an embodiment of the present invention is a system that proposes optimal solutions for disposing of unwanted items. This system works by having users take photos of unwanted items and upload them to the app. The AI ​​analyzes the photos and proposes the optimal solution from three options: reuse, recycle, and reduce. The user can choose the most appropriate method from the proposed options to dispose of the item. For example, when disposing of an old smartphone, the AI ​​suggests selling it secondhand as reuse, reusing parts as recycling, and reducing waste as reduce. The user can choose the optimal method from these options to dispose of the item. Furthermore, by providing specific steps based on the selected solution, the user can smoothly dispose of the item. This allows the AI ​​app to propose optimal solutions and provide specific steps for efficiently disposing of unwanted items.

[0029] The AI ​​app according to the embodiment includes a photographing unit, an analysis unit, a presentation unit, and a procedure provision unit. The photographing unit allows users to take and upload photos of unwanted items. For example, the photographing unit may take photos of the items using a smartphone camera and upload the photos to the app. The photographing unit may also have a function to automatically adjust the resolution and quality of the photos. For example, the photographing unit may automatically adjust the brightness and contrast of the photos and convert them into a format that is easy for the analysis unit to analyze. The photographing unit may also have a function to take and upload multiple photos at once. For example, if a user wants to get rid of multiple items at once, the photographing unit may take consecutive photos of each item and upload them to the app. The analysis unit analyzes the photos uploaded by the photographing unit and proposes the optimal solution from three options: reuse, recycle, and reduce. For example, the analysis unit may use image recognition technology to identify the type and condition of the items and propose the optimal solution based on that information. The analysis unit may also use AI to evaluate the market value and recyclability of the items. For example, the analysis unit evaluates the market value of an item in real time and determines whether it can be reused. The presentation unit presents the solutions proposed by the analysis unit to the user. For example, the presentation unit may display options of reuse, recycle, and reduce on the app screen to allow the user to select. The presentation unit may also have a function to visually display the advantages and disadvantages of each solution. For example, the presentation unit may display the advantages and disadvantages of each solution using graphs or icons to make it easier for the user to select. The procedure providing unit provides specific procedures based on the solutions proposed by the presentation unit. For example, if the user selects reuse, the procedure providing unit may provide listing procedures. If the user selects recycle, the procedure providing unit may also provide procedures for sending the item to a recycling facility. If the user selects reduce, the procedure providing unit may also provide procedures for reducing waste. As a result, the AI ​​app according to the embodiment can propose optimal solutions for efficiently getting rid of unwanted items and provide specific procedures.

[0030] The analysis unit can collect information on recycling facilities for each region and propose an appropriate recycling method. For example, the analysis unit collects information on recycling facilities for each region and proposes an optimal recycling method based on that information. For example, the analysis unit collects information such as the locations, business hours, and accepted items of recycling facilities in the region and provides it to the user. The analysis unit can also propose a recycling method taking into account recycling rules for each region. For example, the analysis unit proposes a method for sorting items and recycling procedures based on the recycling rules for the region. Furthermore, the analysis unit can collect information on recycling events in the region and provide it to the user. For example, the analysis unit collects information on recycling events held in the region and notifies the user. This makes it possible to propose an optimal recycling method based on the recycling facility information for each region.

[0031] The analysis unit can collect information on reusable platforms and propose an appropriate reuse method. The analysis unit, for example, collects information on reusable platforms and proposes an optimal reuse method based on that information. For example, the analysis unit collects selection criteria and listing methods for reusable platforms and provides them to the user. The analysis unit can also propose a reuse method taking into account user ratings and transaction history of the reuse platform. For example, the analysis unit proposes a highly reliable reuse method based on user ratings and transaction history of the platform. Furthermore, the analysis unit can compare fees and service contents of reuse platforms and propose an optimal reuse method. For example, the analysis unit proposes a platform with low fees and comprehensive service contents. This makes it possible to propose an optimal reuse method based on information on reusable platforms.

[0032] The procedure providing unit can provide procedures when reuse is selected. For example, when reuse is selected, the procedure providing unit provides specific listing procedures. For example, the procedure providing unit provides information such as how to take photos of the item, how to write a product description, and how to set a price. The procedure providing unit can also provide transaction procedures, packaging methods, and delivery procedures after listing. For example, the procedure providing unit provides information such as how to contact the trading partner and how to proceed with the transaction, how to select packaging materials and packaging methods, and how to select a delivery company and delivery procedures. Furthermore, the procedure providing unit can also provide procedures for evaluation after the transaction is completed and for after-care. For example, the procedure providing unit provides information such as how to evaluate the trading partner, how to follow up after the transaction, and how to respond if a problem occurs. This makes it possible to provide specific listing procedures when reuse is selected.

[0033] The procedure providing unit can provide delivery procedures when recycling is selected. For example, when recycling is selected, the procedure providing unit provides specific delivery procedures. For example, the procedure providing unit provides information such as how to pack the item, how to select a delivery company, and information about the delivery destination. The procedure providing unit can also provide information such as the recycling facility's acceptance conditions, fees, and points to note when sending. For example, the procedure providing unit provides information such as how to sort the item, how to select packaging materials, and points to note when sending, based on the recycling facility's acceptance conditions. Furthermore, the procedure providing unit can also provide tracking methods after sending and methods for confirming that recycling has been completed. For example, the procedure providing unit provides information such as how to check the delivery status using the delivery company's tracking service and how to obtain receipt confirmation from the recycling facility. In this way, specific delivery procedures can be provided when recycling is selected.

[0034] The procedure providing unit can provide reduction procedures when reduce is selected. For example, when reduce is selected, the procedure providing unit provides specific reduction procedures. For example, the procedure providing unit provides methods for sorting waste and removing reusable parts. The procedure providing unit can also provide information and advice on waste reduction. For example, the procedure providing unit provides ideas and tips that are useful for reducing waste, suggestions for environmentally friendly lifestyles, and the like. Furthermore, the procedure providing unit can also conduct education and awareness-raising activities on waste reduction. For example, the procedure providing unit can provide information on seminars and workshops on waste reduction, allowing the user to participate. In this way, specific reduction procedures can be provided when reduce is selected.

[0035] The photographing unit can automatically record the condition and frequency of use of the item when photographing. For example, the photographing unit automatically evaluates and records the appearance of the item when photographing. For example, the photographing unit evaluates the appearance of the item using image analysis technology and records the presence or absence of scratches or stains. The photographing unit can also ask the user how often the item is used and record that information. For example, the photographing unit prompts the user to input the number of times the item has been used and the period of use, and records that information. Furthermore, the photographing unit can automatically estimate and record the purchase date and period of use of the item. For example, the photographing unit reads the year of manufacture and purchase date of the item from a barcode or two-dimensional code (e.g., a QR code (registered trademark)) and estimates the period of use. This automatically records the condition and frequency of use of the item, thereby improving the accuracy of the analysis. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photography unit can evaluate the appearance of the item using image analysis technology, input that data into the generation AI, and have the generation AI perform a detailed condition evaluation.

[0036] The imaging unit can acquire detailed information by reading the barcode or 2D code of the product when capturing an image. For example, the imaging unit can automatically acquire product information by reading the barcode of the product when capturing an image. For example, the imaging unit can use a barcode scanner to read the barcode of the product and acquire information such as the product model number, manufacturing date, and manufacturer. The imaging unit can also acquire detailed product information by reading the 2D code of the product. For example, the imaging unit can use a 2D code scanner to read the 2D code of the product and acquire information such as the product's instruction manual, warranty information, and manufacturer contact information. The imaging unit can also acquire the manufacturer and year of manufacture of the product by reading the barcode or 2D code. For example, the imaging unit can access a manufacturer's database from the barcode or 2D code and acquire information such as the product's manufacturing year and manufacturer. In this way, detailed product information can be automatically acquired by reading the barcode or 2D code. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input barcode or two-dimensional code data into the generation AI and have the generation AI analyze the detailed information.

[0037] The image capturing unit can acquire region-specific recycling information based on the user's location information when capturing an image. For example, the image capturing unit acquires local recycling facility information based on the user's location information when capturing an image. For example, the image capturing unit acquires the user's location information using a GPS function and provides information such as the location, business hours, and accepted items of local recycling facilities based on the location information. The image capturing unit can also acquire local recycling rules based on the user's location information. For example, the image capturing unit provides item sorting methods and recycling procedures based on the local recycling rules. Furthermore, the image capturing unit can acquire local recycling event information based on the user's location information. For example, the image capturing unit collects information about recycling events held in the region and notifies the user. By acquiring region-specific recycling information based on the user's location information, more appropriate recycling methods can be suggested. Some or all of the above-described processing in the image capturing unit may be performed using, or without, AI. For example, the image capturing unit can input the user's location information data into a generation AI and cause the generation AI to acquire region-specific recycling information.

[0038] The photographing unit can analyze the user's social media activity and acquire information about related items when photographing. For example, the photographing unit can analyze the user's social media posts and acquire information about related items when photographing. For example, the photographing unit can access the user's social media account and analyze the content of the posts and the followers' reactions to collect information about related items. The photographing unit can also analyze the posts of the user's social media followers and acquire information about related items. For example, the photographing unit can analyze the content of the followers' posts and hashtags to collect information about related items. The photographing unit can also analyze the user's social media hashtags and acquire information about related items. For example, the photographing unit can analyze the hashtags used by the user and collect information about items related to those hashtags. In this way, information about related items can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI. For example, the photographing unit can input social media post data into a generation AI and cause the generation AI to acquire information about related items.

[0039] The analysis unit can propose an appropriate solution based on the material and manufacturing year of the item during analysis. For example, the analysis unit proposes a recycling method taking into account the material of the item during analysis. For example, if the item is made of plastic, the analysis unit proposes a plastic recycling method. The analysis unit can also propose a reuse method taking into account the manufacturing year of the item. For example, if the item is old, the analysis unit proposes a method for reusing it as an antique. The analysis unit can also propose a reduction method taking into account the material and manufacturing year of the item. For example, if the item is old and difficult to reuse, the analysis unit proposes a method for reducing waste. In this way, by considering the material and manufacturing year of the item, more appropriate solutions can be proposed. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the material data and manufacturing year data of the item into the generation AI and cause the generation AI to propose an optimal solution.

[0040] The analysis unit can evaluate the market value of an item in real time during analysis and determine whether it can be reused. For example, the analysis unit can evaluate the market value of an item in real time during analysis and determine the possibility of reuse. For example, the analysis unit collects data from auction sites and secondhand goods sales sites to evaluate the market value of the item. The analysis unit can also determine the market value based on expert evaluations. For example, the analysis unit evaluates the value of the item with reference to expert opinions. Furthermore, the analysis unit can suggest reuse methods based on the market value of the item. For example, the analysis unit can suggest reuse methods for items with high market values, and suggest recycling or reduction methods for items with low market values. In this way, the market value of the item can be evaluated in real time to determine the possibility of reuse. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data into a generation AI and have the generation AI determine whether it can be reused.

[0041] During analysis, the analysis unit can propose an appropriate solution based on the user's past product disposal history. For example, during analysis, the analysis unit refers to the user's past product disposal history and proposes an optimal reuse method. For example, the analysis unit retrieves the user's product disposal history from a database and proposes a reuse method based on the retrieved history. The analysis unit can also refer to the user's past product disposal history and propose an optimal recycling method. For example, the analysis unit proposes a recycling method based on the user's product disposal history. Furthermore, the analysis unit can also refer to the user's past product disposal history and propose an optimal reduction method. For example, the analysis unit proposes a waste reduction method based on the user's product disposal history. By referring to the user's past product disposal history, a more appropriate solution can be proposed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past product disposal history data into a generation AI and cause the generation AI to propose an optimal solution.

[0042] The analysis unit can customize solutions based on the user's lifestyle and environmental awareness during analysis. For example, the analysis unit can propose reuse methods taking into account the user's lifestyle during analysis. For example, the analysis unit can propose reuse methods based on the user's family structure, living environment, daily activities, etc. The analysis unit can also propose recycling methods taking into account the user's environmental awareness. For example, the analysis unit can propose recycling methods based on the user's participation in eco-friendly activities and interest in environmental protection. The analysis unit can also propose reduction methods taking into account the user's lifestyle and environmental awareness. For example, the analysis unit can propose waste reduction methods based on the user's lifestyle and environmental awareness. This allows for customization of more appropriate solutions by taking the user's lifestyle and environmental awareness into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's lifestyle data and environmental awareness data into the generation AI and cause the generation AI to customize the solutions.

[0043] The presentation unit can visually display the advantages and disadvantages of the solutions when presenting them. For example, the presentation unit can display the advantages and disadvantages of the solutions in a graph when presenting them. For example, the presentation unit can visually display the advantages and disadvantages of the solutions in a bar graph or pie chart, allowing the user to understand at a glance. The presentation unit can also display the advantages and disadvantages of the solutions as icons. For example, the presentation unit can display the advantages of the solutions with green icons and the disadvantages with red icons, making them easier to visually distinguish. The presentation unit can also display the advantages and disadvantages of the solutions in a list. For example, the presentation unit can list the advantages and disadvantages of the solutions in bullet points, making it easier for the user to compare them. In this way, visually displaying the advantages and disadvantages of the solutions makes it easier for the user to make a selection. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or can be performed without AI. For example, the presentation unit can input data on the advantages and disadvantages of the solutions into the generation AI and cause the generation AI to execute the visual display.

[0044] The presentation unit can prioritize displaying appropriate solutions based on the user's past selection history when presenting solutions. The presentation unit, for example, refers to the user's past selection history when presenting solutions and prioritizes displaying optimal reuse methods. For example, the presentation unit retrieves a history of reuse methods previously selected by the user from a database and prioritizes displaying reuse methods based on the retrieved history. The presentation unit can also refer to the user's past selection history and prioritize displaying optimal recycling methods. For example, the presentation unit prioritizes displaying recycling methods based on the history of recycling methods previously selected by the user. The presentation unit can also refer to the user's past selection history and prioritize displaying optimal reduce methods. For example, the presentation unit prioritizes displaying reduce methods based on the history of reduce methods previously selected by the user. In this way, optimal solutions can be prioritized by referring to the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past selection history data to a generation AI and cause the generation AI to prioritize displaying optimal solutions.

[0045] The presentation unit can select an appropriate display method based on the user's device information at the time of presentation. For example, if the user is using a smartphone at the time of presentation, the presentation unit provides a display method tailored to the screen size. For example, the presentation unit provides a display method optimized for the smartphone's small screen to make it easier for the user to view. Furthermore, if the user is using a tablet, the presentation unit can also provide a display method optimized for a large screen. For example, the presentation unit displays detailed information tailored to the tablet's large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a concise, highly visible display method. For example, the presentation unit displays important information concisely to suit the small screen of the smartwatch. This makes it possible to provide an optimal display method by taking the user's device information into consideration. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input device information data to the generation AI and cause the generation AI to select the optimal display method.

[0046] The presentation unit can analyze the user's social media activity and provide related solutions at the time of presentation. For example, the presentation unit can analyze the user's social media posts and suggest related solutions at the time of presentation. For example, the presentation unit can access the user's social media account and analyze the post content and follower responses to collect related solutions. The presentation unit can also analyze the posts of the user's social media followers and suggest related solutions. For example, the presentation unit can analyze the follower's post content and hashtags to collect related solutions. Furthermore, the presentation unit can analyze the user's social media hashtags and suggest related solutions. For example, the presentation unit can analyze the hashtags used by the user and collect solutions related to those hashtags. In this way, related solutions can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input social media post data to a generation AI and cause the generation AI to suggest related solutions.

[0047] When providing a procedure, the procedure providing unit can customize an appropriate procedure based on the user's past letting-go history. For example, when providing a procedure, the procedure providing unit references the user's past letting-go history and provides an optimal reuse procedure. For example, the procedure providing unit retrieves a history of items the user has previously let go from a database and provides a reuse procedure based on that history. The procedure providing unit can also reference the user's past letting-go history to provide an optimal recycling procedure. For example, the procedure providing unit provides a recycling procedure based on a history of items the user has previously recycled. Furthermore, the procedure providing unit can also reference the user's past letting-go history to provide an optimal reduction procedure. For example, the procedure providing unit provides a waste reduction procedure based on a history of items the user has previously discarded. In this way, the optimal procedure can be customized by referring to the user's past letting-go history. Some or all of the above-described processing by the procedure providing unit may be performed using, or without, AI. For example, the procedure providing unit can input past letting-go history data into a generation AI and cause the generation AI to customize an optimal procedure.

[0048] The procedure providing unit can provide different procedures based on the type and condition of the item when providing the procedures. For example, the procedure providing unit provides reuse procedures according to the type of item when providing the procedures. For example, the procedure providing unit provides appropriate reuse procedures depending on the type of item, such as electronic devices, furniture, or clothing. The procedure providing unit can also provide recycling procedures according to the condition of the item. For example, the procedure providing unit provides different recycling procedures when the item is in like-new condition and when the item has signs of use. Furthermore, the procedure providing unit can also provide reduction procedures according to the type and condition of the item. For example, the procedure providing unit provides different reduction procedures when the item has many reusable parts and when the item has many waste parts. This allows for providing more appropriate procedures by providing different procedures depending on the type and condition of the item. Some or all of the above-described processing by the procedure providing unit may be performed using, or without, AI. For example, the procedure providing unit can input item type and condition data into the generation AI and have the generation AI provide an optimal procedure.

[0049] The procedure providing unit can provide region-specific procedures based on the user's location information when providing the procedures. For example, the procedure providing unit can provide region-specific recycling procedures based on the user's location information when providing the procedures. For example, the procedure providing unit can acquire the user's location information using a GPS function and provide region-specific recycling rules and facility information based on the location information. The procedure providing unit can also provide region-specific reuse procedures based on the user's location information. For example, the procedure providing unit can provide a region-specific reuse platform and trading method. Furthermore, the procedure providing unit can also provide region-specific reduction procedures based on the user's location information. For example, the procedure providing unit can provide region-specific waste reduction methods and waste sorting rules. In this way, region-specific procedures can be provided by taking the user's location information into consideration. Some or all of the above-described processing in the procedure providing unit can be performed using AI, for example, or without AI. For example, the procedure providing unit can input location information data to the generation AI and cause the generation AI to provide region-specific procedures.

[0050] The procedure providing unit can analyze the user's social media activities and provide related procedures when providing the procedures. For example, the procedure providing unit can analyze the user's social media posts and suggest related reuse procedures when providing the procedures. For example, the procedure providing unit accesses the user's social media accounts, analyzes the post content and followers' reactions, and collects related reuse procedures. The procedure providing unit can also analyze the posts of the user's social media followers and suggest related recycling procedures. For example, the procedure providing unit analyzes the follower's post content and hashtags and collects related recycling procedures. The procedure providing unit can also analyze the user's social media hashtags and suggest related Reduce procedures. For example, the procedure providing unit analyzes the hashtags used by the user and collects Reduce procedures related to the hashtags. In this way, related procedures can be suggested by analyzing the user's social media activities. Some or all of the above-described processing in the procedure providing unit may be performed using, or without, AI. For example, the procedure providing unit can input social media post data to a generation AI and have the generation AI suggest related procedures.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can refer to the user's past purchasing history and suggest optimal methods for reusing, recycling, and reducing unwanted items. For example, the analysis unit can retrieve the user's past purchase history from a database and suggest reuse methods based on that. The analysis unit can also identify recyclable items based on the user's purchasing history and suggest appropriate recycling methods. Furthermore, the analysis unit can refer to the user's purchasing history and suggest waste reduction methods for reduction. In this way, the user's purchasing history can be utilized to suggest more appropriate solutions.

[0053] The procedure providing unit can provide region-specific reuse, recycle, and reduce procedures based on the user's location information. For example, the procedure providing unit can obtain the user's location information using a GPS function and provide information on local reuse platforms and recycling facilities based on the location information. The procedure providing unit can also provide local recycling rules and waste sorting methods. Furthermore, the procedure providing unit can provide region-specific reduce procedures and suggest specific methods for waste reduction. In this way, the user's location information can be utilized to provide region-specific procedures.

[0054] The photography unit can analyze the user's social media activity and obtain information about related products. For example, the photography unit accesses the user's social media account and analyzes the content of posts and the reactions of followers to collect information about related products. The photography unit can also analyze posts by the user's social media followers to obtain information about related products. Furthermore, the photography unit can analyze the user's social media hashtags to obtain information about related products. In this way, information about related products can be obtained by analyzing the user's social media activity.

[0055] The analysis unit can propose appropriate solutions based on the user's past product disposal history. For example, the analysis unit retrieves the user's past product disposal history from a database and proposes a reuse method based on that history. The analysis unit can also refer to the user's past product disposal history to propose an optimal recycling method. Furthermore, the analysis unit can also refer to the user's past product disposal history to propose an optimal reduction method. In this way, by referring to the user's past product disposal history, more appropriate solutions can be proposed.

[0056] When providing the procedures, the procedure providing unit can provide different procedures based on the type and condition of the item. For example, the procedure providing unit can provide appropriate reuse procedures depending on the type of item, such as electronic devices, furniture, or clothing. The procedure providing unit can also provide recycling procedures depending on the condition of the item. For example, different recycling procedures are provided for an item that is in like-new condition and an item that has been used. Furthermore, the procedure providing unit can also provide reduction procedures depending on the type and condition of the item. In this way, by providing different procedures depending on the type and condition of the item, more appropriate procedures can be provided.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The photography unit has the function of allowing users to take and upload photos of unwanted items. For example, a user can take a photo of an item using a smartphone camera and upload the photo to the app. The photography unit also has the function of automatically adjusting the resolution and quality of the photo, automatically adjusting the brightness and contrast of the photo, and converting it into a format that is easy for the analysis unit to analyze. Furthermore, the photography unit also has the function of taking and uploading multiple photos at once. Step 2: The analysis unit analyzes the photos uploaded by the photography unit and proposes the optimal solution from three options: reuse, recycle, or reduce. For example, image recognition technology can be used to determine the type and condition of the item, and the optimal solution can be proposed based on that. AI can also be used to evaluate the market value and recyclability of the item, and the market value can be assessed in real time to determine whether it can be reused. Step 3: The presentation unit presents the solutions proposed by the analysis unit to the user. For example, it displays the options of reuse, recycle, and reduce on the app screen, allowing the user to make a selection. It also has a function to visually display the advantages and disadvantages of each solution, using graphs and icons to make it easier for the user to make a selection. Step 4: The procedure provider provides specific procedures based on the solutions presented by the provider. For example, if the user selects "reuse," the procedure provider provides listing procedures, and if the user selects "recycle," the procedure provider provides sending procedures to a recycling facility. Furthermore, if the user selects "reduce," the procedure provider provides waste reduction procedures.

[0059] (Example 2) An AI app according to an embodiment of the present invention is a system that proposes optimal solutions for disposing of unwanted items. This system works by having users take photos of unwanted items and upload them to the app. The AI ​​analyzes the photos and proposes the optimal solution from three options: reuse, recycle, and reduce. The user can choose the most appropriate method from the proposed options to dispose of the item. For example, when disposing of an old smartphone, the AI ​​suggests selling it secondhand as reuse, reusing parts as recycling, and reducing waste as reduce. The user can choose the optimal method from these options to dispose of the item. Furthermore, by providing specific steps based on the selected solution, the user can smoothly dispose of the item. This allows the AI ​​app to propose optimal solutions and provide specific steps for efficiently disposing of unwanted items.

[0060] The AI ​​app according to the embodiment includes a photographing unit, an analysis unit, a presentation unit, and a procedure provision unit. The photographing unit allows users to take and upload photos of unwanted items. For example, the photographing unit may take photos of the items using a smartphone camera and upload the photos to the app. The photographing unit may also have a function to automatically adjust the resolution and quality of the photos. For example, the photographing unit may automatically adjust the brightness and contrast of the photos and convert them into a format that is easy for the analysis unit to analyze. The photographing unit may also have a function to take and upload multiple photos at once. For example, if a user wants to get rid of multiple items at once, the photographing unit may take consecutive photos of each item and upload them to the app. The analysis unit analyzes the photos uploaded by the photographing unit and proposes the optimal solution from three options: reuse, recycle, and reduce. For example, the analysis unit may use image recognition technology to identify the type and condition of the items and propose the optimal solution based on that information. The analysis unit may also use AI to evaluate the market value and recyclability of the items. For example, the analysis unit evaluates the market value of an item in real time and determines whether it can be reused. The presentation unit presents the solutions proposed by the analysis unit to the user. For example, the presentation unit may display options of reuse, recycle, and reduce on the app screen to allow the user to select. The presentation unit may also have a function to visually display the advantages and disadvantages of each solution. For example, the presentation unit may display the advantages and disadvantages of each solution using graphs or icons to make it easier for the user to select. The procedure providing unit provides specific procedures based on the solutions proposed by the presentation unit. For example, if the user selects reuse, the procedure providing unit may provide listing procedures. If the user selects recycle, the procedure providing unit may also provide procedures for sending the item to a recycling facility. If the user selects reduce, the procedure providing unit may also provide procedures for reducing waste. As a result, the AI ​​app according to the embodiment can propose optimal solutions for efficiently getting rid of unwanted items and provide specific procedures.

[0061] The analysis unit can collect information on recycling facilities for each region and propose an appropriate recycling method. For example, the analysis unit collects information on recycling facilities for each region and proposes an optimal recycling method based on that information. For example, the analysis unit collects information such as the locations, business hours, and accepted items of recycling facilities in the region and provides it to the user. The analysis unit can also propose a recycling method taking into account recycling rules for each region. For example, the analysis unit proposes a method for sorting items and recycling procedures based on the recycling rules for the region. Furthermore, the analysis unit can collect information on recycling events in the region and provide it to the user. For example, the analysis unit collects information on recycling events held in the region and notifies the user. This makes it possible to propose an optimal recycling method based on the recycling facility information for each region.

[0062] The analysis unit can collect information on reusable platforms and propose an appropriate reuse method. The analysis unit, for example, collects information on reusable platforms and proposes an optimal reuse method based on that information. For example, the analysis unit collects selection criteria and listing methods for reusable platforms and provides them to the user. The analysis unit can also propose a reuse method taking into account user ratings and transaction history of the reuse platform. For example, the analysis unit proposes a highly reliable reuse method based on user ratings and transaction history of the platform. Furthermore, the analysis unit can compare fees and service contents of reuse platforms and propose an optimal reuse method. For example, the analysis unit proposes a platform with low fees and comprehensive service contents. This makes it possible to propose an optimal reuse method based on information on reusable platforms.

[0063] The procedure providing unit can provide procedures when reuse is selected. For example, when reuse is selected, the procedure providing unit provides specific listing procedures. For example, the procedure providing unit provides information such as how to take photos of the item, how to write a product description, and how to set a price. The procedure providing unit can also provide transaction procedures, packaging methods, and delivery procedures after listing. For example, the procedure providing unit provides information such as how to contact the trading partner and how to proceed with the transaction, how to select packaging materials and packaging methods, and how to select a delivery company and delivery procedures. Furthermore, the procedure providing unit can also provide procedures for evaluation after the transaction is completed and for after-care. For example, the procedure providing unit provides information such as how to evaluate the trading partner, how to follow up after the transaction, and how to respond if a problem occurs. This makes it possible to provide specific listing procedures when reuse is selected.

[0064] The procedure providing unit can provide delivery procedures when recycling is selected. For example, when recycling is selected, the procedure providing unit provides specific delivery procedures. For example, the procedure providing unit provides information such as how to pack the item, how to select a delivery company, and information about the delivery destination. The procedure providing unit can also provide information such as the recycling facility's acceptance conditions, fees, and points to note when sending. For example, the procedure providing unit provides information such as how to sort the item, how to select packaging materials, and points to note when sending, based on the recycling facility's acceptance conditions. Furthermore, the procedure providing unit can also provide tracking methods after sending and methods for confirming that recycling has been completed. For example, the procedure providing unit provides information such as how to check the delivery status using the delivery company's tracking service and how to obtain receipt confirmation from the recycling facility. In this way, specific delivery procedures can be provided when recycling is selected.

[0065] The procedure providing unit can provide reduction procedures when reduce is selected. For example, when reduce is selected, the procedure providing unit provides specific reduction procedures. For example, the procedure providing unit provides methods for sorting waste and removing reusable parts. The procedure providing unit can also provide information and advice on waste reduction. For example, the procedure providing unit provides ideas and tips that are useful for reducing waste, suggestions for environmentally friendly lifestyles, and the like. Furthermore, the procedure providing unit can also conduct education and awareness-raising activities on waste reduction. For example, the procedure providing unit can provide information on seminars and workshops on waste reduction, allowing the user to participate. In this way, specific reduction procedures can be provided when reduce is selected.

[0066] The image capture unit can estimate the user's emotions and determine the timing of capturing images based on the estimated user emotions. The image capture unit, for example, estimates the user's emotions and adjusts the timing of capturing images based on the estimated emotions. For example, if the user is feeling stressed, the image capture unit prompts the user to wait until they are relaxed before capturing images. Furthermore, if the user is in a hurry, the image capture unit can instruct the user to capture images quickly. Furthermore, if the user is enjoying themselves, the image capture unit can encourage the user to capture images as if they were playing a game. For example, the image capture unit may devise a quiz-style capture when the user is enjoying themselves. This allows the user to capture images at a more appropriate timing by adjusting the timing of capturing images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the image capture unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0067] The photographing unit can automatically record the condition and frequency of use of the item when photographing. For example, the photographing unit automatically evaluates and records the appearance of the item when photographing. For example, the photographing unit evaluates the appearance of the item using image analysis technology and records the presence or absence of scratches or stains. The photographing unit can also ask the user how often the item is used and record that information. For example, the photographing unit prompts the user to input the number of times the item has been used and the period of use, and records that information. Furthermore, the photographing unit can automatically estimate and record the purchase date and period of use of the item. For example, the photographing unit reads the year of manufacture and purchase date of the item from a barcode or two-dimensional code (e.g., a QR code) and estimates the period of use. This automatically records the condition and frequency of use of the item, thereby improving the accuracy of the analysis. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit can evaluate the appearance of the item using image analysis technology, input the data into the generation AI, and have the generation AI perform a detailed condition evaluation.

[0068] The imaging unit can acquire detailed information by reading the barcode or 2D code of the product when capturing an image. For example, the imaging unit can automatically acquire product information by reading the barcode of the product when capturing an image. For example, the imaging unit can use a barcode scanner to read the barcode of the product and acquire information such as the product model number, manufacturing date, and manufacturer. The imaging unit can also acquire detailed product information by reading the 2D code of the product. For example, the imaging unit can use a 2D code scanner to read the 2D code of the product and acquire information such as the product's instruction manual, warranty information, and manufacturer contact information. The imaging unit can also acquire the manufacturer and year of manufacture of the product by reading the barcode or 2D code. For example, the imaging unit can access a manufacturer's database from the barcode or 2D code and acquire information such as the product's manufacturing year and manufacturer. In this way, detailed product information can be automatically acquired by reading the barcode or 2D code. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input barcode or two-dimensional code data into the generation AI and have the generation AI analyze the detailed information.

[0069] The photographing unit can estimate the user's emotions and prioritize the items to be photographed based on the estimated user emotions. For example, the photographing unit estimates the user's emotions and prioritizes the items to be photographed based on the estimated emotions. For example, when the user is stressed, the photographing unit prioritizes photographing items that can be easily let go. Furthermore, when the user is relaxed, the photographing unit can prioritize photographing items that can be easily let go. Furthermore, when the user is in a hurry, the photographing unit can prioritize photographing items that can be easily let go. For example, when the user is in a hurry, the photographing unit lists items that can be easily let go and prioritizes photographing them from that list. This prioritizes the items to be photographed based on the user's emotions, allowing for prioritized photographing of more appropriate items. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or without AI. For example, the image capture unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0070] The image capturing unit can acquire region-specific recycling information based on the user's location information when capturing an image. For example, the image capturing unit acquires local recycling facility information based on the user's location information when capturing an image. For example, the image capturing unit acquires the user's location information using a GPS function and provides information such as the location, business hours, and accepted items of local recycling facilities based on the location information. The image capturing unit can also acquire local recycling rules based on the user's location information. For example, the image capturing unit provides item sorting methods and recycling procedures based on the local recycling rules. Furthermore, the image capturing unit can acquire local recycling event information based on the user's location information. For example, the image capturing unit collects information about recycling events held in the region and notifies the user. By acquiring region-specific recycling information based on the user's location information, more appropriate recycling methods can be suggested. Some or all of the above-described processing in the image capturing unit may be performed using, or without, AI. For example, the image capturing unit can input the user's location information data into a generation AI and cause the generation AI to acquire region-specific recycling information.

[0071] The photographing unit can analyze the user's social media activity and acquire information about related items when photographing. For example, the photographing unit can analyze the user's social media posts and acquire information about related items when photographing. For example, the photographing unit can access the user's social media account and analyze the content of the posts and the followers' reactions to collect information about related items. The photographing unit can also analyze the posts of the user's social media followers and acquire information about related items. For example, the photographing unit can analyze the content of the followers' posts and hashtags to collect information about related items. The photographing unit can also analyze the user's social media hashtags and acquire information about related items. For example, the photographing unit can analyze the hashtags used by the user and collect information about items related to those hashtags. In this way, information about related items can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the photographing unit may be performed using, or without, AI. For example, the photographing unit can input social media post data into a generation AI and cause the generation AI to acquire information about related items.

[0072] The analysis unit can estimate the user's emotion and set the presentation method of the analysis result based on the estimated user emotion. For example, the analysis unit estimates the user's emotion and adjusts the presentation method of the analysis result based on the estimated emotion. For example, the analysis unit provides a simple and easy-to-understand analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a key analysis result when the user is in a hurry. For example, the analysis unit displays an analysis result that briefly summarizes the main points when the user is in a hurry. This allows for adjusting the presentation method of the analysis result according to the user's emotion, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0073] The analysis unit can propose an appropriate solution based on the material and manufacturing year of the item during analysis. For example, the analysis unit proposes a recycling method taking into account the material of the item during analysis. For example, if the item is made of plastic, the analysis unit proposes a plastic recycling method. The analysis unit can also propose a reuse method taking into account the manufacturing year of the item. For example, if the item is old, the analysis unit proposes a method for reusing it as an antique. The analysis unit can also propose a reduction method taking into account the material and manufacturing year of the item. For example, if the item is old and difficult to reuse, the analysis unit proposes a method for reducing waste. In this way, by considering the material and manufacturing year of the item, more appropriate solutions can be proposed. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the material data and manufacturing year data of the item into the generation AI and cause the generation AI to propose an optimal solution.

[0074] The analysis unit can evaluate the market value of an item in real time during analysis and determine whether it can be reused. For example, the analysis unit can evaluate the market value of an item in real time during analysis and determine the possibility of reuse. For example, the analysis unit collects data from auction sites and secondhand goods sales sites to evaluate the market value of the item. The analysis unit can also determine the market value based on expert evaluations. For example, the analysis unit evaluates the value of the item with reference to expert opinions. Furthermore, the analysis unit can suggest reuse methods based on the market value of the item. For example, the analysis unit can suggest reuse methods for items with high market values, and suggest recycling or reduction methods for items with low market values. In this way, the market value of the item can be evaluated in real time to determine the possibility of reuse. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data into a generation AI and have the generation AI determine whether it can be reused.

[0075] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and prioritizes the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes proposing solutions that are easy to implement. Furthermore, if the user is relaxed, the analysis unit can prioritize proposing detailed solutions. Furthermore, if the user is in a hurry, the analysis unit can prioritize proposing solutions that can be implemented quickly. For example, when the user is in a hurry, the analysis unit lists solutions that can be implemented immediately and prioritizes proposing them. This allows the analysis results to be prioritized according to the user's emotions, thereby providing more appropriate solutions preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0076] During analysis, the analysis unit can propose an appropriate solution based on the user's past product disposal history. For example, during analysis, the analysis unit refers to the user's past product disposal history and proposes an optimal reuse method. For example, the analysis unit retrieves the user's product disposal history from a database and proposes a reuse method based on the retrieved history. The analysis unit can also refer to the user's past product disposal history and propose an optimal recycling method. For example, the analysis unit proposes a recycling method based on the user's product disposal history. Furthermore, the analysis unit can also refer to the user's past product disposal history and propose an optimal reduction method. For example, the analysis unit proposes a waste reduction method based on the user's product disposal history. By referring to the user's past product disposal history, a more appropriate solution can be proposed. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past product disposal history data into a generation AI and cause the generation AI to propose an optimal solution.

[0077] The analysis unit can customize solutions based on the user's lifestyle and environmental awareness during analysis. For example, the analysis unit can propose reuse methods taking into account the user's lifestyle during analysis. For example, the analysis unit can propose reuse methods based on the user's family structure, living environment, daily activities, etc. The analysis unit can also propose recycling methods taking into account the user's environmental awareness. For example, the analysis unit can propose recycling methods based on the user's participation in eco-friendly activities and interest in environmental protection. The analysis unit can also propose reduction methods taking into account the user's lifestyle and environmental awareness. For example, the analysis unit can propose waste reduction methods based on the user's lifestyle and environmental awareness. This allows for customization of more appropriate solutions by taking the user's lifestyle and environmental awareness into consideration. Some or all of the above-described processing in the analysis unit can be performed using, or without, AI. For example, the analysis unit can input the user's lifestyle data and environmental awareness data into the generation AI and cause the generation AI to customize the solutions.

[0078] The presentation unit can estimate the user's emotions and set a solution presentation method based on the estimated user emotions. The presentation unit, for example, estimates the user's emotions and adjusts the solution presentation method based on the estimated emotions. For example, the presentation unit presents a simple and easy-to-understand solution when the user is stressed. The presentation unit can also present a detailed solution when the user is relaxed. Furthermore, the presentation unit can present a solution that focuses on the main points when the user is in a hurry. For example, the presentation unit displays a solution that briefly summarizes the main points when the user is in a hurry. This allows for adjusting the solution presentation method according to the user's emotions, thereby providing a more appropriate solution. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the presentation unit may be performed using an AI, for example, or without an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0079] The presentation unit can visually display the advantages and disadvantages of the solutions when presenting them. For example, the presentation unit can display the advantages and disadvantages of the solutions in a graph when presenting them. For example, the presentation unit can visually display the advantages and disadvantages of the solutions in a bar graph or pie chart, allowing the user to understand at a glance. The presentation unit can also display the advantages and disadvantages of the solutions as icons. For example, the presentation unit can display the advantages of the solutions with green icons and the disadvantages with red icons, making them easier to visually distinguish. The presentation unit can also display the advantages and disadvantages of the solutions in a list. For example, the presentation unit can list the advantages and disadvantages of the solutions in bullet points, making it easier for the user to compare them. In this way, visually displaying the advantages and disadvantages of the solutions makes it easier for the user to make a selection. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or can be performed without AI. For example, the presentation unit can input data on the advantages and disadvantages of the solutions into the generation AI and cause the generation AI to execute the visual display.

[0080] The presentation unit can prioritize displaying appropriate solutions based on the user's past selection history when presenting solutions. The presentation unit, for example, refers to the user's past selection history when presenting solutions and prioritizes displaying optimal reuse methods. For example, the presentation unit retrieves a history of reuse methods previously selected by the user from a database and prioritizes displaying reuse methods based on the retrieved history. The presentation unit can also refer to the user's past selection history and prioritize displaying optimal recycling methods. For example, the presentation unit prioritizes displaying recycling methods based on the history of recycling methods previously selected by the user. The presentation unit can also refer to the user's past selection history and prioritize displaying optimal reduce methods. For example, the presentation unit prioritizes displaying reduce methods based on the history of reduce methods previously selected by the user. In this way, optimal solutions can be prioritized by referring to the user's past selection history. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input past selection history data to a generation AI and cause the generation AI to prioritize displaying optimal solutions.

[0081] The presentation unit can estimate the user's emotions and set the display order of the solutions based on the estimated user emotions. The presentation unit, for example, estimates the user's emotions and adjusts the display order of the solutions based on the estimated emotions. For example, when the user is stressed, the presentation unit can prioritize displaying solutions that can be easily implemented. Furthermore, when the user is relaxed, the presentation unit can prioritize displaying detailed solutions. Furthermore, when the user is in a hurry, the presentation unit can prioritize displaying solutions that can be quickly implemented. For example, when the user is in a hurry, the presentation unit lists solutions that can be implemented immediately and prioritizes displaying them. In this way, by adjusting the display order of the solutions according to the user's emotions, more appropriate solutions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the presentation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0082] The presentation unit can select an appropriate display method based on the user's device information at the time of presentation. For example, if the user is using a smartphone at the time of presentation, the presentation unit provides a display method tailored to the screen size. For example, the presentation unit provides a display method optimized for the smartphone's small screen to make it easier for the user to view. Furthermore, if the user is using a tablet, the presentation unit can also provide a display method optimized for a large screen. For example, the presentation unit displays detailed information tailored to the tablet's large screen. Furthermore, if the user is using a smartwatch, the presentation unit can also provide a concise, highly visible display method. For example, the presentation unit displays important information concisely to suit the small screen of the smartwatch. This makes it possible to provide an optimal display method by taking the user's device information into consideration. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input device information data to the generation AI and cause the generation AI to select the optimal display method.

[0083] The presentation unit can analyze the user's social media activity and provide related solutions at the time of presentation. For example, the presentation unit can analyze the user's social media posts and suggest related solutions at the time of presentation. For example, the presentation unit can access the user's social media account and analyze the post content and follower responses to collect related solutions. The presentation unit can also analyze the posts of the user's social media followers and suggest related solutions. For example, the presentation unit can analyze the follower's post content and hashtags to collect related solutions. Furthermore, the presentation unit can analyze the user's social media hashtags and suggest related solutions. For example, the presentation unit can analyze the hashtags used by the user and collect solutions related to those hashtags. In this way, related solutions can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input social media post data to a generation AI and cause the generation AI to suggest related solutions.

[0084] The instruction providing unit can estimate the user's emotions and set a method for providing instructions based on the estimated user emotions. The instruction providing unit, for example, estimates the user's emotions and adjusts the method for providing instructions based on the estimated emotions. For example, the instruction providing unit provides simple and easy-to-understand instructions when the user is stressed. The instruction providing unit can also provide detailed instructions when the user is relaxed. Furthermore, the instruction providing unit can provide instructions that focus on the main points when the user is in a hurry. For example, the instruction providing unit displays instructions that briefly summarize the main points when the user is in a hurry. This allows the method for providing instructions to be adjusted according to the user's emotions, thereby providing more appropriate instructions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the instruction providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the procedure providing unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0085] When providing a procedure, the procedure providing unit can customize an appropriate procedure based on the user's past letting-go history. For example, when providing a procedure, the procedure providing unit references the user's past letting-go history and provides an optimal reuse procedure. For example, the procedure providing unit retrieves a history of items the user has previously let go from a database and provides a reuse procedure based on that history. The procedure providing unit can also reference the user's past letting-go history to provide an optimal recycling procedure. For example, the procedure providing unit provides a recycling procedure based on a history of items the user has previously recycled. Furthermore, the procedure providing unit can also reference the user's past letting-go history to provide an optimal reduction procedure. For example, the procedure providing unit provides a waste reduction procedure based on a history of items the user has previously discarded. In this way, the optimal procedure can be customized by referring to the user's past letting-go history. Some or all of the above-described processing by the procedure providing unit may be performed using, or without, AI. For example, the procedure providing unit can input past letting-go history data into a generation AI and cause the generation AI to customize an optimal procedure.

[0086] The procedure providing unit can provide different procedures based on the type and condition of the item when providing the procedures. For example, the procedure providing unit provides reuse procedures according to the type of item when providing the procedures. For example, the procedure providing unit provides appropriate reuse procedures depending on the type of item, such as electronic devices, furniture, or clothing. The procedure providing unit can also provide recycling procedures according to the condition of the item. For example, the procedure providing unit provides different recycling procedures when the item is in like-new condition and when the item has signs of use. Furthermore, the procedure providing unit can also provide reduction procedures according to the type and condition of the item. For example, the procedure providing unit provides different reduction procedures when the item has many reusable parts and when the item has many waste parts. This allows for providing more appropriate procedures by providing different procedures depending on the type and condition of the item. Some or all of the above-described processing by the procedure providing unit may be performed using, or without, AI. For example, the procedure providing unit can input item type and condition data into the generation AI and have the generation AI provide an optimal procedure.

[0087] The procedure providing unit can estimate the user's emotions and prioritize procedures based on the estimated user emotions. The procedure providing unit, for example, estimates the user's emotions and determines the priority of procedures based on the estimated emotions. For example, when the user is stressed, the procedure providing unit prioritizes providing easy-to-follow procedures. Furthermore, when the user is relaxed, the procedure providing unit can prioritize providing detailed procedures. Furthermore, when the user is in a hurry, the procedure providing unit can prioritize providing quick procedures. For example, when the user is in a hurry, the procedure providing unit lists quick procedures and prioritizes providing them from among them. This allows the procedure prioritization based on the user's emotions to prioritize providing more appropriate procedures. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the procedure providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the procedure providing unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0088] The procedure providing unit can provide region-specific procedures based on the user's location information when providing the procedures. For example, the procedure providing unit can provide region-specific recycling procedures based on the user's location information when providing the procedures. For example, the procedure providing unit can acquire the user's location information using a GPS function and provide region-specific recycling rules and facility information based on the location information. The procedure providing unit can also provide region-specific reuse procedures based on the user's location information. For example, the procedure providing unit can provide a region-specific reuse platform and trading method. Furthermore, the procedure providing unit can also provide region-specific reduction procedures based on the user's location information. For example, the procedure providing unit can provide region-specific waste reduction methods and waste sorting rules. In this way, region-specific procedures can be provided by taking the user's location information into consideration. Some or all of the above-described processing in the procedure providing unit can be performed using AI, for example, or without AI. For example, the procedure providing unit can input location information data to the generation AI and cause the generation AI to provide region-specific procedures.

[0089] The procedure providing unit can analyze the user's social media activities and provide related procedures when providing the procedures. For example, the procedure providing unit can analyze the user's social media posts and suggest related reuse procedures when providing the procedures. For example, the procedure providing unit accesses the user's social media accounts, analyzes the post content and followers' reactions, and collects related reuse procedures. The procedure providing unit can also analyze the posts of the user's social media followers and suggest related recycling procedures. For example, the procedure providing unit analyzes the follower's post content and hashtags and collects related recycling procedures. The procedure providing unit can also analyze the user's social media hashtags and suggest related Reduce procedures. For example, the procedure providing unit analyzes the hashtags used by the user and collects Reduce procedures related to the hashtags. In this way, related procedures can be suggested by analyzing the user's social media activities. Some or all of the above-described processing in the procedure providing unit may be performed using, or without, AI. For example, the procedure providing unit can input social media post data to a generation AI and have the generation AI suggest related procedures. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, analysis unit, presentation unit, and procedure providing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit takes a photo of an item using the camera 42 of the smart device 14 and uploads the photo to the app. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the type and condition of the item using image recognition technology and proposes an optimal solution. The presentation unit, realized, for example, by the display 40A of the smart device 14, presents the solution to the user. The procedure providing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides specific procedures based on the selected solution. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described photographing unit, analysis unit, presentation unit, and procedure providing unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit takes a photo of an item using the camera 42 of the smart glasses 214 and uploads the photo to the app. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, determines the type and condition of the item using image recognition technology and proposes an optimal solution. The presentation unit, realized, for example, by the display of the smart glasses 214, presents the solution to the user. The procedure providing unit, realized, for example, by the specific processing unit 290 of the data processing device 12, provides specific procedures based on the selected solution. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, presentation unit, and procedure providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit takes a photo of an item using the camera 42 of the headset-type terminal 314 and uploads the photo to the app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the type and condition of the item using image recognition technology and proposes an optimal solution. The presentation unit presents the solution to the user, for example, by the display 343 of the headset-type terminal 314. The procedure providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides specific procedures based on the selected solution. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, presentation unit, and procedure providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit takes a photo of an item using the camera 42 of the robot 414 and uploads the photo to an app. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the type and condition of the item using image recognition technology and proposes an optimal solution. The presentation unit presents the solution to the user, for example, by using the display of the robot 414. The procedure providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides specific procedures based on the selected solution.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can refer to the user's past purchasing history and suggest optimal methods for reusing, recycling, and reducing unwanted items. For example, the analysis unit can retrieve the user's past purchase history from a database and suggest reuse methods based on that. The analysis unit can also identify recyclable items based on the user's purchasing history and suggest appropriate recycling methods. Furthermore, the analysis unit can refer to the user's purchasing history and suggest waste reduction methods for reduction. In this way, the user's purchasing history can be utilized to suggest more appropriate solutions.

[0092] The presentation unit can estimate the user's emotions and adjust the presentation order of solutions based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit can prioritize and display solutions that can be easily implemented. Also, if the user is relaxed, the presentation unit can prioritize and display detailed solutions. Furthermore, if the user is in a hurry, the presentation unit can prioritize and display solutions that can be quickly implemented. In this way, by adjusting the presentation order of solutions according to the user's emotions, more appropriate solutions can be provided.

[0093] The procedure providing unit can provide region-specific reuse, recycle, and reduce procedures based on the user's location information. For example, the procedure providing unit can obtain the user's location information using a GPS function and provide information on local reuse platforms and recycling facilities based on the location information. The procedure providing unit can also provide local recycling rules and waste sorting methods. Furthermore, the procedure providing unit can provide region-specific reduce procedures and suggest specific methods for waste reduction. In this way, the user's location information can be utilized to provide region-specific procedures.

[0094] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can also provide analysis results that focus on the main points. In this way, by adjusting the way the analysis results are presented according to the user's emotions, more appropriate analysis results can be provided.

[0095] The photography unit can analyze the user's social media activity and obtain information about related products. For example, the photography unit accesses the user's social media account and analyzes the content of posts and the reactions of followers to collect information about related products. The photography unit can also analyze posts by the user's social media followers to obtain information about related products. Furthermore, the photography unit can analyze the user's social media hashtags to obtain information about related products. In this way, information about related products can be obtained by analyzing the user's social media activity.

[0096] The instruction providing unit can estimate the user's emotions and adjust the method of providing instructions based on the estimated emotions. For example, if the user is feeling stressed, the instruction providing unit can provide simple and easy-to-understand instructions. If the user is relaxed, the instruction providing unit can also provide detailed instructions. Furthermore, if the user is in a hurry, the instruction providing unit can also provide instructions that focus on the main points. In this way, by adjusting the method of providing instructions according to the user's emotions, more appropriate instructions can be provided.

[0097] The analysis unit can propose appropriate solutions based on the user's past product disposal history. For example, the analysis unit retrieves the user's past product disposal history from a database and proposes a reuse method based on that history. The analysis unit can also refer to the user's past product disposal history to propose an optimal recycling method. Furthermore, the analysis unit can also refer to the user's past product disposal history to propose an optimal reduction method. In this way, by referring to the user's past product disposal history, more appropriate solutions can be proposed.

[0098] The presentation unit can estimate the user's emotions and adjust the way in which the solution is presented based on the estimated emotions. For example, if the user is feeling stressed, the presentation unit can present a simple and easy-to-understand solution. If the user is relaxed, the presentation unit can also present a detailed solution. Furthermore, if the user is in a hurry, the presentation unit can also present a solution that focuses on the main points. In this way, by adjusting the way in which the solution is presented according to the user's emotions, it is possible to provide a more appropriate solution.

[0099] When providing the procedures, the procedure providing unit can provide different procedures based on the type and condition of the item. For example, the procedure providing unit can provide appropriate reuse procedures depending on the type of item, such as electronic devices, furniture, or clothing. The procedure providing unit can also provide recycling procedures depending on the condition of the item. For example, different recycling procedures are provided for an item that is in like-new condition and an item that has been used. Furthermore, the procedure providing unit can also provide reduction procedures depending on the type and condition of the item. In this way, by providing different procedures depending on the type and condition of the item, more appropriate procedures can be provided.

[0100] The analysis unit can estimate the user's emotions and set priorities for the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize and suggest solutions that are easy to implement. Also, if the user is relaxed, the analysis unit can prioritize and suggest detailed solutions. Furthermore, if the user is in a hurry, the analysis unit can prioritize and suggest solutions that can be implemented quickly. In this way, by prioritizing the analysis results according to the user's emotions, more appropriate solutions can be provided.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The photography unit has the function of allowing users to take and upload photos of unwanted items. For example, a user can take a photo of an item using a smartphone camera and upload the photo to the app. The photography unit also has the function of automatically adjusting the resolution and quality of the photo, automatically adjusting the brightness and contrast of the photo, and converting it into a format that is easy for the analysis unit to analyze. Furthermore, the photography unit also has the function of taking and uploading multiple photos at once. Step 2: The analysis unit analyzes the photos uploaded by the photography unit and proposes the optimal solution from three options: reuse, recycle, or reduce. For example, image recognition technology can be used to determine the type and condition of the item, and the optimal solution can be proposed based on that. AI can also be used to evaluate the market value and recyclability of the item, and the market value can be assessed in real time to determine whether it can be reused. Step 3: The presentation unit presents the solutions proposed by the analysis unit to the user. For example, it displays the options of reuse, recycle, and reduce on the app screen, allowing the user to make a selection. It also has a function to visually display the advantages and disadvantages of each solution, using graphs and icons to make it easier for the user to make a selection. Step 4: The procedure provider provides specific procedures based on the solutions presented by the provider. For example, if the user selects "reuse," the procedure provider provides listing procedures, and if the user selects "recycle," the procedure provider provides sending procedures to a recycling facility. Furthermore, if the user selects "reduce," the procedure provider provides waste reduction procedures.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 photography department takes and uploads photos of unwanted items; an analysis unit that analyzes the photos uploaded by the photography unit and proposes an appropriate solution from three options: reuse, recycle, and reduce; a presentation unit that presents the solution proposed by the analysis unit to a user; a procedure providing unit that provides a procedure based on the solution presented by the presenting unit; Equipped with A system characterized by:

2. The analysis unit Collect information on recycling facilities in each region and propose appropriate recycling methods 2. The system of claim 1.

3. The analysis unit Collect information on reusable platforms and propose appropriate reuse methods 2. The system of claim 1.

4. The procedure providing unit If you choose to reuse, provide instructions 2. The system of claim 1.

5. The procedure providing unit If you choose to recycle, we will provide you with shipping instructions.

2. The system of claim 1.

6. The procedure providing unit If you choose to reduce, provide the reduction steps.

2. The system of claim 1.

7. The imaging unit is Estimate the user's emotions and decide the timing of taking a photo based on the estimated user emotions.

2. The system of claim 1.

8. The imaging unit is Automatically record the condition and frequency of use of the item when photographed 2. The system of claim 1.

Images